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Every skill file I write ends with a self-improvement loop. It sits at the bottom of the skill.md and runs before the skill finishes: - Did any step fail or need a workaround? - Did I correct or reject anything meaningful? - Did you discover something a future run... show more

The Startup Ideas Podcast (SIP) 🧃

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you told it something in the middle of a session, and an hour later it behaved like you never had you were not forgotten, you were never written down an instruction can live in three places and compaction treats them differently: the project root file is re-read from disk and put back, a nested one or a path scoped rule comes back only when the model next opens a file it applies to, and anything you only said out loud comes back never so the rule you typed most recently, in the moment it mattered most, is the one with the shortest life in the room you thought you were configuring it you were talking to it one of those survives the summary

you told it something in the middle of a session, and an hour later it behaved like you never had you were not forgotten, you were never written down an instruction can live in three places and compaction treats them differently: the project root file is re-read from disk and put back, a nested one or a path scoped rule comes back only when the model next opens a file it applies to, and anything you only said out loud comes back never so the rule you typed most recently, in the moment it mattered most, is the one with the shortest life in the room you thought you were configuring it you were talking to it one of those survives the summary

Lagosaur

10,263 görüntüleme • 1 ay önce

Boris Cherny runs Claude Code at Anthropic. His line should stop you mid-scroll: "I don't prompt Claude anymore. I have loops running that prompt Claude and figure out what to do. My job is to write loops." The skill moved. From writing prompts to building the loop around them. A loop that works has three things. Drop one and it burns money in circles. 1. A finish line it can prove. Tests pass or they fail. "Looks done" is not a finish line. 2. A checker that did not write the code. A model grading itself always says yes. 3. Brakes set before you start. A step limit. A money ceiling. A stop condition. That is the whole game. The loop does the work. You design the cage it runs in. Full breakdown below.

Boris Cherny runs Claude Code at Anthropic. His line should stop you mid-scroll: "I don't prompt Claude anymore. I have loops running that prompt Claude and figure out what to do. My job is to write loops." The skill moved. From writing prompts to building the loop around them. A loop that works has three things. Drop one and it burns money in circles. 1. A finish line it can prove. Tests pass or they fail. "Looks done" is not a finish line. 2. A checker that did not write the code. A model grading itself always says yes. 3. Brakes set before you start. A step limit. A money ceiling. A stop condition. That is the whole game. The loop does the work. You design the cage it runs in. Full breakdown below.

shmidt

16,085 görüntüleme • 4 ay önce

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

195,059 görüntüleme • 7 ay önce

THIS IS A PURE F*CKING GOLDMINE Anthropic just shared one of the best tricks for Claude Code. The idea: a skill is one folder with one markdown file, and Claude only reads it when your request actually matches it. You stop pasting the same instructions into every session, and you pay one line of context instead of the whole document. You just make one folder: .claude/skills/review-pr/SKILL.md here's the split: → the description is the only thing sitting in context, roughly one line per skill → the full body loads the second a request matches that description → scripts in the folder get executed, never read into context → commit the folder and every person in the repo has the skill three frontmatter lines do the heavy lifting: → allowed-tools pre-approves the tools, so it runs without a permission prompt → context: fork hands the skill to a subagent that never sees your chat → paths limits it to the files it actually belongs to Description matches. Body loads. Scripts run. JEV engineering is the same move one layer down: the forks that need no thinker (which skill, which tool, retry or stop) go to JEV in milliseconds, and the big models only see the ones that split the full tree > .claude/skills/ /SKILL.md for the repo, ~/.claude/skills/ for your own > only descriptions sit in context, the body loads on match > keep SKILL.md under 500 lines, every line is a recurring cost once loaded > description plus when_to_use is cut at 1,536 characters, put the trigger first > context: fork runs it in a subagent with no conversation history > JEV routes micro-forks in milliseconds paste the tree and this prompt into Claude Code ↓ "Set up skills in this repo from the tree above: 1. Create .claude/skills/ and move the repeated instructions out of CLAUDE.md into one skill per job. 2. Write every description as a trigger sentence first, since the listing is cut at 1,536 characters. 3. Put anything deterministic into scripts/ and pre-approve it with allowed-tools so it runs without a prompt. 4. Route fast deterministic forks (which skill, which tool, retries) through JEV. Show me every change as a diff first. No edits until I say go." ↳ save it, it'll come in handy.

