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Everything Context Engineering & Cookoff between dex, co-founder and CEO of humanlayer, and allen. 0:12 - Meet Dex Horthy (Humanlayer) + Cooking Challenge 1:15 - Tasting Dan Dan Noodles 1:25 - How Dex Got a NASA Internship at 17 3:36 - The Origin of “Context Engineering” 6:24 - What... show more

Latent.Space

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14,971 views • 5 months ago •via X (Twitter)

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Knowing how LLM contexts work and how to work around context limitations – aka “context engineering” – is becoming so important. No better person to explain than dex Timestamps: 00:00 Intro 01:33 Dex’s path into tech 03:34 Early work in platform engineering 05:28 Replicated 11:24 Metalytics 12:36 12-factor agents 18:27 Context engineering 23:38 Harness engineering 26:11 Context overload 30:45 Loop engineering 44:34 Software factories before and after AI 50:33 Automation limits 55:18 Three options for automating 59:00 RPI framework 1:04:16 Intentional compaction 1:11:48 Token harder vs. token smarter 1:16:44 AI slop 1:19:15 HumanLayer 1:29:09 Book recommendation Brought to you by: • Antithesis — with Antithesis, you can use AI agents to work on critical systems without worrying about correctness. Teams like Jane Street, and the etcd community use Antithesis to ship better code, faster. • Buildkite — the CI orchestration platform built for reliable scale. Used by OpenAI, Anthropic, Cursor, Meta, Uber, Ramp, Nvidia, Airbnb and many more. • Sentry — application monitoring software built by developers, for developers. Check out their AI agent, Seer AI, and Sentry MCP. Three interesting learnings from this episode: 1. Lesson learned: Shipping unread code spells disaster within months. Dex experimented with having the model write the code and humans not reviewing anything in July 2025. Four months later, they shut things down and threw the whole system out. Production broke, and no matter how much the team prompted Opus 4.1, the model could not find the root cause. Once fixed, it took three weeks (!!) to re-onboard to a codebase no human had ever read 2. Context engineering 101: figure out where the “dumb zone” begins. As a rule of thumb, the less of the context window that is used, the better the outcomes are. This is because the attention mechanism is quadratic: the more that goes into the context window, the more compute is required to process it all. 3. “You’re completely right!” or “you’re right to push back on that” are phrases that mean it’s time to start a new session. These responses mean the LLM session is trajectory-poisoned, and you’re wasting time and tokens to continue. This is because models are autoregressive.

Knowing how LLM contexts work and how to work around context limitations – aka “context engineering” – is becoming so important. No better person to explain than dex Timestamps: 00:00 Intro 01:33 Dex’s path into tech 03:34 Early work in platform engineering 05:28 Replicated 11:24 Metalytics 12:36 12-factor agents 18:27 Context engineering 23:38 Harness engineering 26:11 Context overload 30:45 Loop engineering 44:34 Software factories before and after AI 50:33 Automation limits 55:18 Three options for automating 59:00 RPI framework 1:04:16 Intentional compaction 1:11:48 Token harder vs. token smarter 1:16:44 AI slop 1:19:15 HumanLayer 1:29:09 Book recommendation Brought to you by: • Antithesis — with Antithesis, you can use AI agents to work on critical systems without worrying about correctness. Teams like Jane Street, and the etcd community use Antithesis to ship better code, faster. • Buildkite — the CI orchestration platform built for reliable scale. Used by OpenAI, Anthropic, Cursor, Meta, Uber, Ramp, Nvidia, Airbnb and many more. • Sentry — application monitoring software built by developers, for developers. Check out their AI agent, Seer AI, and Sentry MCP. Three interesting learnings from this episode: 1. Lesson learned: Shipping unread code spells disaster within months. Dex experimented with having the model write the code and humans not reviewing anything in July 2025. Four months later, they shut things down and threw the whole system out. Production broke, and no matter how much the team prompted Opus 4.1, the model could not find the root cause. Once fixed, it took three weeks (!!) to re-onboard to a codebase no human had ever read 2. Context engineering 101: figure out where the “dumb zone” begins. As a rule of thumb, the less of the context window that is used, the better the outcomes are. This is because the attention mechanism is quadratic: the more that goes into the context window, the more compute is required to process it all. 3. “You’re completely right!” or “you’re right to push back on that” are phrases that mean it’s time to start a new session. These responses mean the LLM session is trajectory-poisoned, and you’re wasting time and tokens to continue. This is because models are autoregressive.

