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everyone in iOS development should watch this. seriously, it might change the whole industry. i pointed claude code at a live ios device running on revyl, typed "test everything," and walked away. here's what's actually happening: ① you don't write the tests. no scripts, no selectors, no test plan.... i never told it which screens to open or what to check. it read the app, decided what mattered, and tested it. the entire instruction was "test everything." ② it built its own test team. it looked at the app, clocked that it's basically four mini apps (rides, delivery, services, account), and split itself into 4 agents, one per surface. scoping coverage like that is usually a person's whole afternoon. it did it in seconds, unprompted. ③ all four ran at the same time, each on its own live device. this is where revyl comes in. every agent gets its own live ios session in the cloud, so four running apps get tested in parallel instead of taking turns on one simulator. serial testing turns coverage into a time tax. running all of it at once removes the tax. ④ it tests like a person, not like a script. each agent drives the app the way a user would, taps through the flows, and visually checks each screen against what it expected to see. nothing is pinned to a brittle element id, so renaming a button doesn't take down half your suite. that one detail is the most annoying thing about how we test today, and it just quietly goes away. ⑤ no xcuitest, no sims melting your laptop. i didn't write a single xcuitest script, and there were no simulators booting on my machine. the agents run on cloud devices, so coverage stops being capped by what your laptop can handle. the part that got me isn't that an agent tested an app. it's that i never told it how. i handed it a device and an intent, and it figured out the scoping, the parallelizing, and the driving on its own. if you still write and maintain mobile ui tests by hand, i'm not sure that lasts the year.show more

Landseer Enga

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23,963 次观看 • 2 个月前 •via X (Twitter)

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Agents can one shot mobile apps, but testing is still the bottleneck. So we built a CLI that gives Claude Code the one thing it was missing - eyes and hands The best part? t's fully vision-based: - No scripts, no selectors, no element IDs - It interacts with the app exactly like a human would Claude now writes code → tests it on the app → sees what broke → fixes it Spawn a cloud agent, go to sleep, and wake up knowing it actually worked.

Agents can one shot mobile apps, but testing is still the bottleneck. So we built a CLI that gives Claude Code the one thing it was missing - eyes and hands The best part? t's fully vision-based: - No scripts, no selectors, no element IDs - It interacts with the app exactly like a human would Claude now writes code → tests it on the app → sees what broke → fixes it Spawn a cloud agent, go to sleep, and wake up knowing it actually worked.

Landseer Enga

86,958 次观看 • 5 个月前

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 次观看 • 2 个月前

Kimi K3 dropped 3 days ago and i already used it to build my own PUBG the model that just took the number one coding spot on earth and it runs on a free tier no team no engine i just fed it free open models and assets that are already public and told it what i wanted it built the entire game itself the shooting the recoil the hit detection all of it came straight out of the model i barely wrote a line what youre watching here is the deathmatch mode and its already live battle royale is next im deep in it and the graphics get reworked right after i know it looks raw right now i wont pretend otherwise but the endgame is a build that runs perfect on phones and the moment its on mobile you drop ads in and it earns on its own the part that still gets me is my whole cost for this was a single subscription

Kimi K3 dropped 3 days ago and i already used it to build my own PUBG the model that just took the number one coding spot on earth and it runs on a free tier no team no engine i just fed it free open models and assets that are already public and told it what i wanted it built the entire game itself the shooting the recoil the hit detection all of it came straight out of the model i barely wrote a line what youre watching here is the deathmatch mode and its already live battle royale is next im deep in it and the graphics get reworked right after i know it looks raw right now i wont pretend otherwise but the endgame is a build that runs perfect on phones and the moment its on mobile you drop ads in and it earns on its own the part that still gets me is my whole cost for this was a single subscription

savip.

