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ANTHROPIC LEAKED A GRAPH WHERE ONE FILE WITH 7 ITEMS CUTS YOUR WORK FROM 8 HOURS TO 40 MINUTES AND TAKES 40 DECISIONS A DAY OFF YOU autonomy isn't a slider you drag to the right - it's a graph with 7 steps, and exactly one edge leads into... each one. your approve is worth nothing until there's a description of what the output should be - otherwise you're approving something nobody defined. you can't run a batch without checkpoints, because one crash in the middle rolls back all 40 tasks at once. the agent already works without you, and until it writes down what it did there's nothing to check the report against. this is where the point of no return sits: past it the loop runs while you sleep. the approval didn't disappear - you moved it onto an evaluator that never gets tired and never lets one through. silence is only allowed when there's a list of what breaks it: money, deletion, anything leaving the system. the last one is the brake - a turn limit and a budget cap, whichever runs out first kills the run. you build the brake before the engine - an agent you can't stop isn't autonomous, it's just unattended. skip one edge and nothing crashes and nobody warns you - the system quietly settles two steps lower and keeps running. save this - 7 steps, 7 artefacts, zero ways to jump one ↓show more

Sprytix

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ANTHROPIC LEAKED 6 RULES THAT RUN 8 LOOPS AT ONCE - AND ONE ENGINEER DOES THE WORK OF A TEAM one engineer with this system replaces an entire team - and costs the company 8 times less You → Opus 5 plan → Fable 5 ×8 execute → Fable 5 review → you: 5 min Opus 5 takes your intent and breaks it into subtasks - a bad plan multiplied across every loop gives the same number of disasters - the plan is the only thing you read carefully one repo - isolated worktrees for each loop - nobody stands on anyone's file - parallel PRs ship while you do nothing the agent runs the tests and fixes until green PR - a red build never reaches your eyes one CLAUDE.md for the whole fleet - without it you get different opinions from every loop and one merge hell the average engineer closes 3-4 tasks a day - this system closes a full sprint overnight with zero working hours Fable 5 pre-reviews every PR before it reaches you - you spend 5 minutes - your throughput is now measured in diffs read per hour bookmark and paste into Claude Code - your first fleet can start tonight

ANTHROPIC LEAKED 6 RULES THAT RUN 8 LOOPS AT ONCE - AND ONE ENGINEER DOES THE WORK OF A TEAM one engineer with this system replaces an entire team - and costs the company 8 times less You → Opus 5 plan → Fable 5 ×8 execute → Fable 5 review → you: 5 min Opus 5 takes your intent and breaks it into subtasks - a bad plan multiplied across every loop gives the same number of disasters - the plan is the only thing you read carefully one repo - isolated worktrees for each loop - nobody stands on anyone's file - parallel PRs ship while you do nothing the agent runs the tests and fixes until green PR - a red build never reaches your eyes one CLAUDE.md for the whole fleet - without it you get different opinions from every loop and one merge hell the average engineer closes 3-4 tasks a day - this system closes a full sprint overnight with zero working hours Fable 5 pre-reviews every PR before it reaches you - you spend 5 minutes - your throughput is now measured in diffs read per hour bookmark and paste into Claude Code - your first fleet can start tonight

Sprytix

37,766 views • 16 days ago

your agent doesn't need a graph. it needs a verifier that isn't doing five jobs at once. a loop is already a graph - one node, one edge back to itself. you add a node when you'd hire a specialist. not before. here is the list of all 5 signals. read it before you draw another node.

your agent doesn't need a graph. it needs a verifier that isn't doing five jobs at once. a loop is already a graph - one node, one edge back to itself. you add a node when you'd hire a specialist. not before. here is the list of all 5 signals. read it before you draw another node.