Gergely Orosz

63,174 views • 22 days ago

Engineers need to communicate effectively when building AI Systems. These terms will help you use a shared vocabulary. This is useful when discussing concepts, reading papers, or collaborating with teammates. Listed in order. 00:00 Agenda 00:28 1. Large Language Model 01:28 2. Tokenization 02:53 3. Vectorization 04:15 4. Attention 07:22 5. Self-Supervised Learning 12:07 6. Transformer 14:32 7. Fine-tuning 17:05 8. Few-shot Prompting 18:11 9. Retrieval Augmented Generation 20:33 10. Vector Database 23:03 11. Model Context Protocol 25:43 12. Context Engineering 28:17 13. Agents 29:19 14. Reinforcement Learning 34:42 15. Chain of Thought 35:55 16. Reasoning Models 36:36 17. Multi-modal Models 38:21 18. Small Language Models 40:24 19. Distillation 41:47 20. Quantization If you are a software engineer looking to transition to AI, click the link below. AI Engineering Course: #AI #SoftwareEngineering #Agents

Engineers need to communicate effectively when building AI Systems. These terms will help you use a shared vocabulary. This is useful when discussing concepts, reading papers, or collaborating with teammates. Listed in order. 00:00 Agenda 00:28 1. Large Language Model 01:28 2. Tokenization 02:53 3. Vectorization 04:15 4. Attention 07:22 5. Self-Supervised Learning 12:07 6. Transformer 14:32 7. Fine-tuning 17:05 8. Few-shot Prompting 18:11 9. Retrieval Augmented Generation 20:33 10. Vector Database 23:03 11. Model Context Protocol 25:43 12. Context Engineering 28:17 13. Agents 29:19 14. Reinforcement Learning 34:42 15. Chain of Thought 35:55 16. Reasoning Models 36:36 17. Multi-modal Models 38:21 18. Small Language Models 40:24 19. Distillation 41:47 20. Quantization If you are a software engineer looking to transition to AI, click the link below. AI Engineering Course: #AI #SoftwareEngineering #Agents

Gaurav Sen

80,115 views • 10 months ago

Anthropic engineer just released a 2-hour workshop on "Graph Engineering" for agentic systems: “80% of our engineers are using self-improving loops. Now everyone is building agentic graphs.” • 00:00 - Introduction to RAG & Graphs • 06:39 - Core of "Graph Engineering" (state, nodes) • 14:29 - 3 feedback loops of Graph agents • 23:06 - Agent evaluation with Graphs • 36:29 - Agent cycles in graphs • 1:15:22 - Agentic RAG & agent context • 1:41:20 - Evaluation datasets based on Graphs This 2-hour workshop will replace 10 paid courses on agentic engineering. Watch it today, then learn how to become a Graph Engineer in the article below.

Anthropic engineer just released a 2-hour workshop on "Graph Engineering" for agentic systems: “80% of our engineers are using self-improving loops. Now everyone is building agentic graphs.” • 00:00 - Introduction to RAG & Graphs • 06:39 - Core of "Graph Engineering" (state, nodes) • 14:29 - 3 feedback loops of Graph agents • 23:06 - Agent evaluation with Graphs • 36:29 - Agent cycles in graphs • 1:15:22 - Agentic RAG & agent context • 1:41:20 - Evaluation datasets based on Graphs This 2-hour workshop will replace 10 paid courses on agentic engineering. Watch it today, then learn how to become a Graph Engineer in the article below.

Codez

395,393 views • 12 days ago

In this episode, Engram co-founder and CEO Dan Biderman joins allen to cook Mediterranean meatballs with yellow rice and talk about building AI that actually learns from you: why long context, RAG, and compaction eventually break down, how Engram compresses knowledge into cartridges and model weights, what continual learning could unlock for long-horizon agents, why token efficiency is inseparable from intelligence, how personal models could improve like Tamagotchis, and what it takes to build the research and infrastructure for millions of continuously updated AI memories. Timestamps: 0:00 Intro 0:26 Engram’s $98M Launch and Meatballs 1:45 From Naval Special Operations to AI Research 4:32 Israeli Military Culture and Founder Maturity 7:12 Why Engram Is Betting on Context and Continual Learning 9:14 Knowledge Cartridges, Compression, and Model Intuition 14:10 Trillion-Token Company Knowledge and Context Rot 18:05 Long-Context Limits, Compaction, and Neural Memory 22:20 Test-Time Training and “Destroying Prefill” 24:31 Harvey and Holistic Enterprise Queries Beyond RAG 27:02 Personal AI Models and Tamagotchi Weights 30:00 What Belongs in Weights vs. Text 32:25 Autonomous Memory and User-Specific Feedback Loops 34:20 Token Efficiency, Model Routing, and Harder Tasks 38:03 Engram’s Research Team and Product Culture 43:02 Hiring Researchers and Infrastructure Engineers 45:25 Doing More With Less 47:41 Where to Find Engram 48:19 Final Taste Test