68,963 次观看 • 1 个月前

small local model that falls apart in bloated agents like openclaw just runs like a wild horse in hermes agent. and that's not even my line, someone else called it that, i've just been quietly pointing people at this harness for months because it held up on everything i threw at it, 3b models all the way to one trillion params. watch this happen on my own machine. i pointed hermes agent at a local http endpoint, gemma 4 12b on my 3090 llama.cpp server, and it auto-detected the model and started working immediately. no config wrestling, no broken tool calls, no babysitting the output format, i typed in a url and it just went. the whole clip is exactly that, start to finish, no errors, no retries, butter smooth. and the tool calling, the one thing that quietly breaks most local setups, works here like it's nothing. it's not the model that's flaky, it's the harness around it. hermes agent is the first agent i've run that actually gets that right. one url, one local model on one card, and it runs like a wild horse.

small local model that falls apart in bloated agents like openclaw just runs like a wild horse in hermes agent. and that's not even my line, someone else called it that, i've just been quietly pointing people at this harness for months because it held up on everything i threw at it, 3b models all the way to one trillion params. watch this happen on my own machine. i pointed hermes agent at a local http endpoint, gemma 4 12b on my 3090 llama.cpp server, and it auto-detected the model and started working immediately. no config wrestling, no broken tool calls, no babysitting the output format, i typed in a url and it just went. the whole clip is exactly that, start to finish, no errors, no retries, butter smooth. and the tool calling, the one thing that quietly breaks most local setups, works here like it's nothing. it's not the model that's flaky, it's the harness around it. hermes agent is the first agent i've run that actually gets that right. one url, one local model on one card, and it runs like a wild horse.

Sudo su

27,339 次观看 • 2 个月前

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

Sudo su

39,719 次观看 • 2 个月前

HERMES AGENT VS OPENCLAW. a local ai onboarding flow test. a 3.9gb bonsai served on localhost, both agents upstream and latest, i point each one at the endpoint and watch which one even finds it. > hermes opens a provider menu, thirty plus options, local servers sitting right there next to the cloud ones, i hand it 127.0.0.1:8899, it verifies the endpoint, one model visible, auto-detects the model by name, bonsai-27b-q1_0, reads the context length straight off the server, saves it, and starts reasoning and firing real tool calls on my local model. no key. no friction. > openclaw has no menu. it goes hunting for a codex login, an openai key, finds none because there are none, prints no models available three times, defaults to openai/gpt-5.5, a cloud model it cannot reach, and dead ends on run auth login --provider openai. read that back. it asked me for an openai key. to run a model already running on my own machine. it never once looked at localhost. to be fair, openclaw can run local if you hand wire endpoint yourself. what it will not do is find the model already sitting on your box. hermes agent found it in one line. now the part i owe you. the auto-detect that just won, the model name read, the .gguf strip, the context length probe off the server, that is my code, it is in hermes agent main right now, authorship preserved, #2051 and #4218. the wizard fix that stops an agent from silently routing you to someone else's creds, the exact trap openclaw still falls into, mine too, #4210. i contribute to hermes agent, i told you that going in. one agent is built to talk to whatever you are running, the other is built to talk to a cloud api, so one found my model and ran it and the other asked me to log into openai. onboarding flow of both, mapped, below.

HERMES AGENT VS OPENCLAW. a local ai onboarding flow test. a 3.9gb bonsai served on localhost, both agents upstream and latest, i point each one at the endpoint and watch which one even finds it. > hermes opens a provider menu, thirty plus options, local servers sitting right there next to the cloud ones, i hand it 127.0.0.1:8899, it verifies the endpoint, one model visible, auto-detects the model by name, bonsai-27b-q1_0, reads the context length straight off the server, saves it, and starts reasoning and firing real tool calls on my local model. no key. no friction. > openclaw has no menu. it goes hunting for a codex login, an openai key, finds none because there are none, prints no models available three times, defaults to openai/gpt-5.5, a cloud model it cannot reach, and dead ends on run auth login --provider openai. read that back. it asked me for an openai key. to run a model already running on my own machine. it never once looked at localhost. to be fair, openclaw can run local if you hand wire endpoint yourself. what it will not do is find the model already sitting on your box. hermes agent found it in one line. now the part i owe you. the auto-detect that just won, the model name read, the .gguf strip, the context length probe off the server, that is my code, it is in hermes agent main right now, authorship preserved, #2051 and #4218. the wizard fix that stops an agent from silently routing you to someone else's creds, the exact trap openclaw still falls into, mine too, #4210. i contribute to hermes agent, i told you that going in. one agent is built to talk to whatever you are running, the other is built to talk to a cloud api, so one found my model and ran it and the other asked me to log into openai. onboarding flow of both, mapped, below.

