Di's Blog

I finished reading Peter Thiel's 'Zero to One: Notes on Startups, or How to Build the Future' today. With the rapid advancements and widespread discussion around AI, the core arguments about technology, human-machine collaboration, and the nature of progress hold up remarkably well. And in some ways, as a manifesto for building a better future, what's written in this book is even more relevant now.

Chapter 2 Party Like It's 1999 outlines four lessons learned from the dot-com crash that became 'dogma' in the startup world, however, Thiel argues that these dogmas are largely incorrect and that the opposite principles are probably more correct:

1. Make incremental advances: Grand visions were seen as bubble-inflating, so small, incremental steps became the preferred path.

   Thiel: It is better to risk boldness than triviality.

2. Stay lean and flexible: Planning was deemed arrogant, and "agnostic experimentation" became the norm.

   Thiel: A bad plan is better than no plan.

3. Improve on the competition: Focus on existing customers and recognizable products, improving on what competitors already offer.

   Thiel: Competitive markets destroy profits.

4. Focus on product, not sales: If a product requires advertising or salespeople, it's not good enough; viral growth is the only sustainable growth.

   Thiel: Sales matters just as much as product.

"The most contrarian thing of all is not to oppose the crowd, but to think for yourself."

In this end this chapter, Thiel is challenging the reader to not simply adopt the prevailing "lessons learned" from the past, but to critically evaluate them. He suggests that true contrarianism isn't just about disagreeing with the majority for the sake of it, but about independent thought and forming your own conclusions, even if those conclusions align with or contradict the crowd. It's about genuine intellectual autonomy.

In Chapter 3 All Happy Companies Are Different, he argues that successful companies are unique and that true value comes from creating a monopoly rather than competing in existing markets. Thiel uses the economic models of "perfect competition" and "monopoly" to explain this difference. In perfect competition, firms sell identical products, have no market power, and thus, in the long run, make no economic profit as new entrants drive prices down. A monopoly, conversely, owns its market, allowing it to set prices and maximize profits due to a lack of close substitutes. He asserts that competition is destructive, leading to a ruthless struggle for survival and zero profits. Monopolies, on the other hand, can afford to focus on long-term innovation, employee well-being, and broader societal impact because they are not constantly battling for survival. Creative monopolies are powerful engines for progress as they introduce entirely new categories of abundance to the world.

He then discusses how both monopolists and non-monopolists tend to misrepresent their market conditions. Monopolists (like Google) downplay their dominance by broadly defining their market to avoid scrutiny, while non-monopolists (like a new restaurant owner) narrowly define their market to appear unique and avoid acknowledging intense competition. Thiel emphasizes that losing sight of competitive reality by focusing on trivial differentiators is a fatal mistake for startups.

"All happy companies are different: each one earns a monopoly by solving a unique problem. All failed companies are the same: they failed to escape competition."

This is the core message from the book. Entrepreneurs should strive to build unique, monopolistic businesses by creating something entirely new.

Chapter 3 primarily focuses on the economic and strategic advantages of monopoly and the destructive nature of perfect competition. Chapter 4: "The Ideology of Competition" shifts the focus to the societal and psychological impact of competition. Competition is not merely an economic concept but a deeply ingrained "ideology" that pervades our society, from education to personal aspirations. He reminds readers that this competition can blind people to real opportunities and lead to irrational behavior and missed chances, and suggests us to recognize and resist the pervasive ideology of competition.

Chapter 5 Last Mover Advantage discusses how a great business is defined by its ability to generate future cash flows and argues that being a last mover (i.e., to make the last great development in a market and enjoy long-term monopoly profits) is more advantageous than being a first mover. It outlines four characteristics of monopoly that contribute to a company's durability:

1. Proprietary Technology: This makes a product difficult to replicate, ideally being at least 10 times better than its closest substitute (e.g., Google's search algorithms, PayPal's payment system for eBay, Amazon's book selection, Apple's integrated design).

2. Network Effects: The product becomes more valuable as more people use it (e.g., Facebook). Thiel emphasizes that such businesses must start with a very small, focused market to get initial users.

3. Economies of Scale: Fixed costs can be spread over increasing sales, making the business stronger as it grows. Software companies are particularly suited for this due to near-zero marginal costs.

4. Branding: A strong brand creates a monopoly (e.g., Apple). However, branding needs to be built on substantive advantages, not just surface-level polish.

"You've probably heard about 'first mover advantage': if you're the first entrant into a market, you can capture significant market share while competitors scramble to get started. But moving first is a tactic, not a goal. What really matters is generating cash flows in the future, so being the first mover doesn't do you any good if someone else comes along and unseats you. It's much better to be the last mover— that is, to make the last great development in a specific market and enjoy years or even decades of monopoly profits. The way to do that is to dominate a small niche and scale up from there, toward your ambitious long-term vision. In this one particular at least, business is like chess. Grandmaster José Raúl Capablanca put it well: to succeed, 'you must study the endgame before everything else.'"

Thiel's advice for startups (1) start small and monopolize, 2) scale up gradually, and 3) don't discrupt) reminds me of Google's AI strategy in recent two years, which seems to align with the 'last mover advantage' mentality. Instead of trying to release one massive, all-encompassing AI that competes directly with established players across every front, Google has released or integrated AI into many "smaller" applications or features (workspace, photos, maps, gemini, etc). Each of these can be seen as a "small market" or specific use case where AI offers a distinct advantage, allowing Google to "monopolize" that particular user experience. After establishing AI capabilities in focused areas, they are integrating these more broadly. Successful AI features in Workspace might then be leveraged for enterprise solutions. Advancements in image recognition from Photos could be applied to broader visual search or other AI models. The iterative development of Bard/Gemini, starting as a conversational AI and gradually expanding its capabilities (multimodality, coding, planning), is a clear example of scaling up. They build upon established user bases and technological strengths. While Google is certainly competing, their strategy doesn't always seem to be about a direct, disruptive frontal assault that immediately aims to destroy an incumbent. Instead, it's often about: 1) leveraging their exsiting ecosystem, 2) focusing on unique capabilities, 3) creating new user behaviors.

In Chapter 6 You Are Not a Lottery Ticket, Thiel described the concept of definite vs. indefinite futures and asserts that the prevailing indefinite optimism, particularly in the US, is unsustainable. He argues that real progress and success require definite plans and individual effort.

“When Baby Boomers grow up and write books to explain why one or another individual is successful, they point to the power of a particular individual's context as determined by chance. But they miss the even bigger social context for their own preferred explanations: a whole generation learned from childhood to overrate the power of chance and underrate the importance of planning."

The core of Chapter 7 Follow the Money applies the power law to venture capital (VC). Venture returns are not normally distributed (where most companies perform average). Instead, they follow a power law: a small handful of companies radically outperform all others, often returning more than the entire rest of the fund combined. People often fail to see the power law, which is a fundamental law of the universe, because it only becomes clear over time; early-stage companies in a portfolio might look similar before exponential growth kicks in. Despite being a niche (less than 1% of new businesses receive VC funding), venture-backed companies disproportionately drive the economy, creating 11% of private sector jobs and generating 21% of GDP. The largest tech companies, all venture-backed, are worth more than all other tech companies combined.

Understanding the power law means focusing on the singular, most important things (e.g., one best market, one dominant distribution strategy). To achieve disproportionate success, one must identify and focus relentlessly on those few critical elements.

In Chapter 8 Secrets, Thiel begins by posing his contrarian question ("What important truth do very few people agree with you on?") in the context of secrets. He states that a good answer to this question implies the existence of secrets – something important, unknown, difficult, but achievable. He argues that secrets still exist and are crucial for progress.Secrets can lead to monumental advancements in science, medicine, and technology (e.g., curing diseases, new energy sources). In business, secrets can lead to valuable companies built on overlooked opportunities, like Airbnb (untapped supply and unaddressed demand in lodging) and Uber/Lyft (connecting drivers and riders). In terms of how to find secrets, Thiel has discussed about 1) secrets of nature from studying physical world, vs. secrets about people from understanding human nature, 2) looking at the fields that matter but haven't been standardized.

Chapter 9 Foundations is around 'Thiel's law': a startup messed up at its foundation cannot be fixed, providing guidance on fundamental level: co-founder relationships, ownership, possession, and control, small boards, full time commitment, equity is the king, founding moment, etc. Chapter 10 The Mechanics of Mafia highlights the importance of company culture. Chapter 11 If You Build It Will They Come stresses that distribution (sales, marketing, advertising) is often underestimated and is just as crucial as product development.

"The founding moment of a company, however, really does happen just once: only at the very start do you have the opportunity to set the rules that will align people toward the creation of value in the future.

The most valuable kind of company maintains an openness to invention that is most characteristic of beginnings. This leads to a second, less obvious understanding of the founding: it lasts as long as a company is creating new things, and it ends when creation stops. If you get the founding moment right, you can do more than create a valuable company: you can steer its distant future toward the creation of new things instead of the stewardship of inherited success. You might even extend its founding indefinitely."

"'Company culture' doesn't exist apart from the company itself: no company has a culture; every company is a culture. A startup is a team of people on a mission, and a good culture is just what that looks like on the inside."

There is a core debate right now around whether AI is going to replace human‘s jobs, and this book offers powerful arguments for the "AI as complement, not replacement" side. Thiel explicitly argued against the "substitution fallacy" in Chapter 12 (Man and Machine), stating that computers and humans have different strengths and will thrive through collaboration. Although Generative AI is unprecedented with its impact on human society nuanced to discuss, I agree there are fundamental differences in intelligence between humans and AI. Human possess intentionality, true innovation, empathy, and emotional intelligence, and human judgment is needed when there are ethical concerns or complex problems. AI as a tool can do augmentation to increase productivity, but not automation. Historically speaking, tech development always creates more jobs than destroyed. While some roles are eliminated, new roles emerge: AI engineers, Prompt Engineers, AI Product Managers, etc. In essence, it's about a redefinition of work, rather than elimination.

"People compete for jobs and for resources; computers compete for neither."

Globalization is about substitution. Technology is about complementarity.

Chapter 13 Seeing Green analyzes the failure of the cleantech bubble, attributing it to a widespread failure to answer the seven critical questions every successful business must address.

1. The Engineering Question: Most offered only incremental, not breakthrough (10x better), technology (e.g., Solyndra's inefficient cylindrical solar cells).
2. The Timing Question: They misjudged market readiness and the slow, linear progress of solar technology compared to exponential tech.
3. The Monopoly Question: They pursued "trillion-dollar markets" that were fiercely competitive, rather than small, defensible niches.
4. The People Question: Teams were often led by "salesman-executives" lacking technical expertise, focusing on fundraising over product. (Thiel suggests a "never invest in a tech CEO that wears a suit" rule.)
5. The Distribution Question: Companies often overlooked effective distribution, leading to complex and inconvenient sales models (e.g., Better Place's battery swapping).
6. The Durability Question: They failed to anticipate competition (e.g., from China) or market shifts (e.g., the rise of fracking).
7. The Secret Question: They based their ventures on "conventional truths" (the need for a cleaner world), which everyone agreed on, rather than unique, hidden insights.

"The 1990s had one big idea: the internet is going to be big. But too many internet companies had exactly that same idea and no others. An entrepreneur can't benefit from macroscale insight unless his own plans begin at the micro-scale. Cleantech companies faced the same problem: no matter how much the world needs energy, only a firm that offers a superior solution for a specific energy problem can make money. No sector will ever be so important that merely participating in it will be enough to build a great company."

Chapter 14 The Founder's Paradox explores the often extreme, contradictory, and seemingly peculiar traits of successful founders, arguing that these unique characteristics are both powerful for a company and carry inherent dangers for the founder. Society needs founders – unusual individuals who can make authoritative decisions, inspire loyalty, and plan long-term, moving companies beyond incrementalism. However, founders must be wary of overestimating their own power and succumbing to their own myth, mistaking public adulation or criticism for truth. The greatest danger for a founder is losing their mind; for a business, it's losing its myth and vision.

The current AI boom feels very much like an "accelerating takeoff " in terms of technological advancement, which is mentioned in the final chapter "Conclusion: Stagnation or Singularity", as one of the Nick Bostrom's four possible patterns for humanity's future. Accelerating Takeoff (Singularity) is the most difficult scenario to imagine: new technologies so powerful that they transcend current understanding, leading to a much better future. Ray Kurzweil's "Singularity is near" concept, based on exponential growth trends, is mentioned as a prominent view of this outcome. However, as Thiel's book is a manifesto for building a better future and criticizes 'indefinite optimism', in the context of AI boom, 'Singularity' is not a predetermined destination, but the choices we make today:

• Are we using AI for "0 to 1" innovation to solve truly hard problems and create new value, or are we just using it for "1 to n" incremental improvements and fierce competition?
• Are we making definite plans for how AI will integrate with and enhance human capabilities, or are we succumbing to "indefinite fears" or blind optimism?
• Are we building companies around unique AI-driven insights that can create sustainable monopolies, or are we simply entering crowded AI markets hoping for a piece of an existing pie?

updating until the end of the month.

Substack

The frontier isn’t volume—it’s discernment. And in that shift, taste has become a survival skill.

Because when abundance is infinite, attention is everything. And what you give your attention to—what you consume, what you engage with, what you amplify—becomes a reflection of how you think.

What matters now is what you do with it. How you filter it. How you recognize signal in the noise. Curation is the new IQ test.

Taste is often dismissed as something shallow or subjective. But at its core, it’s a form of literacy—a way of reading the world. Good taste isn’t about being right. It’s about being attuned. To rhythm, to proportion, to vibe. It’s knowing when something is off, even if you can’t fully articulate why.

Taste is what allows you to skim past the performative noise, the fake depth, the viral bait, and know—instinctively—what’s worth your time.

And that’s what real taste is: a deep internal coherence. A way of filtering the world through intuition that’s been sharpened by attention.

When you sharpen your discernment, you stop being swayed by trends. You stop needing consensus. You stop reacting to every new thing like it’s urgent.

There will always be creators. But the ones who stand out in this era are also curators. People who filter their worldview so cleanly that you want to see through their eyes. People who make you feel sharper just by paying attention to what they pay attention to.

1995 interview with Steve Jobs — “Ultimately, it comes down to taste. It comes down to trying to expose yourself to the best things that humans have done, and then try to bring those things into what you’re doing.”

Good taste isn’t restrictive. It’s expansive. It allows you to contain multitudes without becoming incongruent.

But good taste is deep structure. It’s the throughline in someone’s life. You can see it in the design of their home, the cadence of their speech, the way they treat people, the books on their shelves. Taste is how you live a congruent life. Not in the sense of brand consistency, but in the sense of spiritual alignment. You can change your mind. Explore new spaces. But your values stay intact. Your center holds.

― Taste Is the New Intelligence - Wild Bare Thoughts [Link]

This is an amazing article. In an age of infinite content, taste is your compass. It’s not about elitism—it’s about aligning your attention with what truly matters to you. We can do these:

Learnings and Suggestions:

1. Cultivate Discernment Over Consumption: Prioritize depth over volume in what you read, watch, and engage with. Ask "Is this worth my time?" before consuming content, creating something, or sharing. Trust your intuition—if something feels off, skip it.

2. Curate Your Inputs (Because They Shape Your Outputs): Unfollow accounts, mute topics, and unsubscribe from newsletters that don’t align with your values. Follow thinkers, creators, and curators who consistently offer depth. Set boundaries (e.g., no mindless scrolling after 9 PM). Pause after reading/watching to digest, not just react.

3. Build a "Library Mindset" (Not a Wishlist One): Read books, essays, and long-form work that lingers. Don’t engage with viral content just because it’s popular. Save/share only what resonates deeply—not what’s merely entertaining.

4. Train Your Taste Like a Muscle: Study great art, writing, music, and design to refine your sensibility. Remove distractions, unnecessary commitments, and low-value inputs. Note what ideas/images/sounds stay with you—these reveal your true taste.

5. Embrace Coherence Over Consistency: Your bookshelf, playlists, and feeds should reflect who you are (or aspire to be). Stay open to new influences, but filter them through your core principles. Don’t adopt aesthetics/opinions for status—authenticity matters more.

6. Practice "Vibe Coding" (Like Rick Rubin): Whether in conversations or creativity, prioritize feeling over formulas. In work/life, strip away excess until only the essential remains. If something feels "alive," lean in—even if it defies logic.

7. Reject Cheap Dopamine for Lasting Satisfaction: Opt for the book over the tweet, the slow movie over the clip. After consuming something, ask: Did this uplift or drain me? Regularly eliminate distractions (apps, subscriptions, habits) that don’t serve you.

8. Taste as a Spiritual Practice: Prioritize art/ideas that rearrange your perspective. From your home to your workspace, align space with intention. Engage only with what nourishes, not depletes.

9. Remember: Curation = Power: Amplify only what deserves a wider audience. Your ability to filter signal from noise is a competitive edge. The more you refine your taste, the more it protects you from chaos.

A Primer on US Healthcare - Generative Value [Link]

This article covers an overview of the system (main players), the value chain (how products and services flow through the system and what profitable segments are), incentives (motivation of behaviors), challenges (significant issues within the industry), and potential solutions (software and AI).

It deeply focuses on the interplay between incentives, middlemen, the resulting administrative burden, and AI as the specific technological solution appears to be a key perspective.

BREAKING: UnitedHealth Bleeds. CEO Witty Steps Down. - Sergei Polevikov, AI Health Uncut [Link]

UnitedHealth Abuse Tracker - Matt Stoller, American Economic Liberties Project [Link]

1. Vibe coding is a new approach to software development that utilizes AI tools to assist individuals in creating applications and software without requiring extensive programming knowledge.
2. The term was popularized by Andrej Karpathy, an AI expert, who described it as a method where users interact with AI using natural language to describe their ideas rather than writing traditional code directly.
3. This allows creators, particularly those lacking technical skills, to build functional applications rapidly by simply explaining their requirements to the AI, which generates the relevant code for them.

― What is Vibe Coding? - AI Supremacy [Link]

Who’s the Highest-Paid CEO? - App Economy Insights [Link]

Rick Smith, co-founder and CEO of Axon.

I Summarized Mary Meeker's Incredible 340 Page 2025 AI Trends Deck—Here's Mary's Take, My Response, and What You Can Learn - Nate, Ai & Product [Link]

Nate's overall take is that while Mary Meeker is correct that Generative AI adoption is exploding, real value accrues only where organizations align real-world problems with AI’s actual strengths in workflows. He believes bigger claims demand commensurately bigger evidence.

Carl Dahlman later gave us the three categories that are widely used today:

1. Search and information costs: discovering what is available to purchase and comparing alternatives
2. Bargaining and decision costs: coming to an agreement between buyer and seller, including establishing the final price and terms
3. Enforcement and policing costs: ensuring that both sides holds up their end of the deal
4. Distribution costs: actually getting the good or service to the end consumer

― How To Build AI Agents (2025 Guide) - Max Berry, Max' Prompts [Link]

Key Concepts:

• Transaction Costs: Costs incurred in addition to the actual price of a good or service, necessary to coordinate and execute a transaction. Marketplaces primarily sell the reduction of these costs.
• TAM Expansion: Reducing transaction costs lowers the effective cost of a good or service, increasing demand and expanding the Total Addressable Market (TAM). The degree of TAM expansion relates to the percentage of total cost eliminated.
• Value Distribution: Marketplaces save sellers money on transaction costs and charge them a fee (often similar to what sellers paid previously). They typically pass efficiency gains on to buyers in the form of easier and faster experiences, creating a demand-constrained market. Variable Costs of Addressing Transaction Costs:
  • Low Variable Costs: Addressing search and bargaining costs is highly efficient and has low variable costs. Marketplaces can keep more of the value created here.
  • High Variable Costs: Addressing enforcement and distribution costs involves significant variable costs (e.g., funding returns, building logistics). While these make marketplaces bigger, margins may be lower as value is passed to buyers.

Takeaways:

This article puts the concept of transaction costs as central to understanding marketplaces. Transaction costs are defined as the costs incurred beyond the actual price of a good or service, associated with coordinating and executing the transaction itself. Marketplaces are essentially businesses that sell the reduction of these transaction costs. Studying transaction costs can help determine where marketplaces will succeed, what kind of marketplaces to build, and how to price them.

Looking ahead, the article suggests that the "free lunch" opportunities in many industries are exhausted, pushing marketplaces into high variable cost activities. This implies future marketplaces may be higher scale but potentially lower margin and more operationally intensive. To disrupt incumbent marketplaces, one should look for remaining transaction costs that can be addressed much more efficiently than the current solution. The article suggests disrupting food delivery was possible by building a more efficient network than restaurants had, but disrupting shipping for handmade goods is harder because it requires competing with highly efficient companies like UPS and Fedex.

By far, the largest unsolved transaction costs are in the services industries (e.g., freelancing, home improvement), which constitute two-thirds of consumer spending. Most services marketplaces are currently stuck at the Lead Generation stage, limiting penetration and take rate. This might be partly because much spend is on recurring services where customers leave the marketplace once a good provider is found, leading services marketplaces to rely on high-churn consumer subscriptions. Despite this, there are opportunities, such as Zillow exploring expanding into managed marketplace territory for home services.

An hour a day is all you need. - The Improvement Journal [Link]

The one hour is suggested to be dedicated to three key practices that aim to rebuild an individual from the ground up:

1. Build Something That's Yours: This involves creating something that belongs to you, beyond your job, such as a newsletter, product, service, blog, or by learning/teaching a skill. The purpose is to "plant seeds" that will compound over time, pulling you out of stagnation.
2. Train Like You Want to Be Here for a While: This practice emphasizes physical strength and movement, like walking, running, stretching, lifting, breathing, sleeping deeply, eating real food, and drinking water. It's a message of self-care and an intention to use one's body, which also sharpens mental clarity, as Seneca suggested, "The body should be treated more rigorously, that it may not be disobedient to the mind".
3. Create Enough Silence to Hear Your Own Voice: This habit counters the constant noise and stimulation of modern life. It encourages practices like journaling, meditating, taking walks without headphones, or simply sitting still without a goal or screen. The goal is not productivity, but presence and creating space for reflection and insight, preventing thoughts from being drowned out and actions from remaining unexamined.