In this episode, Engram co-founder and CEO Dan Biderman joins allen to cook Mediterranean meatballs with yellow rice and talk about building AI that actually learns from you: why long context, RAG, and compaction eventually break down, how Engram compresses knowledge into cartridges and model weights, what continual learning could unlock for long-horizon agents, why token efficiency is inseparable from intelligence, how personal models could improve like Tamagotchis, and what it takes to build the research and infrastructure for millions of continuously updated AI memories. Timestamps: 0:00 Intro 0:26 Engram’s $98M Launch and Meatballs 1:45 From Naval Special Operations to AI Research 4:32 Israeli Military Culture and Founder Maturity 7:12 Why Engram Is Betting on Context and Continual Learning 9:14 Knowledge Cartridges, Compression, and Model Intuition 14:10 Trillion-Token Company Knowledge and Context Rot 18:05 Long-Context Limits, Compaction, and Neural Memory 22:20 Test-Time Training and “Destroying Prefill” 24:31 Harvey and Holistic Enterprise Queries Beyond RAG 27:02 Personal AI Models and Tamagotchi Weights 30:00 What Belongs in Weights vs. Text 32:25 Autonomous Memory and User-Specific Feedback Loops 34:20 Token Efficiency, Model Routing, and Harder Tasks 38:03 Engram’s Research Team and Product Culture 43:02 Hiring Researchers and Infrastructure Engineers 45:25 Doing More With Less 47:41 Where to Find Engram 48:19 Final Taste Test

Latent.Space

33,729 views • 24 days ago

Amjad Masad (Amjad Masad), co-founder & CEO of Replit, has spent nearly a decade making programming accessible to everyone. With AI, that vision is closer than ever. In his AI Startup School talk, Amjad traces computing’s arc from mainframes to PCs to a future where AI agents generate software on demand—driving the value of traditional software toward zero and reshaping how companies are built. 00:22 - Evolution of Computing 01:27 - The Rise of Software Engineering 02:16 - Replit's Vision: Making Programming Accessible 02:42 - The Role of AI in Software Development 04:32 - Challenges and Infrastructure for AI Agents 05:03 - Building a Scalable and Secure Environment 06:08 - Future of Software Engineering Agents 09:25 - Autonomy Levels in AI Agents 15:56 - Predictions for the Software Market 18:31 - Impact on Business and Employment 19:58 - The Role of Generalist Employees 26:00 - Audience Q&A

Amjad Masad (Amjad Masad), co-founder & CEO of Replit, has spent nearly a decade making programming accessible to everyone. With AI, that vision is closer than ever. In his AI Startup School talk, Amjad traces computing’s arc from mainframes to PCs to a future where AI agents generate software on demand—driving the value of traditional software toward zero and reshaping how companies are built. 00:22 - Evolution of Computing 01:27 - The Rise of Software Engineering 02:16 - Replit's Vision: Making Programming Accessible 02:42 - The Role of AI in Software Development 04:32 - Challenges and Infrastructure for AI Agents 05:03 - Building a Scalable and Secure Environment 06:08 - Future of Software Engineering Agents 09:25 - Autonomy Levels in AI Agents 15:56 - Predictions for the Software Market 18:31 - Impact on Business and Employment 19:58 - The Role of Generalist Employees 26:00 - Audience Q&A

Y Combinator

224,320 views • 10 months ago

"We are clearly entering a world where a product-minded engineer is now empowered to produce software without writing a line of code for it." In this conversation, Temporal CEO Samar Abbas joins a16z GPs Sarah Wang and Raghu Raghuram to cover: - Why agents are going from short-lived and interactive to long-running and async - Why the engineer of the future manages 15 parallel AI tasks at once - How OpenAI Codex runs millions of concurrent agent executions on Temporal - How real-time context engineering is exploding on Temporal - Why SaaS isn't dead, and value is migrating to APIs 00:00 Introduction 04:03 Temporal's origin story 11:14 Why agents raise the stakes 16:00 Specialized agents need durable RPC 25:20 Deep research agents 30:58 Execution histories as a superpower 39:04 Minimal viable long-running agent architecture 45:07 Context engineering at scale 52:40 Where value accrues: The "five-layer cake" and breakout AI applications Samar Abbas Sarah Wang Raghu Raghuram