Rohit

15,837 views • 11 days ago

Boris Cherny, the engineer who built Claude Code: "I don't talk to an agent anymore. I talk to a loop" a prompt is one instruction you babysit. a loop is a goal the AI works toward on its own: > it plans > does the work > checks itself > fixes what's weak > and repeats until it's done you step out, the work keeps going that's the shift the best engineers quietly moved to. most people hear a line like that and have no idea what it means in practice this breaks it down without the hype: what a loop actually is, when it's worth building, when it's a money trap, and how to run one yourself today watch his clip, then read the article below

Boris Cherny, the engineer who built Claude Code: "I don't talk to an agent anymore. I talk to a loop" a prompt is one instruction you babysit. a loop is a goal the AI works toward on its own: > it plans > does the work > checks itself > fixes what's weak > and repeats until it's done you step out, the work keeps going that's the shift the best engineers quietly moved to. most people hear a line like that and have no idea what it means in practice this breaks it down without the hype: what a loop actually is, when it's worth building, when it's a money trap, and how to run one yourself today watch his clip, then read the article below

Dep

20,909 views • 1 month ago

A DEVELOPER PROVED YOU CAN FIND THE EXACT COMMIT THAT BROKE YOUR CODE IN A HISTORY OF 10,000 -- IN ABOUT 13 STEPS, AUTOMATICALLY.. 33 minutes from a Google engineer on git bisect: the built-in command that binary-searches your entire history to hunt down the one commit that introduced a bug. -> The moment it lands, "When did this break?" stops being an afternoon of guessing. You hand git a good commit, a bad one, and a test and it finds the culprit while you watch. Most people debug a regression by reading diffs by hand, blaming the obvious file, and hoping. Bisect doesn't hope. It cuts the search in half, then in half again, until only the guilty commit is left standing. Reading history by hand was never the skill -> letting git do the binary search for you is. And when an AI agent dumps hundreds of commits into your repo and one quietly breaks production, bisect is what finds the needle without you reading a single line. The bug isn't hiding. It's sitting in one exact commit. Most people just never learned to make git point at it. Bookmark & Watch it today ↓

A DEVELOPER PROVED YOU CAN FIND THE EXACT COMMIT THAT BROKE YOUR CODE IN A HISTORY OF 10,000 -- IN ABOUT 13 STEPS, AUTOMATICALLY.. 33 minutes from a Google engineer on git bisect: the built-in command that binary-searches your entire history to hunt down the one commit that introduced a bug. -> The moment it lands, "When did this break?" stops being an afternoon of guessing. You hand git a good commit, a bad one, and a test and it finds the culprit while you watch. Most people debug a regression by reading diffs by hand, blaming the obvious file, and hoping. Bisect doesn't hope. It cuts the search in half, then in half again, until only the guilty commit is left standing. Reading history by hand was never the skill -> letting git do the binary search for you is. And when an AI agent dumps hundreds of commits into your repo and one quietly breaks production, bisect is what finds the needle without you reading a single line. The bug isn't hiding. It's sitting in one exact commit. Most people just never learned to make git point at it. Bookmark & Watch it today ↓

slash1s

37,773 views • 2 months ago

ANTHROPIC MAPPED OUT 8 LEVELS OF AI AGENTS - AND ANYONE CAN REACH LEVEL 8 WHERE THE SYSTEM EARNS $70K WITHOUT YOU anyone can reach level 8 - one solo developer with the right architecture gets there in a few weeks level 1 - the agent can do one action - and 80% of developers call this a product level 3 - 50 tools in context at once - the model gets confused - most teams get stuck here and lose $30K/month level 4 - Opus 5 grades its own output and rejects before you see it - the first level where quality stops depending on luck level 5 - Opus 5 plans, Fable 5 executes in parallel - one brain, many hands level 7 - while you slept the agent did $3,000 worth of work and sent the report before you woke up level 8 - the fleet finds its own mistakes, patches the graph and tomorrow it is smarter than today bookmark and read below - 8 steps from level 1 to $70K/month without you