How to become friends with literally anyone - April & The Fool [Link]

The article suggests that becoming friends with anyone is to approach interactions with a deep-seated belief in shared humanity, genuine curiosity about individual worldviews, and an open, empathetic demeanor that seeks to understand rather than judge.

• Try to understand people through conversations with a belief in the universal commonality of human nature: The author fundamentally believes that all people are driven by the same core human urges and desires, such as the need to feel loved, respected, and seen. This perspective makes it intuitive to understand others. They see meeting someone new as a "puzzle of empathy" and a "game of commonality," where they try to understand what someone would think, want, need, or crave given their background, values, limitations, and longings.
• From common nature to differences among people due to environmental factors: The author acknowledges that how these needs are defined and achieved varies dramatically due to factors like nationality, gender, religion, cultural heritage, socioeconomic class, hobbies, and upbringing. These "little big differences" are where things become interesting, leading to unique individual personalities and perspectives.
• Follow the reasonableness within their personal worldview to understand motivations and values: The author believes that while people may not always be rational, they are always "reasonable" within their own worldview. This means that everyone has reasons for their actions, and those reasons make sense within their personal framework. Understanding a person's circumstances allows the author to understand their motivations, struggles, and values.
• Be curious and genuine while engaging with people: The author describes themselves as extremely extraverted, loving people, and hating small talk. They are curious about people, viewing them as containing "worlds, histories, stories that span across generations and geographies". This curiosity leads them to give "rapt attention and genuine space to be yourself".
• Assumption of friendship from the beginning, share stories and genuine care: The author approaches new encounters with the assumption that "we are friends" from the moment they meet. They are open, putting "all my cards on the table" and inviting others to reveal theirs. They enjoy conversations, making people feel understood, heard, and cared for.

The key takeaway is that genuine technological advancement, which is real and accelerating, must be distinguished from the business models built around it, which frequently adhere to age-old patterns. When stated purpose and actual function align, it typically indicates that the technology addresses a specific, measurable problem with clear economic value, rather than promising to "transform everything."

Systemantics, or, the art of understanding what’s going on, means recognizing the persistent gaps between what systems proclaim and what they actually do, and capitalizing on that insight.

When the fog dissipates and clarity emerges, the survivors will be those who patiently deciphered the underlying mechanics amidst fleeting illusions.

Enduring AI companies will emerge in two distinct spaces by 2035: unglamorous but essential tools that demonstrably improve margins or reduce costs, and genuine frontier research that reveals entirely new problem spaces. The first category refines what exists; the second invents what doesn't yet.

Real opportunities lie in the quiet spaces between stated ambitions and operational truths. Just as they always have.

― The Art of Understanding What's Going On - Fakepixels [Link]

How I Went From Reading 20 Books Per Year to Over 75 Books - Read and Think Deeply [Link]

Takeaways:

1. Always take a book with you.
2. Give it about 50 pages before you quit. This keeps you from getting stalled on a book that is not resonating with you.
3. Schedule the reading time. e.g., 45 min in the morning, 30 min in the evening, and throughout the day when you get breaks.
4. Weekend sprints. Read in hour-long stretches on weekends or to do several smaller stretches and get through entire sections or even whole books on the weekends.

there are people with half your skills and intelligence living out your dreams, just because they put themselves out there and didn’t overthink it.

Reach out anyway—someone will always have more followers, more free time, a better setup. It’s up to you to push through everything, part the crowd, and make some space for yourself to at least give yourself the chance of getting what you want.

You will never be fully ready and there will never be a perfect time. It’s genuinely not about waiting for the right time to do something when you’re ready, it’s about doing things before you’re ready just to make them exist.

― literally just do things - crystal clear [link]

Papers and Reports

Trends - Artificial Intelligence - Mary Meeker, Bond [Link]

Think Only When You Need with Large Hybrid-Reasoning Models [Link]

Large Reasoning Models (LRMs) improve reasoning via extended thinking (e.g., multi-step traces), but this leads to inefficiencies like overthinking simple queries, increasing latency and token usage. The team Introduces Large Hybrid-Reasoning Models (LHRMs) — the first models that adaptively choose when to think based on query complexity, balancing performance and efficiency. They utilizes a two-stage approach: 1) Hybrid Fine-Tuning (HFT) – cold start using curated datasets labeled as "think" vs. "non-think"; 2) Hybrid Group Policy Optimization (HGPO) – an online RL method that trains the model to pick the optimal reasoning mode. They defines Hybrid Accuracy to evaluate how well the model selects between thinking and non-thinking strategies; correlates strongly with human judgment. Experiments show LHRMs outperform both LRMs and traditional LLMs in reasoning accuracy and response quality, while also reducing unnecessary computation.

The 2025 State of B2B - Monetization - Kyle Poyar [Link]

The report summarizes a poll of 240 software companies about their pricing strategies. Key findings indicate a decline in flat-rate and seat-based pricing models, with hybrid pricing (combining subscriptions and usage) emerging as the dominant approach, especially for companies incorporating AI capabilities. The report also highlights a growing interest in outcome-based pricing among AI-native companies and stresses the importance of pricing agility and clear ownership of pricing strategy within organizations.

The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity - Apple Machine Learning Research [Link]

The authors analyzed the thinking process and reasoning traces of LRMs in several smart ways:

• A custom pipeline using regex identifies and extracts potential solution attempts from the LRM's thinking traces.
• Extracted solutions are rigorously verified against puzzle rules and constraints using specialized simulators for step-by-step correctness.
• Records the accuracy of valid solutions and their relative position within the reasoning trace for behavioral insights.
• Categorizes LRM thinking patterns (e.g., overthinking, late success, collapse) by analyzing how solution correctness and presence vary with problem complexity.
• Examines how the proportion of correct solutions changes sequentially within the thinking trace, revealing dynamic accuracy shifts.
• Pinpoints the initial incorrect step in a solution sequence to understand the depth of correct reasoning before error.
• Quantifies thinking token usage to analyze scaling of effort with complexity, noting an unexpected decline at high complexity.

How much do language models memorize? - Meta, Google, NVIDIA, and Cornell University [Link]

This paper proposed a new method to quantify how much information a language model "knows" about a datapoint.

They formally separate memorization into two components by two novel definitions of memorization: unintended memorization (information about a specific dataset) and generalization (information about the true data-generation process).

There are several interesting findings:

• By training models on uniform random bitstrings (eliminating generalization), they precisely measure model capacity, finding GPT-style transformers store 3.5 to 4 bits per parameter.
• Their framework shows that the double descent phenomenon occurs when the data size exceeds the model capacity, suggesting that models are "forced" to generalize when they can no longer individually memorize datapoints.
• The paper develops and validates a scaling law that predicts membership inference performance based on model capacity and dataset size, indicating that membership inference becomes harder with larger datasets relative to model capacity.

To understand their smart methods:

• They proposed a very clever approach to understand memorization and model capacity in LM. They isolate unintended memorization by training models on uniform random bitstrings.

  • No Generalization Signal: When training on truly random data, there are no underlying patterns, rules, or structures for the model to generalize from. Each bitstring is an independent, random piece of information.
  • Only Memorization is Possible: In this scenario, the only way for the model to "learn" or perform well on this data (i.e., predict the next bit in a sequence or identify if it was part of the training set) is to literally memorize the specific bitstrings it has seen. Any "knowledge" the model gains is purely about the individual data points.
  • Total Memorization as Measured: Therefore, when generalization is effectively zero, the information the model stores about the random bitstrings directly reflects its total memorization capacity for that type of information. There's no "general knowledge" to distinguish; it's all about remembering specific instances.

  Therefore, they are measuring the maximum amount of distinct, specific information the model can store.

  They equal total memorization to model capacity. In machine learning, model capacity generally refers to the size and complexity of the functions a model is capable of learning. It's the model's ability to fit a wide variety of patterns in the data. A model with higher capacity can potentially fit more complex relationships or memorize more specific data points.

  The paper further quantifies this by showing that GPT-style models have a capacity of approximately 3.6 bits-per-parameter. This indicates that each parameter in the model effectively acts as a certain amount of storage for information, reflecting the overall capacity of the neural network architecture.

• The fundamental challenge in understanding and evaluating language models is the ambiguity and conflation of "memorization" (copy or reproduce a specific sequence in the training data) and "learning." (truly understand and generalize a pattern or concept) This is exactly what they addressed by decomposing memorization into unintended memorization and generalization. The decomposition enables controlled measurement and the use of random bitstrings is the key innovation.

  About the Double Descent Phenomena: When a model's capacity exceeds the generalizable patterns in the data, it starts to memorize individual data points. As data size increases relative to capacity, the model is "forced" to generalize more, leading to a decrease in unintended memorization and an improvement in performance.

  The core insight is that as models become massively overparameterized (far beyond what's needed to simply fit the training data), they find "simpler" interpolating solutions that generalize better, often due to the implicit biases of optimization algorithms like Stochastic Gradient Descent (SGD).

  Intuition for double descent:

  1. "Under-parameterized" Regime (Classical ML): Model Capacity < Data Size: very generalizable, low test error
  2. "Interpolation Threshold" (The Peak): Model Capacity ~ Data Size: peak of test error, due to overfitting the noise
  3. "Over-parameterized" Regime (Double Descent / Modern Deep Learning): Model Capacity >> Data Size: robust generalization happens, test error goes down again.

• Here is a concept Membership Inference Attacks (MIAs): These are attacks that attempt to determine whether a specific data point was part of a model's training dataset or not. A successful MIA indicates that the model has "memorized" that specific data point. "Scaling Laws for Membership Inference" in the paper refers to predictive relationships that describe how the success rate of a MIA changes as a function of various model and data characteristics, such as model capacity and dataset size.

  They basically propose that membership inference success is inversely related to how "generalizable" the data point is within the model's capacity. In other words, membership inference is easier when the model has high capacity relative to the dataset size, leading to more unintended memorization of individual data points. Membership inference becomes harder (i.e., less successful for an attacker) when the dataset size is very large compared to the model's capacity, or when the model starts to "grok" (generalize) more.

LlamaFirewall: An open source guardrail system for building secure AI agents - Meta AI [Link]

Reasoning models don't always say what they think - Anthropic [Link]

The core issue explored is whether the Chain-of-Thought accurately reflects the model's true reasoning process ("faithfulness"). The article's research suggests that models frequently do not faithfully report their reasoning. Researchers tested this by subtly providing hints (both correct and incorrect) to models during evaluations. They then checked if the models admitted using these hints in their Chain-of-Thought explanations. The results showed that models (Claude 3.7 Sonnet and DeepSeek R1) rarely mentioned the hints they used, even when those hints led them to a different answer or involved potentially problematic information (like "unauthorized access"). Instead, models often created fake rationales for their answers.

The researchers also studied reward hacking by constructing synthetic Reinforcement Learning (RL) environments with injected "reward hacks." They then finetuned an earlier snapshot of Claude 3.7 Sonnet in these environments using outcome-based RL. By setting up the environments this way, they could observe if the models learned to pick the incorrect hint answer to maximize their reward, and then check if their Chain-of-Thought verbalized this reward-seeking behavior. They found that models learned the reward hacks very quickly (selecting the incorrect hint answer over 99% of the time), but rarely verbalized these hacks in their Chain-of-Thought (less than 2% of examples in most environments).

Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task - MIT [Link]

This experimental study combines neuroscience (EEG), educational psychology, and human-AI interaction to examine how AI tools like ChatGPT influence cognitive engagement during essay writing. The result shows that brain-only group showed stronger and broader neural connectivity, especially in theta bands, indicating deeper internal ideation and cognitive engagement. LLM group exhibited reduced alpha and theta connectivity, suggesting externalized and narrower thought patterns—relying more on ChatGPT suggestions rather than internal generation of ideas. So it's saying if you rely heavily on AI, you will get dumber.

YouTube and Podcasts

Sundar Pichai: CEO of Google and Alphabet | Lex Fridman Podcast [Link]

Jared Isaacman: What went wrong at NASA | The All-In Interview [Link]

Naval Ravikant On The 4 Books That CHANGED His Life (Financially And Philosophically) [Link]

Chamath Palihapitiya: Zuckerberg, Rogan, Musk, and the Incoming “Golden Age” Under Trump - Tucker Carison [Link]

Satya Nadella on AI Agents, Rebuilding the Web, the Future of Work, and more - Rowan Cheung [Link]

Jeff Bezos: Amazon and Blue Origin | Lex Fridman Podcast - Lex Fridman [Link]

WWDC25: Platforms State of the Union - Apple [Link]

#385 Michael Dell - Founders [Link]

IPOs and SPACs are Back, Mag 7 Showdown, Zuck on Tilt, Apple's Fumble, GENIUS Act passes Senate - All-In Podcasts [Link]

Articles and Blogs

Everything Google Announced at I/O 2025 - WIRED [Link]

Launch Hugging Face Models In Colab For Faster AI Exploration - Medium [Link]

My AI Skeptic Friends Are All Nuts - Thomas Ptacek [Link]

The author argues that LLMs as agents are improving developer productivity, and suggests that while the hype around AI can be annoying, the technology's impact is real and profound. He believes that those who don't embrace AI in their coding practices will be left behind.

My Brain Finally Broke - The New Yorker [Link]

The author shares a disorienting sense of reality's erosion, attributing it to various factors, including the relentless pace of digital information, the overwhelming nature of political events, and the insidious proliferation of AI. This environment fosters a collective cognitive detachment and erosion of critical faculties, making it challenging to discern truth, engage effectively, and maintain a grounded sense of self and world.

New Tools

Meet the Foundation Models framework - Apple [Link]

The iPhone maker has launched the Foundation Models framework to allow users to run a 3B parameter model locally. The framework is part of Apple Intelligence suite and allows developers to access it using three lines of code. The model can be used to generate text, extract summaries, and tag structured information from unstructured text.

Users should be aware of strength and weakness. It's only available on Apple Intelligence-enabled devices with OS version 26+. You need to use Xcode Playgrounds to prototype with real model output. You can use Instruments profiling template to measure latency and token overhead. There is no support for fine-tuning or external model deployment

Connect Your MCP Client to the Hugging Face Hub - HuggingFace [Link]

HuggingFace releases open-source MCP server to allow accessing its tools from VSCode and Claude Desktop.

New Book List

Some book names from my daily readings recently caught my attention and might be the next book to read for me:

• How Innovation Works: And Why It Flourishes in Freedom - Matt Ridley (2021) ✅

• The Rational Optimist: How Prosperity Evolves (P.s.)- Matt Ridley (2021) ✅

• Incerto - Nassim Nicholas Taleb (2021) ✅

  • Fooled by Randomness; The Black Swan; The Bed of Procrustes; Antifragile; Skin in the Game

• The Beginning of Infinity - David Deutsch (2012) ✅

• The E-Myth Revisited: Why Most Small Businesses Don't Work and What to Do About It - Michael E. Gerber

• Start with Why: How Great Leaders Inspire Everyone to Take Action - Simon Sinek

• The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses - Eric Ries ✅

• Grit: The Power of Passion and Perseverance - Angela Duckworth

• Mindset: The New Psychology of Success - Carol S. Dweck

• 7 Powers: The Foundations of Business Strategy - Hamilton Helmer (2016) ✅

• The Great CEO Within: The Tactical Guide to Company Building - Matt Mochary (2019) ✅

• The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers―Straight Talk on the Challenges of Entrepreneurship - Ben Horowitz (2014) ✅

• The Interpretation of Financial Statements – Benjamin Graham & Spencer B. Meredith (1998)

• Inside the Ultimate Money Mind – Robert G. Hagstrom (2023)

• The Creative Act: A Way of Being - Rick Rubin (2023)

• In the Company of Giants: Candid Conversations With the Visionaries of the Digital World - Rama Dev Jager (1998)

"How Leaders Learn" by David Novak is a great book for active learners. It has three chapters: "Learn from", "Learn to", and "Learn by".

Active learners are like artists—constantly refining, adapting, and evolving. They approach life as an masterpiece-in-progress, understanding that each new insight adds depth and clarity to the bigger picture. The book encourages active learning and defines it as a mindset - a daily discipline of seeking out knowledge from people, experience, and failures, staying open to feedback, new perspectives, and uncomfortable truths, and taking actions to test ideas, adapting and refining.

An active learner is somebody who seeks out ideas and insights and then pairs them with action and execution. They learn with purpose. The result is greater possibilities-for them and the people around them.

It's as Eric Hoffer, the American philosopher, wrote in Reflections on the Human Condition: "In a time of drastic change, it is the learners who inherit the future." They can't wait to discover the next idea, and the next, and the next, because behind every idea is a world of possibility and a brighter future.

Warren Buffett once told me what he looks for in the companies he acquires. He said, "I'm looking to buy companies that are run by painters." When I asked for an explanation, he said, "Most great artists have a hard time letting go of their paintings. They're in love with the painting. They are constantly adding a dab of color here, a little more texture there. I'm looking for the boss who is always tweaking their company, constantly trying to make it better. No matter how successful they may have already been, what they still see is a masterpiece-in-progress." He calls Berkshire Hathaway a museum for these masterpieces, but he expects the people who run them to keep making progress, to keep changing and expanding.

This book covers a lot of good practices, some of which I learned through experience and have been implementing in daily life, but I've never clearly summarized them in words like this author does (e.g., learn from failure and success, learn to ask better questions, learn to develop pattern thinking, learn to reflect, learn by tackling problems, etc); some are common sense to people but not easy to follow (e.g. learn to see the world the way it really is, learn to make and check your own judgments, learn by being your best self, learn by seeking new challenges, learn by making everyone count, learn by recognizing on purpose, etc); others are new ideas and wise advice to me that are incredibly enlightening (e.g., learn from new environment, learn to trust in positive intentions, learn to be humble and confident, learn by simplying, learn by teaching, etc).

My Learnings:

1. I carry good values and get rid of bad values from my upbringings and move on, but never go back and think about weakness and blind spots that were developed implictly.

   Our upbringings shape us-the good and bad experiences, the normal experiences of our day-to-day lives. When you choose to learn from your upbringing, you learn who you are, your strengths and weaknesses, your unique perspective, and your blind spots.

2. I'm the type of person who stick to one thing or one job, do the best, and get the most learnings from it - greedy but probably not the most efficient approach. So this is the top one advice for me: "Not moving means not growing" and "Choose environment wisely and don't stand still".

   New environments bring uncertainty and risk, two things humans really don't like. The brain weighs threats of loss heavier than it does opportunities for gain. Whether it's a move to a new city or a move to a new company, we don't know the people or the culture and we don't know if we'll succeed when we get there. The brain tells us it's best if we just stay where we are, in our more certain, less risky, known environment. But that's not always the right choice. Josh Waitzkin, child chess prodigy, subject of the book and movie Searching for Bobby Fischer, and later a tai chi world champion, wrote in The Art of Learning, "Growth comes at the expense of previous comfort or safety.

   However, not every new environment is good for you, it requires some luck and judgment.

   When looking at a new environment, evaluate it for these four sources of learning:

   • New knowledge, skills, or systems
   • New ideas and innovative thinking
   • New people and their perspectives and opinions
   • New influences that lead to personal growth

   Some new environments aren't going to advance your learning; they might even slow you down.

   First, make sure the new environment will offer opportunities to learn and grow in any area that's important to you right now, like I did. This is especially true when you have an ambition but aren't sure how to get there.

   As important as this work is, the next important step is to insert ourselves in an environment filled with people who routinely do what we're struggling to imagine." This is the whole point of choosing a new environment.

   Second, choose an environment that's suited to you. Understanding your personal ideal environment is an important aspect of self-awareness.

   Third, choose an environment that will exert the right influences on you, so that you're not only learning new skills, new knowledge, and new ideas, but also absorbing better collaboration, better leadership, better self-management, or whatever area of personal growth you think you need to work on.

   It's not only about growth, new environment can shape a person.

   Our social and cultural environments have a huge impact on our thinking and behavior. In Infuencer, psychologist Joseph Grenny and his coauthors explain that if you want to change behavior, you have to make changes to the social and structural environment. In Atomic Habits, James Clear argues that our environments usually matter more than our motivation when it comes to building new habits: "Especially over a long time period, your personal characteristics tend to get overpowered by your environment."

   You can either fight that truth or leverage it to learn more and grow more. Eric Gleacher recognized the power of environment and how it could not only offer new skills but also shape the person he would become at a surprisingly young age.

3. Have a look at what a getting-things-done talent looks like and fill the gap. Although people all succeed in different ways and no one's success is replicable, becoming a 'working genius' is at least a good option to start.

   Invention: creating novel ideas or solutions

   Discernment: evaluating and analyzing ideas and situations

   Galvanizing: organizing and inspiring others to take action

   Enablement: providing encouragement and assistance

   Tenacity: pushing projects to completion

   If you're wondering who you should turn to, always start with people who have applied their ideas in the real world and can prove that they work.

   Next, ask, Will they actually fill my gaps, or will they hold back their best ideas or try to elevate their ego by making what they know seem complex and hard to understand? Will they make their knowledge simple and clear? Essentially, you're asking, Is this person an active learner? Because active learners love helping people fill their gaps.

   A final tip: if you want people to share their know-how with you, you need to spread know-how. You need to be willing to share with them, too.

4. Human has instinct to avoid social pain or negative truth about themselves, when someone tells a less positive truth, we need to fight our instincts and always listen.

   When somebody cares enough and is brave enough to tell you the truth, your best course of action is to fight your instincts to dismiss it or hide from it. Overcome your brain's biological drive to protect you. Shut down the voice in your head telling you they're wrong. Don't run out of the room. Take some deep calming breaths (that really works), remind yourself that this person probably has a good reason for bringing the truth to your attention, and listen.

   Active learners work through this set of mental gymnastics every day. They work on their humility and maintaining an open mind (more on this in part two because they see the value truth-tellers bring.