"We are clearly entering a world where a product-minded engineer is now empowered to produce software without writing a line of code for it." In this conversation, Temporal CEO Samar Abbas joins a16z GPs Sarah Wang and Raghu Raghuram to cover: - Why agents are going from short-lived and interactive to long-running and async - Why the engineer of the future manages 15 parallel AI tasks at once - How OpenAI Codex runs millions of concurrent agent executions on Temporal - How real-time context engineering is exploding on Temporal - Why SaaS isn't dead, and value is migrating to APIs 00:00 Introduction 04:03 Temporal's origin story 11:14 Why agents raise the stakes 16:00 Specialized agents need durable RPC 25:20 Deep research agents 30:58 Execution histories as a superpower 39:04 Minimal viable long-running agent architecture 45:07 Context engineering at scale 52:40 Where value accrues: The "five-layer cake" and breakout AI applications Samar Abbas Sarah Wang Raghu Raghuram

a16z

45,721 views • 5 months ago

Here's my conversation with Jensen Huang, CEO of NVIDIA, the most valuable & one of the most influential companies in the history of human civilization. It is the engine powering the AI revolution. This was a fascinating & inspiring conversation, in parts super-technical on engineering of every part of the AI stack, memory, power, supply chain (TSMC, ASML, etc), in parts about leadership & psychology, and in parts personal & philosophical about life, consciousness, mortality, and human nature. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Introduction 0:33 - Extreme co-design and rack-scale engineering 3:18 - How Jensen runs NVIDIA 22:40 - AI scaling laws 37:40 - Biggest blockers to AI scaling laws 39:23 - Supply chain 41:18 - Memory 47:24 - Power 52:43 - Elon and Colossus 56:11 - Jensen's approach to engineering and leadership 1:01:37 - China 1:09:50 - TSMC and Taiwan 1:15:04 - NVIDIA's moat 1:20:41 - AI data centers in space 1:24:30 - Will NVIDIA be worth $10 trillion? 1:34:39 - Leadership under pressure 1:48:25 - Video games 1:55:16 - AGI timeline 1:57:29 - Future of programming 2:11:01 - Consciousness 2:17:22 - Mortality

Here's my conversation with Jensen Huang, CEO of NVIDIA, the most valuable & one of the most influential companies in the history of human civilization. It is the engine powering the AI revolution. This was a fascinating & inspiring conversation, in parts super-technical on engineering of every part of the AI stack, memory, power, supply chain (TSMC, ASML, etc), in parts about leadership & psychology, and in parts personal & philosophical about life, consciousness, mortality, and human nature. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Introduction 0:33 - Extreme co-design and rack-scale engineering 3:18 - How Jensen runs NVIDIA 22:40 - AI scaling laws 37:40 - Biggest blockers to AI scaling laws 39:23 - Supply chain 41:18 - Memory 47:24 - Power 52:43 - Elon and Colossus 56:11 - Jensen's approach to engineering and leadership 1:01:37 - China 1:09:50 - TSMC and Taiwan 1:15:04 - NVIDIA's moat 1:20:41 - AI data centers in space 1:24:30 - Will NVIDIA be worth $10 trillion? 1:34:39 - Leadership under pressure 1:48:25 - Video games 1:55:16 - AGI timeline 1:57:29 - Future of programming 2:11:01 - Consciousness 2:17:22 - Mortality

Lex Fridman

2,532,134 views • 4 months ago

AI completely changes the way we can talk to data. Hex is leading the way by building collaborative, shareable analytics workspaces. I sat down with CEO Barry McCardel to discuss how agent workflows, conversational interfaces, and context-aware models are reshaping analysis. We cover how Hex aims to make everyone a data person by unifying analysis and AI in one workflow, how Hex is replacing outdated fragmented workflows, and the importance of trust and accuracy in AI systems. We also go over Hex’s awesome product launch videos, Barry’s emphasis on being fun and weird, and lessons he learned from Palantir. Enjoy! 0:00 The challenge of trustworthy data 1:19 Context management, observability, and governance 9:24 The promise and reality of data democratization 15:51 The impact of AI on data workflows 31:39 Breadth, depth, and speed: the three dimensions of AI in data 48:04 The rise of ambient analytics 55:56 The modern data stack and industry consolidation 1:03:40 Acquisitions and owning the data insight layer 1:06:06 Lessons from Palantir: forward-deployed engineering 1:13:13 Commitment engineering and customer collaboration 1:17:11 Brand, launch videos, and having fun in SaaS Barry McCardel Hex

Sarah Wang

41,746 views • 8 months ago

What if we're actually in the middle of the third golden age of software engineering? This is what Grady Booch sees happening. If you are anxious about the state of the industry, you want to watch/listen to Grady's longer-term perspective and stories. Watch the full episode here: 00:00 Intro 01:58 The first golden age of software engineering 18:59 The software crisis 33:01 The second golden age of software engineering 42:21 Y2K and the Dotcom crash 45:47 Early AI 47:34 The third golden age of software engineering 51:48 Why software engineers will very much be needed 58:46 Grady responds to Dario Amodei 1:06:54 New skills engineers will need to succeed 1:10:04 Resources for studying complex systems 1:14:33 How to thrive during periods of change Brought to you by: • Statsig — ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar – The makers of SonarQube, the industry standard for automated code review. Join me online at the Sonar Summit on March 3rd, where I talk about practical tactics for the AI era. • WorkOS – Everything you need to make your app enterprise-ready.

