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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...

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Dami-Defi

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He is 19, already built an AI system making $2.3M a year - and now teaches how to do it at Stanford 01:03 - he lost his job at Anthropic because of prompt engineering 09:34 - Context Engineering gives 10x to coding speed 28:47 - Opus 5 builds an AI system making $2.3M a year from scratch after watching I deleted all my prompts and switched to Context Engineering - first $20k+ in a month. Save & watch - the article below is a step by step guide to build an AI system
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He is 19, already built an AI system making $2.3M a year - and now teaches how to do it at Stanford 01:03 - he lost his job at Anthropic because of prompt engineering 09:34 - Context Engineering gives 10x to coding speed 28:47 - Opus 5 builds an AI system making $2.3M a year from scratch after watching I deleted all my prompts and switched to Context Engineering - first $20k+ in a month. Save & watch - the article below is a step by step guide to build an AI system

Noisy

67,247 Aufrufe • vor 13 Tagen

Most people lump prompt engineering, context engineering, and graph engineering together. Here's the simple breakdown: - Prompt engineering: how you ask the AI a better question. - Context engineering: how you give the AI better information. - Graph engineering: how you design the work around the AI so it stops living inside one giant chat. Example: researching a startup idea. The chat way: - One model, one pass. - It decides what matters - Researches the market - Interprets the evidence - Writes the recommendation - Grades its own confidence. The graph way: - A planner breaks the question into angles - 5 researchers split up: customer, competitors, distribution, pricing, risks - A skeptic tries to kill the weak findings - A merger turns what survives into a one-page recommendation - You approve before you act Same written report. Completely different work behind it. Think: - Prompt = the question - Context = the information - Graph = the workflow Design the workflow. That's where it clicks.
2:11

Most people lump prompt engineering, context engineering, and graph engineering together. Here's the simple breakdown: - Prompt engineering: how you ask the AI a better question. - Context engineering: how you give the AI better information. - Graph engineering: how you design the work around the AI so it stops living inside one giant chat. Example: researching a startup idea. The chat way: - One model, one pass. - It decides what matters - Researches the market - Interprets the evidence - Writes the recommendation - Grades its own confidence. The graph way: - A planner breaks the question into angles - 5 researchers split up: customer, competitors, distribution, pricing, risks - A skeptic tries to kill the weak findings - A merger turns what survives into a one-page recommendation - You approve before you act Same written report. Completely different work behind it. Think: - Prompt = the question - Context = the information - Graph = the workflow Design the workflow. That's where it clicks.

The Startup Ideas Podcast (SIP) 🧃

17,562 Aufrufe • vor 9 Tagen

🎨 Introducing Prompt Canvas — a novel UX for prompt engineering Building LLM applications requires new and dedicated tools for prompt engineering. With Prompt Canvas in LangSmith, you can: • Collaborate with an AI agent to draft, refine, and edit your prompts • Define custom quick actions to standardize prompting strategies used across the org ✍️ Learn more: ➡️ Try it out:
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🎨 Introducing Prompt Canvas — a novel UX for prompt engineering Building LLM applications requires new and dedicated tools for prompt engineering. With Prompt Canvas in LangSmith, you can: • Collaborate with an AI agent to draft, refine, and edit your prompts • Define custom quick actions to standardize prompting strategies used across the org ✍️ Learn more: ➡️ Try it out:

LangChain

43,733 Aufrufe • vor 1 Jahr

Anthropic just changed how they think about Claude 5. Not with a new model. Not with a benchmark. With a completely different philosophy for building AI systems. Most people will miss it. They're still trying to write better prompts. Anthropic is optimizing something else entirely: Context. Here's what every AI builder should learn from it: 1. Stop telling AI exactly what to do. Start telling it what success looks like. Older models needed rigid instructions. Newer models perform better when you define the objective and let them make the decisions. The goal is no longer more control. It's more clarity. 2. Context is a budget, not a storage unit. Every extra sentence competes for the model's attention. A massive context file doesn't make AI smarter. It often makes reasoning worse. The best systems don't load everything. They load only what's relevant. 3. Great tools beat great prompts. Most people spend hours tweaking prompt wording. Anthropic is investing in better interfaces instead. Clear parameters. Structured inputs. Well-designed tools. If the interface removes ambiguity, the model makes better decisions before it even starts reasoning. 4. Show reality instead of describing it. Want a specific coding style? Provide the codebase. Want a certain design language? Share the mockup. Want consistent outputs? Give it tests to satisfy. Concrete references consistently outperform long written instructions. 5. Deliver information when it's needed. Not everything belongs in the initial context. Documentation. Style guides. Verification. Reviews. Load them only when they're relevant. Think of context like RAM, not a hard drive. 6. The real advantage is system design. Prompt engineering isn't disappearing. It's becoming one small part of a much bigger stack. Memory. Retrieval. Context architecture. Tool design. Evaluation. Every improvement compounds. The biggest takeaway from Anthropic's update? The next generation of AI won't be won by the people writing the longest prompts. It'll be won by the people building the smartest systems. Same models. Completely different results.
0:30

Anthropic just changed how they think about Claude 5. Not with a new model. Not with a benchmark. With a completely different philosophy for building AI systems. Most people will miss it. They're still trying to write better prompts. Anthropic is optimizing something else entirely: Context. Here's what every AI builder should learn from it: 1. Stop telling AI exactly what to do. Start telling it what success looks like. Older models needed rigid instructions. Newer models perform better when you define the objective and let them make the decisions. The goal is no longer more control. It's more clarity. 2. Context is a budget, not a storage unit. Every extra sentence competes for the model's attention. A massive context file doesn't make AI smarter. It often makes reasoning worse. The best systems don't load everything. They load only what's relevant. 3. Great tools beat great prompts. Most people spend hours tweaking prompt wording. Anthropic is investing in better interfaces instead. Clear parameters. Structured inputs. Well-designed tools. If the interface removes ambiguity, the model makes better decisions before it even starts reasoning. 4. Show reality instead of describing it. Want a specific coding style? Provide the codebase. Want a certain design language? Share the mockup. Want consistent outputs? Give it tests to satisfy. Concrete references consistently outperform long written instructions. 5. Deliver information when it's needed. Not everything belongs in the initial context. Documentation. Style guides. Verification. Reviews. Load them only when they're relevant. Think of context like RAM, not a hard drive. 6. The real advantage is system design. Prompt engineering isn't disappearing. It's becoming one small part of a much bigger stack. Memory. Retrieval. Context architecture. Tool design. Evaluation. Every improvement compounds. The biggest takeaway from Anthropic's update? The next generation of AI won't be won by the people writing the longest prompts. It'll be won by the people building the smartest systems. Same models. Completely different results.

Evan Luthra

38,751 Aufrufe • vor 14 Tagen

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 01:45 - Build an LLM from scratch 17:22 - Learn how to actually use AI models 30:03 - Master prompt engineering 52:35 - Coordinate 100 agents as one human Most people are still learning how to prompt one model. Jeff Dean is showing the full stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating agent systems is the new one. This is 27 years of Google AI compressed into one hour. Bookmark and watch it today Then read the full Graph Engineering guide below ↓
1:06:12

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 01:45 - Build an LLM from scratch 17:22 - Learn how to actually use AI models 30:03 - Master prompt engineering 52:35 - Coordinate 100 agents as one human Most people are still learning how to prompt one model. Jeff Dean is showing the full stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating agent systems is the new one. This is 27 years of Google AI compressed into one hour. Bookmark and watch it today Then read the full Graph Engineering guide below ↓

Lunar

99,204 Aufrufe • vor 5 Tagen

EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.
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EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.