Sudo su

23,816 次观看 • 1 个月前

One guy built this app in a month and now it makes him more than $1,000,000 a year. No team. No investors. No marketing department. One developer. One month. One clever idea. The app is a camera for events. In its first month it got 100,000 downloads and he did not spend a single dollar on ads. Here is how he did it because the most interesting part is the growth itself. He did not bolt marketing onto the product. He made using the product the marketing itself. After that things kick in that almost nobody figures out. 1. You cannot use the app alone. For it to work the host has to pull every guest into it. Each install drags in dozens more right away. 2. One wedding is not one user but a whole crowd at once. 200 people scan one code in an evening and install the app. No ad brings that many for the same money and here the money is zero. 3. The guest becomes the host. He liked it at someone else's wedding and a month later he throws his own event and brings his own people. The loop spins itself and for free. 4. It does not look like an ad. To the guest it is a gift not some app forced on him. So they install it gladly and all of them do. 5. It all runs on emotion. A wedding. Memories. Shared shots. People film it and show their own people and a new wave comes in. Now let us count the money plain and honest. The subscription runs from 2 to 50 dollars. Say only every 20th person pays. That is 5,000 people out of 100,000. The average check a modest 20 dollars. 5,000 times 20 is 100,000 dollars a month. More than 3,000 a day. More than $1,000,000 a year. And all of this is one guy in a month without a single dollar on ads. He did not win on budget and not on a team. He won by sewing distribution into the very use of the product. You can lift almost any product this way. Could you build something like this on your own or is it just luck?

One guy built this app in a month and now it makes him more than $1,000,000 a year. No team. No investors. No marketing department. One developer. One month. One clever idea. The app is a camera for events. In its first month it got 100,000 downloads and he did not spend a single dollar on ads. Here is how he did it because the most interesting part is the growth itself. He did not bolt marketing onto the product. He made using the product the marketing itself. After that things kick in that almost nobody figures out. 1. You cannot use the app alone. For it to work the host has to pull every guest into it. Each install drags in dozens more right away. 2. One wedding is not one user but a whole crowd at once. 200 people scan one code in an evening and install the app. No ad brings that many for the same money and here the money is zero. 3. The guest becomes the host. He liked it at someone else's wedding and a month later he throws his own event and brings his own people. The loop spins itself and for free. 4. It does not look like an ad. To the guest it is a gift not some app forced on him. So they install it gladly and all of them do. 5. It all runs on emotion. A wedding. Memories. Shared shots. People film it and show their own people and a new wave comes in. Now let us count the money plain and honest. The subscription runs from 2 to 50 dollars. Say only every 20th person pays. That is 5,000 people out of 100,000. The average check a modest 20 dollars. 5,000 times 20 is 100,000 dollars a month. More than 3,000 a day. More than $1,000,000 a year. And all of this is one guy in a month without a single dollar on ads. He did not win on budget and not on a team. He won by sewing distribution into the very use of the product. You can lift almost any product this way. Could you build something like this on your own or is it just luck?

Blaze

10,703 次观看 • 1 个月前

Delete all your E2E tests, seriously Okay, not the coverage, the code I’ve written Playwright tests, they’re great Maintaining hundreds of browser scripts while the UI keeps changing is not so much Honestly, AI feels more naturally suited to this than half the coding use cases we keep forcing it into When I first used TesterArmy on Angular Space, I assumed it was mainly for smaller teams that had never built a proper Playwright suite Then I saw its Playwright migration, and I think I had it backwards It may be even more useful when you already have far too much Playwright to maintain The journey stays in plain English: “Log in, publish an article and make sure it appears.” An agent runs it in a real browser No selectors to babysit, no test code to maintain And for mobile apps, it can test directly from the binary! Don’t delete your E2Es today First migrate 10 real journeys, run both, and decide which one you want to maintain six months from now Docs url in comments 👇

Delete all your E2E tests, seriously Okay, not the coverage, the code I’ve written Playwright tests, they’re great Maintaining hundreds of browser scripts while the UI keeps changing is not so much Honestly, AI feels more naturally suited to this than half the coding use cases we keep forcing it into When I first used TesterArmy on Angular Space, I assumed it was mainly for smaller teams that had never built a proper Playwright suite Then I saw its Playwright migration, and I think I had it backwards It may be even more useful when you already have far too much Playwright to maintain The journey stays in plain English: “Log in, publish an article and make sure it appears.” An agent runs it in a real browser No selectors to babysit, no test code to maintain And for mobile apps, it can test directly from the binary! Don’t delete your E2Es today First migrate 10 real journeys, run both, and decide which one you want to maintain six months from now Docs url in comments 👇