What if we're actually in the middle of the third golden age of software engineering? This is what Grady Booch sees happening. If you are anxious about the state of the industry, you want to watch/listen to Grady's longer-term perspective and stories. Watch the full episode here: 00:00 Intro 01:58 The first golden age of software engineering 18:59 The software crisis 33:01 The second golden age of software engineering 42:21 Y2K and the Dotcom crash 45:47 Early AI 47:34 The third golden age of software engineering 51:48 Why software engineers will very much be needed 58:46 Grady responds to Dario Amodei 1:06:54 New skills engineers will need to succeed 1:10:04 Resources for studying complex systems 1:14:33 How to thrive during periods of change Brought to you by: • Statsig — ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar – The makers of SonarQube, the industry standard for automated code review. Join me online at the Sonar Summit on March 3rd, where I talk about practical tactics for the AI era. • WorkOS – Everything you need to make your app enterprise-ready.

Gergely Orosz

86,896 views • 6 months ago

AI and crypto… How will these two powerful technologies collide, and what happens when they do? Today’s episode is a special one. It’s a round table on some of the most compelling ideas at the intersection of AI and crypto, sourced from members of the a16z crypto team. You’re going to hear from builders, researchers, and deal makers who are all wrestling with the big question: How will these two powerful technologies collide, and what happens when they do? with Sam Broner Scott Kominers Liz Harkavy Carra Wu jay mattg Timestamps: (0:00) Introduction (1:44) Universal Identity for AI Agents (5:25) Why Crypto Makes a Difference (10:07) Context in AI: Memory and Interoperability (15:00) Vibe Coding Applications (19:32) Registries with Global Context (21:47) Honkaku Mystery Novels (24:13) Monetization Models for AI x Crypto (28:36) Overcoming Technical Challenges (31:34) AI Agents and the Rise of Web Crawlers (36:05) Collapse of the Social Contract (39:21) AI Bots and Blockchain Solutions (44:31) Proof of Personhood: Ensuring Human-ness (50:41) Future of Personalized Advertising (1:01:54) Final Thoughts Enjoy the full episode here on X.

AI and crypto… How will these two powerful technologies collide, and what happens when they do? Today’s episode is a special one. It’s a round table on some of the most compelling ideas at the intersection of AI and crypto, sourced from members of the a16z crypto team. You’re going to hear from builders, researchers, and deal makers who are all wrestling with the big question: How will these two powerful technologies collide, and what happens when they do? with Sam Broner Scott Kominers Liz Harkavy Carra Wu jay mattg Timestamps: (0:00) Introduction (1:44) Universal Identity for AI Agents (5:25) Why Crypto Makes a Difference (10:07) Context in AI: Memory and Interoperability (15:00) Vibe Coding Applications (19:32) Registries with Global Context (21:47) Honkaku Mystery Novels (24:13) Monetization Models for AI x Crypto (28:36) Overcoming Technical Challenges (31:34) AI Agents and the Rise of Web Crawlers (36:05) Collapse of the Social Contract (39:21) AI Bots and Blockchain Solutions (44:31) Proof of Personhood: Ensuring Human-ness (50:41) Future of Personalized Advertising (1:01:54) Final Thoughts Enjoy the full episode here on X.

a16z crypto

20,803 views • 1 year ago

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing

In this episode, OpenAI Chief Research Officer Mark Chen joins allen to flambé shrimp, cook Korean stew, and chat about being at the frontier of AI research: why scaling laws and pre-training still matter, how OpenAI chooses research bets and allocates compute, what it means to develop research taste, why evals are in crisis, how to avoid benchmark-maxing, and what it will take for models to handle long-horizon real-world work, multimodal reasoning, and eventually end-to-end AI research. Timestamps: 0:00 Intro 0:28 The Soup Story 1:52 From Trading to AI Research 3:21 How to Develop Research Taste 5:23 RL, Evals, and Superhuman Benchmarks 8:17 Cooking Begins on the Impulse Stove 8:53 Scaling Laws, Pre-Training, and Reasoning 12:33 OpenAI’s Research Roadmap and Compute Allocation 15:48 What Makes a Great Researcher 19:33 The Evals Crisis and Benchmark-Maxing 24:34 Jagged Intelligence, Context, and Long-Horizon Learning 27:14 Shrimp Flambé and New Research Bets 31:32 Multimodal Models and One Architecture 32:36 Vibe Researching and End-to-End AI Research 34:36 Failed Bets, Postmortems, and OpenAI’s Alpha 37:07 Final Taste Test 37:53 Overrated vs. Underrated AI Research 41:00 Closing