THIS IS A PURE F*CKING GOLDMINE Anthropic just shared one of the best tricks for Claude Code. The idea: a skill is one folder with one markdown file, and Claude only reads it when your request actually matches it. You stop pasting the same instructions into every session, and you pay one line of context instead of the whole document. You just make one folder: .claude/skills/review-pr/SKILL.md here's the split: → the description is the only thing sitting in context, roughly one line per skill → the full body loads the second a request matches that description → scripts in the folder get executed, never read into context → commit the folder and every person in the repo has the skill three frontmatter lines do the heavy lifting: → allowed-tools pre-approves the tools, so it runs without a permission prompt → context: fork hands the skill to a subagent that never sees your chat → paths limits it to the files it actually belongs to Description matches. Body loads. Scripts run. JEV engineering is the same move one layer down: the forks that need no thinker (which skill, which tool, retry or stop) go to JEV in milliseconds, and the big models only see the ones that split the full tree > .claude/skills/ /SKILL.md for the repo, ~/.claude/skills/ for your own > only descriptions sit in context, the body loads on match > keep SKILL.md under 500 lines, every line is a recurring cost once loaded > description plus when_to_use is cut at 1,536 characters, put the trigger first > context: fork runs it in a subagent with no conversation history > JEV routes micro-forks in milliseconds paste the tree and this prompt into Claude Code ↓ "Set up skills in this repo from the tree above: 1. Create .claude/skills/ and move the repeated instructions out of CLAUDE.md into one skill per job. 2. Write every description as a trigger sentence first, since the listing is cut at 1,536 characters. 3. Put anything deterministic into scripts/ and pre-approve it with allowed-tools so it runs without a prompt. 4. Route fast deterministic forks (which skill, which tool, retries) through JEV. Show me every change as a diff first. No edits until I say go." ↳ save it, it'll come in handy.

xsmryze

15,075 görüntüleme • 14 gün önce

Can your agent finally edit your videos? Yup, it's a repo called video use, by Browser Use. You put the raw footage in a folder. Then Claude Code or Codex edits it. It removes the filler words, cuts the dead space, adds the subtitles, and renders the file. Then it checks its own cut points. Most creators use the same small set of edits in every video. That style stays in your head and in your hands. This repo puts it in a file that an agent can read. People ask for a full YouTube channel on day one. They see bad results, and they stop. Start with one format instead. - One founder loom into a 60 second launch video - 3 clips out of one podcast recording Then make the format a system. Then you operate the system, or you sell it. - Real estate agents need listing videos - Coaches need clips - Agencies need ad variations Manual installation with git clone is possible. For 95% of people, the agent is faster. Here's how i intalled it: - Gave the setup prompt to Claude Code with shell access - Let it read install.md - Installed the repo - Connected FFmpeg - Registered the skill

Can your agent finally edit your videos? Yup, it's a repo called video use, by Browser Use. You put the raw footage in a folder. Then Claude Code or Codex edits it. It removes the filler words, cuts the dead space, adds the subtitles, and renders the file. Then it checks its own cut points. Most creators use the same small set of edits in every video. That style stays in your head and in your hands. This repo puts it in a file that an agent can read. People ask for a full YouTube channel on day one. They see bad results, and they stop. Start with one format instead. - One founder loom into a 60 second launch video - 3 clips out of one podcast recording Then make the format a system. Then you operate the system, or you sell it. - Real estate agents need listing videos - Coaches need clips - Agencies need ad variations Manual installation with git clone is possible. For 95% of people, the agent is faster. Here's how i intalled it: - Gave the setup prompt to Claude Code with shell access - Let it read install.md - Installed the repo - Connected FFmpeg - Registered the skill

The Startup Ideas Podcast (SIP) 🧃

15,605 görüntüleme • 2 ay önce

Hyperagent is great if you want agents without having to deal with configuration, servers, or any complexity whatsoever. You don't need a Mac Mini or a server. Every agent runs in the cloud, in its own isolated and secure environment. You get: 1. The ability to learn any new API as a skill 2. Browser support 3. Code execution 4. Image and video generation 5. Integration with hundreds of tools Despite the simplicity, I think their best differentiator is the ability for these agents to learn new skills. Basically, you can teach an agent how your company does marketing, and it will never forget your rules and processes. Obviously, it also lets you deploy your agents to Slack with a single click, so everyone can use them. You don't even need to mention them since they will pay attention to chats and act when necessary. I built a simple agent that allows me to translate any message into French. I installed it on Slack, and every time I ping it, it gives me translations. Took me 10 minutes tops.