ANTHROPIC MAPPED OUT 8 LEVELS OF AI AGENTS - AND ANYONE CAN REACH LEVEL 8 WHERE THE SYSTEM EARNS $70K WITHOUT YOU anyone can reach level 8 - one solo developer with the right architecture gets there in a few weeks level 1 - the agent can do one action - and 80% of developers call this a product level 3 - 50 tools in context at once - the model gets confused - most teams get stuck here and lose $30K/month level 4 - Opus 5 grades its own output and rejects before you see it - the first level where quality stops depending on luck level 5 - Opus 5 plans, Fable 5 executes in parallel - one brain, many hands level 7 - while you slept the agent did $3,000 worth of work and sent the report before you woke up level 8 - the fleet finds its own mistakes, patches the graph and tomorrow it is smarter than today bookmark and read below - 8 steps from level 1 to $70K/month without you

Noisy

12,083 views • 17 days ago

your agent loop needs 8 exits. most people ship only one. (explained with triggers) 1) goal met → an evaluator scores the output against a rubric, and the run stops on a pass. → fires when the work is measurably done, not when the model says it is done. 2) turn cap → a hard ceiling on iterations, counted and enforced by the harness, not the prompt. → fires on the task it was never going to finish, before you pay to find that out. 3) budget cap → a limit on tokens or dollars, whichever one runs out first. → fires mid-run, which is exactly why it is the exit that saves you the 3am bill. 4) wall clock → a deadline on elapsed time, independent of how much progress was made. → fires when the run collides with a deploy window or the start of business hours. 5) no progress → hash the state every turn and compare it against the last few. → fires when three turns in a row change nothing. busy is not the same as moving. 6) human interrupt → an approval gate before risky steps, plus a kill switch that lives outside the loop. → fires whenever you decide, and it is the one exit the model cannot argue with. 7) error threshold → a counter of consecutive failures that resets on any success. → fires at n in a row, so it halts instead of retrying into the same wall all night. 8) external event → a webhook or a poll on whatever the task was actually about. → fires when the PR merged or the ticket closed and the work stopped mattering. a loop with one exit hangs. a loop with eight is a system. write the exits before you write the prompt.

your agent loop needs 8 exits. most people ship only one. (explained with triggers) 1) goal met → an evaluator scores the output against a rubric, and the run stops on a pass. → fires when the work is measurably done, not when the model says it is done. 2) turn cap → a hard ceiling on iterations, counted and enforced by the harness, not the prompt. → fires on the task it was never going to finish, before you pay to find that out. 3) budget cap → a limit on tokens or dollars, whichever one runs out first. → fires mid-run, which is exactly why it is the exit that saves you the 3am bill. 4) wall clock → a deadline on elapsed time, independent of how much progress was made. → fires when the run collides with a deploy window or the start of business hours. 5) no progress → hash the state every turn and compare it against the last few. → fires when three turns in a row change nothing. busy is not the same as moving. 6) human interrupt → an approval gate before risky steps, plus a kill switch that lives outside the loop. → fires whenever you decide, and it is the one exit the model cannot argue with. 7) error threshold → a counter of consecutive failures that resets on any success. → fires at n in a row, so it halts instead of retrying into the same wall all night. 8) external event → a webhook or a poll on whatever the task was actually about. → fires when the PR merged or the ticket closed and the work stopped mattering. a loop with one exit hangs. a loop with eight is a system. write the exits before you write the prompt.

Hanako

181,373 views • 24 days ago

There's an AI that runs 19 models at the same time on a Mac Mini. It works while you sleep. Not one brain. Nineteen. Each one a specialist. Here's what the Perplexity Personal Computer actually does: → You say "build me a website." → One model researches your competitors. → Another designs the layout. → Another writes the copy. → Another makes the images. → Then it publishes the whole thing. By itself. No clicking. No dragging. No switching between apps. It runs 24/7 on your Mac Mini. Always on. Never stops. You go to bed. You wake up. Your AI already finished the work. It also has access to your real files, folders, and apps. Not stuck in a chat window anymore. And unlike OpenClaw, it has a kill switch, audit logs, and confirmation steps before anything sensitive happens. We went from AI as a tool to AI as a worker. Save this. In two years, everyone will have one of these.

