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Introducing Loopany 🔁 Stop prompting, Design the loop. A loop management space that connects to your team’s own local agents: - Scaffold loop contracts, state, and logs - Add programmable triggers - Run self-improving cycles - Start from built-in loop templates We baked in everything we’ve learned from running... show more

Jason Zhou

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Introducing Loop The autonomous business manager that runs your outreach, your follow-ups, and your pipeline,while you focus on the work only you can do. Loop doesn't live in your inbox. Your inbox lives in Loop. Reads every lead like a teammate who's been on your team for years Writes the reply, makes the call, chases the deal when email goes cold Pauses the moment a human actually needs to show up Replaces the patchwork of CRMs, outreach tools, and follow-up apps with one system that does the work Stop managing tools. Start running your loop. comment "loop" and we'll send you in. loop

Introducing Loop The autonomous business manager that runs your outreach, your follow-ups, and your pipeline,while you focus on the work only you can do. Loop doesn't live in your inbox. Your inbox lives in Loop. Reads every lead like a teammate who's been on your team for years Writes the reply, makes the call, chases the deal when email goes cold Pauses the moment a human actually needs to show up Replaces the patchwork of CRMs, outreach tools, and follow-up apps with one system that does the work Stop managing tools. Start running your loop. comment "loop" and we'll send you in. loop

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Today we're introducing Wave. A proactive product agent that helps teams build self-improving products. Every product team runs the same loop. Build, ship, use, learn. AI has made building and shipping extraordinarily fast. Understanding usage and learning still happen by hand. Wave runs the whole loop: - Analyzes your Amplitude data for signals across analytics, feedback, session replays, error logs, agent traces, and experiment results - Surfaces opportunities in the form of full product specs that you or your agents can approve and ship - Tracks the experiments and outcomes of what you've built so you can start the loop over again Your product learns what works and gets smarter every cycle:

Today we're introducing Wave. A proactive product agent that helps teams build self-improving products. Every product team runs the same loop. Build, ship, use, learn. AI has made building and shipping extraordinarily fast. Understanding usage and learning still happen by hand. Wave runs the whole loop: - Analyzes your Amplitude data for signals across analytics, feedback, session replays, error logs, agent traces, and experiment results - Surfaces opportunities in the form of full product specs that you or your agents can approve and ship - Tracks the experiments and outcomes of what you've built so you can start the loop over again Your product learns what works and gets smarter every cycle:

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Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.

Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.

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The guy who kicked off the entire "loop engineering" wave Peter Steinberger: "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents." One post. 6.5M views in a week. In this talk he walks the real stack: the agent loop, a verifier that fails its own work and retries, and a loop that rewrites the agent while he sleeps. Worth more than any $500 vibe-coding course. Watch it, then read the full breakdown of the 4 loops below.

The guy who kicked off the entire "loop engineering" wave Peter Steinberger: "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents." One post. 6.5M views in a week. In this talk he walks the real stack: the agent loop, a verifier that fails its own work and retries, and a loop that rewrites the agent while he sleeps. Worth more than any $500 vibe-coding course. Watch it, then read the full breakdown of the 4 loops below.

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Anthropic Research Lead: "99% of our engineers run swarms of 300+ self-improving agents" "Close the loop, give the model a way to verify its own output" In a 20-minute session, an Anthropic Team member breaks down how to build agents that improve themselves The real setup is Claude running through loops, plan mode, and dynamic workflows Better than most $300 agent courses Bookmark and watch the talk Then read the article below

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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

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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

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We tripled the fps in one day everyone!! 15 yesterday, 40 to 50 today. Day 32 of building our own community open source, open world game with a loop of AI agents. the whole day went into performance and it paid off next stop is locking 60

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Anthropic research lead: "99% of our engineers are running swarms of 300+ self-improving agents. close the agent loop. Give the model a way to verify its own output" in a 20-minute session, Anthropic team member explains how to build a model that improves itself. Claude + loops + plan mode + dynamic workflows -that’s the secret. Watch the talk, then save the playbook below.

Anthropic research lead: "99% of our engineers are running swarms of 300+ self-improving agents. close the agent loop. Give the model a way to verify its own output" in a 20-minute session, Anthropic team member explains how to build a model that improves itself. Claude + loops + plan mode + dynamic workflows -that’s the secret. Watch the talk, then save the playbook below.

Codez

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Anthropic engineer: "Over 90% of Anthropic engineers use a self-improving agentic loop for coding. The key is to close the loop: give the agent a way to verify and improve its own work" In this 12-minute talk, an Anthropic engineer explains how to build an agent that continuously improves itself. Claude + routines + loops + CLAUDE.md - that's the foundation. Skip Netflix tonight. Save this post!

Anthropic engineer: "Over 90% of Anthropic engineers use a self-improving agentic loop for coding. The key is to close the loop: give the agent a way to verify and improve its own work" In this 12-minute talk, an Anthropic engineer explains how to build an agent that continuously improves itself. Claude + routines + loops + CLAUDE.md - that's the foundation. Skip Netflix tonight. Save this post!

unicode

62,153 görüntüleme • 1 ay önce

The future of vibecoding is looping. ( my recent app cnvs has done $5.7k in 5 days and was made primarily with design looping ) you can easily break free from ai slop and create designs that feel artisan and worth paying for. im begging you to bookmark this 10 min beginner guide and start setting up loops, it will change everything for you. Timestamps : 0:27 – The ultra-simple looping diagram explained 1:18 – The best loop to start with: the design loop 1:38 – Using Codex + GPT Image Gen for the mockup 3:57 – Handing the mockup to Claude Code 4:45 – Loop starts (screenshot → compare → iterate) 7:31 – Loop finished : reviewing the result

