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Everyone is still optimizing prompts The people running 20 agents stopped doing that a year ago Prompt → Context → Harness → Loops Matt Pocock put his whole workflow in one hour and called it "just copy him" 384,000 watched it, almost none copied the third layer a rule... show more

Jordan | 先生

12,647 subscribers

74,307 görüntüleme • 16 gün önce •via X (Twitter)

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Matt Pocock profil fotoğrafı

Matt Pocock16 gün önce

God this slop-repost meta is boring, can't you add something new to the conversation?

Jordan | 先生 profil fotoğrafı

Jordan | 先生16 gün önce

Well, yeah, fair enough. However the breakdown under it isn't a repost though, my own article with 10 steps, diagrams etc If any of it is wrong I'd rather hear it from you than not.

Matt Pocock profil fotoğrafı

Matt Pocock16 gün önce

I just don't understand why you're using my face to advertise your article Just post your article If it's good, it'll get attention

gsimone profil fotoğrafı

gsimone16 gün önce

@mattpocockuk > bookmark it before it's gone what

Jordan | 先生 profil fotoğrafı

Jordan | 先生16 gün önce

@mattpocockuk bookmark this tweet before it's gone in your timeline

Anders Abjorn | sales → AI profil fotoğrafı

Anders Abjorn | sales → AI16 gün önce

@mattpocockuk It's a difference To prompt like it's Google and works with AI like AI is supposed to be used

Danny Yaroslavski profil fotoğrafı

Danny Yaroslavski16 gün önce

@mattpocockuk Stop tweeting like an llm

AI Mastery Guide profil fotoğrafı

AI Mastery Guide16 gün önce

@mattpocockuk Almost nobody copied the layer

AJ ◥ profil fotoğrafı

AJ ◥16 gün önce

Grok, full technical breakdown: thesis, 5 core arguments with timestamps, 3 counterpoints, lessons, methodologies with paper links, tools/platforms, cited researchers/companies, 2 direct research-team applications, SOTA gap, and implementation details (hyperparams, architecture sizes, or costs) if mentioned. No fluff.

badbutar profil fotoğrafı

badbutar16 gün önce

@mattpocockuk Prompts are suggestions, harnesses are the bouncers

CryptoAI_Insider profil fotoğrafı

CryptoAI_Insider16 gün önce

@mattpocockuk Most people build fragile agents because they skip the harness entirely.

hylanderdev profil fotoğrafı

hylanderdev16 gün önce

@mattpocockuk At least unslop your content before posting lmao

DEV profil fotoğrafı

DEV16 gün önce

@mattpocockuk Most are stuck at the prompt level. The real value’s in those deeper layers.

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OpenAI CEO, Sam Altman: "You don't need to write prompts anymore. You need Loops and Graphs." In 38 minutes he explains why his own engineers stopped writing prompts, and what they build instead Prompts → Agents → Loops → Graphs a prompt buys you one answer a graph buys you the whole job, and you are not in it watch it today then save the full graph engineering guide below before everyone catches up ↓

OpenAI CEO, Sam Altman: "You don't need to write prompts anymore. You need Loops and Graphs." In 38 minutes he explains why his own engineers stopped writing prompts, and what they build instead Prompts → Agents → Loops → Graphs a prompt buys you one answer a graph buys you the whole job, and you are not in it watch it today then save the full graph engineering guide below before everyone catches up ↓

Hanako

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Harness vs. Graphs, clearly explained! a harness is great, and most people think it is the whole thing: retries, timeouts, a sandbox, a log, the context it assembles before every call. all of that is real work, and all of it wraps exactly one call. run it a hundred times and you have one call, made very safely, a hundred times. Graph engineering fixes this by moving the decision up a layer: not how safely one call is made, but which calls exist to be made at all. you need both, and here is the sentence that resolves the whole confusion: the harness is everything around one call. the graph is everything between them. ↳ around one call: retry, timeout, sandbox, log, assemble the context, hand back a result ↳ between calls: split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs the harness does not go away when you build a graph. it moves under each node, and now there are five of them, each wrapping a call you would never have made by hand. the trick is knowing which layer a failure belongs to. turn a piece off and run it again. if the call still works, it was the harness. if the wrong step runs at all, it was the graph. people spend weeks hardening a harness around a node that should not have existed. one thing to know before you scale it. most of what people call their agent is a harness with a chat box on it. ↳ it retries, it times out, it logs, it assembles context, it holds one call up beautifully ↳ it has never once decided that a second call should exist, and that is the entire difference that last one catches careful people. a harness that never fails is not evidence the system is right. it is evidence one call went well, which is the smallest possible claim. and the one that eats whole nights: a harness cannot save you from the wrong step running. you can retry a bad decision three times with a clean log and perfect isolation, and all you bought was three copies of it. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Harness vs. Graphs, clearly explained! a harness is great, and most people think it is the whole thing: retries, timeouts, a sandbox, a log, the context it assembles before every call. all of that is real work, and all of it wraps exactly one call. run it a hundred times and you have one call, made very safely, a hundred times. Graph engineering fixes this by moving the decision up a layer: not how safely one call is made, but which calls exist to be made at all. you need both, and here is the sentence that resolves the whole confusion: the harness is everything around one call. the graph is everything between them. ↳ around one call: retry, timeout, sandbox, log, assemble the context, hand back a result ↳ between calls: split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs the harness does not go away when you build a graph. it moves under each node, and now there are five of them, each wrapping a call you would never have made by hand. the trick is knowing which layer a failure belongs to. turn a piece off and run it again. if the call still works, it was the harness. if the wrong step runs at all, it was the graph. people spend weeks hardening a harness around a node that should not have existed. one thing to know before you scale it. most of what people call their agent is a harness with a chat box on it. ↳ it retries, it times out, it logs, it assembles context, it holds one call up beautifully ↳ it has never once decided that a second call should exist, and that is the entire difference that last one catches careful people. a harness that never fails is not evidence the system is right. it is evidence one call went well, which is the smallest possible claim. and the one that eats whole nights: a harness cannot save you from the wrong step running. you can retry a bad decision three times with a clean log and perfect isolation, and all you bought was three copies of it. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