5. Pursue the truth of the world, don't be delusional. Although 'we see the world as we are, not as it is' (Adaptation of Anaïs Nin's famous quote), we at least should be aware of this.

   Andy Pearson: "Learn to see the world the way it really is, not how you wish it to be."

   Darrow: "Chase after the truth like all hell and you'll free yourself, even though you never touch its coat tails."

   In their book Decisive, Chip Heath and Dan Heath explained that a sound decision-making process is more important than data and analysis, because no matter what, that data or our analysis of it is often flawed. We interpret it based on what we wish or what we assume or what we think, not what is.

   Good process can lead to better analysis, they explained, but analysis without good process won't produce the best learning. You need both to orient yourself to reality.

   When you see the world the way it really is, the right action becomes very clear.

   One of the best ways to be a better critical thinker is to make sure that your information is as close to the source as possible. If you don't go to the source yourself, you might be letting one perception after another influence what you end up hearing or learning. You won't know if you're seeing reality.

   When you're trying to see the world the way it really is, it's important to not be blinded by good news, something a good process can help you overcome.

   A great way to stay grounded is to not only chase the truth but also deal in it. Active learners know the value of being honest and transparent. They tell it like it is, because they know when they do, there's a greater chance others will, too.

6. I love pattern thinking and I seek out actively, but I still limit myself by a passive pursue of richer life experience.

   To prepare to make that leap, active learners expose themselves to as many patterns from as many disciplines as they can. Being curious about the world around us in the hope that we'll discover a new way of thinking about a problem or a new way of seeing an opportunity is core to active learning. Active learners read, listen, travel, try new things, explore hobbies and interests. They explore trends and insights from different disciplines, industries, cultures. Then they apply what they've absorbed to problems or goals. Those habits have helped me come up with some of my most successful ideas.

   You might think of a pattern-thinking moment as an aha moment or a stroke of inspiration, but active learners don't wait for the moment to hit them; they work to find it.

   Peter Georgescu, chairman emeritus of advertising giant Young & Rubicam and author of The Source of Success, said of pattern thinking, "A creative solution is a leap, and that leap is supported and fed and nurtured by experiences in life. The richer your life experience is, the more creative you'll become."

7. About reflection and thinking, the book elaborates two modes: focus mode and diffese mode. It resonates with me as I do see the benefits of switching between data science work during the day time and freestyle dancing in the evening in terms of developing creative ideas and unstuck myself from difficult problems.

   In her talk, she described two modes of thinking: focus mode and diffuse mode. Focus mode is exactly what it sounds like. It's how we think when we're trying to accomplish a task or memorize something. Our thinking is usually confined to neural paths we've already created. Diffuse mode is a more "relaxed set of neural states" that allows our thinking to take off, range widely, and process or even create new ideas. When we are learning, we need both. And when we feel stuck in our thinking, unable to understand a concept, unable to unravel a challenge, we especially need the diffuse mode.

8. A combination of confidence and humility is a good characteristic. I've never thought about them deeply as a combo, that's why I've never found the sweet spot.

   Confidence is important because nobody will follow you unless they believe you know where you're going and you'll find a way to get there. If that confidence isn't tempered by humility, though, it becomes arrogance.

   Humility is just the recognition that you can't do it by yourself whatever "it" is-either because you simply can't, because you don't know enough, or because it won't be as fun or fulfilling if you go it alone.

   Confidence is simply the expectation that you'll find a way to win-somehow.

9. People have good side and bad side. If you believe in their good side, they do so. From another perspective, it's often not their fault if they choose to express the bad side.

   In any relationship, business or personal, somebody has to trust more or trust first to break inertia and build up positive momentum.

   As important as it is for us to trust in positive intentions, if we want people to trust in ours, we need to behave accordingly. We need to build a well of trust to draw on.

   We're all human; we're all going to lose our tempers or handle a delicate situation poorly or not show as much compassion as we should or make a poor judgment call. When we're on the receiving end, if we can take a breath, find a little empathy, and trust that the other person has good intentions that didn't pan out, we can avoid a total breakdown in the flow of ideas and learning and collaboration.

   I read a striking definition of trust recently: "Trust is a relationship of reliance." Aren't we all reliant on each other if we want to learn, grow, and expand our possibilities? We can choose to support that relationship or tear it down. If we choose the second option, we're only limiting ourselves. If we choose the first, the possibilities are infinite.

10. This is from my experience: I only think hard, struggle and learn, when I'm dealing with my own unique life path, I don't take time to think when I follow other's path or live to other's expectation.

    You may know the quote often attributed to Oscar Wilde: "Be yourself; everyone else is taken." (What he actually wrote is more cynical: "Most people are other people. Their thoughts are someone else's opinions, their lives a mimicry, their passions a quotation." Maybe because of my background and the potential prejudgments that came with it, I've spent most of my life working hard to just be me to understand who that person is, the contributions I have to offer, what I believe, and my purpose and passions. If I hadn't followed this path, I would have missed out on so much learning.

    Active learners know that it's hard to learn when your mental energy is focused on trying to be somebody other than yourself. Instead of being open and curious, you'll be defensive. You'll be putting up barriers and withholding your brilliance. And then the people around you will do the same. Most of us can sense when people aren't being authentic, and it makes us trust them less.

    Active learners like Marvin pursue authenticity by recognizing their unique value and talents, figuring out what matters to them and why, and then leveraging it to have a positive impact.

    It's all about bringing who you are to the moment so that you're comfortable and open-minded enough to learn important lessons and ideas as they arise.

11. Everyone knows we need do the right things, but when it comes difficult situations, would you like to prioritize it above all else?

    This is vital, because over time, depending on environments and circumstances and your own choices, your sense of right and wrong can suffer from stepwise degradation. You stray over the line, stray a bit further the next time, justifying one bad action after another. Stray too far over the line and you can lose sight of it entirely. Eventually, you lose the ability to know what doing the right thing looks like.

    The best thing that happens when we do the right thing is that we feel good about our choices and the impact we're having on the world, and that inspires us to keep doing the right thing. Values aren't some thing you write down on a piece of paper and then put in a drawer or hang on the wall. Values are something you use to take good action. It isn't always the easy choice, but it's always the best choice and the one that helps you learn the most powerful lessons.

12. Input and output are different things. We collect information by inputing knowledge from outside, and we make sense of those knowledge by neural-networking it within our brain and outputing it in a little different way which requires our logical, critical, and creative thinkings.

    Two things happen in the brain that help us "learn what we know." One is that we believe ideas more when we share them with others verbally, especially if we're trying to convince others that they're true. Psychologists call it the "saying is believing effect." Want to convince yourself to make time to exercise three times a week? Try convincing somebody else that they could fit a simple exercise regimen into their schedule. Another is that speaking (and writing) brings a different part of our brain into play than just thinking, which changes how we think about an idea. It's one reason that we can struggle and struggle to come up with a solution to a problem, but almost as soon as we explain the problem to another person out loud, a good solution pops into our head. Talking it out forces us to slow down, zoom out (simplify), and order our thoughts.

    Sometimes, it's audience's engagement and support force us further along the learning journey.

    I learned things I didn't know, and I learned what I already knew, as Timo put it, as I analyzed leadership, considered it from different angles, and expanded or supported my ideas. Active learners use this process to codify their ideas into something digestible and easily shared. When you codify it, you can scale it.

    Teaching well also forces you to stay on top of your game, to continually look for new material to keep your ideas current and relevant. And it forces you to learn good storytelling, an invaluable skill. Stories are stickier than almost any other kind of information. If you want an idea to stay with people, you better be able to convey it in a relevant, compelling story with emotion and tension.

13. Many know "people go first", few know how to do it. If you want them to care about what you care about, you need to care about them first.

    Active learners understand that people-not knowledge or results-should be the priority. How we support people, how we show our gratitude for them, how we show our interest or concern for them has a much greater impact, especially over time, than the latest quarterly earnings or the latest market rankings. I've said it before: I really like to win. But you don't win for long if the people who make the winning possible don't know how much they count.

    I have always admired Geoff Colvin, senior editor-at-large of Fortune magazine and author of books like Talent Is Overrated and Humans Are Underrated. When he joined me on my podcast, he described the kinds of high-value work that only humans can do and that technology or AI can't: empathy, collaboration, and the insights or learning we generate along the way.

Substack

The greatest muscle you can build is urgency. Decrease the time between having an idea and getting it done. Everything changes – Codie Sanchez

You either chase your one big goal with everything you’ve got, or nothing will happen. Trying to be balanced is what’s wrong with society.

Success in any field comes from IMBALANCE.

Hard work only feels bad if it’s building someone else’s dream, not yours.

The most important thing in your career: Speed.

If you answer emails fast, walk fast, talk fast, get sh*t done fast, you will make a lot of money. No sense of urgency, you won't. Nick Huber

― The Most Successful People I Know Have a Psychopathic Sense of Urgency - Unfiltered by Tim Denning [Link]

UBER: Distribution is The King - Capitalist Letters [Link]

Great business with potential of continuous growth and expansion, and cheap stock price.

Ecosystem is a huge advantage because it creates cross-platform efficiencies.

The concept of network effect was first laid out in 1985, by Carl Shapiro and Michael Katz in their seminal paper “Network Externalities, Competition, and Compatibility.”

According to Russ Harris, author of The Happiness Trap, values are “how we want to be, what we want to stand for, and how we want to relate to the world around us.”

Values are attributes of the person we want to be.

― How Successful People Timebox - Nir Eyal's Substack [Link]

Identify your values -> turn values into time commitments -> create a timeboxed calendar -> track distractions -> reflect and refine weekly. Do remember to schedule fun activities, use flexible categories, and be aware that the goal isn't finishing tasks.

Some key ideas in the article backed by behaviorial science:

• According to The Happiness Trap by Russ Harris (Acceptance and Commitment Therapy - ACT), productivity should align with personal values (e.g., health, relationships, growth) rather than just task completion.
• People often ignore realistic time estimates in favor of optimistic ones, leading to overpacked schedules and missed deadlines.

Especially in the Bay Area, the problem isn’t mediocrity—it’s misdirected excellence. Kids under Chua’s parenting style rarely have a choice in their own extracurriculars from elementary through high school. (I doubt being vice-president of the National Honor Society is a dream to most.) Sure, it can produce a passable overachiever who knows how to get A’s. But to produce someone capable of real vision, high agency, and contrarian thinking, the irony is that that overachiever may be ill-prepared as we approach an era where AI handles rote tasks and the knowledge economy demands more creativity.

― How to Raise High-Agency Kids - Rebecca Wang [Link]

True excellence and future success come from fostering agency—self-directed purpose, curiosity, and ownership—rather than forcing kids to conform to hyper-competitive, checklist-driven achievement cultures (like those common in the Bay Area).

What's the root problem? - misdirected excellence

What's the solution? - Give kids structure (boundaries, values) but autonomy (freedom to pursue interests).

This isn’t about faking confidence. It’s about understanding the low-pressure way to join a group.

Our ability to notice intricate details allows us to ask the specific questions that make others feel truly seen.

In a world where everyone is clamoring to be heard, the ability to observe and truly listen becomes your superpower.

Robert Greene's The 48 Laws of Power completed the picture with "Never Outshine the Master", a lesson teaching the power of blending in rather than disrupting. Don't announce your presence; become part of the scenery, then contribute when appropriate.

― The Spy Trick to Joining Any Conversation (Even If You're Anxious) - AnifragileADHD [Link]

For neurodivergent individuals (ADHD, social anxiety, etc.), socializing isn’t about performing—it’s about strategic observation and gradual integration. This article is backed by psychology and behavioral science.

Small tips:

1. Stand inside the group (not on edges) and listen silently at first.
2. Linger quietly to blend into the social environment.
3. Wait for a group member to naturally include you.
4. Ask open-ended questions about others’ interests.
5. Sustain conversation with follow-up questions.

Articles and Blogs

Scientists discover quantum computing in the brain - Brighter [Link]

This research bridges quantum physics, biology, and information theory, suggesting that life evolved to exploit quantum mechanics for survival and intelligence. It challenges reductionist views of biology and could redefine our understanding of consciousness, disease, and even the origins of life.

Here are the 19 US AI startups that have raised $100M or more in 2025 - TechCrunch [Link]

Just as “internet” evolved from buzzword to business backbone, AI is following the same playbook.

― In 2025, venture capital can’t pretend everything is fine any more - Pivot to AI [Link]

Venture capital in 2025 is a dying industry clinging to AI as its last hope, with most investment funneled into OpenAI and a few other hyped players while the rest of the startup ecosystem collapses. The sector, which thrived on zero-interest-rate euphoria, now faces a harsh reality: no exits, frozen IPOs, and a market unwilling to fund early-stage ventures. VCs blame Trump’s chaotic tariffs—despite many having supported him—but the real issue is their own inability to adapt to a normal economy. The NVCA report offers no solutions, just desperate optimism, as the industry’s leaders—many of whom lucked into success—flail in ideological fringe movements and pray for a miracle. The only remaining question is whether AI will keep the bubble inflated long enough for them to cash out before it all implodes.

The walled garden cracks: Nadella bets Microsoft’s Copilots—and Azure’s next act—on A2A/MCP interoperability - VentureBeat [Link]

Nadella’s endorsement signals Microsoft’s commitment to open protocols over proprietary ecosystems, aligning with his long-standing advocacy for interoperability (e.g., ONNX, GitHub’s multi-model approach). By backing A2A (agent-to-agent communication) and MCP (model-data context standardization), Microsoft ensures Copilot, Foundry, and Azure AI can seamlessly integrate with third-party AI agents and tools. This move preempts enterprise concerns about vendor lock-in, a criticism of past Microsoft products.

Car Companies Are In A Billion-Dollar Software War, And Everyone's Losing - InsideEVs [Link]

why it's so hard to shift from lagacy automaker to SDV (software designed vehicle) company?

• Cultural shift: Legacy automakers treated software as an afterthought, not a core product. Now, they must adopt a Silicon Valley-like approach.
• Supplier dependence: Traditional automakers rely on suppliers for ECUs, creating a tangled web of software layers.
• Safety vs. agility: They must balance "move fast and break things" with "zero defects or recalls."
• Hybrid challenges: Slowing EV demand means SDV systems must also work with internal-combustion vehicles, complicating power and update logistics.

Legacy automakers must become software companies to survive, but the transition is painfully slow and expensive. The winners will be those who can blend Silicon Valley speed with automotive-grade reliability—something no traditional automaker has fully achieved yet.

8 Reasons Leadership Is Hard And Why Few Are Prepared To Lead - Forbes [Link]

The most inspiring leaders today aren’t just adapting—they’re rewriting the rules. Leadership isn’t a pinnacle; it’s a daily practice of courage and reinvention. The world doesn’t need more bosses; it needs architects of possibility.

Summary:

1. The Myth of the Omniscient Leader

Shift: From "knowing it all" to curiosity-driven collaboration.

• Action: Adopt a "Learn It All" mindset (Microsoft’s Satya Nadella famously replaced "Know It All" with this).
  
• Tool: Host "No Answers Meetings" where leaders openly discuss unsolved problems, inviting teams to co-create solutions. Example: Google’s "20% Time" empowers employees to explore innovations beyond their core roles, democratizing problem-solving.

2. Embracing the Illusion of Control

Shift: From command-and-control to adaptive stewardship.

• Action: Practice "Scenario Planning" (like Shell Oil’s famed strategy) to prepare for multiple futures, not just one.
  
• Mindset: View volatility as a laboratory for innovation. Spotify’s "Fail Fast, Learn Fast" approach rewards experimentation.
  
• Quote: "The art of leadership is not to control, but to unleash." — Reed Hastings, Netflix.

3. The Leadership Pipeline Crisis

Root Cause: Short-term efficiency has gutted long-term talent development.

• Fix: Reverse Mentorship Programs (e.g., GE’s junior employees mentor execs on digital trends).
  
• Metric: Track "Readiness Ratios"—how many high-potentials are prepared for next-level roles?
  
• Warning: Deloitte’s research shows 89% of executives see "weak leadership benches" as their top threat.

4. The Death of Cookie-Cutter Playbooks

New Rule: Context over conformity.

• Action: Build "Modular Strategies"—flexible frameworks adjusted in real-time (like Amazon’s "Working Backwards" method).
  
• Tool: Use "Pre-Mortems" (anticipating failures before launch) to stress-test strategies. Example: Blockbuster’s rigid playbook failed, while Netflix’s pivot to streaming embraced uncertainty.

5. Respect as a Daily Earned Currency

Key: Authenticity > Authority.

• Action: Practice "Radical Transparency" (like Bridgewater Associates’ culture of brutal honesty).
  
• Tool: Replace "All Hands Meetings" with "All Hearts Meetings"—forums for empathy and vulnerability. Example: Edelman’s Trust Barometer shows employees trust "a peer like me" 3x more than CEOs.

6. Rebuilding Trust in Judgment

Antidote: Inclusive Decision-Making.

• Action: Form "Shadow Boards" (e.g., Gucci’s millennial council advising execs).
  
• Rule: For major decisions, require "Disagree & Commit" (document dissent but align once decided). Example: Patagonia’s CEO involves employees in sustainability bets, building trust through shared stakes.

7. Titles vs. Influence

New Power Model: Fluid Hierarchies.

• Action: Adopt "Holacracy Lite" (like Zappos’ role-based authority, not title-based).
  
• Symbolic Step: Drop "CEO" for "Chief Enabler" (as some startups do to signal servant leadership).
  
• Stat: 72% of Gen Z workers prefer "Project Leaders" over "Managers" (McKinsey, 2024).

8. The Reinvention Imperative

Framework: "Learn, Unlearn, Relearn" (Alvin Toffler’s future-proofing mantra).

• Tool: "Skills Gap Heatmaps"—quarterly self-assessments on emerging competencies (e.g., AI literacy).
  
• Example: Adobe’s "Kickbox" program gives employees $1,000 to test new ideas, forcing leaders to adapt.

The Path Forward: Leadership as a Dynamic Practice

Your closing question—"Will you be one of them?"—is the call to action. Leaders who thrive will:
1. Lead with Questions, not answers.
2. Treat Trust as Currency, not a given.
3. Build Antifragile Teams (Nassim Taleb’s concept of growing stronger through chaos).
4. Measure Success in Learning Cycles, not quarterly profits alone.

Microsoft Follows Competitors Amazon, Meta, and Google in Employee Productivity Crackdown [Link]

The pandemic hiring spree, rising interest rates, and the AI arms race have forced tech giants to abandon the "growth at all costs" mindset. Instead, they’re:

• Prioritizing speed (fewer managers = faster decisions)
• Maximizing output per employee (via stack ranking and attrition policies)
• Investing savings into AI (where Microsoft is battling Google and OpenAI)

Master The Psychology Of Building An Unforgettable Personal Brand - Forbes [Link]

When your brand is rooted in internal conviction, it radiates effortlessly. The right opportunities find you.

"My worth isn’t measured by likes; it’s measured by impact."

"If they don’t buy, it’s not a rejection—it’s a mismatch."

"Outcomes are data, not identity."

"Consistency today compounds into authority tomorrow."

Zero to One: Learning Agentic Patterns - Philschmid [Link]

This guide explores techniques such as prompt chaining, routing, parallelization, reflection, tool integration, planning, and multi-agent collaboration. It features practical code examples for each pattern, enabling the development of efficient, context-aware workflows with Google DeepMind Gemini. Emphasis is placed on structured strategies to enhance task delegation and agent coordination.

Our research shows that by 2030, data centers are projected to require \(\text{\$6.7}\) trillion worldwide to keep pace with the demand for compute power. Data centers equipped to handle AI processing loads are projected to require \(\$5.2\) trillion in capital expenditures, while those powering traditional IT applications are projected to require \(\$1.5\) trillion in capital expenditures (see sidebar “What about non-AI workloads?”). Overall, that’s nearly \(\$7\) trillion in capital outlays needed by 2030—a staggering number by any measure.

To qualify our \(\$5.2\) trillion investment forecast for AI infrastructure, it’s important to note that our analysis likely undercounts the total capital investment needed, as our estimate quantifies capital investment for only three out of five compute power investor archetypes—builders, energizers, and technology developers and designers—that directly finance the infrastructure and foundational technologies necessary for AI growth (see sidebar “Five types of data center investors”). Approximately 15 percent (\(\$0.8\) trillion) of investment will flow to builders for land, materials, and site development. Another 25 percent (\(\$1.3\) trillion) will be allocated to energizers for power generation and transmission, cooling, and electrical equipment. The largest share of investment, 60 percent (\(\$3.1\) trillion), will go to technology developers and designers, which produce chips and computing hardware for data centers. The other two investor archetypes, operators, such as hyperscalers and colocation providers, and AI architects, which build AI models and applications, also invest in compute power, particularly in areas such as AI-driven automation and data center software. But quantifying their compute power investment is challenging because it overlaps with their broader R&D spending.

― The cost of compute: A $7 trillion race to scale data centers - McKinsey [Link]

The Comfortable Life is Killing You - Poetic Outlaws [Link]

Meaning is forged in resistance - Meaning is a byproduct of engagement with resistance. Joy emerges when we meet challenges worthy of our souls. To paraphrase Camus: The struggle itself is enough.

Agentic AI Is Already Changing the Workforce - Harvard Business Review [Link]

Papers and Reports

The power of one: How standout firms grow national productivity - McKinsey Global Institute [Link]

Productivity growth is crucial for economic prosperity. The report suggests that instead of waiting for all firms to improve, targeted support for high-potential firms could accelerate national productivity gains.

Identifying and scaling AI 
 use cases - OpenAI [Link]

OpenAI ads, but useful for pitching GenAI use cases. It offers guidance on identifying and scaling AI use cases within organizations, noting that AI adoption is rapidly increasing and demonstrating significant benefits for early adopters. It emphasizes three key steps for businesses: understanding where AI can add value by focusing on repetitive tasks, skill bottlenecks, and navigating ambiguity; teaching teams fundamental AI use cases like content creation, research, and automation; and prioritizing opportunities using an impact/effort framework to determine which projects to pursue and scale.