There's an AI that runs 19 models at the same time on a Mac Mini. It works while you sleep. Not one brain. Nineteen. Each one a specialist. Here's what the Perplexity Personal Computer actually does: → You say "build me a website." → One model researches your competitors. → Another designs the layout. → Another writes the copy. → Another makes the images. → Then it publishes the whole thing. By itself. No clicking. No dragging. No switching between apps. It runs 24/7 on your Mac Mini. Always on. Never stops. You go to bed. You wake up. Your AI already finished the work. It also has access to your real files, folders, and apps. Not stuck in a chat window anymore. And unlike OpenClaw, it has a kill switch, audit logs, and confirmation steps before anything sensitive happens. We went from AI as a tool to AI as a worker. Save this. In two years, everyone will have one of these.

Julian Goldie SEO

25,880 views • 5 months ago

nobody gets what scotty actually did yet. everything in this video came out of one chat box. the film. the product shots. the voiceover. the music. you write one line. it hands you back a plan. scenes, script, tone, runtime. you approve it and you're out of the loop. but the thing isn't just making it. it publishes too. connect youtube, shopify, whatever. scotty formats and posts on its own. think of it this way. one agent is doing what a four-tool team used to do. yuh. live now: 👇

nobody gets what scotty actually did yet. everything in this video came out of one chat box. the film. the product shots. the voiceover. the music. you write one line. it hands you back a plan. scenes, script, tone, runtime. you approve it and you're out of the loop. but the thing isn't just making it. it publishes too. connect youtube, shopify, whatever. scotty formats and posts on its own. think of it this way. one agent is doing what a four-tool team used to do. yuh. live now: 👇

Leo Barrett ⚡

27,332 views • 14 days ago

HOW TO USE AI LOOPS TO RUN YOUR BUSINESS 24/7 A lot has been written about loop engineering for building products. Almost nothing about using loops to run the business itself. That's the bigger idea. A loop is when you give an agent a goal, a way to check its own work, and permission to keep trying until it hits that goal. Build. Verify. Repeat. Stop when the condition is met. Here's what it looks like in practice: 1/SEO loop You're position 30 for a term you want. The loop runs once a month, makes changes, checks where you rank, and keeps pushing until you're on page one. This is running in production right now on Inbox Zero. 2/Ads loop You're spending $100 a day and losing money. The loop tests creative, checks profitability, kills what fails, and keeps going until the account is in the black. 3/Eval loop Your AI feature is only 88% accurate. The loop keeps adjusting the prompt and swapping the model until it passes 90%. 4/LLM visibility loop People search in ChatGPT now, not just Google. Same loop, new scoreboard. Are we the answer or not? The whole thing hinges on one thing: a metric that comes back black and white. Where do I rank? Did it hit profitability? Did the evals pass? Give an agent that scoreboard and it runs for months. Loops used to run for 30 minutes. These run for a year. Take a step, sleep, wake up next month, take another one. You're basically hiring an agency that never sleeps, gets paid in tokens instead of invoices, and undoes its own mistakes when the number goes down. Full episode on The Startup Ideas Podcast (SIP) 🧃 watch