Hyperagent is great if you want agents without having to deal with configuration, servers, or any complexity whatsoever. You don't need a Mac Mini or a server. Every agent runs in the cloud, in its own isolated and secure environment. You get: 1. The ability to learn any new API as a skill 2. Browser support 3. Code execution 4. Image and video generation 5. Integration with hundreds of tools Despite the simplicity, I think their best differentiator is the ability for these agents to learn new skills. Basically, you can teach an agent how your company does marketing, and it will never forget your rules and processes. Obviously, it also lets you deploy your agents to Slack with a single click, so everyone can use them. You don't even need to mention them since they will pay attention to chats and act when necessary. I built a simple agent that allows me to translate any message into French. I installed it on Slack, and every time I ping it, it gives me translations. Took me 10 minutes tops.

Santiago

213,793 görüntüleme • 3 ay önce

Right now, I have a few dozen tools to automate my life. Every single thing that I do more than once every week is now a tool: either a Claude Code skill, a scheduled workflow, or an application. • I spend the time doing this once. • I schedule it. • I forget about it (or at least, try to) CREAO is one of the platforms I've used extensively for building some of these automations. They are partnering with me on this post. They are one of the only platforms where you can go from a conversation to a scheduled agent that quickly. This is how it works: 1. You describe the problem you want to solve 2. CREAO's agent builds the logic and executes it 3. You can iterate with the agent to improve the solution 4. When you are done, you turn that solution into a mini app 5. You can schedule that app to run any time you need it Something important: These agents are deterministic. They don't use an LLM to generate answers, so they will always return the same output given the same input. This is critical: when you're automating something that runs every Monday at 9 am, you need it to return the same result every time, not a "creative" answer.

Right now, I have a few dozen tools to automate my life. Every single thing that I do more than once every week is now a tool: either a Claude Code skill, a scheduled workflow, or an application. • I spend the time doing this once. • I schedule it. • I forget about it (or at least, try to) CREAO is one of the platforms I've used extensively for building some of these automations. They are partnering with me on this post. They are one of the only platforms where you can go from a conversation to a scheduled agent that quickly. This is how it works: 1. You describe the problem you want to solve 2. CREAO's agent builds the logic and executes it 3. You can iterate with the agent to improve the solution 4. When you are done, you turn that solution into a mini app 5. You can schedule that app to run any time you need it Something important: These agents are deterministic. They don't use an LLM to generate answers, so they will always return the same output given the same input. This is critical: when you're automating something that runs every Monday at 9 am, you need it to return the same result every time, not a "creative" answer.

Santiago

13,727 görüntüleme • 7 ay önce

One of the best applications for agentic loops is SEO. Here's why it works: the outcome is "black or white." You moved up this month, you moved down, or you stayed the same. That's the entire requirement for a loop — a metric that grades the work for you. Build it in five parts: 1) Access. - Connect it to your real data, not your description of your data. - Google Search Console has an API. - DataForSEO shows you the four pages ranking above yours. 2) Action. - Let it audit and fix before it experiments. - Meta tags, JSON-LD, a missing sitemap. - Quick wins on most sites. 3) Memory. - A markdown file where it logs every change it made and why. 4) Judgment. - Every two weeks it reopens that file and checks its own work: I rewrote that description — did it rank us higher or lower? 5) Reversal. - A change that took you from position 20 to 30 gets undone. - Nothing here is set in stone. Then put it on a routine so it picks up where it left off without you. The catch: this takes months, not days. Months of nothing, then month four, bam bam bam, page one. So find the number in your business that can't be argued with. Build the loop around that one.

One of the best applications for agentic loops is SEO. Here's why it works: the outcome is "black or white." You moved up this month, you moved down, or you stayed the same. That's the entire requirement for a loop — a metric that grades the work for you. Build it in five parts: 1) Access. - Connect it to your real data, not your description of your data. - Google Search Console has an API. - DataForSEO shows you the four pages ranking above yours. 2) Action. - Let it audit and fix before it experiments. - Meta tags, JSON-LD, a missing sitemap. - Quick wins on most sites. 3) Memory. - A markdown file where it logs every change it made and why. 4) Judgment. - Every two weeks it reopens that file and checks its own work: I rewrote that description — did it rank us higher or lower? 5) Reversal. - A change that took you from position 20 to 30 gets undone. - Nothing here is set in stone. Then put it on a routine so it picks up where it left off without you. The catch: this takes months, not days. Months of nothing, then month four, bam bam bam, page one. So find the number in your business that can't be argued with. Build the loop around that one.