ZeroSearch: Incentivize the Search Capability of LLMs without Searching - Alibaba Group [Link]

Traditional RL training requires massive API calls to services like Google Search, costing hundreds of thousands of dollars. ZeroSearch replaces this with a simulated search environment where the LLM itself generates both relevant and irrelevant documents in response to queries.

Real search engines return unpredictable results, complicating training. While ZeroSearch uses curriculum-based rollouts, gradually degrading document quality to teach the model to discern useful information.

It has a cost reduction up to 88% and its performance surpasses real search engines.

AI Global, Global Sector Trends on Generative AI [Link]

Gen AI Traffic Share update - Similarweb @Twitter [Link]

Subdomains and pages only (below).

YouTube and Podcasts

Fed Hesitates on Tariffs, The New Mag 7, Death of VC, Google's Value in a Post-Search World - All-In Podcast [Link]

The Physical Turing Test: Jim Fan on Nvidia's Roadmap for Embodied AI - Sequoia Capital [Link]

This lecture introduces the Physical Turing Test, a new benchmark for robotics. Jim Fan from NVIDIA breaks down why solving this is hard—and what tools researchers are using to make progress.

5 Types of AI Agents: Autonomous Functions & Real-World Applications - IBM Technology [Link]

This lecture covers reflex agents, model-based agents, goal-based systems, utility-based frameworks, and learning agents.

Stanford Webinar - Agentic AI: A Progression of Language Model Usage - Stanford Online [Link]

How to connect AI agents to third-party tools using MCP - Underfitted [Link]

Llamacon 2025 - Conversation with Mark Zuckerberg and Satya Nadella - Meta Developers [Link]

关税大棒下的苹果：一场全球供应链的迁徙风暴 - 硅谷101 [Link]

E191｜小而美的机会来了，聊聊这轮AI Agent进化新范式 - 硅谷101 [Link]

Sundar Pichai, CEO of Alphabet | The All-In Interview [Link]

Trump's Big Week: Middle East Trip, China Deal, Pharma EO, "Big, Beautiful Bill" with Ben Shapiro - All-In Podcast [Link]

枪声背后的信任危机：“病不起”的美国人 - 硅谷101 [Link]

Bond crisis looming? GOP abandons DOGE, Google disrupts Search with AI, OpenAI buys Jony Ive's IO - All-In Podcast [Link]

Microsoft Build 2025 | Satya Nadella Opening Keynote - Microsoft [Link]

Exciting new products - copilot studio, foundry local, microsoft discovery, etc!

Google I/O '25 Keynote - Google [Link]

AI mode finally - Smart move to embrace next-gen search. Android XR glass is launching, and Gentle Monster + Warby Parker will be the first eyewear partners. Genimi App has Agent mode is coming. And many more!

NVIDIA CEO Jensen Huang Keynote at COMPUTEX 2025 - NVIDIA [Link]

NVLink Fusion, DGX Spark AI Computer, DGX Station Super Computer, FTX Pro Server, AI Robotics, etc.

I'll tell you my hiring experience. We have about 30 people at 8090 and the way that I have found it to work the best is you have senior people act as mentors and then you have an overwhelming corpus of young very talented people who are AI native. And if you don't find that mix, what you have instead are L7s from Google and Amazon and Meta who come to you with extremely high salary demands and stock demands and they just don't thrive. And part of why they don't thrive is that they push back on the tools and how you use them. They push back on all these things that the tools help you get to it faster. M this is why I think it's so important for the young folks to just jump in with two feet and be AI native from the jump because you're much more hirable frankly to the to the emergent company and the bigger companies you'll have a lot of these folks that see the writing on the wall may not want to adapt as fast as otherwise. Another way for example that you can measure this is if you look inside your company on the productivity lift of some of these coding assistants for people as a distribution of age. What you'll see is the younger people leverage it way more and have way more productivity than older folks. And I'm not saying that as an aegis comment. I'm saying that it's an actual reflection of how people are reacting to these tools. What you're describing is a paradigm shift. It is a big leap. Is you know it's like when I went to college, when I took computer science, it was object-oriented programming. It was like C++. It was compiled languages. It was gnarly. It was nasty work. And then you had these highle abstracted languages. And I used to remember at Facebook, I would just get so annoyed because I was like, why is everybody using PHP and Python? This is like not even real. But I was one of these old lights who didn't understand that I just had to take the leap. And what it did was it grew the top of the funnel of the number of developers by 10x. And as a result, what you had were all of these advancements for the internet. And I think what's happening right now is akin to the same thing where you're going to grow the number of developers upstream by 10x. But in order to embrace that, you just have to jump in with two feet. And if you're very rigid in how you think the job should be done technically, I think you're just going to get left behind. - Chamath Palihapitiya

― AI Doom vs Boom, EA Cult Returns, BBB Upside, US Steel and Golden Votes - All-In Podcast [Link]

Substack

A Parquet file is composed of Row Groups, Column Chunk, and Pages.

Parquet is a self-described file format that contains all the information needed for the application that consumes the file. This allows the software to efficiently understand and process the file without requiring external information. Thus, the metadata is the crucial part of Parquet. They include Magic Number, FileMetadata, and PageHeader.

Google Dremel (the query engine behind BigQuery) inspired Parquet’s approach to implementing nested and repeated field storage. In a 2010 paper introducing Dremel, Google detailed its method for efficiently handling nested and repeated fields in analytics workloads using definition level (for nested fields) and repetition level (for array-like fields). I wrote an article about this approach seven months ago; you can read it here:

― I spent 8 hours learning Parquet. Here’s what I discovered - Vu Trinh [Link]

The overall BigQuery architecture includes independent components for query execution, storage, a container management system, and a shuffler service:

• Colossus: A distributed storage system that holds and stores data.
• Dremel: The distributed query engine.
• Borg is Google’s large-scale cluster management system that can reliably manage and orchestrate compute resources. (Borg is the predecessor of Kubernetes.) We will return to Borg when discussing the Vortex architecture.
• Dedicate shuffle service: Dremel was inspired by the map-reduce paradigm to operate and manage the data shuffle between stages efficiently; Google built a separate shuffle service on top of disaggregated distributed memory. This service backs BigQuery and supports other services, such as Google Dataflow.

― I spent 4 hours learning the architecture of BigQuery's storage engine - Vu Trinh [Link]

1. Extract: The process’s first step is extraction. The needed data is gathered from various sources, such as relational databases or third-party APIs
2. Transform: Extracted data undergoes many potential transformations, including cleaning, filtering, combining from different sources, and formatting to conform to a target schema.
3. Load: The transformed data is loaded into the destination with the predefined schema and constrained.

ELT solves many of the problems associated with ETL.

Most transformation logic can now be handled within the data warehouse using SQL, making it more accessible for users such as data analysts or data scientists. This eliminates the potential performance bottleneck of ETL pipelines.

Most importantly, ELT allows you to keep raw data in the warehouse. This approach offers several advantages. You don’t need to plan transformation logic in advance; instead, the logic can evolve over time based on analytical needs—an especially valuable benefit in today’s agile software development environment.

― ETL and ELT - Vu Trinh [Link]

Apache Airflow Overview - Vu Trinh [Link]

How did Airbnb build their semantic layer? - Vu Trinh [Link]

In a meltdown, discipline beats brilliance.

Over the years, I’ve found that having a simple rule-based system helps me stay grounded. Here are the 4 rules I follow to protect my portfolio:

• I invest a fixed amount monthly — rain or shine.
• I don’t add to losers — keeping them relatively small.
• I don’t sell winners — staying the course and being patient.
• I invest for at least 5 years — to give compounding time to work.

― Bear Market Survival Guide - App Economy Insights [Link]

Salesforce & AI Strategy - Generative Value [Link]

This article discusses the history of Salesforce, what made it successful, the state of the business, and the AI opportunity (or threat) today.

Everything Wrong with MCP - Shrivu's Substack [Link]

How to future-proof your career in the age of AI - Operator's Handbook [Link]

Key Takeaways:

The author’s call to "lean into human strengths while actively engaging with AI" is a compelling middle path. The essay underscores that the future belongs to those who combine AI literacy with irreplaceable human skills—judgment, influence, and adaptability.

Human Competitive Advantages:

• Judgment & Conviction: Ability to make decisions with incomplete/ambiguous data. Distinguishing impactful work from "interesting but useless" projects. Simplifying complexity into actionable frameworks.
• Influence & Execution: Navigating organizational politics and incentives. Building trust and adoption for AI-driven outputs. Understanding unspoken processes and relationships.

Actionable Skills to Cultivate:

• Develop "taste" by studying excellence in your field.
• Gain hands-on experience to pressure-test AI outputs.
• Learn to align stakeholders and drive consensus.
• Build strong interpersonal relationships and reputation.

Adaptability as the Ultimate Skill:

• AI will keep evolving, so continuous learning and flexibility are critical.
• Focus on areas where humans add unique value (judgment, influence, creativity).

This is a very interesting point: "Develop "taste" by studying excellence in your field."

Just like any skill, taste sharpens with exposure and effort. The more you study, critique, and create, the better you’ll get at recognizing—and producing—excellence. In a world flooded with AI-generated content, the people who thrive will be those who can separate the remarkable from the mediocre.

Blogs and Articles

How Airbnb Standardized Metric Computation at Scale - Airbnb Blog [Link]

Digital hygiene - Andrej Karpathy [Link]

Good tips and tricks for digital hygiene, given the pervasive nature of internet fraud and the data collection practices of major tech companies.

Measuring AI Ability to Complete Long Tasks - METR [Link]

The "think" tool: Enabling Claude to stop and think in complex tool use situations - Anthropic [Link]

Anthropic introduces a "think" tool designed to enhance Claude's complex problem-solving by providing a dedicated space for structured reasoning during tasks. This tool differs from extended thinking by allowing Claude to pause and consider necessary information mid-response, particularly beneficial for multi-step processes and tool use. Evaluations on benchmarks like τ-Bench demonstrated significant performance improvements, especially in policy-heavy domains like airline customer service, where optimized prompting alongside the "think" tool proved most effective.

Tiny Agents: a MCP-powered agent in 50 lines of code - HuggingFace [Link]

Anthropic CEO wants to open the black box of AI models by 2027 - Techcrunch [Link]

Powerful AI will shape humanity’s destiny, and we deserve to understand our own creations before they radically transform our economy, our lives, and our future.

― The Urgency of Interpretability - Dario Amodei [Link]

Interpretability isn’t just academic—it’s a prerequisite for safe, controllable AI. The window to solve it is narrowing as AI grows more powerful. By steering resources toward this goal now, we might avoid a future where humanity builds systems it doesn’t understand but can’t afford to stop.

The Jobs That Will Fall First As AI Takes Over The Workplace - Forbes [Link]

Takeaways:

1. Timeline for Disruption:
   • By 2030: 30% of U.S. jobs could be automated (McKinsey).
   • By 2035: White-collar restructuring in finance, legal, and media (Larry Fink, Jamie Dimon).
   • By 2045: 50% of jobs may be fully automated (Goldman Sachs).
   • By 2050: AI could dominate 60-80% of jobs, depending on innovation pace.
2. Most Vulnerable Jobs (Near-Term):
   • Administrative: Data entry, scheduling, customer service (60% automatable, per IPPR).
   • Finance & Legal: Bookkeeping, contract drafting, paralegal work (AI tools like Harvey already achieve 90% accuracy).
   • Creative & Media: Basic graphic design, copywriting, journalism (30% at risk by 2035, Pew Research).
   • Routine STEM Tasks: Coding, data analysis (40% automatable by 2040, WEF).
3. More Resilient Jobs (Longer-Term):
   • Healthcare: Nursing, therapy, and patient care (empathy-driven roles).
   • Skilled Trades: Construction, repair, maintenance (physical labor is harder to automate).
   • Education & Leadership: Teaching, high-level management (requires emotional intelligence).

To protect career:

• Focus on critical thinking, creativity, and AI collaboration (e.g., prompt engineering, AI-augmented decision-making).
• Target Resilient Sectors- Healthcare, education, skilled trades, and AI-adjacent roles (e.g., cybersecurity, AI ethics).
• Push for employer or government-sponsored programs to transition into hybrid (human + AI) roles.
• Embrace Hybrid Roles- Jobs that combine technical skills with human judgment (e.g., AI-assisted healthcare diagnostics) will thrive.

As Ray Dalio warns, the economy faces a "great deleveraging" where AI disrupts jobs faster than new ones emerge. The key is adaptability—those who proactively reinvent their skills today will shape the workforce of tomorrow.

Curation is the new leadership superpower. Here are 3 ways to adopt a curation mindset - FastCompany [Link]

The most transformative leaders of the next decade will be those who master the art of curation—seeing their role as a conduit for the best ideas, not the source of them.

The Obsolescence of the "Omniscient Leader": The pace of change, hyper-specialization, and interconnected challenges (e.g., AI, climate, global markets) make it impossible for one person to have all the answers. Leaders must shift from being "the smartest in the room" to becoming "architects of collective intelligence."

Curation as the Core Leadership Skill:

1. Curating Talent: Prioritize cognitive diversity over homogeneity. Example: Diverse teams solve problems faster (39% efficiency boost).
2. Curating Ideas: Create systems where unconventional thinking flourishes (e.g., Google’s 20% time → Gmail, Maps). Actively seek "outliers" (contrarians, outsiders) to challenge groupthink.
3. Curating Innovation: Design for "structured serendipity" (e.g., Pixar’s open office, IDEO’s cross-industry brainstorming). Embrace cross-disciplinary collisions (e.g., NASA’s tech inspiring sportswear, biomimicry in architecture).

How to Cultivate a Curation Mindset:

• Facilitate, don’t dictate: Ask better questions; let solutions emerge from debate (e.g., Amazon’s "Disagree and Commit").
• Optimize for collaboration, not just efficiency: Space matters (physical or virtual).

Perplexity CEO says its browser will track everything users do online to sell ‘hyper personalized’ ads - TechCrunch [Link]

Perplexity is building a browser (Comet) to track user behavior across the web—explicitly to fuel targeted advertising. It highlights the company’s ambition to emulate Google’s surveillance-capitalism playbook.

Perplexity’s move confirms that the AI search revolution is less about displacing Google’s model than replicating it—with AI as a smarter wrapper for the same ads.

Today’s Most Crucial Leadership Skill Is Systems Thinking - Forbes [Link]

Leaders who master systems thinking don’t just survive uncertainty—they thrive in it, turning complexity into competitive advantage.

Five Key Tools of Systems Thinking for Strategic Leaders

1. Problem Statements: Move from surface-level fixes to systemic solutions. Example: Instead of asking, “How do we get customers to recycle?”, ask, “How can we redesign products and infrastructure for circularity?”
2. Stakeholder Mapping: Identify all affected parties—not just obvious ones. Example: For electric vehicles, consider miners of critical minerals, urban planners, and regulators, not just automakers and buyers.
3. Iceberg Analysis: Look beneath visible events to uncover hidden structures and mindsets. Example: Employee burnout isn’t just about workload—it’s shaped by corporate culture, incentive systems, and societal norms.
4. Causal Loops: Visualize feedback loops to see how actions create ripple effects. Example: A cost-cutting measure in one department may increase inefficiencies elsewhere.
5. Iteration & Testing: Embrace adaptive strategies, not rigid plans. Example: Pilot small-scale solutions, measure impact, and refine before full rollout.

Perplexity CEO shares the Elon Musk–inspired mantra that helped him build the $9 billion rival to OpenAI - Fortune [Link]

Srinivas’s journey highlights resilience, speed, and Silicon Valley’s tight-knit founder network as key drivers of startup success.

1. "It’s Only Over When You Give Up" – Aravind Srinivas, CEO of AI search startup Perplexity, draws inspiration from Elon Musk’s perseverance during SpaceX’s early failures. He told Harvard students that success comes from relentless self-belief, even when others doubt you.
2. Rocketing Valuation – Perplexity, competing with Google and OpenAI, grew from a 1B to 9B and is now in talks to raise funds at an 18B valuation.
3. Forget Pitch Decks, Build Fast – Srinivas advises founders to focus on rapid product iteration rather than lengthy business plans. He admits he doesn’t even know how to make a pitch deck—Perplexity’s success came from live demos.
4. OpenAI Alumni Network – Despite competing with OpenAI, Srinivas maintains a strong relationship with Sam Altman (his former boss at OpenAI). This mirrors the "PayPal Mafia" dynamic, where ex-OpenAI employees now lead major AI firms like Anthropic and Safe Superintelligence.

Marc Andreessen predicts one of the few jobs that may survive the rise of AI automation - Fortune [Link]

Andreessen’s logic suggests focusing on roles where trust, psychology, and networks matter more than data crunching. But don’t underestimate AI’s ability to creep into those domains too.

How To Get Noticed Without Self-Promotion By Using Strategic Visibility - Forbes [Link]

Core Lessons:

1. Hard Work ≠ Visibility: Doing great work is necessary but insufficient. If leaders don’t know what you’re doing, they can’t reward it. Waiting for annual reviews is too late—visibility requires consistent, intentional updates.
2. Humility Has a Hidden Cost: While modesty is admirable, staying silent can render you invisible. Gallup’s data on declining engagement (just 36% in 2020) highlights how disengagement hurts promotion prospects. Visibility isn’t ego-driven; it’s about ensuring your impact is recognized.
3. Visibility ≠ Bragging: Framing contributions as useful knowledge (e.g., "Here’s how I solved X") builds trust and leadership credibility. Sharing wins, failures, and best practices helps the team and positions you as a problem-solver.
4. Tactical Ways to Increase Visibility
   • Share knowledge: Lead "lessons learned" sessions or contribute to internal newsletters.
   • Mentor others: Their success reflects your leadership.
   • Speak up strategically: One substantive insight per meeting > empty chatter.
   • Volunteer for high-impact projects: Align with organizational priorities.
   • Write internally: Document best practices to showcase thought leadership.
5. Emotional Intelligence (EQ) Matters More Than Extroversion
   • Visibility is about meaningful engagement, not being the loudest.
   • Avoid self-deprecating language ("I’m sorry, but…")—speak with conviction.
6. What Leaders Actually Notice
   • Initiative, influence, and alignment with goals matter more than face-time.
   • Working smart (not just late) and collaborating effectively signal leadership potential.

YouTube and Podcast

DOGE updates + Liberation Day Tariff Reactions with Ben Shapiro and Antonio Gracias - All-In Podcast [Link]

2027 Intelligence Explosion: Month-by-Month Model — Scott Alexander & Daniel Kokotajlo - Dwarkesh Patel [Link]

Trump vs Harvard, Nvidia export controls, how DEI killed Hollywood with Tim Dillon - All-In Podcast [Link]

E187 | 关税战难解美国制造业困境，旧秩序正在崩溃 - 硅谷101播客 [Link]

How DeepSeek Rewrote the Transformer [MLA] - Welch Labs [Link]

A lecture explaining the architecture and optimizations behind DeepSeek R1, a language model that improves Transformer efficiency.

Live Demo: Reinforcement Fine-Tuning for LLMs — Build Smarter Models with Less Data l Tutorial - Predibase [Link]

This video was talking about why RFT beats supervised fine-tuning (SFT) in reasoning tasks, giving live demo of an end-to-end RFT workflow, and PyTorch-to-Triton case study showing real-world impact.

Model Context Protocol (MCP), clearly explained (why it matters) - Greg Isenberg [Link]

Trump Rally or Bessent Put? Elon Back at Tesla, Google's Gemini Problem, China's Thorium Discovery - All-In Podcast [Link]

Suffering is mostly mental anguish and mental pain and it just means you don't want to do the task at hand.

The kind of fame that pure actors and celebrities have, I wouldn't want, but the kind of fame that's earned because you did something useful, why dodge that.

People will always want more status uh but I think you can be satisfied at a certain level of wealth.

Not the kind of confidence that would say I have the answer but the kind of confidence that I will figure it out and I know what I want or only I am a good arbiter of what I want.

Pride is the enemy of learning, so when I look at my friends and colleagues, the ones who are still stuck in the past and have grown the least are the ones who were the proudest, because they sort of feel like they already had the answers and so they don't want to correct themselves publicly.

I think everybody puts themselves first that's just human nature, you're here because you survive you're a separate organism.

The happier you are, the more you can sustain doing something, the more likely you're going to do something that will in turn make you even happier, and you'll continue to do it, and you'll outwork everybody else. The more free you are the better you can allocate your time.

There are no problems in the real world other than maybe things that inflict pain on your body. Everything else has to become a problem in your mind first.

Your family is broken but you're going to fix the world. People are running out there to try and fix the world when their own lives are a mess.

I think the only true test of intelligence is if you get what you want out of life and there are two parts to that one is getting what you want so you know how to get it and the second is wanting the right things knowing what to want in the first place.

Usually I think people end up there because they are going on autopilot with sort of societal expectations or other people's expectations or out of guilt or out of like mimetic desire.

Probably the biggest regret will be staying in the relationship after you knew it was over, exactly you should have left sooner, the moment you knew it wasn't going to work out, you should have moved on.

We are naturally hardwired to be pessimists but modern society is very different despite whatever problems you may have with modern society, it is far far safer than living in the jungle and just trying to survive and the opportunities.

Leave all those labels alone. It's better just to look at the problem at hand, look at reality the way it is, try to take yourself out of the equation in a sense.

The less you think about yourself the more you can think about a mission or about God or about a child or something like that.

I don't think there are any formulas i think it's unique to each person it's like asking a successful person how did you become successful each one of them will give you a different story uh you can't follow anyone else's path.

A lot of change is more about desire and understanding than it is about uh forcing yourself or trying to domesticate yourself.

When your mind is under stress, it's because it has two conflicting desires at once... and anxiety I think is sort of this pervasive unidentifiable stress where you're just kind of stressed out all the time and you're not even sure why and you can't even identify the underlying problem. I think the reason for that is because you have so many unresolved problems unresolved stress points that have piled up in your life that you can no longer identify what the problems are.

Life is going to play out the way it's going to play out there will be some good and some bad most of it is actually just up to your interpretation.