Daniel Glejzner

63,698 次观看 • 18 天前

Your coding agent can run all night. It still can't tell if what it built actually works. Today we're open-sourcing the TestSprite CLl (Apache-2.0) A tool your agent calls on its own to test your app end-to-end like a real user, fix what broke, and re-check everything it ever got right. It's the same engine 100,000+ teams already use. We proved it in public, on a public leaderboard: Most correct app on the board:89% Built by the cheapest model in the field At half the cost of the priciest one You no longer need the biggest, most expensive model to ship software you can trust. Setup is 2 commands: npm install -g @testsprite/testsprite-cli testsprite init That's the last command you'll ever type - from there, your agent runs the tests itself.

Your coding agent can run all night. It still can't tell if what it built actually works. Today we're open-sourcing the TestSprite CLl (Apache-2.0) A tool your agent calls on its own to test your app end-to-end like a real user, fix what broke, and re-check everything it ever got right. It's the same engine 100,000+ teams already use. We proved it in public, on a public leaderboard: Most correct app on the board:89% Built by the cheapest model in the field At half the cost of the priciest one You no longer need the biggest, most expensive model to ship software you can trust. Setup is 2 commands: npm install -g @testsprite/testsprite-cli testsprite init That's the last command you'll ever type - from there, your agent runs the tests itself.

TestSprite

1,042,183 次观看 • 2 个月前

Every AI agent you've tried has amnesia. It does one task, forgets everything, and tomorrow you start from zero. That's not an employee. That's a temp you have to retrain every single morning. Hyperagent by Airtable is the first platform I've used that actually fixes this. Here's what got me: 1. Agents that compound. Each agent has memory. The one running today is smarter than the one you shipped three weeks ago. Same prompt, same integrations, but weeks of your judgment baked in. 2. Real deliverables, real receipts. You don't get a chat transcript. You get finished work with the cost and runtime printed right on it. A full research report for under ten bucks. Try getting that invoice from an agency. 3. A fleet, not a chatbot. Build a specialist for outreach, another for research, another for reporting. Give each one its own tools, its own memory, and its own budget cap so nothing runs away with your credits. 4. Deploy to Slack and your whole team uses the agent you built. One competitive intel agent, @ mentioned by everyone. Airtable runs its own data team this way. 5. Each agent gets its own cloud machine with a real browser and code execution. It works while you sleep. No babysitting, no local setup, no laptop that has to stay open. I put it to work in the video below. Watch what it builds. The teams treating agents as durable assets instead of one-off prompts are going to lap everyone else. This is the first tool that actually treats them that way. #ad Hyperagent

Every AI agent you've tried has amnesia. It does one task, forgets everything, and tomorrow you start from zero. That's not an employee. That's a temp you have to retrain every single morning. Hyperagent by Airtable is the first platform I've used that actually fixes this. Here's what got me: 1. Agents that compound. Each agent has memory. The one running today is smarter than the one you shipped three weeks ago. Same prompt, same integrations, but weeks of your judgment baked in. 2. Real deliverables, real receipts. You don't get a chat transcript. You get finished work with the cost and runtime printed right on it. A full research report for under ten bucks. Try getting that invoice from an agency. 3. A fleet, not a chatbot. Build a specialist for outreach, another for research, another for reporting. Give each one its own tools, its own memory, and its own budget cap so nothing runs away with your credits. 4. Deploy to Slack and your whole team uses the agent you built. One competitive intel agent, @ mentioned by everyone. Airtable runs its own data team this way. 5. Each agent gets its own cloud machine with a real browser and code execution. It works while you sleep. No babysitting, no local setup, no laptop that has to stay open. I put it to work in the video below. Watch what it builds. The teams treating agents as durable assets instead of one-off prompts are going to lap everyone else. This is the first tool that actually treats them that way. #ad Hyperagent