HOW TO USE AI LOOPS TO RUN YOUR BUSINESS 24/7 A lot has been written about loop engineering for building products. Almost nothing about using loops to run the business itself. That's the bigger idea. A loop is when you give an agent a goal, a way to check its own work, and permission to keep trying until it hits that goal. Build. Verify. Repeat. Stop when the condition is met. Here's what it looks like in practice: 1/SEO loop You're position 30 for a term you want. The loop runs once a month, makes changes, checks where you rank, and keeps pushing until you're on page one. This is running in production right now on Inbox Zero. 2/Ads loop You're spending $100 a day and losing money. The loop tests creative, checks profitability, kills what fails, and keeps going until the account is in the black. 3/Eval loop Your AI feature is only 88% accurate. The loop keeps adjusting the prompt and swapping the model until it passes 90%. 4/LLM visibility loop People search in ChatGPT now, not just Google. Same loop, new scoreboard. Are we the answer or not? The whole thing hinges on one thing: a metric that comes back black and white. Where do I rank? Did it hit profitability? Did the evals pass? Give an agent that scoreboard and it runs for months. Loops used to run for 30 minutes. These run for a year. Take a step, sleep, wake up next month, take another one. You're basically hiring an agency that never sleeps, gets paid in tokens instead of invoices, and undoes its own mistakes when the number goes down. Full episode on The Startup Ideas Podcast (SIP) 🧃 watch

GREG ISENBERG

82,335 views • 1 month ago

300 agents just finished your research in 40 minutes. congrats, now go read 300 documents. that's what a swarm gives you. a pile. the useful part isn't in any single one of them. it's in how they link up. 100 assets have 4,950 possible connections between them. nobody holds that in their head. kimi agent swarm keeps the links. 300 agents in one launch, up to 4,000 steps. every asset and every wallet an agent touches becomes a node. two agents hit the same counterparty, it draws an edge between them. you don't get 300 answers. you get one map. point it at your own trading portfolio and the picture gets uncomfortable. positions you thought were unrelated land in the same cluster, because the same market maker quotes all of them. one bridge lights up as the hub half your holdings route through. that's your single point of failure, and nobody asked for it in the prompt. Renaissance hedge fund keeps around a hundred PhDs on staff to catch things like that. it also doesn't die when you close the tab. next launch adds to the same graph. that's the gap opening right now. one guy re-reads 300 docs every week. the other one has a base that gets smarter every run. my friend broke down the full framework and wrote the guide in the article below. read it, then go look at what your last agent run left you with.

300 agents just finished your research in 40 minutes. congrats, now go read 300 documents. that's what a swarm gives you. a pile. the useful part isn't in any single one of them. it's in how they link up. 100 assets have 4,950 possible connections between them. nobody holds that in their head. kimi agent swarm keeps the links. 300 agents in one launch, up to 4,000 steps. every asset and every wallet an agent touches becomes a node. two agents hit the same counterparty, it draws an edge between them. you don't get 300 answers. you get one map. point it at your own trading portfolio and the picture gets uncomfortable. positions you thought were unrelated land in the same cluster, because the same market maker quotes all of them. one bridge lights up as the hub half your holdings route through. that's your single point of failure, and nobody asked for it in the prompt. Renaissance hedge fund keeps around a hundred PhDs on staff to catch things like that. it also doesn't die when you close the tab. next launch adds to the same graph. that's the gap opening right now. one guy re-reads 300 docs every week. the other one has a base that gets smarter every run. my friend broke down the full framework and wrote the guide in the article below. read it, then go look at what your last agent run left you with.

cvxv666

469,132 views • 3 days ago

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.

ChatGPT can write you a thousand trading strategies from one sentence. 999 will quietly bleed your account dry. Every single one of them looks like a winner. One method pulls the real one out. Quant desks have run it for years. It's called loop engineering. It's not a smarter prompt. It's a loop: generate a strategy, test it, score why it failed, feed that back, and run it again until one survives data it has never seen. Man Group runs $150 billion. They built an AI that writes and tests hundreds of strategies a week. And second agent whose only job is to hunt the ones that secretly overfit and kill them before a cent goes live. > describe an idea -> backtest 5 years in 12 seconds > read why it died -> feed that back in > run it again until one survives data it never saw AI can write a thousand strategies. Loop engineering is the only thing that tells you which one was ever real. 89% of retail lost money in 2025. Almost none of it from bad ideas. All of it from trading the first guess and never running it through backtest loop. Horizon just put the whole thing in a chat box, free while the beta's open: You're not losing to smarter traders. You're losing to the ones who opened the tab. Anyone can generate a strategy now. Almost no one runs the loop that proves it. That gap is the whole edge. You'll mean to check it later. Later is a "waitlist closed" page.