Latent.Space

62,796 views • 1 month ago

Software is eating labor & moats still matter: AI can do cheaply what has never been worth a person's time. Whole verticals have opened up, and unlike in previous product cycles, AI can actually complete work end-to-end. a16z GPs David Haber, Alex Rampell, and Erik Torenberg sat down for a conversation on how AI changed software and what fundamentals still hold true. They cover why moats still matter, the "greenfield strategies" that AI makes possible, the difference between differentiation & defensibility, why context is king, and more. 0:00 Intro 1:12 Why moats still matter 8:48 Why you won't vibecode Microsoft 10:09 The Goldilocks zone of pricing 11:21 Greenfield strategies 16:22 Steel man: Brand and velocity as moats 17:44 "Context is king" 19:58 Feature vs. product vs. company 27:05 Platform risk: Will they compete or tax you? 35:26 Will AI consolidate to winner-take-most? 39:16 Why Dropbox survived 44:06 Why AI is different: It's consensus 48:18 Jobs won't disappear – $1 tasks will explode David Haber Alex Rampell Erik Torenberg

a16z

61,997 views • 8 months ago

Understanding Valiant and Fogo ft. Russ from Valiant in this episode of the Touch Grass Podcast, i sat down with rusty to talk about > what it really takes to build a competitive dex > dex design choices: ux, liquidity, and tighter spreads > dual-flow batch auctions and orderbook evolution > how fogo enables entirely new dex mechanics and much more! key timestamps - 0:41 - building a future-proof dex with @reoffgenaud 1:51 - how andy and rusty met at solana founding village 3:03 - being a dex maxi: ux, assets, and execution 4:02 - what makes valiant different 5:24 - how the Fogo vision came together 7:29 - traction: 1.5m site visits, 6k mainnet wallets (ex-bots) 8:06 - fogo fishing + fish tradable for usdc 9:02 - fogo fests and ecosystem events 11:07 - the thesis behind valiant 14:16 - dual-flow batch auctions and tighter spreads 15:14 - the fogo ecosystem 16:40 - what if a better-suited chain than fogo emerges? 18:35 - hardest problem as a spot dex founder: asset onboarding 19:24 - #1 advice for builders in the fogo ecosystem

Understanding Valiant and Fogo ft. Russ from Valiant in this episode of the Touch Grass Podcast, i sat down with rusty to talk about > what it really takes to build a competitive dex > dex design choices: ux, liquidity, and tighter spreads > dual-flow batch auctions and orderbook evolution > how fogo enables entirely new dex mechanics and much more! key timestamps - 0:41 - building a future-proof dex with @reoffgenaud 1:51 - how andy and rusty met at solana founding village 3:03 - being a dex maxi: ux, assets, and execution 4:02 - what makes valiant different 5:24 - how the Fogo vision came together 7:29 - traction: 1.5m site visits, 6k mainnet wallets (ex-bots) 8:06 - fogo fishing + fish tradable for usdc 9:02 - fogo fests and ecosystem events 11:07 - the thesis behind valiant 14:16 - dual-flow batch auctions and tighter spreads 15:14 - the fogo ecosystem 16:40 - what if a better-suited chain than fogo emerges? 18:35 - hardest problem as a spot dex founder: asset onboarding 19:24 - #1 advice for builders in the fogo ecosystem

Rachit Agarwal

11,811 views • 7 months ago

Full interview with the founder and CEO of Propr Lou sits down with me to discuss Hyperliquid and the future of prop trading Timestamps: 00:00:00 Intro 00:00:16 Meet Louis: from TradFi to crypto 00:01:52 How the crypto market has changed 00:03:49 Why build on Hyperliquid 00:06:52 How propr got started 00:08:40 Scaling a lean team (and the grind) 00:12:24 What a prop firm actually is 00:15:33 Agents, bots, and the future of trading 00:18:32 What Louis is most proud of 00:21:02 Thinking about the token and TGE 00:24:14 Where propr goes in 5 years 00:25:57 Best and worst trades 00:27:50 Life outside crypto + shoutouts