The Startup Ideas Podcast (SIP) 🧃

11,505 görüntüleme • 3 ay önce

you let the old sessions get cleaned up, they were only taking space the conversations went, the conclusions drawn from them did not transcripts are deleted after the retention period, and the memory directory is excluded from that sweep on purpose, so a note stays until you or the model edits it which means the evidence has an expiry date and the verdict does not on the directory i audited, thirty nine stored conclusions, sixteen of them with no date on the file at all you did not clear anything you deleted the part you could have checked and kept the part that argues the transcript was the receipt the memory is the story about it

you let the old sessions get cleaned up, they were only taking space the conversations went, the conclusions drawn from them did not transcripts are deleted after the retention period, and the memory directory is excluded from that sweep on purpose, so a note stays until you or the model edits it which means the evidence has an expiry date and the verdict does not on the directory i audited, thirty nine stored conclusions, sixteen of them with no date on the file at all you did not clear anything you deleted the part you could have checked and kept the part that argues the transcript was the receipt the memory is the story about it

Lagosaur

34,063 görüntüleme • 2 ay önce

HERMES AGENT /LEARN IS ONE OF THE MOST UNDERRATED FEATURE. SHOW IT A WORKFLOW ONCE. IT SAVES THE SKILL. REPEATS IT FOREVER WITHOUT RE-EXPLAINING. you publish a YouTube video: write the title, create the thumbnail, write the description, set tags, schedule it. do it once with Hermes watching. type /learn. Hermes distills that workflow into a reusable skill. next time you say "publish this video," the agent follows the exact same steps. no re-explaining. no missed steps. WHAT YOU CAN TEACH IT: → show it your publishing workflow → /learn → give it an article → /learn → walk it through a client onboarding process → /learn → feed it API documentation → /learn → show it how you process invoices → /learn anything you do more than twice should become a /learn skill. THREE WAYS TO USE /LEARN: from a workflow you walked through: do the task once in chat. then /learn. Hermes extracts the procedure from the conversation. from a directory: /learn ~/docs/youtube-publishing/ reads every file. creates the skill. from a URL: /learn https:// any-documentation-page. com reads the page. distills the skill. the skill saves to ~/.hermes/skills/ with a SKILL.md containing: → when to trigger → step-by-step procedure → pitfalls to avoid → verification steps available as a slash command from that moment. WHY THIS CHANGES DAILY WORK: before /learn: you explain the same process to the agent every time you start a new session. session ends. context lost. start over. after /learn: you explain once. the skill persists. every future session already knows the procedure. the agent follows it without prompting. the boring repetitive tasks you do every week become one-command skills you never think about again. comment LEARN and I'll send you the full guide on turning any workflow into a permanent Hermes skill. full Hermes architecture deep-dive in the article 👇

HERMES AGENT /LEARN IS ONE OF THE MOST UNDERRATED FEATURE. SHOW IT A WORKFLOW ONCE. IT SAVES THE SKILL. REPEATS IT FOREVER WITHOUT RE-EXPLAINING. you publish a YouTube video: write the title, create the thumbnail, write the description, set tags, schedule it. do it once with Hermes watching. type /learn. Hermes distills that workflow into a reusable skill. next time you say "publish this video," the agent follows the exact same steps. no re-explaining. no missed steps. WHAT YOU CAN TEACH IT: → show it your publishing workflow → /learn → give it an article → /learn → walk it through a client onboarding process → /learn → feed it API documentation → /learn → show it how you process invoices → /learn anything you do more than twice should become a /learn skill. THREE WAYS TO USE /LEARN: from a workflow you walked through: do the task once in chat. then /learn. Hermes extracts the procedure from the conversation. from a directory: /learn ~/docs/youtube-publishing/ reads every file. creates the skill. from a URL: /learn https:// any-documentation-page. com reads the page. distills the skill. the skill saves to ~/.hermes/skills/ with a SKILL.md containing: → when to trigger → step-by-step procedure → pitfalls to avoid → verification steps available as a slash command from that moment. WHY THIS CHANGES DAILY WORK: before /learn: you explain the same process to the agent every time you start a new session. session ends. context lost. start over. after /learn: you explain once. the skill persists. every future session already knows the procedure. the agent follows it without prompting. the boring repetitive tasks you do every week become one-command skills you never think about again. comment LEARN and I'll send you the full guide on turning any workflow into a permanent Hermes skill. full Hermes architecture deep-dive in the article 👇