The gut is what decides the head, is kind of what rationalizes it afterwards, the gut is the ultimate decision maker.

You can't change other people, you can change your reaction to them.

If you do want to change someone's behavior, I I think the only effective way to do it is to compliment them when they do something you want, not to insult them or be negative or critical when they do something you don't want.

If you can't decide, the answer is no.

Almost invariably the advice that you would give yourself 10 years ago is still the advice that you need to hear today.

On mental things, I think understanding is way more important once you see the truth of something you cannot unsee it... when we really do see something clearly, it changes our behavior immediately, and that is far more efficient than trying to change your behavior through repetition.

Truth is often painful, if it wasn't, we'd all be seeing truth all the time. Reality is always reflecting truth that's all it is why would you not have accessed it already exactly... wisdom is the set of things that cannot be transmitted. If they could be transmitted you know we'd read the same five philosophy books, and we'd all be done, we'd all be wise. You have to learn it for yourself, it has to be rediscovered for yourself in your own context.

You're probably better off only caring about things that are local or things that you can affect. So if you really care about something that's in the news, then by all means care about it but make a difference go do something about it.

Desire is a contract to be unhappy until you get what you want.

The real currency of life is attention it's what you choose to pay attention to and and what you do about it.

― 44 Harsh Truths About Human Nature - Naval Ravikant (4K) - Chris Williamson [Link]

Key Learnings:

• Someone who can do the job peacefully or happily is more effective than someone with unnecessary emotional turmoil
• Fame sought for its own sake is fragile and leads to a constant need to perform.
• People often say things they don't really believe, driven by a desire to be seen as something they are not.
• Status is zero-sum and insatiable, unlike wealth. Status is often comparative, like leaderboards, where one person's gain can be another's loss.
• Self-esteem comes from aligning actions with internal values, especially when difficult. Genuine sacrifice, doing something you want less for something you value more, can build self-esteem.
• True confidence is not having all the answers but the self-belief to figure things out.
• Pride is an enemy of learning and can lead to being stuck in past mistakes.
• Everyone puts themselves first; unapologetic self-prioritization is rare but perhaps more honest. Much of what appears as altruism might be a waste of time if it goes against one's true desires.
• Happiness and freedom are intertwined with efficiency and productivity.
• Many emotional problems arise from the mind creating problems where none exist in the real world. He advises observing one's thoughts objectively to realize unnecessary emotional energy expenditure.
• People often try to fix the world while their own lives are in disarray. He questions the credibility of those who cannot manage their own lives but seek to solve global issues.
• True intelligence is getting what you want out of life by wanting the right things and knowing how to get them.
• Many people go through life unconsciously following societal or mimetic desires. He emphasizes the importance of thinking things through for oneself rather than blindly following others.
• Staying too long in bad situations (relationships, jobs) is a common regret.
• We are naturally hardwired for pessimism due to evolutionary pressures to avoid ruin.
• Humans are dynamic and labels like optimist, pessimist, introvert, extrovert are self-limiting.
• Overthinking about oneself can lead to misery; focusing on something bigger can bring happiness. Overthinking and rumination do not help with happiness.
• There are no universal formulas for success or happiness; each person's path is unique.
• Lasting change comes from desire and understanding, not forcing oneself. He suggests aligning actions with genuine wants for maximal effectiveness.
• Anxiety often stems from having many unresolved and conflicting desires.
• Our interpretations of experiences shape our reality. The same experience can lead to different emotional responses based on individual interpretation.
• The "gut" is the ultimate decision-maker, representing refined judgment accumulated through evolution and experience. He advises trusting this instinct once it's developed.
• You cannot change other people, only your reaction to them. He adds that people change through their own insights or trauma, not by being told to.
• Negative reinforcement is less effective than positive reinforcement in changing behavior.
• If faced with a difficult choice and unable to decide, the answer is often "no." He also suggests that when choosing between two equal options, take the more painful path in the short term.
• Understanding is more important than discipline for mental change.
• Truth, though often painful, is constantly reflected by reality; wisdom is the personal rediscovery and contextual application of timeless truths. He also mentions that many important life lessons are "unteachable" in the sense that they must be experienced firsthand to be truly understood.
• Memorization is becoming less valuable in the age of readily available information; understanding, judgment, and taste are more crucial. He links understanding to solving real problems and finding generalizable truths.
• Philosophy evolves with new knowledge and perspectives. He explains how advancements in science and technology lead to different philosophical outlooks, and even moral philosophy progresses over time.
• Many philosophical paradoxes can be resolved by considering different scales and timeframes. Naval suggests that seemingly contradictory questions like free will and determinism can be understood by shifting perspectives.
• Coordination is essential for societal function; pure libertarianism is unsustainable.
• Modern AI, while powerful, currently lacks true creativity and deep understanding.
• Meaning can be more important than moment-to-moment happiness.
• In an age of news saturation, it's a battle to maintain focus on what truly matters and what one can influence. He emphasizes that attention is the real currency of life and should be spent consciously. Attention, not time or money, is the most fundamental resource in life.
• Getting past one's past is a skill achieved by processing it to be rid of it, not to dwell on it.

I think agents are real, but I think that we are far away from that because we're still at the phase of how do you build reliable software in production for an enterprise versus the toy apps that you see on the internet which is like let me vibe code something. I think these things are worlds apart still. - Chamath Palihapitiya

I think we have not yet figured out how to move the budgets from experimentation to mainline production. Meaning where large chunks of the US economy are comfortable enough with the ways in which hallucinations are managed such that they will replace legacy deterministic code with this new probabilistic model generated code meaning model enabled code. - Chamath Palihapitiya

― Trump's First 100 Days, Tariffs Impact Trade, AI Agents, Amazon Backs Down - All-In Podcast [Link]

Papers and Reports

Orchestrating Agents and Data for Enterprise: A Blueprint Architecture for Compound AI [Link]

This paper contributes to the enterprise AI landscape by offering a comprehensive architectural blueprint for deploying agentic, modular, and data-integrated AI systems that can efficiently leverage LLMs and enterprise assets.

Github

Google Gemini 2.0 with MCP (Model Context Protocol) Servers - Gemini Samples [Link]

Maestro - A Framework for Claude Opus, GPT and local LLMs to Orchestrate Subagents - maestro [Link]

MCP-Agent [Link]

Local Deep Researcher [Link]

News

Accelerate Generalist Humanoid Robot Development with NVIDIA Isaac GR00T N1 - NVIDIA [Link]

Announcing the Agent2Agent Protocol (A2A) - Google for Developers [Link]

Key Takeaways:

1. A2A is an open-source protocol backed by 50+ tech giants (e.g., Salesforce, SAP, Cohere) and consultancies (e.g., Accenture, Deloitte). It allows agents from different vendors/frameworks to communicate, share data, and coordinate tasks without being locked into a single platform.
2. Solving Enterprise Pain Points: Breaks down silos by letting agents interoperate across HR (Workday), CRM (Salesforce), ERP (SAP), and other systems. Example: A hiring manager’s agent can autonomously source candidates, schedule interviews, and run background checks by collaborating with specialized agents.
3. Design Principles:
   • Agent-Centric: Supports unstructured, multi-agent collaboration (beyond rigid "tool" roles).
   • Built on Standards: Uses HTTP, JSON-RPC, and SSE for easy integration.
   • Secure by Default: Enterprise-grade auth (aligned with OpenAPI).
   • Long-Running Tasks: Handles tasks lasting hours/days with real-time updates.
   • Multimodal: Supports text, audio, video, and UI negotiations (e.g., web forms, iframes).

The Chinese goods Americans most rely on, from microwaves to Barbies - Financial Times [link]

Apple Vision Pro 2 Reportedly Cheaper & Lighter, Mac-Tethered Headset Coming Too - Upload [Link]

Others

Machine learning (ML) solutions applied to business problems across various industries:

• ML and LLM system design: 500 case studies to learn from [Link]
• Machine Learning and Data Science Applications in Industry - Firmai [Link]
• Business Machine Learning - Firmai [Link]
• ML System Design Case Studies Repository [Link]
• 500+ Artificial Intelligence Project List with Code [Link]

Blogs and Articles

How to Build a Graph RAG App - Steve Hedden [Link]

Walking through building a graph rag app that improves LLM accuracy using knowledge graphs. It covers data preparation, search refinement with MeSH terms, and article summarization.

We believe that, in 2025, we may see the first AI agents “join the workforce” and materially change the output of companies. We continue to believe that iteratively putting great tools in the hands of people leads to great, broadly-distributed outcomes.

― Reflections - Sam Altman [Link]

Structured Report Generation Blueprint with NVIDIA AI [Link] [YouTube]

Sky-T1: Train your own O1 preview model within $450 - NovaSky [Link]

Agents - Chip Huyen [Link]

This guide provides a comprehensive exploration of AI-powered agents, focusing on their capabilities, planning, tool selection, and failure modes. It delves into the factors determining an agent's performance, how LLMs can plan, and how to augment planning capabilities. It also provides insights into agent failures and how to evaluate them effectively.

The Batch Issue 284 - DeepLearning.AI - Andrew Ng [Link]

Andrew Ng highlights AI Product Management’s growth as software becomes cheaper to build.

DeepSeek V3 LLM NVIDIA H200 GPU Inference Benchmarking - DataCrunch [Link]

Global-batch load balance almost free lunch to improve your MoE LLM training - Qwen [Link]

MoE models struggle with expert underutilization due to micro-batch-level load balancing, which fails when data within a batch lacks diversity. This results in poor expert specialization and model performance.

The paper proposes global-batch load balancing, where expert selection frequencies are synchronized across all parallel groups, ensuring more effective domain specialization and improved performance.

Global-batch load balancing outperforms micro-batch balancing in all tested configurations. It shows improved performance and expert specialization, with models achieving better results across various data sizes and domains.

How to Evaluate LLM Summarization - Isaac Tham [Link]

A quantitative, research-backed framework for evaluating LLM summaries, focusing on conciseness and coherence. This guide explores challenges in summarization evaluation, defines key quality metrics (conciseness, coherence), and improves the Summarization Metric in the DeepEval framework. Includes a GitHub notebook for applying these methods to assess summaries of long-form content systematically.

We just gave sight to smolagents - HuggingFace [Link]

This tutorial is about how to integrate vision capabilities into autonomous agents using smolagents. It explains passing images to agents in two ways: at initialization or dynamically via callbacks. It demonstrates building a web-browsing agent with vision using the MultiStepAgent class and helium. The agent performs actions like navigation, popup handling, and dynamic webpage analysis.

On DeepSeek and Export Controls - Dario Amodei [Link]

Highlighting export controls' impact on AI geopolitics.

Workflows and Agents - LangGraph [Link]

Review of common patterns for agentic systems.

Constitutional Classifiers: Defending against universal jailbreaks - Anthropic [Link] [YouTube]

Anthropic invites everyone to test its new safety classifier that eradicates jailbreaks and further increases Claude's over-refusal rate.

Open-source DeepResearch – Freeing our search agents - HuggingFace [Link]

Hugging Face challenges OpenAI’s Deep Research with an open-source alternative, beating previous SOTA by 9 points.

Choosing the Right AI Agent Framework: LangGraph vs CrewAI vs OpenAI Swarm - Yi Zhang [Link]

Compare LangGraph, CrewAI, and OpenAI Swarm frameworks for building agentic applications with hands-on examples. Understand when to use each framework, and get a preview of debugging and observability topics in Part II.

How to Scale Your Model - Google DeepMind [Link]

Learn how to scale LLMs on TPUs by understanding hardware limitations, parallelism, and efficient training techniques. Explore how to estimate training costs, memory needs, and optimize performance using strategies like data, tensor, pipeline, and expert parallelism. Gain hands-on experience with LLaMA-3, and learn to profile and debug your code.

Three Observations - Sam Altman [Link]

Sam outlines AI trends: AI’s scaling limits, cost reduction, and the future of autonomous agents.

How to deploy and fine-tune DeepSeek models on AWS - HuggingFace [Link]

Deploy and fine-tune DeepSeek-R1 models on AWS using Hugging Face with GPUs, SageMaker, and EC2 Neuron.

Building a Universal Assistant to connect with any API - Pranav Dhoolia [Link]

Convert any OpenAPI spec into an MCP-compatible API assistant without writing custom integration code. Use a generic MCP server to expose API endpoints dynamically. This approach simplifies integration, expands compatibility, and makes scaling API support more efficient.

From PDFs to Insights: Structured Outputs from PDFs with Gemini 2.0 - Philschmid [Link]

Learn to convert PDFs into structured JSON using Gemini 2.0. Set up the SDK, process files, manage tokens, and define JSON schemas with Pydantic. Covers real-world examples like invoices and forms, best practices, and cost management, works within the free tier.

The Hidden Ways We Really Work Together - Microsoft [Link]

Managing LLM implementation projects - Piotr Jurowiec [Link]

Discover how to implement LLMs from initial planning to deployment. Establish project goals, select suitable architectures, preprocess data, train and evaluate models, optimize hyperparameters, and incorporate domain expertise. Tackle challenges such as hallucinations, security risks, regulatory compliance, and scalability limitations. Develop systematic workflows for building and managing LLM-based applications.

How to build a ChatGPT-Powered AI tool to learn technical things fast - AWS [Link]

What Problem Does The Model Context Protocol Solve? - AIhero [Link]

Learn how the Model Context Protocol (MCP) simplifies integrating large language models (LLMs) with external APIs.

MCP acts as a connector between LLMs and external data sources, facilitating interactions with tools without requiring LLMs to understand intricate APIs. By providing a standardized interface, it streamlines integrations with platforms like GitHub, enhancing workflow speed and efficiency.

Most AI value will come from broad automation, not from R&D - Epoch AI [Link]

Epoch AI's article argues against the popular notion that the primary economic benefit of artificial intelligence will stem from its application in research and development. Instead, the authors posit that AI's most significant value will arise from its widespread deployment in automating existing labor across various sectors.

Substack

Tencent: Betting Big on AI - App Economy Insights [Link]

Tencent's proprietary HunYuan framework has developed into a central AI platform catering to both consumers and enterprises. Originally centered on text and conversational AI, HunYuan has expanded to support multimodal capabilities, including image, video, and 3D generation, where it has attained top rankings in industry benchmarks.

Tencent has a Dual-Core AI strategy: It combines its proprietary T1 model with external AI, such as DeepSeek’s R1, in a “double-core” approach. Yuanbao chatbot utilizes both—T1 for deep reasoning and R1 for quick responses—while WeChat Search enhances accuracy by integrating T1 with DeepSeek.

Google: Biggest Deal Ever - App Economy Insights [Link]

Alphabet has completed its largest acquisition to date with a $32 billion deal to acquire cloud security startup Wiz. If successful, this move could redefine GCP’s security portfolio, strengthening its stance as AI-driven cloud computing becomes the focal point.

Papers and Reports

Whitepaper Agents - Authors: Julia Wiesinger, Patrick Marlow and Vladimir Vuskovic [Link]

Google’s whitepaper explains how AI agents use reasoning, tools, and external data to automate tasks, turning large language models (LLMs) into workflow automation systems. Google suggests using LangChain for prototyping and Vertex AI for scaling production-ready agents. its framework provides a standardized approach to ensure reliable AI agent execution.

Key Components

1. Decision Engine – The LLM plans and executes tasks using reasoning methods like ReAct or Chain-of-Thought.
2. Tool Integration – Agents interact with APIs, databases, and real-time data.
3. Orchestration Layer – Manages task execution and decision-making.

Tool Types

1. Extensions – Directly call APIs for automation.
2. Functions – Allow developers to control execution.
3. Data Stores – Use retrieval-augmented generation (RAG) for external data access.

Use Cases

Agents handle tasks like personalized recommendations, workflow automation, and database queries. For example, they can fetch a user’s purchase history and generate tailored responses.

Introducing smolagents, a simple library to build agents - HuggingFace [Link]

Memory Layers at Scale - Meta [Link]

2 OLMo 2 Furious - Alien AI [Link]

HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs [Link]

This paper shows how to build domain-specific reasoning models using a two-stage training process. HuatuoGPT-o1, a medical LLM, enhances complex reasoning using this two-stage approach: (1) supervised fine-tuning (SFT) with complex Chain-of-Thought (CoT) and (2) reinforcement learning (RL) using a verifier to refine reasoning.

Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps - Google DeepMind [Link]

Google DeepMind introduces noise search method, outperforming traditional denoising in diffusion models.

Chain of Agents: Large language models collaborating on long-context tasks - Google Research [Link] [Paper]

SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training - Google DeepMind [Link] [Link]

Explaining why reinforcement learning outperforms supervised fine-tuning for model generalization.

Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge [Link]

A new preference optimization algorithm for LLM-as-a-Judge models.

Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks [Link]

Generative AI models often produce hallucinations, making them less reliable and reducing trust in AI systems. In this work, a multi-agent system is designed using over 300 prompts to induce hallucinations. AI agents at different levels review and refine outputs using distinct language models, structured JSON communication, and the OVON framework for seamless interaction. New KPIs are introduced to measure hallucination levels.

ELEGNT: Expressive and Functional Movement Design for Non-Anthropomorphic Robot - Apple [Link] [Link]

This is very cool.

π0 and π0-FAST: Vision-Language-Action Models for General Robot Control - Hugging Face [Link]

Hugging Face publishes the first open-source robotics foundation models for real-world applications.

Claude’s extended thinking - Anthropic [Link]

Claude 3.7 Sonnet introduces extended thinking, visible reasoning, and improved agentic capabilities for complex tasks.

Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers [Link]

This study evaluated the ability of LLMs to generate novel, expert-level research ideas compared to human experts by recruiting over 100 NLP researchers for idea generation and blind reviews. Results showed that LLM-generated ideas were rated as more novel than human ideas (p < 0.05) but slightly less feasible. While LLMs demonstrated promising ideation capabilities, challenges such as limited idea diversity and unreliable self-evaluation were identified, highlighting areas for improvement in developing effective research agents.

Transformers without Normalization [Link]

Yann LeCun and his team have proposed Dynamic Tanh (DyT) as an alternative to conventional normalization layers in deep learning models. This innovative method, leveraging the scaled tanh function, delivers performance on par with or superior to techniques like LayerNorm and RMSNorm. Notably, its ability to lower computational costs while preserving model efficiency makes it particularly compelling.

Energy [Link]

Most serious issues:

• Aging and overburdened energy infrastructure is the most serious issue.
• Energy demand is at its highest growth rate in 20 years. EV adoption and AI workloads are accelerating the strain on the grid.
• There is increased frequency of extreme weather events causing outages.
• The U.S. experienced twice as many weather-related power outages from 2014–2023 compared to 2000–2009.

Some key trends:

• Energy demand is rising rapidly, especially due to data centers, AI, and EV adoption.
• Extreme weather is causing more frequent and severe power outages.
• The transition to renewables is accelerating, but grid interconnection delays are slowing progress.
• Grid infrastructure is aging and requires massive investment, but funding gaps remain.
• Transformer shortages and long lead times are hindering grid expansion and maintenance.
• Cybersecurity threats and physical attacks on substations are emerging risks.

YouTube and Podcasts

Fixing the American Dream with Andrew Schulz - All-In Podcast [Link]

E179｜DeepSeek技术解析：为何引发英伟达股价下跌？- 硅谷101播客 [Link]

This Year in Uber’s AI-Driven Developer Productivity Revolution - Gradle [Link]

GraphRAG: The Marriage of Knowledge Graphs and RAG: Emil Eifrem - AI Engineer [Link]

Great intro to Graph RAG from Prof Emil Eifrem. Check out neo4j genai ecosystem.

Some articles mentioned: "Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering" [Link], and "GraphRAG: Unlocking LLM discovery on narrative private data" [Link].

Building a fully local "deep researcher" with DeepSeek-R1 - LangChain [Link]

This tutorial reviews DeepSeek R1's training methods, explains downloading the model via Ollama, and demonstrates JSON-mode testing. Test its local "deep research" assistant, which performs web research and iterative summarization with reflection for improved results.

Building Effective Agents with LangGraph - LangChain [Link]

This video shows the difference between agents and workflows and when to use each. You'll Implement patterns like prompt chaining, parallelization, and routing using LangGraph. The session covers building agents, applying advanced patterns, and understanding how LangGraph enhances automation and optimization in AI systems.

Nvidia's GTC 2025 Keynote: Everything Announced in 30 Minutes - Amrit Talks [Link]

How I use LLMs - Andrej Karpathy [Link]

White House BTS, Google buys Wiz, Treasury vs Fed, Space Rescue - All-In Podcast [Link]

Satya Nadella – Microsoft’s AGI Plan & Quantum Breakthrough - Dwarkesh Partel [Link]

Satya Nadella discusses AI, AGI skepticism, economic growth, quantum computing, AI pricing, gaming models, and legal challenges. Notes of insights impressed me:

1. Nadella believes hyperscalers (like Microsoft Azure, AWS, and Google Cloud) will be major beneficiaries of AI advancements. The exponential growth in compute demand for AI workloads—both for training and inference—will drive massive infrastructure needs. Hyperscalers are well-positioned to meet this demand due to their ability to scale compute, storage, and AI accelerators efficiently.
2. He argues that hyperscale infrastructure is not a winner-takes-all market. Enterprises and corporations prefer multiple suppliers to avoid dependency on a single vendor. This structural dynamic ensures competition and prevents monopolization.
3. While there may be a few dominant closed-source AI models, Nadella predicts that open-source alternatives will act as a check, preventing any single entity from monopolizing the AI model space. He draws parallels to the coexistence of closed-source (e.g., Windows) and open-source systems in the past.
4. He highlights that governments worldwide are unlikely to allow private companies to dominate AI entirely. Regulatory and state involvement will likely shape the landscape, further preventing a winner-takes-all scenario.
5. In consumer markets, network effects can lead to winner-takes-all dynamics (e.g., ChatGPT's early success). However, in enterprise markets, multiple players will thrive across different categories.
6. He disagrees with the notion that AI models or cloud infrastructure will become commoditized. At scale, the complexity of managing hyperscale infrastructure and the know-how required to optimize it create significant barriers to entry and sustain profitability.
7. Microsoft aims to build a versatile hyperscale fleet capable of handling large training jobs, inference workloads, and specialized tasks like reinforcement learning (RL). The company focuses on distributed computing, global data center placement, and high utilization of resources to meet diverse AI demands.
8. Nadella envisions a future where AI agents and specialized models will drive even greater compute demand. He emphasizes the importance of building infrastructure that can support both training and inference at scale, while also accommodating evolving AI research and development.
9. Microsoft Research (MSR) has a history of investing in fundamental, curiosity-driven research, often with no immediate payoff. Nadella emphasizes the importance of maintaining this culture, even if the benefits may only materialize decades later. Nadella highlights the difficulty of transitioning from research breakthroughs to scalable products. The role of leadership is to ensure that innovations are not only technically sound but also commercially viable.
10. Nadella envisions quantum computing being accessed via APIs, similar to how cloud services are used today. This could democratize access to quantum capabilities for research and industry.