Full interview with the founder and CEO of Propr Lou sits down with me to discuss Hyperliquid and the future of prop trading Timestamps: 00:00:00 Intro 00:00:16 Meet Louis: from TradFi to crypto 00:01:52 How the crypto market has changed 00:03:49 Why build on Hyperliquid 00:06:52 How propr got started 00:08:40 Scaling a lean team (and the grind) 00:12:24 What a prop firm actually is 00:15:33 Agents, bots, and the future of trading 00:18:32 What Louis is most proud of 00:21:02 Thinking about the token and TGE 00:24:14 Where propr goes in 5 years 00:25:57 Best and worst trades 00:27:50 Life outside crypto + shoutouts

ryandcrypto

42,836 views • 2 months ago

Wondering why your maker-turned-manager suddenly seems distracted in meetings? Maybe they're addicted to coding agents! In this episode of Lightcone, Calvin French-Owen — a co-founder of Segment and former engineer on OpenAI's Codex team — joins us to talk about why coding agents suddenly feel so powerful, the differences between Codex, Claude Code, and Cursor, and what the future of work will look like. 0:00 Intro 1:15 Garry can’t stop using Claude Code! 4:00 Contrast with IDE’s, context-splitting 6:23 Distribution models, top down vs bottom up 9:11 Licensing and optimization 12:28 Tips on becoming a top 1% user of coding agents 17:36 When can the agents work 24-48 hour running jobs on their own? 21:34 Can the agent teach things like architecture? 26:27 Will the next generation have even better taste and multitasking ability? 29:58 Maker vs manager schedules 31:36 How would Calvin build Segment now? 35:52 The importance of testing 38:52 The Claude bots are talking (to each other!) 40:10 Examples of complex issues, how will the tools evolve 43:00 Outro

Wondering why your maker-turned-manager suddenly seems distracted in meetings? Maybe they're addicted to coding agents! In this episode of Lightcone, Calvin French-Owen — a co-founder of Segment and former engineer on OpenAI's Codex team — joins us to talk about why coding agents suddenly feel so powerful, the differences between Codex, Claude Code, and Cursor, and what the future of work will look like. 0:00 Intro 1:15 Garry can’t stop using Claude Code! 4:00 Contrast with IDE’s, context-splitting 6:23 Distribution models, top down vs bottom up 9:11 Licensing and optimization 12:28 Tips on becoming a top 1% user of coding agents 17:36 When can the agents work 24-48 hour running jobs on their own? 21:34 Can the agent teach things like architecture? 26:27 Will the next generation have even better taste and multitasking ability? 29:58 Maker vs manager schedules 31:36 How would Calvin build Segment now? 35:52 The importance of testing 38:52 The Claude bots are talking (to each other!) 40:10 Examples of complex issues, how will the tools evolve 43:00 Outro

Y Combinator

18,418 views • 6 months ago

The Dario Amodei interview. 0:00:00 - What exactly are we scaling? 0:12:36 - Is diffusion cope? 0:29:42 - Is continual learning necessary? 0:46:20 - If AGI is imminent, why not buy more compute? 0:58:49 - How will AI labs actually make profit? 1:31:19 - Will regulations destroy the boons of AGI? 1:47:41 - Why can’t China and America both have a country of geniuses in a datacenter? Look up Dwarkesh Podcast on Youtube, Spotify, Apple Podcasts, etc.

The Dario Amodei interview. 0:00:00 - What exactly are we scaling? 0:12:36 - Is diffusion cope? 0:29:42 - Is continual learning necessary? 0:46:20 - If AGI is imminent, why not buy more compute? 0:58:49 - How will AI labs actually make profit? 1:31:19 - Will regulations destroy the boons of AGI? 1:47:41 - Why can’t China and America both have a country of geniuses in a datacenter? Look up Dwarkesh Podcast on Youtube, Spotify, Apple Podcasts, etc.

Dwarkesh Patel

2,059,199 views • 5 months ago

What is it like to be a founding engineer at a startup, and how do you become one? Michelle Lim joined Warp as the founding engineer, and she's now a founder at Flint AI where she is hiring founding engineers. We talk through her journey and tactics helpful for anyone wanting to make an impact at a startup. Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Linear ⁠ — ⁠ The system for modern product development. I really liked how Michelle was realistic how, as a founding engineer, you have two jobs at once: #1: Software engineering. Build the stuff you need to do #2: Everything else to help the business succeed, including lots of things that are not to do with software engineering! Do all of these once you did job #1 well. In return, you get to learn a lot on how a business is built - which will help you if and when you become a founder yourself! Thanks for the great convo, Michelle, and keep it up!

What is it like to be a founding engineer at a startup, and how do you become one? Michelle Lim joined Warp as the founding engineer, and she's now a founder at Flint AI where she is hiring founding engineers. We talk through her journey and tactics helpful for anyone wanting to make an impact at a startup. Watch or listen: • YouTube: • Spotify: • Apple: Brought to you by: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Linear ⁠ — ⁠ The system for modern product development. I really liked how Michelle was realistic how, as a founding engineer, you have two jobs at once: #1: Software engineering. Build the stuff you need to do #2: Everything else to help the business succeed, including lots of things that are not to do with software engineering! Do all of these once you did job #1 well. In return, you get to learn a lot on how a business is built - which will help you if and when you become a founder yourself! Thanks for the great convo, Michelle, and keep it up!