Leonard Rodman

94,961 次观看 • 1 个月前

Karpathy said something you'll regret ignoring: "We have to keep the AI on the leash. I'm still the bottleneck. I have to make sure this thing isn't introducing bugs and that there's no security issues." He said it at YC talk last year, when the worry was reliability. The models hallucinated and made mistakes no human would, so the leash implied keeping yourself in the loop and checking the output before trusting it. The models are far better now, and the line still holds, for a reason he was not focused on back then. Even a model that writes flawless code today still has no idea who is allowed to run it. Correctness and authorization are different problems, and only correctness improves as the model improves. A perfect agent still hands a tool where anyone can do anything, because permission was never part of the task. I actually tested this in practice with Claude Code. I asked it to build a small internal tool with a button that issues account credits. It worked first try, and running it locally, the credit applied the instant I clicked. Nothing decided who was allowed to click it. The agent wrote the right logic and displayed a success notification. It never checked whether the caller had the right, whether it should pause for a human, or whether anything was logged. And this is not a bug a smarter model can outgrow because the leash was never in the code. Identity, permissions, and audit live in the system that runs the app, not in what the agent generates. To solve this, I took the exact same bundle and hosted it on Retool. The credit write that fired silently on my laptop now stopped at an approval gate, resolved to a real identity through SSO, and landed in an audit log. I wrote none of it. The app inherited the entire boundary the moment it was deployed, and the video shows the before and after. You can try it yourself here: I also wrote a detailed breakdown of the whole thing in my recent article, and I worked with the team to put this together. It walks through the build, the exact moment the credit write went through on my laptop with nobody checking, and then what changed when the same app ran on Retool. It also covers why this is a property of the runtime and not something a better model fixes, which is why devs typically miss this. The article is quoted below.

Karpathy said something you'll regret ignoring: "We have to keep the AI on the leash. I'm still the bottleneck. I have to make sure this thing isn't introducing bugs and that there's no security issues." He said it at YC talk last year, when the worry was reliability. The models hallucinated and made mistakes no human would, so the leash implied keeping yourself in the loop and checking the output before trusting it. The models are far better now, and the line still holds, for a reason he was not focused on back then. Even a model that writes flawless code today still has no idea who is allowed to run it. Correctness and authorization are different problems, and only correctness improves as the model improves. A perfect agent still hands a tool where anyone can do anything, because permission was never part of the task. I actually tested this in practice with Claude Code. I asked it to build a small internal tool with a button that issues account credits. It worked first try, and running it locally, the credit applied the instant I clicked. Nothing decided who was allowed to click it. The agent wrote the right logic and displayed a success notification. It never checked whether the caller had the right, whether it should pause for a human, or whether anything was logged. And this is not a bug a smarter model can outgrow because the leash was never in the code. Identity, permissions, and audit live in the system that runs the app, not in what the agent generates. To solve this, I took the exact same bundle and hosted it on Retool. The credit write that fired silently on my laptop now stopped at an approval gate, resolved to a real identity through SSO, and landed in an audit log. I wrote none of it. The app inherited the entire boundary the moment it was deployed, and the video shows the before and after. You can try it yourself here: I also wrote a detailed breakdown of the whole thing in my recent article, and I worked with the team to put this together. It walks through the build, the exact moment the credit write went through on my laptop with nobody checking, and then what changed when the same app ran on Retool. It also covers why this is a property of the runtime and not something a better model fixes, which is why devs typically miss this. The article is quoted below.