Instrumenting & Evaluating LLMs - Hamei Husain [Link]

How to Get Your Data Ready for AI Agents (Docs, PDFs, Websites) - Dave Ebbelaar [Link]

The AI Cold War, Signalgate, CoreWeave IPO, Tariff Endgames, El Salvador Deportations - All-In Podcast [Link]

Github

a smol course - HuggingFace [Link]

Good course to help you to align LLM with specific use cases. It includes instruction tuning, preference alignment using DPO/ORPO, LoRA, prompt tuning, and multimodal model adaptation, and it covers creating synthetic datasets, evaluation, and efficient inference.

AI Hedge Fund [Link]

POC for educational purposes.

build-your-own-x [Link]

Interesting resources / tutorials for building technologies from scratch to deepen practical understanding.

Open R1- HuggingFace [Link]

A fully open reproduction of DeepSeek-R1.

AI Tutor Using RAG, Vector DB, and Groq [Link]

GenAI Agents: Comprehensive Repository for Development and Implementation [Link]

DeepSeek Open Infra [Link]

Awesome MCP Servers [Link]

This repository provides access to a selection of curated Model Context Protocol (MCP) servers designed for seamless AI model-resource interaction. It features both production-ready and experimental servers, offering capabilities like file access, database connections, and API integrations. There are frameworks, tutorials, and practical tips to enhance model deployment and maximize resource efficiency in real-world applications.

News

Replit Integrates xAI [Link]

Expand your research with proprietary financial data - Perplexity [Link]

Crunchbase, FactSet, and DeepSeek R1 now power enterprise insights.

Google to acquire cloud security startup Wiz for $32 billion after deal fell apart last year - CNBC [Link]

Leave it to Manus - Manus [Link]

I just finished reading "Daring Greatly: How the Courage to Be Vulnerable Transforms the Way We Live, Love, Parent, and Lead" by Brené Brown. This is the second book of hers I have read. Her words feel like whispers from God.

What she says about wholeheartedness:

“Wholehearted living is about engaging in our lives from a place of worthiness. It means cultivating the courage, compassion, and connection to wake up in the morning and think, No matter what gets done and how much is left undone, I am enough. It's going to bed at night thinking, Yes, I am imperfect and vulnerable and sometimes afraid, but that doesn't change the truth that I am also brave and worthy of love and belonging.”

What she says about vulnerability:

“Vulnerability is based on mutuality and requires boundaries and trust. It's not oversharing, it's not purging, it's not indiscriminate disclosure, and it's not celebrity-style social media information dumps. Vulnerability is about sharing our feelings and our experiences with people who have earned the right to hear them. Being vulnerable and open is mutual and an integral part of the trust-building process.”

“If we're going to find our way out of shame and back to each other, vulnerability is the path and courage is the light. To set down those lists of what we're supposed to be is brave. To love ourselves and support each other in the process of becoming real is perhaps the greatest single act of daring greatly.”

What she says about perfectionism: (I love this part!)

“The problem was thankfully never fixed, and in time the box overflowed as more and more art piled up. I think the dilemma exists because art, among all the other tidy categories, most closely resembles what it is like to be human. To be alive. It is our nature to be imperfect. To have uncategorized feelings and emotions. To make or do things that don't sometimes necessarily make sense.

Art is all just perfectly imperfect.

My fixation with these words from Leonard Cohen's song "Anthem" comes from how much comfort and hope they give me as I put "enough" into practice: "There's a crack in everything. That's how the light gets in."”

What she says about oversharing:

“It's an important question, and the answer is that I don't tell stories or share vulnerabilities with the public until I've worked through them with the people I love. I have my own boundaries around what I share and what I don't share and I stay mindful of my intentions.

First, I only share stories or experiences that I've worked through and feel that I can share from solid ground. I don't share what I define as "intimate" stories, nor do I share stories that are fresh wounds.

Second, I follow the rule that I learned in my graduate social work training. Sharing yourself to teach or move a process forward can be healthy and effective, but disclosing information as a way to work through your personal stuff is inappropriate and unethical.

Last, I only share when I have no unmet needs that I'm trying to fill. I firmly believe that being vulnerable with a larger audience is only a good idea if the healing is tied to the sharing, not to the expectations I might have for the response I get.

What she says about disengagement:

“Disengagement is the issue underlying the majority of problems I see in families, schools, communities, and organizations and it takes many forms, including the ones we discussed in the "Armory" chapter. We disengage to protect ourselves from vulnerability, shame, and feeling lost and without purpose. We also disengage when we feel like the people who are leading us—our boss, our teachers, our principal, our clergy, our parents, our politicians-aren't living up to their end of the social contract.”

“The gap starts here: We can't give people what we don't have. Who we are matters immeasurably more than what we know or who we want to be. The space between our practiced values (what we're actually doing, thinking, and feeling) and our aspirational values (what we want to do, think, and feel) is the value gap, or what I call "the disengagement divide." It's where we lose our employees, our clients, our students, our teachers, our congregations, and even our own children.”

What she says about vulnerabilities in Sales:

“My answer was no. And yes. In that scenario vulnerability is recognizing and owning that you don't know something; it's looking the customer in the eye and saying, "I don't know the answer to that, but I'll find out. I want to make sure you have the correct information." I explained that the unwillingness to engage with the vulnerability of not knowing often leads to making excuses, dodging the question, or-worst-case scenario-bullshitting. That's the deathblow in any relationship, and the one thing I've learned from talking to people who sell for a living is that sales is all about relationships.”

And her Daring Greatly Leadership Manifesto:

“To the CEOs and teachers. To the principals and the managers. To the politicians, community leaders, and decision makers:

We want to show up, we want to learn, and we want to inspire.

We are hardwired for connection, curiosity, and engagement.

We crave purpose, and we have a deep desire to create and contribute.

We want to take risks, embrace our vulnerabilities, and be courageous.

When learning and working are dehumanized, when you no longer see us and no longer encourage our daring, or when you only see what we produce or how we perform, we disengage and turn away from the very things that the world needs from us: our talent, our ideas, and our passion.

What we ask is that you engage with us, show up beside us, and learn from us.

Feedback is a function of respect; when you don't have honest conversations with us about our strengths and our opportunities for growth, we question our contributions and your commitment.

Above all else, we ask that you show up, let yourself be seen, and be courageous. Dare Greatly with us.”

Substack

The key aspect of managing up is to learn to speak the language of your counterpart. If you can speak their language you can understand their goals and fears, and you can communicate at the level they are. You'll be in a better position to be an effective report. — Umberto Nicoletti, Head of R&D at Proemion

The better we understand the goals that our managers have, the less surprising their actions will be. […] Some of the situations where managers act in ways that most dismay or surprise us are when they are acting on their fears and worries. - Joe Chippindale, CTO Coach

― Frameworks for Managing Up as a Software Engineer - High Growth Engineer [Link]

Building Trust:

1. Sincerity — you are honest and transparent, even when it’s uncomfortable. This includes admitting mistakes early, being upfront with challenges, and sharing both good and bad news, without sugar-coating the latter.
2. Reliability — this is about consistency and following through. You do what you say you'll do, you set realistic expectations, and communicate proactively through regular update habits. More on this later in the updates section.
3. Care — you have their best interests in mind. This means understanding their goals and challenges, being proactive in helping them succeed, and showing empathy when things get tough.
4. Competence — finally, you deliver results. This goes beyond technical skills: it's about delivering business value, learning and growing from feedback, and understanding the big picture.

Speaking their language:

1. Map their context

   1. What makes you successful? — What are your goals and concerns?
   2. What makes me successful? — How can I help you reach your goals?

   The only way for you to be successful is to make your manager successful. To do that, you need to be able to map your goals and concerns into their own.

2. Translate impact across altitudes

   For any item you report to your manager, the question you should ask yourself is: why should my manager care about this? And, more subtly: what does my manager care about this?

3. Create explicit agreements

   1. Scope of ownership — do you know what decisions you can make autonomously vs when you need to involve your manager?
   2. Success criteria — how do you know if what you do is successful? Do you know how impact will be measured?
   3. Mutual expectations — do you know what your manager needs from you? And do they know what you need from them?

Creating effective updates

1. Define your update stack

   1. Async messages (daily) — about significant progress or blockers.
   2. Written reports (weekly) — structured updates about key results and next steps.
   3. 1:1s (weekly or biweekly) — deeper conversations about growth, wellbeing, and strategy.

2. Make every update count

   1. Why does this matter to my manager?
   2. What should they do with this information?

3. Build a feedback loop

   Use 1:1s, retrospectives, and feedback moments to inspect your update process: what's working? What feels like noise? What critical information is missing?

JD Vance's AI Summit Paris Speech - Artificial Intelligence Survey [Link] [YouTube]

Here are some of JD Vance's main points regarding AI, on behalf of the Trump Administration:

• Vance emphasizes AI's potential for revolutionary applications and economic innovation and advocates against being too risk-averse. This is the main stance of this optimistic speech - more of AI opportunity, less of AI safety.
• He states the administration aims to ensure American AI technology remains the gold standard and the U.S. is the preferred partner for AI expansion. The U.S. wants to partner with other countries in the AI revolution with openness and collaboration, but this requires international regulatory regimes that foster creation rather than strangling it.
• He expresses concern that excessive regulation could stifle the AI industry and supports a deregulatory approach. He mentions the development of an AI action plan that avoids overly precautionary regulatory regimes, while ensuring that all Americans benefit from the technology and its transformative potential. The administration is troubled by foreign governments tightening regulations on U.S. tech companies with international footprints. Vance states that preserving an open regulatory environment has encouraged American innovators to experiment.
• He stresses that American AI should not be co-opted for authoritarian censorship and should be free from ideological bias.
• He notes the importance of building the most powerful AI systems in the U.S. with American-designed and manufactured chips.
• He believes AI should be a tool for job creation and making workers more productive, prosperous, and free. The administration will always center American workers in its AI policy and ensure that AI makes workers more productive. For all major AI policy decisions coming from the federal government, the Trump Administration will guarantee American workers a seat at the table.

Elon Musk Blocked a Bill to Stop Amazon from Helping Kids Kill Themselves - BIG by Matt Stoller [Link]

In December, Elon Musk pushed for the reduction of government funding legislation, which led to the removal of several provisions. One provision removed due to Musk's intervention was the Youth Poisoning Prevention Act, which would have prevented consumers from buying concentrated sodium nitrite, a chemical often used in teenage suicides. This chemical, while used in low concentrations as a food preservative, is lethal in high concentrations and has no household uses.

The article highlights that Musk, who has significant political power, can make harmful mistakes, sometimes unknowingly. The author notes that the removal of the provision was considered a mistake that could be fixed. Despite bipartisan support for the priorities, there has been no action taken to reinstate them. He questions whether anyone will address and rectify the issues that arise from actions taken by figures like Musk and Trump.

Deep Research, information vs. insight, and the nature of science - Interconnects [Link]

This is a very interesting point: the article considers how AI might challenge Thomas Kuhn's theories of scientific revolutions. Kuhn's The Structure of Scientific Revolutions describes how science evolves, with scientists forming paradigms around theories and using them to gain knowledge until limitations necessitate a new paradigm. Here's how AI might challenge Kuhn's theories:

• AI is accelerating scientific progress, potentially faster than paradigms can be established. The fundamental unit of scientific progress is reducing so quickly that it redefines experimentation methods.
• Kuhn emphasizes that scientific knowledge is a process, not a set of fixed ideas. AI's emergence challenges this.
• Kuhn suggests science is done by a community that slowly builds out the frontier of knowledge, rather than filling in a known space. The article questions how the dynamics of science will change with AI systems.
• Kuhn states that to reject one paradigm requires the simultaneous substitution of another. The article implies that AI's rapid advancements may disrupt this process.

Check out this impressive list of stories they’ve broken since Trump took office:

• Elon Musk Plays DOGE Ball—and Hits America’s Geek Squad (Steven Levy, 1/22/25)
• DOGE Will Allow Elon Musk to Surveil the US Government From the Inside (Vittoria Elliott, 1/24/25)
• Elon Musk Lackeys Have Taken Over the Office of Personnel Management (Vittoria Elliott, 1/28/25)
• Elon Musk Is Running the Twitter Playbook on the Federal Government (Zoë Schiffer, 1/28/25)
• Elon Musk Tells Friends He’s Sleeping at the DOGE Offices in DC (Zoë Schiffer, 1/29/25)
• Government Tech Workers Forced to Defend Projects to Random Elon Musk Bros (Makena Kelly, Vittoria Elliott, 1/30/25)
• Elon Musk’s Friends Have Infiltrated Another Government Agency (Makena Kelly, Zoë Schiffer, 1/31/25)
• US Government Websites Are Disappearing in Real Time (Vittoria Elliott, Dhruv Mehrotra, Dell Cameron, 2/1/25)
• DOGE Staff Had Questions About the ‘Resign’ Email. Their New HR Chief Dodged Them (Makena Kelly, 2/1/25)
• The Young, Inexperienced Engineers Aiding Elon Musk’s Government Takeover (Vittoria Elliott, 2/2/25)
• Pronouns Are Being Forcibly Removed From Government Signatures (Dell Cameron, 2/3/25)
• Elon Musk Ally Tells Staff ‘AI-First’ Is the Future of Key Government Agency (Makena Kelly, 2/3/25)
• Elon Musk’s DOGE Is Still Blocking HIV/AIDS Relief Exempted From Foreign Aid Cuts (Kate Knibbs, 2/3/25)
• A 25-Year-Old With Elon Musk Ties Has Direct Access to the Federal Payment System (Vittoria Elliott, Dhruv Mehrotra, Leah Feiger, Tim Marchman, 2/4/25)
• Federal Workers Sue to Disconnect DOGE Server (Dell Cameron, 2/4/25)
• State Department Puts ‘All Direct Hire’ USAID Personnel on Administrative Leave (Kate Knibbs, 2/4/25)
• Elon Musk’s Takeover Is Causing Rifts in Donald Trump’s Inner Circle (Jake Lahut, 2/5/25)

When It Comes to Covering Musk's Government Takeover, WIRED Is Showing Everyone How It's Done - The Present Age [Link]

The Media Is Missing the Story: Elon Musk Is Staging a Coup - The Present Age [Link]

Mainstream media is refusing to tell the truth. WIRED deserves support.

"Character Limit - How Elon Musk Destroyed Twitter" by Kate Conger and Ryan Mac, as a reference.

2025: the Year of Datacenter Mania - AI Supremacy [Link]

This is an overview of what's happening and going to happen around Data Center Construction, covering a wide range of areas.

AI Expansion and Energy Demand:

The AI race is intensifying, leading to significant capital expenditure by Big Tech and raising concerns about potential harmful consequences. AI data centers' power demands are rapidly increasing, with estimates of needing 10 gigawatts of additional capacity in 2025 alone.

Goldman Sachs Research projects a 165% increase in data center power demand by 2030. By 2027, global AI data center power demand could reach 68 GW and 327 GW by 2030, compared to a total global data center capacity of 88 GW in 2022. Training AI models could require up to 1 GW in a single location by 2028 and 8 GW by 2030.

Infrastructure and Logistical Challenges:

Power infrastructure delays are increasing wait times for grid connections, which can take four to seven years in key regions. Data centers face struggles with local and state permits, especially for backup generators and environmental impact assessments.

A lack of data center infrastructure in the U.S. could cause a shift of construction to other countries. Countries with greater compute access may gain economic and military advantages.

Environmental and Health Concerns:

There are growing concerns that the impact of data centers on human health is being overlooked, and one of President Biden's executive orders acknowledges that data centers are harmful to health.

The environmental cost of AI includes concerns about water consumption, air pollution, electronic waste, and critical materials, in addition to public health concerns around pollution.

Energy Solutions and the Nuclear Option:

To meet AI’s growing power needs, some experts advocate for nuclear energy as the most viable long-term solution. Nuclear energy produces no carbon emissions during operation and offers a reliable, constant energy supply. Tech giants like Microsoft and Google are recognizing nuclear energy’s potential, with Microsoft exploring small modular reactors (SMRs). The adoption of nuclear energy faces obstacles such as high upfront costs, regulatory hurdles, and public skepticism.

Global AI Race and Investments:

The EU is mobilizing $200 billion in AI investments, signifying a global race for AI leadership. The UAE is investing billions in AI data centers in France and is implicated, along with SoftBank and Oracle, in OpenAI's data center project in Abilene, Texas.

The Question of Sustainability:

AI's rapid expansion is testing the limits of power infrastructure, natural resources, and sustainability efforts. If AI continues to expand at its current rate, there is a risk of a gridlocked future limited by energy availability. The future of AI depends on sustainability and the willingness to sacrifice energy for intelligence.

Amazon: Outspending Everyone - App Economy Insights [Link]

Some highlights:

1. Amazon is planning to invest over $100 billion in 2025, primarily in AI-related infrastructure. This is more than any other company, and a 20% increase from 2024.
2. AWS revenue grew 19% Y/Y, and roughly half of the growth is attributed to AI. AWS has a 30% market share in cloud infrastructure. Amazon is focused on custom silicon (Trainium and Inferentia) to improve AI efficiency.
3. The de minimis exemption, which allows imports under $800 to avoid US tariffs, gives companies like Shein and Temu a competitive edge. Amazon Haul was launched last year to compete directly with these companies. Should the de minimis loophole be eliminated, Amazon's superior logistics network could give it an advantage in fulfillment and reliability.
4. Amazon Prime's multi-faceted membership is highly effective at reducing churn. A 2022 study by the National Research Group found that Prime has one of the lowest churn rates, second only to cloud storage and music streaming services. Amazon's detailed purchase data provides advertisers with a valuable advantage, enabling highly targeted CTV ads with industry-leading returns on ad spend (ROAS).

Uber’s Three-Pronged AV Strategy:

1. Fleet partnerships: Uber isn’t building its own AVs. Instead, it partners with companies like Waymo, Motional, and Aurora, integrating their fleets into Uber’s network.
2. Hybrid model: AVs can’t handle all trips—human drivers will fill gaps, handling extreme weather, complex routes, and peak hours for decades.
3. Fleet infrastructure: Uber is investing in charging depots and fleet management to maximize AV asset utilization.

While Tesla is vertically integrated, its rideshare strategy may take a different path. If Tesla adopts an asset-light model, Tesla owners—not Tesla itself—would decide whether to list their AVs on Uber. If maximum utilization is the goal, Uber could be the logical choice.

When it comes to demand aggregation, Uber remains the undisputed leader—its network effects ensure that as long as it aggregates supply, demand will follow, and gross profit will scale.

While the rideshare market will become more fragmented, Uber could still be the biggest fish in a much larger pond. After all, Uber is already the Airbnb for cars.

Tesla has a massive opportunity once the pieces fall into place. But with auto sales under pressure and market share declining, it still faces a long road ahead before claiming the top spot in any market.

― Tesla vs. Uber: Collision Course? - App Economy Insights [Link]

Uber's business model is one of my favorite business models. Not only because it's asset-light, its network effect, etc, but also because it created millions of jobs.

Uber's AV strategy is designed to balance innovation with practicality, ensuring that the company remains competitive while minimizing risks and costs. By leveraging partnerships, maintaining a hybrid model, and investing in infrastructure, Uber is well-positioned to lead the transition to autonomous mobility.

While a partnership between Uber and Tesla is possible and could offer significant synergies, it is not guaranteed. The decision would depend on whether both companies can align their goals and overcome competitive tensions. If Tesla decides to prioritize its own ride-hailing network (Tesla Network), it may choose to compete rather than collaborate with Uber. However, if Tesla sees more value in leveraging Uber’s platform and customer base, a partnership could be a strategic move for both companies.

Microsoft: AI Efficiency Paradox - App Economy Insights [Link]

Google: Capex Arms Race - App Economy Insights [Link]

The End of Search, The Beginning of Research - One Useful Thing [Link]

Huang’s take: “We've really only tapped consumer AI and search and some amount of consumer generative AI, advertising, recommenders, kind of the early days of software. […] Future reasoning models can consume much more compute.”

DeepSeek-R1, he said, has “ignited global enthusiasm” and will push reasoning AI into even more compute-intensive applications.

― NVIDIA: AI's 3 Scaling Laws - App Economy Insights [Link]

Huang introduced a framework for AI’s evolving compute demands, outlining three scaling laws:

• Pre-training scaling: Traditional model growth through data consumption, now enhanced by multimodal learning and reasoning-based data.
• Post-training scaling: The fastest-growing compute demand, driven by reinforcement learning from human and AI feedback. This phase now exceeds pre-training in compute usage due to the generation of synthetic data.
• Inference & reasoning scaling: The next major shift, where AI engages in complex reasoning (e.g., chain-of-thought, search). Inference already requires 100x more compute than early LLMs and could scale to millions of times more.

Jensen Huang outlined a three-layer AI transformation across industries:

• Agentic AI (Enterprise AI): AI copilots and automation tools boosting productivity in sectors like automotive, finance, and healthcare.
• Physical AI (AI for Machines): AI-driven training systems for robotics, warehouses, and autonomous vehicles.
• Robotic AI (AI in the Real World): AI enabling real-world interaction and navigation, from self-driving cars to industrial robots.