Dami-Defi

13,353 Aufrufe • vor 1 Monat

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 AI “Slop” Actually Means 8:06 - From Engineer to Founder 12:54 - How Context Engineering Is Changing 15:23 - The “Dumb Zone” of Context Windows 18:59 - Will AI Replace Software Engineers? 21:27 - Chaotic Final Plating 22:00 - Taste Test: Who Cooked It Better? 24:24 - Judges Decide the Winner Look up Latent Space on Youtube, Spotify, Apple Podcasts, etc.
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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 AI “Slop” Actually Means 8:06 - From Engineer to Founder 12:54 - How Context Engineering Is Changing 15:23 - The “Dumb Zone” of Context Windows 18:59 - Will AI Replace Software Engineers? 21:27 - Chaotic Final Plating 22:00 - Taste Test: Who Cooked It Better? 24:24 - Judges Decide the Winner Look up Latent Space on Youtube, Spotify, Apple Podcasts, etc.

Latent.Space

14,971 Aufrufe • vor 5 Monaten

Happy to properly launch Anna, the proactive AI agent for parents! Uncovering a bit of the technology behind the scenes! Building Anna is where I learned: 💾 Memory as plain text sucks. You need structured memory. Like a full-blown PostgreSQL DB that stores your tasks and calendar in a structured manner. Most harnesses are good at coding-related stuff. Let it do the query. Don't let it vibe-search the memory. Let it vibe your SQL query 💭 Dreaming is a useful concept for enhancing memory to feed the LLM context. But DO NOT vibe your dream. Asking your agent to "hey, just dream and keep the relevant memory around" is a recipe for deleting a bunch of important information and keeping trash around. Your dream needs to have some Taxonomy (or better, Ontology). What information is important? For who? With what object? What can they do? And again, these are impossible to describe and act well without a proper schema 🔄 Loop Engineering is important for smoothing out rough edges in the system we build. But even expensive loop engineering with a state-of-the-art model can't out-engineer bad system design. The highest leverage an AI Engineer can do is actually building the right system design, and having an eye on both product delight and engineering scalability There are several more insights that I plan to cover in a dedicated video about Agentic AI Engineering. But it's actually a huge relief that the future of software engineering... is still software engineering
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Use this ai workflow to get 1000s of followers/day (Steps and tools included) Step 1: Find 3 posts that went massive Step 2: Ask Claude to break down WHY they worked Step 3: Extract the psychological patterns Step 4: Build prompts that interview YOU for context Tools: Claude 4.1 extended thinking (not basic GPT) Cost: $20/month Time: 10 min setup → infinite personalized content The KEY: Context is KING You have to feed it YOUR voice parameters You have to give it audience psychology You're one prompt away from automating your content creation like a pro Stop using garbage prompts Start reverse-engineering what works Use this playbook
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Use this ai workflow to get 1000s of followers/day (Steps and tools included) Step 1: Find 3 posts that went massive Step 2: Ask Claude to break down WHY they worked Step 3: Extract the psychological patterns Step 4: Build prompts that interview YOU for context Tools: Claude 4.1 extended thinking (not basic GPT) Cost: $20/month Time: 10 min setup → infinite personalized content The KEY: Context is KING You have to feed it YOUR voice parameters You have to give it audience psychology You're one prompt away from automating your content creation like a pro Stop using garbage prompts Start reverse-engineering what works Use this playbook

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INTRODUCING FACTORY Factory is the Command Center where developers and agentic AI collaborate to understand, plan, and build enterprise software. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems powered by frontier LLMs. In your Factory, • 🤖 Droid Mode unlocks cutting-edge agentic capabilities. Accelerate your development with AI that autonomously pulls in tickets, reads error logs, retrieves relevant context, executes code, and solves complex, long-running tasks. • 🪡 Threads let you jump into deep work with all relevant context dynamically surfaced in front of you. No more crawling through Github, Slack, Google Drive, Notion, Jira, Slack, or context-switching between them. • ⚡ Workflows transform and centralize your organization’s best practices into executable, AI-powered processes. Automate repetitive tasks like building integrations, bug-fixing, PRD creation, release notes, version updates, and more. We're working with some of the most innovative organizations in software — from AI-forward enterprises like MongoDB to high-growth organizations across the world. Our enterprise platform combines advanced engineering system indexing, state-of-the-art retrieval and search, and reliable agentic systems using frontier LLMs like o3 and Claude 3.7 Sonnet 1/7

Factory

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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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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

Box

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.Tensol turns OpenClaw into full-time AI employees for your company. They handle repetitive workflows across support, engineering, sales and more — running 24/7 in a secure environment, connected to your tools, with full context of your business. Congrats on the launch pratik and Oliviero Pinotti!
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.Tensol turns OpenClaw into full-time AI employees for your company. They handle repetitive workflows across support, engineering, sales and more — running 24/7 in a secure environment, connected to your tools, with full context of your business. Congrats on the launch pratik and Oliviero Pinotti!