cvxv666

173,703 views • 1 month ago

A NASA fellow claims the modern world runs on one equation. Your phone signal, wifi, satellites, this post reaching your screen. All of it obeys one line of math written in the 1860s, before the light bulb existed. His words. The man is Ali Alqaraghuli, a former NASA postdoctoral fellow who now runs two hardware companies. And the way he teaches it is what makes the video worth it. He does not derive the equation. He teaches you to read it. His claim is that school taught you to read math wrong, and one symbol proves it: what you were told is a plus sign is actually something else. Once you see it, the whole equation turns from notation into a sentence. Then he uses that sentence to walk you through how a signal actually gets from your phone to a satellite and back. One viewer called that section straight up magic. It is 4 minutes of it. The comments are the proof. A physics student wrote it explained what a semester with his professor could not. An electrical engineer admitted this is where the equations finally clicked. One person wrote: I cried and I do not even know why. His larger point: the wireless world is not magic and never was. It is one readable line, and reading it is a skill nobody bothered to teach you. One equation. Everything wireless. 14 minutes. It is in the video.

A NASA fellow claims the modern world runs on one equation. Your phone signal, wifi, satellites, this post reaching your screen. All of it obeys one line of math written in the 1860s, before the light bulb existed. His words. The man is Ali Alqaraghuli, a former NASA postdoctoral fellow who now runs two hardware companies. And the way he teaches it is what makes the video worth it. He does not derive the equation. He teaches you to read it. His claim is that school taught you to read math wrong, and one symbol proves it: what you were told is a plus sign is actually something else. Once you see it, the whole equation turns from notation into a sentence. Then he uses that sentence to walk you through how a signal actually gets from your phone to a satellite and back. One viewer called that section straight up magic. It is 4 minutes of it. The comments are the proof. A physics student wrote it explained what a semester with his professor could not. An electrical engineer admitted this is where the equations finally clicked. One person wrote: I cried and I do not even know why. His larger point: the wireless world is not magic and never was. It is one readable line, and reading it is a skill nobody bothered to teach you. One equation. Everything wireless. 14 minutes. It is in the video.

ooaes

28,543 views • 15 days ago

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.

cvxv666

103,698 views • 1 month ago

somebody just torched their entire career for us someone inside Anthropic wrote down exactly how they wire their AIs to work as a team instead of one at a time, every number, and let the whole thing walk out the door it's called Graph Engineering: you stop asking one AI to do the whole job and start running a crew. one plans, a few go and look, one checks the others for lies their own measurement: the crew came back with 96% of the quality at 46% of the price. same models, same question. they only changed who is allowed to talk to whom Anthropic raised $65,000,000,000 to figure this out. it costs you $0 run one tonight in a normal chat, 3 moves: 1. give it the job and make it write the plan before it touches anything 2. run each step in its own fresh chat so nothing bleeds into the next 3. in one clean chat at the end, make it check the work against sources and mark whatever it could not confirm save this, then open the piece below: how many workers a job actually needs, and where one more starts making it worse ↓

somebody just torched their entire career for us someone inside Anthropic wrote down exactly how they wire their AIs to work as a team instead of one at a time, every number, and let the whole thing walk out the door it's called Graph Engineering: you stop asking one AI to do the whole job and start running a crew. one plans, a few go and look, one checks the others for lies their own measurement: the crew came back with 96% of the quality at 46% of the price. same models, same question. they only changed who is allowed to talk to whom Anthropic raised $65,000,000,000 to figure this out. it costs you $0 run one tonight in a normal chat, 3 moves: 1. give it the job and make it write the plan before it touches anything 2. run each step in its own fresh chat so nothing bleeds into the next 3. in one clean chat at the end, make it check the work against sources and mark whatever it could not confirm save this, then open the piece below: how many workers a job actually needs, and where one more starts making it worse ↓