Grab: The Uber Slayer - App Economy Insights [Link]

DeepSeek isn’t a threat—it’s validation. If AI inference costs are falling, Meta stands to benefit more than almost any other company. Instead of challenging its strategy, DeepSeek reinforces that heavy AI investments will pay off—not the other way around.

― Meta: DeepSeek Tailwinds - App Economy Insights [Link]

Elon Musk and spiky intelligence - Silver Bulletin [Link]

Interesting study on spiky intelligence, using Elon as a case study. Concepts highlights:

Spiky Intelligence: This refers to individuals who exhibit exceptional abilities in certain areas while being deficient in others. It contrasts with the idea of general intelligence (the "g factor"), where most cognitive abilities are positively correlated. Spiky intelligence is often seen in people who excel in abstract, analytical reasoning but may lack emotional intelligence, empathy, or practical judgment.

Berkson’s Paradox: This statistical phenomenon explains why successful individuals often appear to have significant weaknesses. In highly competitive fields, it’s rare to find people who excel in all dimensions, so success often goes to those with a few standout traits.

YouTube and Podcasts

DOGE vs USAID, Crypto Framework, Google's $75B AI Spend, US Sovereign Wealth Fund, GLP-1s - All-In Podcast [Link]

DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters - Lex Fridman Podcast #459 [Link] [Transcript]

This is a very good one. 5 hours intro and overview of current AI landscape.

Those driver jobs weren't even there 10 years ago. Uber came along and created all these driver jobs. DoorDash created all these driver jobs. So what technology does—yes, technology destroys jobs—but it replaces them with opportunities that are even better. And then, either you can go capture that opportunity yourself, or an entrepreneur will come along and create something that allows you to capture those opportunities. AI is a productivity tool. It increases the productivity of a worker; it allows them to do more creative work and less repetitive work. As such, it makes them more valuable. Yes, there is some retraining involved, but not a lot. These are natural language computers—you can talk to them in plain English, and they talk back to you in plain English. But I think David is absolutely right. I think we will see job creation by AI that will be as fast or faster than job destruction. You saw this even with the internet. Like, YouTube came along—look at all these YouTube streamers and influencers. That didn’t used to be a job. New jobs—really, opportunities—because 'job' is the wrong word. 'Job' implies someone else has to give it to me, like they're handed out, as if it's a zero-sum game. Forget all that—it's opportunities. After COVID, look at how many people are making money by working from home in mysterious little ways on the internet that you can't even quite grasp. - Naval Ravikant

you know as long as you remain adaptive and you keep learning and you learn how to take advantage of these tools you should do better and if you wall yourself off from the technology and don't take advantage of it that's when you put yourself at risk - David Sacks

If you trained on the open web, your model should be open source. – Naval Ravikant.

To keep the conversation moving, let me segue a point that came up that was really important into tariffs. And the point is, even though the internet was open, the U.S. won a lot of the internet—a lot of U.S. companies won the internet. And they won that because we got there "the firstest with the mostest," as they say in the military. And that matters because a lot of technology businesses have scale economies and network effects underneath, even basic brand-based network effects. If you go back to the late '90s and early 2000s, very few people would have predicted that we would have ended up with Amazon basically owning all of e-commerce. You would have thought it would have been perfect competition and very spread out. And that applies to how we end up with Uber as basically one taxi service or how we end up with Airbnb. Meta—Airbnb—it's just network effects, network effects, network effects rule the world around me. But when it comes to tariffs and when it comes to trade, we act like network effects don't exist. The classic Ricardian comparative advantage dogma says that you should produce what you're best at, I produce what I'm best at, and we trade. And then, even if you want to charge me more for it—if you want to impose tariffs for me to ship to you—I should still keep tariffs down because I'm better off. You're just selling me stuff cheaply—great. Or if you want to subsidize your guys—great, you're selling me stuff cheaply. The problem is, that is not how most modern businesses work. Most modern businesses have network effects. As a simple thought experiment, suppose that we have two countries, right? I'm China, you're the U.S. I start out by subsidizing all of my companies and industries that have network effects. So I'll subsidize TikTok, I'll ban your social media but push mine. I will subsidize my semiconductors, which tend to have winner-take-all dynamics in certain categories. Or I'll subsidize my drones and then, exactly—BYD, self-driving, whatever. And then, when I win, I own the whole market and I can raise prices. And if you try to start up a competitor, it's too late—I've got network effects. Or if I've got scale economies, I can lower my price to zero, crash you out of business, no one in their right mind will invest, and then I'll raise prices right back up. So you have to understand that certain industries have hysteresis, or they have network effects, or they have economies of scale—and these are all the interesting ones. These are all the high-margin businesses. So in those, if somebody is subsidizing or they're raising tariffs against you to protect their industries and let them develop, you do have to do something. You can't just completely back down. - Naval Ravikant

I think Sam and his team would do better to leave the nonprofit part alone, leave an actual independent nonprofit board in charge, and then have a strong incentive plan and a strong fundraising plan for the investors and the employees. So I think this is workable. It's just that trying to grab it all seems way off, especially when it was built on open algorithms from Google, open data from the web, and nonprofit funding from Elon and others. - Naval Ravikant

― JD Vance's AI Speech, Techno-Optimists vs Doomers, Tariffs, AI Court Cases with Naval Ravikant - All-In Podcast [Link]

"AI won't take your job; it's someone using AI that will take your job." – Richard Baldwin. The discussion around AI's impact on jobs is often framed as a zero-sum game, but the reality is more nuanced. While AI will displace certain jobs (e.g., self-driving cars replacing drivers), it will also create new opportunities and industries that we can't yet fully envision. The key is adaptability—those who learn to use AI tools will thrive, while those who resist will fall behind.

The Stablecoin Future, Milei's Memecoin, DOGE for the DoD, Grok 3, Why Stripe Stays Private - All-In Podcast [Link]

How to build full-stack apps with OpenAI o1 pro - Part 1 - Mckay Wrigley [Link]

Learn app development using OpenAI o1-Pro with a structured six-prompt workflow.

Open Deep Research - LangChain [Link]

Build and run a deep research agent with LangGraph Studio, customize configurations, compare architectures, and analyze costs.

Paper and Reports

Probabilistic weather forecasting with machine learning [Link]

GenCast's success stems from its ability to generate ensembles of sharp, realistic weather trajectories and well-calibrated probability distributions.

The methodology of GenCast involves several key components:

• GenCast employs a second-order Markov assumption, meaning it conditions its predictions on the two previous weather states, rather than just one. This is done because conditioning on two previous time steps works better than one.
• GenCast is implemented as a conditional diffusion model. Diffusion models are generative machine learning methods that can model the probability distribution of complex data and generate new samples by iteratively refining a noisy initial state. The model predicts a residual with respect to the most recent weather state. The sampling process begins with random noise, which is then refined over a series of steps.
• At each step of the iterative refinement process, GenCast uses a denoiser neural network. This network is trained to remove noise that has been artificially added to atmospheric states. The architecture of the denoiser includes an encoder, a processor, and a decoder. The encoder maps the noisy target state to an internal representation on a refined icosahedral mesh, the processor is a graph transformer, and the decoder maps the internal mesh representation back to a denoised target state.
• GenCast uses a noise distribution that respects the spherical geometry of global weather variables. Rather than using independent and identically distributed (i.i.d.) Gaussian noise on the latitude-longitude grid, it samples isotropic Gaussian white noise on the sphere and projects it onto the grid.
• GenCast's performance is evaluated using various metrics, including:
  • CRPS (Continuous Ranked Probability Score): Measures the skill of a probabilistic forecast.
  • RMSE (Root Mean Squared Error): Measures how closely the mean of an ensemble of forecasts matches the ground truth.
  • Spread/Skill Ratios and Rank Histograms: Used to evaluate the calibration of the forecast distributions.
  • Brier Skill Score: Evaluates probabilistic forecasts of binary events, specifically the prediction of extreme weather events.
  • Relative Economic Value (REV): Characterizes the potential value of a forecast over a range of probability decision thresholds.
  • Spatially Pooled CRPS: Evaluates forecasts aggregated over circular spatial regions of varying sizes to assess the model's ability to capture spatial dependencies.
  • Regional Wind Power Forecasting: Evaluates the model's ability to predict wind power generation at wind farm locations using a standard idealized power curve.
  • Tropical Cyclone Track Prediction: Uses the TempestExtremes tropical cyclone tracker to extract cyclone trajectories from the forecast and analysis data. The model's ability to forecast cyclone tracks is evaluated using position error and track probability.

The United States currently leads the world in data centers and AI compute, but unprecedented demand leaves the industry struggling to find the power capacity needed for rapidly building new data centers. Failure to address current bottlenecks may compel U.S. companies to relocate AI infrastructure abroad, potentially compromising the U.S. competitive advantage in compute and AI and increasing the risk of intellectual property theft.

― AI's Power Requirements Under Exponential Growth - RAND [Link] [pdf]

Genome modeling and design across all domains of life with Evo 2 - Arc Institute [Link]

Evo 2 is a powerful genome modeling and design tool that operates across all domains of life. It can analyze and generate genetic sequences from molecular to genome scale. It accurately assigns likelihood scores to human disease variants, distinguishing between pathogenic and benign mutations in both coding and noncoding regions. It can predict whether genes are essential or nonessential using mutational likelihoods, helping in bacterial and phage gene essentiality studies. It can generate large-scale DNA sequences with structured features like tRNAs, promoters, and genes with intronic structures. It provides zero-shot fitness predictions for protein and non-coding RNA sequences, correlating well with experimental fitness measurements. It robustly predicts the pathogenicity of various mutation types, achieving state-of-the-art performance for noncoding and splice variants.

Large Action Models: From Inception to Implementation [Link]

Microsoft Research published one of the most complete papers in this area, outlining a complete framework for large action models (LAMs) models. The core idea is to bridge the gap between the language understanding capability of LLMs and the need for real-world action execution.

Towards an AI co-scientist [Link]

Uncovering the Impact of Chain-of-Thought Reasoning for Direct Preference Optimization: Lessons from Text-to-SQL [Link]

Direct Preference Optimization (DPO) does not consistently improve performance in the Text-to-SQL task and sometimes even degrades it. Existing Standard Fine-Tuning (SFT) methods are limited by the lack of high-quality training data, and prompting-based methods are expensive, slow, and raise data privacy concerns.

To solve the problems, they generate synthetic CoT solutions to improve training datasets, leading to more stable and significant performance improvements in DPO. They integrate execution-based feedback to refine the model’s SQL generation process, making the optimization process more reliable. And they create a quadruple-based preference dataset to help the model learn to distinguish between correct and incorrect SQL responses more effectively.

MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking [Link]

Google DeepMind developed an innovative approach - Myopic Optimization with Non-myopic Approval (MONA), to mitigate multi-step reward hacking. This MONA methodology is built on two key principles. The first is myopic optimization, where agents focus on maximizing rewards for immediate actions rather than planning multi-step strategies. This ensures that agents do not develop complex, unintelligible tactics. The second principle is non-myopic approval, where human overseers assess the agent's actions based on their expected long-term utility. These evaluations serve as the primary mechanism for guiding agents toward behavior aligned with human-defined objectives, without relying on direct feedback from outcomes.

The Ultra-Scale Playbook: Training LLMs on GPU Clusters - Hugging Face [Link]

This book from Hugging Face explains 5D parallelism, ZeRO, CUDA kernel optimizations, and compute-communication overlap in large-scale AI training. It breaks down scaling bottlenecks, PyTorch internals, and parallelism techniques like ZeRO-3, pipeline, sequence, and context parallelism.

Articles and Blogs

The research found six distinct leadership styles, each springing from different components of emotional intelligence. The styles, taken individually, appear to have a direct and unique impact on the working atmosphere of a company, division, or team, and in turn, on its financial performance. And perhaps most important, the research indicates that leaders with the best results do not rely on only one leadership style; they use most of them in a given week—seamlessly and in different measure—depending on the business situation. Imagine the styles, then, as the array of clubs in a golf pro’s bag. Over the course of a game, the pro picks and chooses clubs based on the demands of the shot. Sometimes he has to ponder his selection, but usually it is automatic. The pro senses the challenge ahead, swiftly pulls out the right tool, and elegantly puts it to work. That’s how high-impact leaders operate, too.

Leaders who have mastered four or more—especially the authoritative, democratic, affiliative, and coaching styles—have the very best climate and business performance.

The leader can build a team with members who employ styles she lacks.

― Leadership That Gets Results - Harvard Business Review [Link]

WeatherNext - Google DeepMind [Link]

GenCast predicts weather and the risks of extreme conditions with state-of-the-art accuracy - Google DeepMind [Link]

Morgan Stanley stated that ASICs perform exceptionally well in certain specific application scenarios, but are highly dependent on the custom needs of particular clients; the development cost of ASICs is usually lower, but their system costs and Software deployment costs may be much higher than GPUs that can be commercially scaled, leading to a higher total cost of ownership. In addition, NVIDIA's CUDA ecosystem is very mature and widely used in Global Cloud Computing Services, with a market position that remains as solid as ever.

― Morgan Stanley: ASICs are overheated, and NVIDIA's position is difficult to shake. - moomoo [Link]

NVIDIA possesses a robust competitive advantage in the AI chip market due to its mature ecosystem, continuous R&D investments, and strong technical capabilities.

• NVIDIA's CUDA ecosystem is well-established, enabling clients to easily deploy and run various workloads. The maturity of this ecosystem means that customers may find it easier to use NVIDIA products compared to adapting software for ASICs or other alternatives.
• NVIDIA has a leading position in the AI chip market, which is reinforced by its presence on every cloud platform across the globe. Investments within NVIDIA's ecosystem benefit from global dissemination, further solidifying its market dominance.
• NVIDIA invests significantly in R&D. The company is expected to invest approximately \(\$16\) billion in R&D this year. This level of investment allows NVIDIA to maintain a 4-5 year development cycle and continuously introduce leading high-performance chips. Custom ASIC development budgets are typically smaller (less than \(\$1\) billion), giving NVIDIA an edge in innovation.
• NVIDIA is difficult to surpass in providing high-end training capabilities. The company focuses on training multi-modal AGI models.

This means DeepResearch can identify cross-domain links or examples that might otherwise be overlooked, offering fresh perspectives. In professional settings, this can support more well-rounded decision-making – for example, a product manager can quickly gather insights from scientific research, market data, and consumer opinions in one place, rather than relying on multiple teams or lengthy research processes. It makes you multifaceted!

― #87: Why DeepResearch Should Be Your New Hire - Turing Post [Link]

Deep Research and Knowledge Value - Stratechery [Link]

OpenAI launched Deep Research in ChatGPT, which is an agentic capability that conducts multi-step research on the internet for complex tasks. It synthesizes knowledge in an economically valuable way but does not create new knowledge.

As demonstrated in the article, it can be useful for researching people and companies before conducting interviews. However, it can also produce reports that are completely wrong by missing major entities in an industry.

This is a good point - The Internet revealed that news was worthless in terms of economic value because the societal value does not translate to economic value. Deep Research reveals how much more could be known, but the increasing amount of "slop" makes it more difficult to find the right information. Information that matters and is not on the Internet has future economic value wrapped up in it.

Proprietary data is valuable, and AI tools like Deep Research make it more difficult to harvest alpha from reading financial filings. Prediction markets may become more important as AI increases the incentive to keep things secret.

As a summary of the impact - Deep Research is a good value, but it is limited by the quality of information on the Internet and the quality of the prompt. There is value in the search for and sifting of information, and this may be lost with on-demand reports. AI will replace knowledge work. Secrecy is a form of friction that imposes scarcity on valuable knowledge. Deep Research is not yet good at understanding some things.

Massive Foundation Model for Biomolecular Sciences Now Available via NVIDIA BioNeMo - NVIDIA Blog [Link]

Grok 3: Intelligence, Performance & Price Analysis - Artificial Analysis [Link]

Grok resets the AI race - The Verge [link]

Grok-3 (chocolate) is the first-ever model to break 1400 score and is now #1 in Arena.

Grok 3 Beta — The Age of Reasoning Agents - Grok Blog [Link]

Motivated by unmet needs in the modern scientific discovery process and building on recent AI advances, including the ability to synthesize across complex subjects and to perform long-term planning and reasoning, we developed an AI co-scientist system. The AI co-scientist is a multi-agent AI system that is intended to function as a collaborative tool for scientists. Built on Gemini 2.0, AI co-scientist is designed to mirror the reasoning process underpinning the scientific method. Beyond standard literature review, summarization and “deep research” tools, the AI co-scientist system is intended to uncover new, original knowledge and to formulate demonstrably novel research hypotheses and proposals, building upon prior evidence and tailored to specific research objectives.

― Accelerating scientific breakthroughs with an AI co-scientist - Google Blog [Link]

An Interview with Uber CEO Dara Khosrowshahi About Aggregation and Autonomy - Stratechery [Link]

OpenAI Deep Research Guide - DAIR.AI [Link]

This is a super helpful guide to Deep Research Tools.

You provide use cases and examples, very inspiring, and tips, etc.

Studies on the brain affirm the benefits of Tom’s visualization technique: Imagining something in vivid detail can fire the same brain cells actually involved in doing that activity. The new brain circuitry appears to go through its paces, strengthening connections, even when we merely repeat the sequence in our minds. So to alleviate the fears associated with trying out riskier ways of leading, we should first visualize some likely scenarios. Doing so will make us feel less awkward when we actually put the new skills into practice.

― Primal Leadership: The Hidden Driver of Great Performance - Harvard Business Review [Link]

Imagine it, fake it, and make it.

Our research tells us that three conditions are essential to a group’s effectiveness: trust among members, a sense of group identity, and a sense of group efficacy.

― Building the Emotional Intelligence of Groups - Harvard Business Review [Link]

Team is so important to leaders.

Interrupt the ascent.

When people are continually promoted within their areas of expertise, they don’t have to stray far from their comfort zones, so they seldom need to ask for help, especially if they’re good problem solvers. Accordingly, they may become overly independent and fail to cultivate relationships with people who could be useful to them in the future. What’s more, they may rely on the authority that comes with rank rather than learning how to influence people. A command-and-control mentality may work in certain situations, particularly in lower to middle management, but it’s usually insufficient in more senior positions, when peer relationships are critical and success depends more on the ability to move hearts and minds than on the ability to develop business solutions.

― The Young and the Clueless - Harvard Business Review [Link]

Don't fall into the independence trap.

Accelerating scientific breakthroughs with an AI co-scientist - Google Research [Link]

News and Comments

Introducing deep research - Open AI [Link]

Introducing Perplexity Deep Research - Perplexity [Link]

Trend is on deep research.

Shopify Tells Employees to Just Say No to Meetings - Bloomberg [Link]

Who will control the future of AI? - The Washington Post [Link]

Sam promotes a U.S.-led strategy to ensure AI development aligns with democratic values and remains under the leadership of the U.S. and its allies.

This new architecture used to develop the Majorana 1 processor offers a clear path to fit a million qubits on a single chip that can fit in the palm of one’s hand.

Microsoft is now one of two companies to be invited to move to the final phase of DARPA’s Underexplored Systems for Utility-Scale Quantum Computing (US2QC) program – one of the programs that makes up DARPA’s larger Quantum Benchmarking Initiative – which aims to deliver the industry’s first utility-scale fault-tolerant quantum computer, or one whose computational value exceeds its costs.

― Microsoft’s Majorana 1 chip carves new path for quantum computing - Microsoft [Link]

In the near term, Google’s approach with superconducting qubits (like Willow) is more mature. This technology has already demonstrated impressive benchmarks and is backed by years of incremental improvements. Its error correction techniques, while still challenging, are well‑studied, and scaling up using transmon qubits is an area where significant progress has been made.

On the other hand, Microsoft’s topological approach with Majorana 1 aims to use a completely new type of qubit—one that is “protected by design” thanks to its topological nature. In theory, this means lower error rates and potentially a much more scalable architecture with fewer physical qubits needed per logical qubit. However, this method is still very experimental, and questions remain over whether true Majorana zero modes have been reliably created and controlled.

In summary, for near‑term practical applications, Google’s path appears to be the safer bet. But if Microsoft’s topological qubit platform can overcome its technical hurdles, it may ultimately provide a more efficient and scalable route to fault‑tolerant quantum computing.

Tencent's Weixin app, Baidu launch DeepSeek search testing - Reuters [Link]

OpenAI tries to ‘uncensor’ ChatGPT - Techcrunch [Link]

Elon Musk Ally Tells Staff ‘AI-First’ Is the Future of Key Government Agency - WIRED [Link]

Thomas Shedd, a former Tesla engineer and ally of Elon Musk, is implementing an "AI-first strategy" at the General Services Administration's Technology Transformation Services (TTS). Shedd envisions the agency operating like a software startup, automating tasks and centralizing federal data. This shift is causing concern among GSA staff, who report being thrown into unexpected meetings and facing potential workforce cuts. Shedd is promoting collaboration between TTS and the United States Digital Services (DOGE), though specifics about the new AI-driven projects and data repository remain unclear. A cybersecurity expert expressed concern that automating government tasks is difficult and the attempt is raising red flags. Employees also voiced concerns regarding working hours and potential job losses.

OpenAI o3-mini - OpenAI [Link]

Introducing deep research - OpenAI [Link]

Gemini 2.0 is now available to everyone - Google DeepMind [Link]

The all new le Chat: Your AI assistant for life and work - Mistral AI [Link]

My thoughts regarding the AI landscape at the current stage:

As open-source AI becomes more affordable, it is poised to become as ubiquitous and accessible as electricity—financially viable for everyone. The AI and AGI arms race, whether between nations, open- and closed-source models, or competing companies, is effectively over or should be over, and the outcome is clear. Compute power still remains essential, but semiconductor giants like NVIDIA should look beyond language model training and inference, shifting their focus to the next frontiers, such as robotics and world models. Now is the time for developers and startups to concentrate on the vertical integration of AI, where real economic value can be realized.