YanXbt

13,150 görüntüleme • 4 ay önce

your team put the deploy skill in the repo so everyone would run the same steps it works, and on your laptop it is not the one running the order is enterprise first, then personal, then project a folder of your own with the same name wins, quietly, on your machine only no error, no warning, the command runs and does something slightly different than it does for them and the command is the folder name, not the name field inside the file, so two skills collide over a directory you named months ago and forgot the shared checklist is shared right up until someone has a file of their own with the same name

your team put the deploy skill in the repo so everyone would run the same steps it works, and on your laptop it is not the one running the order is enterprise first, then personal, then project a folder of your own with the same name wins, quietly, on your machine only no error, no warning, the command runs and does something slightly different than it does for them and the command is the folder name, not the name field inside the file, so two skills collide over a directory you named months ago and forgot the shared checklist is shared right up until someone has a file of their own with the same name

Lagosaur

12,613 görüntüleme • 1 ay önce

Do you know the pain of wanting to send a client an invoice, then you have to open your laptop, find your invoice document and start typing it in Or if you're one of those who use Canva for it You need an invoice and you need it in 3 minutes so you can close a deal I GOT YOU, I will show you how I made an invoice for my company using AI and then created a Webapp for it, the WebApp installs like an android app and I can open it and use anytime without internet. That's the next video Let me show you how I did my own P.S: It was my first time, so we do it together... Video dropping soon

Do you know the pain of wanting to send a client an invoice, then you have to open your laptop, find your invoice document and start typing it in Or if you're one of those who use Canva for it You need an invoice and you need it in 3 minutes so you can close a deal I GOT YOU, I will show you how I made an invoice for my company using AI and then created a Webapp for it, the WebApp installs like an android app and I can open it and use anytime without internet. That's the next video Let me show you how I did my own P.S: It was my first time, so we do it together... Video dropping soon

Richard U. | Web Developer

27,624 görüntüleme • 3 ay önce

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

Sprytix

154,158 görüntüleme • 4 ay önce

i genuinely don't understand why everyone isn't using this yet Andrej Karpathy, a co-founder of OpenAI, posted a simple idea that hit 16 million views: stop using AI to write code, use it to build a second brain. you give Grok Bot one folder on its own cloud computer, feed it your emails, links and photos from your phone, and it reads them, links them, and files them into a living wiki of everything you know. it compounds like interest, the more you feed it, the smarter it gets. doesn't matter if you're a developer, a salesperson or a student, you need this the full setup: > create a bot in Grok Bot, name it Brain and add Gmail and Google Calendar in Connect apps. it runs on its own cloud computer, so it keeps working while your laptop is shut > paste the prompt from chapter 07 of my article below into its chat. one message builds the folder, the routines and the rules > it reads the last 100 emails you sent and writes voice.md, 10 rules of how you write, each with a real line of yours. no more "hope this finds you well" drafts > label any email "Brain" and every night at 11 PM it saves the original and files it on the page of that person or project > at 7:30 AM you get one page with what came in, what it drafted and what waits for your yes, ranked by the 5 projects you actually work on > correct it once and the fix goes into rules.md, which it rereads before every task. every Sunday it tells you what you decided and forgot five minutes to set up, and you never start from a blank chat again. every prompt is in the 7-step course below. or paste the whole article into Brain's chat and it builds the brain for you. bookmark this

i genuinely don't understand why everyone isn't using this yet Andrej Karpathy, a co-founder of OpenAI, posted a simple idea that hit 16 million views: stop using AI to write code, use it to build a second brain. you give Grok Bot one folder on its own cloud computer, feed it your emails, links and photos from your phone, and it reads them, links them, and files them into a living wiki of everything you know. it compounds like interest, the more you feed it, the smarter it gets. doesn't matter if you're a developer, a salesperson or a student, you need this the full setup: > create a bot in Grok Bot, name it Brain and add Gmail and Google Calendar in Connect apps. it runs on its own cloud computer, so it keeps working while your laptop is shut > paste the prompt from chapter 07 of my article below into its chat. one message builds the folder, the routines and the rules > it reads the last 100 emails you sent and writes voice.md, 10 rules of how you write, each with a real line of yours. no more "hope this finds you well" drafts > label any email "Brain" and every night at 11 PM it saves the original and files it on the page of that person or project > at 7:30 AM you get one page with what came in, what it drafted and what waits for your yes, ranked by the 5 projects you actually work on > correct it once and the fix goes into rules.md, which it rereads before every task. every Sunday it tells you what you decided and forgot five minutes to set up, and you never start from a blank chat again. every prompt is in the 7-step course below. or paste the whole article into Brain's chat and it builds the brain for you. bookmark this