Argona

872,514 views • 1 day ago

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

193,592 views • 4 months ago

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,186 views • 2 months ago

MIT GAVE DEBUGGING ITS OWN FULL LECTURE -- BECAUSE MOST DEVS STILL HUNT BUGS WITH PRINT STATEMENTS LIKE IT'S 1995 54 minutes that take the thing you do every single day, badly, and show you the tools schools never bothered to teach. -> The moment it lands, you realize "add a print, run it, add another print" was never debugging. It was guessing with extra steps. Most people never learned the real toolkit. A proper debugger, breakpoints, tracing what the program actually does instead of what you assume it does. In 2026 guessing isn't good enough -> the bug is buried in code you didn't even write, and printf won't find it for you. An AI agent will generate hundreds of lines in seconds. When one of them quietly breaks, finding it runs on exactly the skills in this lecture. Anyone can add a print statement. The person who can actually trace a bug to its source is the one who ships while everyone else guesses. Bookmark & Watch it ↓

MIT GAVE DEBUGGING ITS OWN FULL LECTURE -- BECAUSE MOST DEVS STILL HUNT BUGS WITH PRINT STATEMENTS LIKE IT'S 1995 54 minutes that take the thing you do every single day, badly, and show you the tools schools never bothered to teach. -> The moment it lands, you realize "add a print, run it, add another print" was never debugging. It was guessing with extra steps. Most people never learned the real toolkit. A proper debugger, breakpoints, tracing what the program actually does instead of what you assume it does. In 2026 guessing isn't good enough -> the bug is buried in code you didn't even write, and printf won't find it for you. An AI agent will generate hundreds of lines in seconds. When one of them quietly breaks, finding it runs on exactly the skills in this lecture. Anyone can add a print statement. The person who can actually trace a bug to its source is the one who ships while everyone else guesses. Bookmark & Watch it ↓

slash1s

10,864 views • 2 months ago

Your trading strategy didn't break. The market it was built for quietly stopped existing. Read that twice. It's most of why 89% of retail finished 2025 in the red. There's now an app that does the entire job of a $400,000 quant. You type a trading idea in plain English. It writes the code, backtests 5 years in 12 seconds, runs thousands of simulations, and tells you cold whether your edge is dead or the regime just changed. No code. No Python. No $25,000 terminal. 20,000 already inside. Waitlist stops at 25,000: That distinction is the whole game, and you never had a way to see it. Every strategy is a bet that one thing stays true. Momentum bets trends continue. Mean reversion bets ranges hold. When the regime flips, the assumption dies and your strategy bleeds with nothing wrong in the code. You stare at the logic for a month and never find the bug, because there isn't one. So you delete it, or refit it to the last drawdown and build something that would have survived the pain you already felt and nothing coming next. The desks never had that problem. 92% of institutional volume is automated. Only 45% of retail is. They test 100 strategies for every 1 you test by hand, and kill 97 of them on purpose, because they can tell a dead edge from a normal drawdown. Now that exact loop costs $0. One hypothesis used to cost a fund $87,500 to test. With Horizon you get unlimited, in seconds, and a winner deploys live in 90 seconds and runs without your hands on it.

Your trading strategy didn't break. The market it was built for quietly stopped existing. Read that twice. It's most of why 89% of retail finished 2025 in the red. There's now an app that does the entire job of a $400,000 quant. You type a trading idea in plain English. It writes the code, backtests 5 years in 12 seconds, runs thousands of simulations, and tells you cold whether your edge is dead or the regime just changed. No code. No Python. No $25,000 terminal. 20,000 already inside. Waitlist stops at 25,000: That distinction is the whole game, and you never had a way to see it. Every strategy is a bet that one thing stays true. Momentum bets trends continue. Mean reversion bets ranges hold. When the regime flips, the assumption dies and your strategy bleeds with nothing wrong in the code. You stare at the logic for a month and never find the bug, because there isn't one. So you delete it, or refit it to the last drawdown and build something that would have survived the pain you already felt and nothing coming next. The desks never had that problem. 92% of institutional volume is automated. Only 45% of retail is. They test 100 strategies for every 1 you test by hand, and kill 97 of them on purpose, because they can tell a dead edge from a normal drawdown. Now that exact loop costs $0. One hypothesis used to cost a fund $87,500 to test. With Horizon you get unlimited, in seconds, and a winner deploys live in 90 seconds and runs without your hands on it.