The future of vibecoding is looping. ( my recent app cnvs has done $5.7k in 5 days and was made primarily with design looping ) you can easily break free from ai slop and create designs that feel artisan and worth paying for. im begging you to bookmark this 10 min beginner guide and start setting up loops, it will change everything for you. Timestamps : 0:27 – The ultra-simple looping diagram explained 1:18 – The best loop to start with: the design loop 1:38 – Using Codex + GPT Image Gen for the mockup 3:57 – Handing the mockup to Claude Code 4:45 – Loop starts (screenshot → compare → iterate) 7:31 – Loop finished : reviewing the result

Max Blade

22,493 görüntüleme • 1 ay önce

THIS 11-PAGE CLAUDE LOOP ENGINEERING PLAYBOOK IS THE CHEAT CODE FOR SENIOR DEV PROMOTIONS typing prompts line by line won't get you hired. you need to know how to design self-prompting loops: > Discovery: agent finds its own work from logs, failing tests, and commits > Handoff: tasks execute in isolated git worktrees to prevent parallel collisions > Verification: a second reviewer agent audits the builder's code to catch blind spots > Persistence and Scheduling: code writes to disk and loops run 24/7 on a timer the shift is simple: stop prompting the agent, build the system that prompts it read the full setup blueprint 👇

THIS 11-PAGE CLAUDE LOOP ENGINEERING PLAYBOOK IS THE CHEAT CODE FOR SENIOR DEV PROMOTIONS typing prompts line by line won't get you hired. you need to know how to design self-prompting loops: > Discovery: agent finds its own work from logs, failing tests, and commits > Handoff: tasks execute in isolated git worktrees to prevent parallel collisions > Verification: a second reviewer agent audits the builder's code to catch blind spots > Persistence and Scheduling: code writes to disk and loops run 24/7 on a timer the shift is simple: stop prompting the agent, build the system that prompts it read the full setup blueprint 👇

Mr. Buzzoni

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Introducing watchmen. It writes the skill files your coding agents should already have from your own sessions, shared across Claude Code, Codex, and pi, so you stop paying tokens to re-explain what your agent learned last week. Local and open-source.

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Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.

Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.

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Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

LangChain

33,192 görüntüleme • 1 yıl önce

From lathe to loop! Our team crafted our very own YoYo on the QUICK TURN 200ML, a turning centre built for small, precision parts that demand smooth finishes and perfect balance. Watch the video to see it come alive 🤯 #CNC #Turning #YoYo #WYYC

From lathe to loop! Our team crafted our very own YoYo on the QUICK TURN 200ML, a turning centre built for small, precision parts that demand smooth finishes and perfect balance. Watch the video to see it come alive 🤯 #CNC #Turning #YoYo #WYYC

MazakEurope

15,625 görüntüleme • 1 yıl önce

Today, we launched browser use inside Codex to further close the build & verify loop for local development! Now, you can ask Codex to build your front end, and test it like a user would by clicking through the app. Codex sees everything a user sees through vision & checks the network/console logs to help debug & fix any issues that it finds. This change brings us closer to fully autonomous coding agents that delivers high quality and tested changes. Watch Codex test my app in the browser, catch & fix a real bug, and doing that loop again with a brand new feature.

James Sun

226,793 görüntüleme • 3 ay önce

Introducing Helmor The open-source, local-first answer to Conductor. A more refined, faster GUI for orchestrating coding agents. No cloud. One-click import from Conductor. AI made coding faster. Helmor is about finishing the rest of the loop: orchestration, workspaces, review, testing, and merge. We believe the next generation of GUI agent orchestration should be built in the open — by the community.

Introducing Helmor The open-source, local-first answer to Conductor. A more refined, faster GUI for orchestrating coding agents. No cloud. One-click import from Conductor. AI made coding faster. Helmor is about finishing the rest of the loop: orchestration, workspaces, review, testing, and merge. We believe the next generation of GUI agent orchestration should be built in the open — by the community.

Caspian 東澔

121,766 görüntüleme • 3 ay önce

Axultan, our head of product, on how we engineered Higgsfield Supercomputer, the first engine that runs your work end to end. Agents plan, execute, and grade their own output, so the human in the loop drops to near zero. What part of your work would you hand off first?

Axultan, our head of product, on how we engineered Higgsfield Supercomputer, the first engine that runs your work end to end. Agents plan, execute, and grade their own output, so the human in the loop drops to near zero. What part of your work would you hand off first?

Higgsfield AI 🧩

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Less than two weeks before he left Google to co-found Discovery Loop, Jeff Dean described the automated research loop the new company is named for, giving no hint he was about to leave to go do it. "There's this sort of the foundation of the scientific method of you propose an experiment, you implement what you need to run the experiment, and you evaluate the experiment, and then you get results from that." "And I think there are more and more problems that are now possible to implement where that whole loop of running not just a few experiments, but running many, many experiments because you're able to automate that loop and make the latency of that loop extremely low is going to be really, really important." "It's going to enable us to tackle lots of different problem domains in science and engineering and machine learning, model design itself, and also in engineering tasks like designing chips."

Less than two weeks before he left Google to co-found Discovery Loop, Jeff Dean described the automated research loop the new company is named for, giving no hint he was about to leave to go do it. "There's this sort of the foundation of the scientific method of you propose an experiment, you implement what you need to run the experiment, and you evaluate the experiment, and then you get results from that." "And I think there are more and more problems that are now possible to implement where that whole loop of running not just a few experiments, but running many, many experiments because you're able to automate that loop and make the latency of that loop extremely low is going to be really, really important." "It's going to enable us to tackle lots of different problem domains in science and engineering and machine learning, model design itself, and also in engineering tasks like designing chips."

Fireside Alpha

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