DeepSeek - Background

DeepSeek began as a research offshoot of High-Flyer—a hedge fund that had already amassed a large GPU inventory (reportedly 10,000 Nvidia A100s in 2021). Over time, this resource base appears to have grown, with estimates suggesting that—when you account for research, ablation experiments, and shared infrastructure with trading—the effective pool might be closer to 50,000 GPUs. This expansive compute power enables DeepSeek to run many experiments simultaneously and quickly iterate on new architectures.

By leveraging a shared infrastructure with its hedge fund operations, DeepSeek can reinvest profits from quant trading into AI research. This model of “doing more with less” not only challenges the notion that massive, multibillion-dollar compute expenditures are necessary to build world-class AI models but also has broader implications for the industry. It raises questions about the future economics of AI development and the potential for more cost-efficient, research-driven models to shift market dynamics, as seen by the notable impact on Nvidia’s stock and market sentiment.

Export Controls on GPUs to China

In essence, the U.S. government originally imposed limits on chips that exceed certain thresholds in both interconnect bandwidth and compute (FLOPs) to restrict China’s ability to train massive AI models. Early on, chips that combined high interconnect speeds with high FLOPs were off‐limits.

For example, the H100—one of Nvidia’s top GPUs—was deemed too powerful. In response, Nvidia developed the H800, which maintained the same floating point performance (FLOPs) as the H100 but had its interconnect bandwidth intentionally reduced to meet U.S. export criteria. However, when the government later decided to tighten controls further (targeting chips solely on FLOPs), even the H800 was banned. This led Nvidia to innovate once again with the H20, a chip that now offers full interconnect bandwidth (and even improved memory characteristics over the H100) but with a deliberate cut in overall FLOPs to satisfy export rules.

The strategic rationale behind these controls is to “decap” China’s compute—especially for large-scale AI training—by limiting how many of the most advanced GPUs (and thus the overall density of compute) can be legally acquired. While Chinese companies can still purchase GPUs to train models, the overall capacity available for training (which is critical for developing super-powerful AI) is being capped. This is seen as a way to maintain U.S. and allied leadership in AI, particularly in a world where super-powerful AI may soon offer decisive military and economic advantages.

Sidenote - GPUs for AI

Keys GPU Specifications:

• FLOPS (Compute Power): Critical for training large models (e.g., GPT-4) but less critical for inference tasks like reasoning.
• Memory Bandwidth/Capacity: Determines how much data (e.g., KV cache in transformers) can be stored and accessed quickly, crucial for long-sequence tasks.
• Interconnect Speed: Affects communication between GPUs in clusters, important for distributed training but less regulated now.

H20 vs. H100: Tradeoffs for AI Workloads:

• H20 (China-Specific): has its strength in higher memory bandwidth and capacity than H100, making it better suited for reasoning tasks (e.g., long-context inference, chain-of-thought). However, FLOPS (≈1/3 of H100 on paper, ≈50-60% in practice) is reduced, limiting its utility for training.

  Regulatory Context: Designed to comply with U.S. export controls that focus on FLOPS, allowing Nvidia to ship 1M units to China in 2023 (20-25% of total GPUs).

• H100: Optimized for FLOPS-heavy training but less efficient for memory-bound inference tasks

Why Memory Matters for Reasoning:

• KV Cache in Transformers stores keys/values of all tokens in a sequence for attention mechanisms. Memory demands grow quadratically with sequence length (e.g., 10K+ tokens in reasoning tasks).
• Autoregressive Generation: Output tokens require sequential processing, forcing repeated KV cache access. This limits parallelism and increases memory pressure. Tasks like agentic AI or chain-of-thought involve generating long outputs (10K+ tokens), stressing memory bandwidth/capacity.

DeepSeek - Technical Comments

Paper: DeepSeek-V3 Technical Report

GPU Infrastructure with Nvidia Hardware

• DeepSeek trains on Nvidia GPUs. These are equipped with many cores (organized into streaming multiprocessors, or SMs) that perform the heavy lifting during both training and inference.
• The GPUs they used were those legally available in China, which imposed certain limitations—especially on interconnect bandwidth between units. This meant that DeepSeek needed to overcome hardware constraints that might not be present with the very latest high-end GPUs elsewhere.

Custom Low-Level Optimization

• Instead of relying solely on Nvidia’s standard NCCL (Nvidia Communications Collectives Library) for handling inter-GPU communications, DeepSeek’s engineers developed custom scheduling techniques. They even scheduled communications at the SM level, which is more granular than the typical approach.
• Their implementation involved programming approaches that went deep into the hardware—down to using PTX (an intermediate assembly-like language for CUDA). This allowed them to squeeze extra efficiency from each GPU by reducing the overhead in communication between layers of the model.

Efficiency via Architectural Choices

• One of the key innovations was using a sparse Mixture of Experts (MoE) architecture. With a model that can have hundreds of billions of parameters overall but only activates a fraction (e.g., around 37 billion at a time), the compute and memory demands are dramatically reduced. This architectural choice means that even if the hardware isn’t the absolute latest, it can still be very cost-effective by not needing to run every parameter for every token.
• DeepSeek's novel attention mechanism MLA (Multi-Head Latent Attention) reduces memory usage by 80–90% compared to traditional transformer attention. This optimization lowers computational costs, especially for long-context processing, without sacrificing performance.
• By optimizing both the hardware usage (through custom scheduling and low-level programming) and the model architecture (via MoE and MLA), DeepSeek manages to cut down on the cost of training. This is crucial given the significant compute expense associated with large-scale language models.

Pre-Training and Context Window Extension

• Pre-trained on 14.8 trillion tokens drawn from a multilingual corpus (primarily English and Chinese) with a higher proportion of math and programming content compared to previous iterations.
• Utilizes a two-phase extension (via the YaRN framework) to expand the context length from 4K tokens to 32K and finally to 128K tokens.
• Reported training cost for V3 is approximately $5.58 million, consuming about 2.788 million GPU-hours on Nvidia H800 GPUs. This figure is significantly lower than the hundreds of millions typically reported by US rivals.

Post-Training: Supervised Fine-Tuning & Reinforcement Learning

• V3 is fine-tuned on a carefully curated dataset of approximately 1.5 million examples (both reasoning and non-reasoning tasks) to improve instruction-following and output formatting.
• DeepSeek employs GRPO—a group relative policy optimization method—to reward outputs based on correctness (accuracy rewards) and presentation (format rewards).
• R1 leverages RL to fine-tune the reasoning process, rewarding chain-of-thought quality and encouraging the model to generate self-reflective “aha moments.”

Speed-to-Market and Safety Tradeoffs

• DeepSeek prioritizes rapid deployment over extensive safety testing, avoiding delays and costs associated with ethical reviews (common in Western firms like Anthropic). This "ship-first" approach reduces development cycle expenses.

• Releasing model weights publicly attracts third-party hosting and innovation, indirectly expanding reach without bearing full infrastructure costs.

The Tech and Business Perspective

The release of DeepSeek-R1 marks a pivotal moment in the AI industry, igniting discussions about open-source dominance, market disruption, and geopolitical implications.

Industry Leaders Weigh In:

Yann LeCun (Meta’s Chief AI Scientist)

LeCun emphasized the growing power of open-source models over proprietary approaches:

"To people who see the performance of DeepSeek and think China is surpassing the US in AI. You are reading this wrong. The correct reading is: Open source models are surpassing proprietary ones."

Andrej Karpathy (OpenAI Co-founder)

Karpathy pointed out the continued need for large-scale computing while praising DeepSeek’s efficiency:

"Does this mean you don't need large GPU clusters for frontier LLMs? No, but you have to ensure that you're not wasteful with what you have, and this looks like a nice demonstration that there's still a lot to get through with both data and algorithms."

Satya Nadella (Microsoft CEO)

Nadella underscored the significance of DeepSeek, highlighting its role in making AI reasoning more accessible:

"We should take the developments out of China very, very seriously." "DeepSeek has had some real innovations. … Obviously, now all that gets commoditized." "When token prices fall, inference computing prices fall, that means people can consume more, and there will be more apps written."

Mark Zuckerberg (Meta CEO)

Zuckerberg acknowledged DeepSeek's novel infrastructure optimizations:

"DeepSeek had a few pretty novel infrastructure optimization advances, which, fortunately, they published them, so we can not only observe what they did, but we can read about it and implement it, so that'll benefit us." "Always interesting when there's someone who does something better than you. Let's make sure we are on it."

Aravind Srinivas (Perplexity AI CEO)

Srinivas stressed the importance of foundational innovation:

"We need to build, not just wrap existing AI."

Marc Andreessen (Andreessen Horowitz Co-founder)

He likened DeepSeek-R1 to a historic milestone:

"DeepSeek R1 is AI's Sputnik moment."

Tim Cook (Apple CEO)

Cook gave a measured response during an earnings call:

"In general, I think innovation that drives efficiency is a good thing."

Academic and Research Perspectives

AI Researchers on DeepSeek-R1:

Timnit Gebru (AI Ethics Researcher)

Gebru reflected on past AI development priorities:

"At Google, I asked why they were fixated on building THE LARGEST model. Why are you going for size? What function are you trying to achieve? They responded by firing me."

Ethan Mollick (Wharton AI Professor)

Mollick focused on accessibility rather than capabilities:

"DeepSeek is a really good model, but it is not generally a better model than o1 or Claude. But since it is both free and getting a ton of attention, I think a lot of people who were using free 'mini' models are being exposed to what an early 2025 reasoner AI can do and are surprised."

Andrew Ng (AI Researcher and Entrepreneur)

Ng saw the market reaction as an opportunity for developers:

"Today's 'DeepSeek selloff' in the stock market—attributed to DeepSeek V3/R1 disrupting the tech ecosystem—is another sign that the application layer is a great place to be. The foundation model layer being hyper-competitive is great for people building applications."

Global Academic Community Response:

Huan Sun from Ohio State University noted that DeepSeek's affordability is expanding LLM adoption in research. Cong Lu from the University of British Columbia highlighted R1’s rapid adoption, surpassing 3 million downloads on Hugging Face in a week. Meanwhile, safety concerns emerged as studies revealed R1 is 11 times more likely to generate harmful content compared to OpenAI models, prompting calls for better safeguards.

Impact Discussion

Market and Industry Impact

The release of DeepSeek-R1 caused massive shifts in financial markets. U.S. tech stocks collectively lost \(\$1\) trillion, with Nvidia suffering record losses due to the rising competition from this cost-efficient model. Investors are recalibrating AI development strategies as DeepSeek achieved comparable performance to OpenAI’s models at just \(\$6\) million versus OpenAI’s \(\$100\) million.

Integration into Cloud Ecosystems

AWS and Microsoft Azure have incorporated DeepSeek-R1, enabling developers to explore its capabilities securely and cost-effectively. The emergence of cost-effective models like DeepSeek R1 is forcing a shift in AI economics, emphasizing efficiency over massive capital investments. As a result, competition in the AI sector is intensifying, ushering in a “warring states era” where companies are scrambling for innovation in cost-effective models.

Geopolitical and National Security Implications

The success of DeepSeek R1 has intensified concerns that the U.S. is losing its technological edge to China. Policymakers are reassessing export controls on advanced chips in light of DeepSeek's ability to innovate using restricted hardware. Security concerns have also prompted the U.S. Navy to ban the use of DeepSeek R1 due to potential security and ethical risks, fueling debates over the implications of adopting foreign-developed AI systems.

Open-Source vs Proprietary Models

DeepSeek R1 is accelerating the democratization of AI by lowering barriers for smaller developers and researchers, fostering innovation. However, transparency concerns remain as DeepSeek has not disclosed its training data, raising ethical and bias-related questions.

Ethical and Technical Questions

Concerns have emerged regarding potential censorship, as some versions of DeepSeek R1 appear to align with Chinese narratives. Additionally, skepticism exists over whether DeepSeek’s reported costs and capabilities are fully accurate, with some experts questioning the factors that contributed to its success.

Public Sentiment and the Future of AI

Public reaction to DeepSeek-R1 has been mixed. Some view this as a “Sputnik moment,” encouraging U.S. firms to accelerate AI innovation while leveraging open-source models to stay competitive. Others see it as a wake-up call, with former President Donald Trump urging U.S. industries to adapt quickly to maintain leadership in AI development.

Persistence in LangGraph

Persistence is a cornerstone for building robust and production-grade applications. LandGraph introduces a game-changing feature that ensures application states are stored and retrievable at any point. This redefines reliability and scalability in workflow management. This capability is especially vital when executing workflows involving interruptions, user inputs, or debugging. Whether you're building a simple app or an enterprise-grade system, persistence ensures your application is always ready to handle interruptions and user interactions gracefully.

The "Persisting Agent Stage" enables seamless workflows, especially in user-facing applications. Here’s why this feature is critical:

1. Human-in-the-Loop Workflows: Many applications rely on user input to make decisions or advance processes. With persistence, LandGraph allows the graph execution to pause, checkpoint the state into persistent storage, and resume later. This means the application can wait for user input and continue without losing context.
2. Debugging and History: Persistence creates a robust mechanism for saving the application state after every step. This makes debugging easier and enables the creation of detailed execution histories.
3. Support for Multi-Session Scenarios: Applications often require users to switch between sessions while maintaining their progress. Persistence ensures continuity by saving states into persistent storage.

At the heart of this feature is the CheckPointer object, a persistence layer implemented by LandGraph. Here’s how it works:

• Integration with Databases The CheckPointer can save states into various database types, including:

  • Document databases: Firestore, MongoDB
  • Relational databases: PostgreSQL, SQLite, MySQL
  • Graph databases: Neo4j, AWS Neptune

  For example, the following section will focus on persisting states into an SQLite database, a popular choice for local environments. The process can also be extended to managed cloud databases like Google Cloud SQL or AWS RDS.

• State Management As each node in the graph executes, the CheckPointer saves the updated state into the database. This ensures that states are recoverable after interruptions, enabling the graph to resume execution from exactly where it left off.

To implement persistence, follow these simple steps:

1. Import the CheckPointer object from LandGraph.
2. Create an instance of CheckPointer and configure it with a connection string (local or cloud-based database).
3. Pass the CheckPointer instance to your graph during creation. LandGraph will handle state persistence automatically after each node execution.

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from langgraph.checkpoint.sqlite import SqliteSaver

memory = SqliteSaver.from_conn_string(":checkpoints.sqlite:")
graph = workflow.complie(checkpointer=memory)

The result is that you can pause the graph, fetch user input, and continue execution seamlessly, all while ensuring states are securely stored in your chosen database.

MemorySaver + Interrupts = Human In The Loop

Human-in-the-loop systems are essential to modern applications, allowing seamless integration of human feedback into automated workflows. With the help of the MemorySaver feature, you can build applications using LangGraph that pause, capture user input, and resume execution effortlessly.

In workflows involving human interaction, there are moments where the application needs to pause, gather feedback from the user, and then continue processing. For instance, consider a sequence of tasks where:

1. A process executes its initial steps.
2. The system pauses to collect human input.
3. The workflow resumes, incorporating the user’s feedback.

This type of flow requires interrupts to halt the execution and persistence to save the current state of the workflow. Langraph provides the tools to manage both seamlessly.

Implementation

To illustrate, let’s build a straightforward graph with the following steps:

1. Start with a simple initial node.
2. Execute a task and pause for human feedback.
3. Resume execution with the updated state and complete the workflow.

We use Langraph's MemorySaver, a checkpointing tool that saves the workflow’s state in memory after each node’s execution. This ephemeral storage method is perfect for local testing and prototyping. Here’s a simplified version of the setup:

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from dotenv import load_dotenv

load_dotenv()
from typing import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import MemorySaver


class State(TypedDict):
    input: str
    user_feedback: str


def step_1(state: State) -> None:
    print("---Step 1---")


def human_feedback(state: State) -> None:
    print("---human_feedback---")


def step_3(state: State) -> None:
    print("---Step 3--")


builder = StateGraph(State)
builder.add_node("step_1", step_1)
builder.add_node("human_feedback", human_feedback)
builder.add_node("step_3", step_3)
builder.add_edge(START, "step_1")
builder.add_edge("step_1", "human_feedback")
builder.add_edge("human_feedback", "step_3")
builder.add_edge("step_3", END)


memory = MemorySaver()

graph = builder.compile(checkpointer=memory, interrupt_before=["human_feedback"])

graph.get_graph().draw_mermaid_png(output_file_path="graph.png")

The graph visualization by using Mermaid.ink is here:

MemorySaver Implementations

Integrating human feedback into automated systems is a growing trend in AI development. It bridges the gap between machine automation and human judgment, enabling better decision-making, improved accuracy, and adaptability. In this section, we explore how to incorporate human-in-the-loop functionality into a graph-based system while leveraging memory storage to track execution states. This walkthrough showcases the process from initialization to final execution.

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from dotenv import load_dotenv

load_dotenv()
from typing import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import MemorySaver


class State(TypedDict):
    input: str
    user_feedback: str


def step_1(state: State) -> None:
    print("### Step 1 ###")


def human_feedback(state: State) -> None:
    print("### Human Feedback ###")


def step_3(state: State) -> None:
    print("### Step 3 ###")


builder = StateGraph(State)
builder.add_node("step_1", step_1)
builder.add_node("human_feedback", human_feedback)
builder.add_node("step_3", step_3)
builder.add_edge(START, "step_1")
builder.add_edge("step_1", "human_feedback")
builder.add_edge("human_feedback", "step_3")
builder.add_edge("step_3", END)


memory = MemorySaver()

graph = builder.compile(checkpointer=memory, interrupt_before=["human_feedback"])

graph.get_graph().draw_mermaid_png(output_file_path="graph.png")

if __name__ == "__main__":
    thread = {"configurable": {"thread_id": "1"}}

    initial_input = {"input": "hello world"}

    for event in graph.stream(initial_input, thread, stream_mode="values"):
        print(event)

    print(graph.get_state(thread).next)

    user_input = input("How do you want to update the state? ")

    graph.update_state(thread, {"user_feedback": user_input}, as_node="human_feedback")

    print("### State after update ###")
    print(graph.get_state(thread))

    print(graph.get_state(thread).next)

    for event in graph.stream(None, thread, stream_mode="values"):
        print(event)

The graph’s execution is tied to a thread variable, a dictionary initialized with a thread_id. This serves as a session or conversation identifier, distinguishing various graph runs. For simplicity, the thread_id is set to 1, though a more robust implementation would use a UUID. The graph processes events using graph.stream(), which accepts the initial input and thread details. Events are streamed in value mode, and each event is printed for transparency.

During execution:

• Input is processed.
• Node executions are logged.
• Interruptions allow for dynamic human input.

Running the graph in debug mode provides insights into:

• Memory storage (memory.storage) containing nested objects that log the graph state.
• Transition logs for each node, showing updates or lack thereof.

At an interrupt, human feedback is solicited using Python's built-in input() function. This input updates the state dynamically. Once human input is integrated, the graph resumes execution. Subsequent steps process the updated state, leading to the graph’s completion.

SqliteSaver

Switching from an ephemeral memory-based state saver to a persistent database saver can significantly enhance the durability and traceability of your graph’s execution. In this section, we’ll explore how to replace the in-memory MemorySaver with an SQLiteSaver for long-term storage and easy debugging.

The MemorySaver is transient, meaning all state information vanishes after the program stops. By using an SQLite database, you can:

• Persist graph states across runs.
• Debug and troubleshoot using a structured database.
• Resume executions exactly where they were interrupted.

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import sqlite3

from dotenv import load_dotenv
from langgraph.checkpoint.sqlite import SqliteSaver

load_dotenv()
from typing import TypedDict
from langgraph.graph import StateGraph, START, END


class State(TypedDict):
    input: str
    user_feedback: str


def step_1(state: State) -> None:
    print("### Step 1 ###")


def human_feedback(state: State) -> None:
    print("### Human Feedback ###")


def step_3(state: State) -> None:
    print("### Step 3 ###")


builder = StateGraph(State)
builder.add_node("step_1", step_1)
builder.add_node("human_feedback", human_feedback)
builder.add_node("step_3", step_3)
builder.add_edge(START, "step_1")
builder.add_edge("step_1", "human_feedback")
builder.add_edge("human_feedback", "step_3")
builder.add_edge("step_3", END)

conn = sqlite3.connect("checkpoints.sqlite", check_same_thread=False)
memory = SqliteSaver(conn)
graph = builder.compile(checkpointer=memory, interrupt_before=["human_feedback"])

graph.get_graph().draw_mermaid_png(output_file_path="graph.png")

if __name__ == "__main__":
    thread = {"configurable": {"thread_id": "1"}}

    initial_input = {"input": "hello world"}

    for event in graph.stream(initial_input, thread, stream_mode="values"):
        print(event)

    print(graph.get_state(thread).next)

    user_input = input("How do you want to update the state: ")

    graph.update_state(thread, {"user_feedback": user_input}, as_node="human_feedback")

    print("### State after update ###")
    print(graph.get_state(thread))

    print(graph.get_state(thread).next)

    for event in graph.stream(None, thread, stream_mode="values"):
        print(event)

We start by importing the required modules. Then Initialize a connection to your SQLite database. The check_same_thread=False flag ensures thread-safe database operations, essential for stopping and restarting execution across different threads. After that we create an instance of SQLiteSaver and pass it the SQLite connection. This saver integrates seamlessly with the graph execution pipeline, persisting states to the SQLite database.

1. Initial Execution: Run the graph with the SQLiteSaver. After execution, you’ll see a new file, checkpoints.sqlite, created in your project directory.
2. Inspect the Database: Use your IDE’s database tools (e.g. SQLite3 Editor for VS Code) to load and inspect the checkpoints.sqlite file. You’ll find a table storing graph states, similar to what you’d see with MemorySaver, but now it’s persistent.

Changing the thread_id allows you to simulate a new session while retaining access to previous runs. When resuming, the graph starts from the last recorded state. You can verify this by inspecting the database entries for the new thread_id.

For enhanced traceability, integrate Langsmith for tracking and debugging. Langsmith provides detailed insights, including thread metadata and execution traces.