cvxv666

40,765 views • 2 months ago

ANTHROPIC LEAKED A 6-AGENT ASSEMBLY LINE WHERE ONE PROMPT GOES IN AND A FINISHED PR COMES OUT your name shows up in this process exactly once - at the very end, for 5 minutes. prompt → planner → coder → tester → critic → scribe → PR Opus 5 plans once - a bad plan executed by five perfect agents is still a bad result. Sonnet 5 runs 3 parallel lanes - and they only work because the planner made the steps independent. tests loop until green and red goes back to the coder - the critic never sees a broken build. Opus 5 as the critic rejects to the plan, not the code - tests ask "does it run", the critic asks "should it exist". the PR description writes itself from the trace, not from memory - cut this seat and you get 8 PRs nobody can tell apart. one human step in the whole thing: approve or send back - 5 minutes instead of 5 hours. save the setup - 2 models, 6 seats, 2 return paths ↓

ANTHROPIC LEAKED A 6-AGENT ASSEMBLY LINE WHERE ONE PROMPT GOES IN AND A FINISHED PR COMES OUT your name shows up in this process exactly once - at the very end, for 5 minutes. prompt → planner → coder → tester → critic → scribe → PR Opus 5 plans once - a bad plan executed by five perfect agents is still a bad result. Sonnet 5 runs 3 parallel lanes - and they only work because the planner made the steps independent. tests loop until green and red goes back to the coder - the critic never sees a broken build. Opus 5 as the critic rejects to the plan, not the code - tests ask "does it run", the critic asks "should it exist". the PR description writes itself from the trace, not from memory - cut this seat and you get 8 PRs nobody can tell apart. one human step in the whole thing: approve or send back - 5 minutes instead of 5 hours. save the setup - 2 models, 6 seats, 2 return paths ↓

Sprytix

38,726 views • 6 days ago

A screen recording is the best context you can give an AI agent. It's also the worst. A few seconds of video is thousands of images. Feed that to an agent and you instantly blow up its memory. So I built a tool that fixes it. How it works: Hit record and just do your thing. Click around, type, and talk out loud about what you're doing. While you record, it quietly captures the stuff that actually matters: your voice transcription, every click and the exact thing you clicked, what you typed, and the shortcuts you press. When you stop, it doesn't hand the agent the whole video. It writes a short, readable summary: a timeline of what you said and did. The agent reads that tiny summary first. Then, only when it needs to actually see a moment, it pulls that one exact frame from the video, like flipping to a single page instead of reading the whole book. The result: the agent understands everything you did! Full context. Less tokens.

A screen recording is the best context you can give an AI agent. It's also the worst. A few seconds of video is thousands of images. Feed that to an agent and you instantly blow up its memory. So I built a tool that fixes it. How it works: Hit record and just do your thing. Click around, type, and talk out loud about what you're doing. While you record, it quietly captures the stuff that actually matters: your voice transcription, every click and the exact thing you clicked, what you typed, and the shortcuts you press. When you stop, it doesn't hand the agent the whole video. It writes a short, readable summary: a timeline of what you said and did. The agent reads that tiny summary first. Then, only when it needs to actually see a moment, it pulls that one exact frame from the video, like flipping to a single page instead of reading the whole book. The result: the agent understands everything you did! Full context. Less tokens.

Milind S

12,376 views • 1 month ago