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Anthropic engineer: "At Anthropic, we don't write prompts anymore We build harnesses" In just 42 minutes, she shows how the Claude team builds harnesses that prompt themselves Agents → Harness → Loops → Graphs → Self-Improving Systems If this were a $500 course, people would call it one of the best agent courses of the year But it's completely free And it's better than most paid agent engineering courses Watch it today Then use the step-by-step harness engineering guide below

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Anthropic engineer: "At Anthropic, we don't write prompts anymore We build loops" In just 42 minutes, she shows how the Claude team builds loops that can prompt themselves If this were a $400 course, people would probably call it one of the best agent courses of the year It's completely free Watch it, then use the step-by-step guide below to build your first loop

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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 ↓

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Anthropic engineer: "At Anthropic, we don't write prompts anymore We build loops" In just 42 minutes, she shows how the Claude team builds loops that can prompt themselves If this were a $400 course, people would probably call it one of the best agent courses of the year It's completely free Watch it, then use the step-by-step guide below to build your first loop

Lunar

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Google just released a free 2-hour course On going from one prompt to a complete multi-agent graph: 17:44 - Build your first AI agent 39:30 - Create self-improving agent loops 1:12:38 - Connect those loops into graphs 1:34:26 - Make agents regulate themselves 1:55:05 - Orchestrate the full multi-agent system Most people are still building one agent and stopping there Google is teaching the entire stack: Prompt → Agents → Loops → Graphs → Multi-Agent Systems Single agents are the old workflow Self-regulating agent graphs are the new one This free 2-hour course is worth more than most paid agent engineering programs Bookmark and watch it today Then read the full architecture below ↓

Google just released a free 2-hour course On going from one prompt to a complete multi-agent graph: 17:44 - Build your first AI agent 39:30 - Create self-improving agent loops 1:12:38 - Connect those loops into graphs 1:34:26 - Make agents regulate themselves 1:55:05 - Orchestrate the full multi-agent system Most people are still building one agent and stopping there Google is teaching the entire stack: Prompt → Agents → Loops → Graphs → Multi-Agent Systems Single agents are the old workflow Self-regulating agent graphs are the new one This free 2-hour course is worth more than most paid agent engineering programs Bookmark and watch it today Then read the full architecture below ↓

rari

43,167 görüntüleme • 1 ay önce

Met someone whose agents ship more code in a week than my whole team does in a month. I asked him what actually made him this good - which loop framework, which prompt pack. He laughed and sent me a Stanford lecture. Andrew Ng - CS230, Intro to Deep Learning. Free, an hour long. "Everyone memorizes loop tricks," he said. "Almost nobody understands the thing the loop is wrapped around." I watched it last night. Halfway through, I realized I'd spent a year duct-taping agents together without ever understanding what was happening underneath any of it. Bookmark it. Watch it today - then the loop guide below finally clicks.

Met someone whose agents ship more code in a week than my whole team does in a month. I asked him what actually made him this good - which loop framework, which prompt pack. He laughed and sent me a Stanford lecture. Andrew Ng - CS230, Intro to Deep Learning. Free, an hour long. "Everyone memorizes loop tricks," he said. "Almost nobody understands the thing the loop is wrapped around." I watched it last night. Halfway through, I realized I'd spent a year duct-taping agents together without ever understanding what was happening underneath any of it. Bookmark it. Watch it today - then the loop guide below finally clicks.

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Andrew Ng just released a 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents that loop, rewrite themselves, and run without you: 09:14 - Build your first AI agent 33:11 - Run agents with loop engineering 1:02:46 - Turn agent loops into graphs 1:30:15 - Build agents that rewrite themselves 1:49:05 - Run the full graph system without you Most people are still building one agent and calling it done. Andrew Ng is already teaching everything after: Prompt → Agents → Loops → Graphs → Self-Improving Systems Single agents are the old workflow. Systems that improve and run without you are the new one. This 2-hour watch is worth more than most $500 agent engineering courses. Bookmark and watch it before everyone catches up. Then read how to run 1,000 agents from one prompt below ↓

Andrew Ng just released a 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents that loop, rewrite themselves, and run without you: 09:14 - Build your first AI agent 33:11 - Run agents with loop engineering 1:02:46 - Turn agent loops into graphs 1:30:15 - Build agents that rewrite themselves 1:49:05 - Run the full graph system without you Most people are still building one agent and calling it done. Andrew Ng is already teaching everything after: Prompt → Agents → Loops → Graphs → Self-Improving Systems Single agents are the old workflow. Systems that improve and run without you are the new one. This 2-hour watch is worth more than most $500 agent engineering courses. Bookmark and watch it before everyone catches up. Then read how to run 1,000 agents from one prompt below ↓

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