Gergely Orosz

17,126 views • 8 months ago

Interview with founding member of Oculus, hardware-pioneer, and CEO of Framework, Nirav Patel (Nirav Patel) 0:00 Breaking into Framework HQ 1:11 Experience at Apple 2:05 Becoming employee 0001 at Oculus 3:20 What worked for Oculus? 4:30 Founding Framework 6:32 GTM for Framework & Linusv 9:48 Advice for hardware 12:22 Nirav on AI Hardware 15:36 Raising little, huge impact 17:00 Hardest technical challenge 18:33 Emerging research/tech 19:06 How do you think? 20:26 Master plan 22:01 Consumer Hardware mistakes 23:12 Why Consumer Hardware? 23:48 Helpful founder resources 25:17 Branding for Framework 27:08 Framework manufacturing story 24:45 Fundraising is hard 35:45 Judging Consumer Hardware 36:47 Current Life Philosophy

Interview with founding member of Oculus, hardware-pioneer, and CEO of Framework, Nirav Patel (Nirav Patel) 0:00 Breaking into Framework HQ 1:11 Experience at Apple 2:05 Becoming employee 0001 at Oculus 3:20 What worked for Oculus? 4:30 Founding Framework 6:32 GTM for Framework & Linusv 9:48 Advice for hardware 12:22 Nirav on AI Hardware 15:36 Raising little, huge impact 17:00 Hardest technical challenge 18:33 Emerging research/tech 19:06 How do you think? 20:26 Master plan 22:01 Consumer Hardware mistakes 23:12 Why Consumer Hardware? 23:48 Helpful founder resources 25:17 Branding for Framework 27:08 Framework manufacturing story 24:45 Fundraising is hard 35:45 Judging Consumer Hardware 36:47 Current Life Philosophy

Jason Carman

45,147 views • 2 years ago

These guys literally dropped a masterclass on the basics of technical AI: 3:22 - How LLMs actually work 5:24 - What temperature is 7:05 - What context windows are 7:59 - Building a tool from scratch 10:28 - Why MCP exists 12:03 - What makes an agent an agent 12:43 - The power of skills 16:47 - What LangChain does 20:50 - How RAG kills hallucinations 23:36 - When you don't need a framework 25:26 - How routing works 28:34 - Why routers save millions 31:35 - Mock interview: LLM or ML? 35:50 - Why models hallucinate

These guys literally dropped a masterclass on the basics of technical AI: 3:22 - How LLMs actually work 5:24 - What temperature is 7:05 - What context windows are 7:59 - Building a tool from scratch 10:28 - Why MCP exists 12:03 - What makes an agent an agent 12:43 - The power of skills 16:47 - What LangChain does 20:50 - How RAG kills hallucinations 23:36 - When you don't need a framework 25:26 - How routing works 28:34 - Why routers save millions 31:35 - Mock interview: LLM or ML? 35:50 - Why models hallucinate

Aakash Gupta

75,101 views • 14 days ago

This conversation with my friend Sebastian Mallaby changed my mind on AI: The Truth About AI They Don't Want You To Know Watch/listen on X, YouTube, Spotify, Apple, or wherever you get your podcasts. Timestamps 0:00 Sebastian Mallaby Introduction 2:04 What drew Sebastian to Demis Hassabis? 3:17 Is the progress of AI impressive? 5:01 The fascinating character that is Demis 7:12 Why is this not a Silicon Valley story? 8:31 Sebastian "He isn't motivated by money." 12:17 What is DeepMind and why does it matter? 13:40 Is AI an existential risk? 15:25 Anthony "People need to understand what is going on." 18:31 The relationship between capital and transformative tech 21:06 What is AGI and who gets there first? 23:00 Is China ahead of the US in AI race? Where is Europe in this? 26:02 Has this changed Sebastian's view of the future? 27:21 Five Words

This conversation with my friend Sebastian Mallaby changed my mind on AI: The Truth About AI They Don't Want You To Know Watch/listen on X, YouTube, Spotify, Apple, or wherever you get your podcasts. Timestamps 0:00 Sebastian Mallaby Introduction 2:04 What drew Sebastian to Demis Hassabis? 3:17 Is the progress of AI impressive? 5:01 The fascinating character that is Demis 7:12 Why is this not a Silicon Valley story? 8:31 Sebastian "He isn't motivated by money." 12:17 What is DeepMind and why does it matter? 13:40 Is AI an existential risk? 15:25 Anthony "People need to understand what is going on." 18:31 The relationship between capital and transformative tech 21:06 What is AGI and who gets there first? 23:00 Is China ahead of the US in AI race? Where is Europe in this? 26:02 Has this changed Sebastian's view of the future? 27:21 Five Words

Anthony Scaramucci

24,994 views • 4 months ago