Argona

74,527 görüntüleme • 23 gün önce

Trump to Hegseth: You did a job in Iran last night. You knocked the hell out of them. We’re winning that one very big btw. They don't have any aircraft, anything having to do with aircraft or ships. They're all at the bottom of the sea or at the bottom of a runway

Trump to Hegseth: You did a job in Iran last night. You knocked the hell out of them. We’re winning that one very big btw. They don't have any aircraft, anything having to do with aircraft or ships. They're all at the bottom of the sea or at the bottom of a runway

Acyn

156,677 görüntüleme • 2 ay önce

I built a Claude skill that helps you review a report as an analyst. Now, it thinks like a senior analyst for you. The readme file will explain how to use it as a skill on Claude or Copilot or Chatgpt Use & let me how it works. My gift to U

I built a Claude skill that helps you review a report as an analyst. Now, it thinks like a senior analyst for you. The readme file will explain how to use it as a skill on Claude or Copilot or Chatgpt Use & let me how it works. My gift to U

David Abu _

45,698 görüntüleme • 7 ay önce

I gave Qoder a task instead of a prompt. And it actually did the work. It read the context, made a plan, used the tools it needed, executed the task, and verified the result before finishing. That’s the big difference with Qoder. It feels less like chatting with AI and more like handing a task to an AI agent on your computer. You describe the outcome you want. Qoder can then: • Read your workspace and context • Plan how to complete the task • Execute commands and make file changes • Use the browser or desktop when needed • Let you pause, adjust, or take over • Verify the result when it’s done And right now, Qwen3.8-Flash runs at 0.0x Credits through September 30. New users can also get 600 Credits in total. Try Qoder here: If you already use AI for coding or everyday computer work, this is worth experimenting with.

I gave Qoder a task instead of a prompt. And it actually did the work. It read the context, made a plan, used the tools it needed, executed the task, and verified the result before finishing. That’s the big difference with Qoder. It feels less like chatting with AI and more like handing a task to an AI agent on your computer. You describe the outcome you want. Qoder can then: • Read your workspace and context • Plan how to complete the task • Execute commands and make file changes • Use the browser or desktop when needed • Let you pause, adjust, or take over • Verify the result when it’s done And right now, Qwen3.8-Flash runs at 0.0x Credits through September 30. New users can also get 600 Credits in total. Try Qoder here: If you already use AI for coding or everyday computer work, this is worth experimenting with.

Shushant Lakhyani

143,473 görüntüleme • 1 ay önce

Karpathy compressed the agent problem into 1 sentence: "If you can't evaluate then you can't auto research it, right?" That's the rule I keep coming back to with long-running coding agents Before you launch /goal or /loop, write the verifier: - what counts as done - what evidence proves it - which checks run every pass - which artifact gets saved - which failure sends it back into the loop Then let the agent run The loop can keep going because proof sits outside the agent's own explanation Tests, screenshots, benchmark curves, browser runs, changed files That's how you get autonomy without babysitting a transcript for 6 hours Read the full breakdown on goals, verifiers, loops, artifacts, and session memory in the article below

Karpathy compressed the agent problem into 1 sentence: "If you can't evaluate then you can't auto research it, right?" That's the rule I keep coming back to with long-running coding agents Before you launch /goal or /loop, write the verifier: - what counts as done - what evidence proves it - which checks run every pass - which artifact gets saved - which failure sends it back into the loop Then let the agent run The loop can keep going because proof sits outside the agent's own explanation Tests, screenshots, benchmark curves, browser runs, changed files That's how you get autonomy without babysitting a transcript for 6 hours Read the full breakdown on goals, verifiers, loops, artifacts, and session memory in the article below

Alpha Batcher

45,339 görüntüleme • 4 ay önce

Today we're launching Claude for marketing. One skill turns it into a full marketing team: > Drop your URL: ads, launch films and social cuts come back > Say "punchier" or "different hook": it re-runs on the spot > Every format, every ratio, one message Comment "VIRAL" and I'll send you the skill. 👇

Today we're launching Claude for marketing. One skill turns it into a full marketing team: > Drop your URL: ads, launch films and social cuts come back > Say "punchier" or "different hook": it re-runs on the spot > Every format, every ratio, one message Comment "VIRAL" and I'll send you the skill. 👇

Motion

96,491 görüntüleme • 2 ay önce