Y Combinator

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Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.
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Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,166 Aufrufe • vor 2 Monaten

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!
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New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

200,950 Aufrufe • vor 1 Jahr

.Aaron Levie says AI agents may take years to diffuse from engineering into the rest of knowledge work. "The models are really good at code, and the work is verifiable." "You have 5 or 10 things that make agents work in an enterprise context for engineering." "The users are less technical, the data is much more fragmented, the systems are much more legacy." "That's why it's going to be a number of years for this diffusion to roll from what we're seeing in Silicon Valley, into the rest of knowledge work."
1:27

.Aaron Levie says AI agents may take years to diffuse from engineering into the rest of knowledge work. "The models are really good at code, and the work is verifiable." "You have 5 or 10 things that make agents work in an enterprise context for engineering." "The users are less technical, the data is much more fragmented, the systems are much more legacy." "That's why it's going to be a number of years for this diffusion to roll from what we're seeing in Silicon Valley, into the rest of knowledge work."

MTS

13,046 Aufrufe • vor 3 Monaten

Google Deepmind CEO Demis Hassabis just explained the real AI skill gap: "The next generation will do things that used to take teams of 10, 20, 30, 50 people" Most people hear that and think AI just makes them faster Wrong It changes what one person can become > researcher > designer > developer > marketer > analyst > support > ops All wired into one workflow Agents research Tools write Automations publish Memory compounds This is not prompt engineering This is leverage The next 5 years won't belong to better prompt writers They'll belong to better workflow builders Bookmark and watch the full intreview
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Google Deepmind CEO Demis Hassabis just explained the real AI skill gap: "The next generation will do things that used to take teams of 10, 20, 30, 50 people" Most people hear that and think AI just makes them faster Wrong It changes what one person can become > researcher > designer > developer > marketer > analyst > support > ops All wired into one workflow Agents research Tools write Automations publish Memory compounds This is not prompt engineering This is leverage The next 5 years won't belong to better prompt writers They'll belong to better workflow builders Bookmark and watch the full intreview

rari

39,959 Aufrufe • vor 1 Monat

Google Brain founder, Andrew Ng: "One of the most important skills of the future is the ability to tell a computer exactly what you want so it can do it for you." In this talk, he explains why the biggest opportunity isn't just using AI. It's learning how to turn work into agentic workflows, break tasks into steps, give agents the right tools and context, add evals, and make the whole system reliable. That's the difference between prompting an AI and engineering an agent that actually works. Watch it today, then read the full guide in the article below.
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Google Brain founder, Andrew Ng: "One of the most important skills of the future is the ability to tell a computer exactly what you want so it can do it for you." In this talk, he explains why the biggest opportunity isn't just using AI. It's learning how to turn work into agentic workflows, break tasks into steps, give agents the right tools and context, add evals, and make the whole system reliable. That's the difference between prompting an AI and engineering an agent that actually works. Watch it today, then read the full guide in the article below.

