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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 次观看 • 3 个月前 •via X (Twitter)

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🆕 No Vibes Allowed: Solving Hard Problems in Complex Codebases dex returns for the epic sequel to his viral talk on Context Engineering, sharing how to avoid slop and stay out of the dumb zone with Harness Engineering with TONs of practical advice/examples

🆕 No Vibes Allowed: Solving Hard Problems in Complex Codebases dex returns for the epic sequel to his viral talk on Context Engineering, sharing how to avoid slop and stay out of the dumb zone with Harness Engineering with TONs of practical advice/examples

AI Engineer

35,993 次观看 • 7 个月前

This video by explains the fundamentals of context engineering in just 14 minutes. Context Engineering is effectively the #1 job of engineers building AI agents. do not sleep on it.

This video by explains the fundamentals of context engineering in just 14 minutes. Context Engineering is effectively the #1 job of engineers building AI agents. do not sleep on it.

ℏεsam

75,150 次观看 • 11 个月前

CONTEXT ENGINEERING > PROMPT ENGINEERING Everyone is obsessed with writing better prompts. The next generation of AI builders is focused on context engineering instead. • Prompt engineering shapes the question. Context engineering shapes everything the AI sees. • Great prompts can't save an agent missing critical context, memory, or tools. • AI failures often come from missing information, not weak models. • Context engineering combines prompts, memory, RAG, state management, and tool access into one system. What powers effective AI agents? → Memory: Remembers preferences, past interactions, and ongoing tasks. → State Management: Tracks progress across multi-step workflows. → RAG: Retrieves only the most relevant information when needed. → Tools: Connects AI to APIs, databases, code execution, and real-world actions. → Dynamic Prompts: Enriches instructions with live context at runtime. The key insight: Prompt Engineering = Better Questions Context Engineering = Better Systems The future of AI isn't building smarter prompts. It's building smarter environments for AI to think, remember, retrieve, and act.

CONTEXT ENGINEERING > PROMPT ENGINEERING Everyone is obsessed with writing better prompts. The next generation of AI builders is focused on context engineering instead. • Prompt engineering shapes the question. Context engineering shapes everything the AI sees. • Great prompts can't save an agent missing critical context, memory, or tools. • AI failures often come from missing information, not weak models. • Context engineering combines prompts, memory, RAG, state management, and tool access into one system. What powers effective AI agents? → Memory: Remembers preferences, past interactions, and ongoing tasks. → State Management: Tracks progress across multi-step workflows. → RAG: Retrieves only the most relevant information when needed. → Tools: Connects AI to APIs, databases, code execution, and real-world actions. → Dynamic Prompts: Enriches instructions with live context at runtime. The key insight: Prompt Engineering = Better Questions Context Engineering = Better Systems The future of AI isn't building smarter prompts. It's building smarter environments for AI to think, remember, retrieve, and act.

Dami-Defi

10,107 次观看 • 15 天前

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

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Gaurav Sen

80,025 次观看 • 9 个月前

8 months ago we started explaining enterprise AI on YouTube. 2M views later, here are the 5 shifts that actually defined 2025. Timestamps 00:36 Prompt Engineering → Agent Engineering 00:51 Prompting best practices 01:19 How context engineering focuses on curating information 01:51 Evolution of assistants to agents 02:12 Agents handling complex tasks 02:58 How security became foundational 03:36 The correct security architecture for organizations 04:23 The emergence of model flexibility 05:26 The importance of governance and trust

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Box

460,406 次观看 • 6 个月前

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Anjney Midha

31,764 次观看 • 10 个月前

you might've heard about context engineering, but nobody has explained what it really is... that's why I made a full practical walkthrough on how to use it to make your AI outputs personalized and consistent in this walkthrough, I go over: – what context engineering actually is (without the jargon) – how to train AI on your own data and preferences – how to get outputs that sound like you wrote them (not generic AI) reply "context" and I'll DM it to you (must be following)

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Tyler

20,009 次观看 • 10 个月前

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224,320 次观看 • 9 个月前

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a16z

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Sarah Wang

41,746 次观看 • 6 个月前

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Introducing Dex: the self-driving workspace for operators. Dex is the first agent system with full operational context and a self-updating knowledge base. Every datapoint from your workspace is ingested, synced, and structured into compounding context for agents to take action. Comment "DEX" or tag Dex access. First 1,000 sign-ups get 7 days free. After, join our rolling waitlist. Sign up at joindex [dot] com for a fun surprise. How dex works (threads)

Regina Lin

69,087 次观看 • 2 个月前

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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 次观看 • 4 个月前

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a16z crypto

20,772 次观看 • 11 个月前

AI PM jobs pay $250K+. Here's your roadmap to break in, from the guy who teaches AI PM at Stanford, UCLA, and Maven: 1:21 - AI PM Salaries 9:13 - Live Demo: Agent + RAG 38:48 - Context Engineering vs Prompt Engineering 43:28 - Complete Roadmap: Zero to AI PM at Top Companies

AI PM jobs pay $250K+. Here's your roadmap to break in, from the guy who teaches AI PM at Stanford, UCLA, and Maven: 1:21 - AI PM Salaries 9:13 - Live Demo: Agent + RAG 38:48 - Context Engineering vs Prompt Engineering 43:28 - Complete Roadmap: Zero to AI PM at Top Companies

Aakash Gupta

16,026 次观看 • 7 个月前

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

62,074 次观看 • 7 天前

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 次观看 • 7 个月前

Dex is live. Escape the context-switching nightmare. Your browser just got a brain.

Dex is live. Escape the context-switching nightmare. Your browser just got a brain.

Regina Lin

56,074 次观看 • 8 个月前