Rahul

11,553 Aufrufe • vor 3 Tagen

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.
1:33:10

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 Aufrufe • vor 28 Tagen

We are announcing potpie AI $2.2M pre-seed fundraise to advance Spec-Driven Development for large enterprise codebases. The round is led by Emergent Ventures , with participation from All In Capital , DeVC , and PointOne Capital , along with the support of some amazing angel investors from companies including Atlassian, OpenAI, Meta, Razorpay and Flobiz. As AI accelerates code generation, the constraint inside large enterprises has shifted from coding to maintenance and assurance. The limiting factor is no longer writing code, but understanding complex systems, aligning teams around intent, and safely evolving large, interdependent codebases. In most organizations, specifications exist as static documents, while production systems evolve independently. Context is fragmented across repositories, tickets, logs, reviews, and floating documents making reliable AI adoption difficult. We are building the foundational layer that makes Spec-Driven Development executable at scale. By unifying engineering context and operationalizing the spec as a structured source of truth, we enable AI systems to reason with architectural awareness rather than surface-level code completion. We are already working with large enterprise customers, including Fortune 500 organizations. This milestone allows us to deepen those partnerships and support more teams transitioning from experimental AI usage to structured, production-grade AI-first engineering workflows. If you are leading engineering at scale and evaluating how AI should integrate into mission-critical systems, we would love to chat with you!
2:17

We are announcing potpie AI $2.2M pre-seed fundraise to advance Spec-Driven Development for large enterprise codebases. The round is led by Emergent Ventures , with participation from All In Capital , DeVC , and PointOne Capital , along with the support of some amazing angel investors from companies including Atlassian, OpenAI, Meta, Razorpay and Flobiz. As AI accelerates code generation, the constraint inside large enterprises has shifted from coding to maintenance and assurance. The limiting factor is no longer writing code, but understanding complex systems, aligning teams around intent, and safely evolving large, interdependent codebases. In most organizations, specifications exist as static documents, while production systems evolve independently. Context is fragmented across repositories, tickets, logs, reviews, and floating documents making reliable AI adoption difficult. We are building the foundational layer that makes Spec-Driven Development executable at scale. By unifying engineering context and operationalizing the spec as a structured source of truth, we enable AI systems to reason with architectural awareness rather than surface-level code completion. We are already working with large enterprise customers, including Fortune 500 organizations. This milestone allows us to deepen those partnerships and support more teams transitioning from experimental AI usage to structured, production-grade AI-first engineering workflows. If you are leading engineering at scale and evaluating how AI should integrate into mission-critical systems, we would love to chat with you!

Aditi Kothari

41,878 Aufrufe • vor 5 Monaten

AutoGPT might be the next big step in AI. Here's why Karpathy recently said "AutoGPT is the next frontier of prompt engineering" AutoGPT is the equivalent of giving GPT-based models a memory and a body. You can now give a task to an AI agent and have it autonomously come up with a plan, execute on it, browse the web, and use new data to revise the strategy until the task is completed. It can analyze the market and come up with a trading strategy, customer service, marketing, finance, or other tasks that requires continuous updates. There are three components to it: 1. Architecture: It leverages GPT-4 and GPT-3.5 via API. 2. Autonomous Iterations: AutoGPT can refine its outputs by self-critical review, building on its previous work and integrating prompt history for more accurate results. 3. Memory Management: Integration with Pinecone allows for long-term memory storage, enabling context preservation and improved decision-making. 4. Multi-functionality: Capabilities include file manipulation, web browsing, and data retrieval, distinguishing AutoGPT from previous AI advancements by broadening its application scope.
0:29

AutoGPT might be the next big step in AI. Here's why Karpathy recently said "AutoGPT is the next frontier of prompt engineering" AutoGPT is the equivalent of giving GPT-based models a memory and a body. You can now give a task to an AI agent and have it autonomously come up with a plan, execute on it, browse the web, and use new data to revise the strategy until the task is completed. It can analyze the market and come up with a trading strategy, customer service, marketing, finance, or other tasks that requires continuous updates. There are three components to it: 1. Architecture: It leverages GPT-4 and GPT-3.5 via API. 2. Autonomous Iterations: AutoGPT can refine its outputs by self-critical review, building on its previous work and integrating prompt history for more accurate results. 3. Memory Management: Integration with Pinecone allows for long-term memory storage, enabling context preservation and improved decision-making. 4. Multi-functionality: Capabilities include file manipulation, web browsing, and data retrieval, distinguishing AutoGPT from previous AI advancements by broadening its application scope.

Lior Alexander

808,102 Aufrufe • vor 3 Jahren