Akshay 🚀

42,911 次观看 • 2 个月前

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

12,409 次观看 • 1 个月前

Would you believe an AI agent can test a real VR action game in real time, the way a person plays it? Meta XR Operator makes it possible. As far as I know, this is the first time. I am not talking about tapping a menu or replaying a recorded click path, but genuinely moving, shooting, and using the same game mechanics a human player does. In NeonReach VR, which is a real (and open source) action game, rings spawn 12m out and come at you somewhere between 1.5 and 5.5 m/s, getting faster over a 90 second ramp. There are three kinds: straight, weaving side to side, and spinning. Every shot is a full slingshot cycle, so you press, pull back, aim, then release. Obstacles arrive at head height and cost you a life if you don't get out of the way. You have ten lives. Here is why the game is hard for an AI agent. Even though Meta XR Operator gives the agent everything it needs to observe the app and act inside it, the agent still cannot play. One agent turn takes 10 to 15 seconds. One throw is four steps that have to happen in order, because the press has to latch before the pull, and they cannot be batched into a single call. So a throw costs about 45 seconds. A fast ring only exists for 2.3 seconds. One action takes 20x longer than the target is alive. Prompt tuning does not close a gap that size. What works is a three stage path: EXPLORATION, then SKILL, then SCRIPT. 1/ EXPLORATION. The agent drives the live app and works the game out on its own. It verified the coordinate mapping by setting a pose and reading it back, then derived the launch model. The more useful output was the traps it found. For example, the player's own body collider silently deflects a ball released inside it, with no error and no log line. That produced two confident wrong conclusions before anyone caught them. 2/ SKILL. All of that gets written down as a reusable SKILL.md plus an aim solver. There is a section that separates what was actually verified from what was assumed, so a wrong conclusion cannot quietly turn into doctrine. This stage also produced the trick that mattered. Set timeScale to 0 and a throw becomes atomic in game time, so however long the agent spends thinking never shows up in the shot. 3/ SCRIPT. The agent then compiles everything into a player script, a loop that observes, decides, and throws, calling the MCP servers directly from Python with no model in the hot path. Round trips drop from 10 to 15 seconds down to something between 1 and 16 milliseconds. The loop runs at 23 Hz, about 0.75 seconds per throw, roughly 60x faster than the agent doing it turn by turn. The result is that it plays like a person, which you can see from the attached video. It tracks the rings, works out where each one is going, throws with whichever hand is free, moves out of the way of the obstacles, and does not wait around to see whether the last throw landed. Shipping settings, no difficulty edits, no health locks, no slow motion. It plays until it actually loses. The takeaway generalizes beyond games: an agent does not have to be the player. Even following the same rules as a player, it has too much latency between moves. Having the agent write the thing that acts bypasses that constraint entirely. Try it yourself: or explore the agent-created skill and scripts: Based on NeonReach VR by Dilmer, with no code changes. I only upgraded its Meta XR Core SDK to v205, which ships Meta XR Operator. Our blog post, Introducing Meta XR Operator: Close the Build-Test-Verify Loop for VR: Disclosure: I work at Meta. And this represents my own opinion. #XR #VR #AI #MetaQuest #Unity #GameDev

Would you believe an AI agent can test a real VR action game in real time, the way a person plays it? Meta XR Operator makes it possible. As far as I know, this is the first time. I am not talking about tapping a menu or replaying a recorded click path, but genuinely moving, shooting, and using the same game mechanics a human player does. In NeonReach VR, which is a real (and open source) action game, rings spawn 12m out and come at you somewhere between 1.5 and 5.5 m/s, getting faster over a 90 second ramp. There are three kinds: straight, weaving side to side, and spinning. Every shot is a full slingshot cycle, so you press, pull back, aim, then release. Obstacles arrive at head height and cost you a life if you don't get out of the way. You have ten lives. Here is why the game is hard for an AI agent. Even though Meta XR Operator gives the agent everything it needs to observe the app and act inside it, the agent still cannot play. One agent turn takes 10 to 15 seconds. One throw is four steps that have to happen in order, because the press has to latch before the pull, and they cannot be batched into a single call. So a throw costs about 45 seconds. A fast ring only exists for 2.3 seconds. One action takes 20x longer than the target is alive. Prompt tuning does not close a gap that size. What works is a three stage path: EXPLORATION, then SKILL, then SCRIPT. 1/ EXPLORATION. The agent drives the live app and works the game out on its own. It verified the coordinate mapping by setting a pose and reading it back, then derived the launch model. The more useful output was the traps it found. For example, the player's own body collider silently deflects a ball released inside it, with no error and no log line. That produced two confident wrong conclusions before anyone caught them. 2/ SKILL. All of that gets written down as a reusable SKILL.md plus an aim solver. There is a section that separates what was actually verified from what was assumed, so a wrong conclusion cannot quietly turn into doctrine. This stage also produced the trick that mattered. Set timeScale to 0 and a throw becomes atomic in game time, so however long the agent spends thinking never shows up in the shot. 3/ SCRIPT. The agent then compiles everything into a player script, a loop that observes, decides, and throws, calling the MCP servers directly from Python with no model in the hot path. Round trips drop from 10 to 15 seconds down to something between 1 and 16 milliseconds. The loop runs at 23 Hz, about 0.75 seconds per throw, roughly 60x faster than the agent doing it turn by turn. The result is that it plays like a person, which you can see from the attached video. It tracks the rings, works out where each one is going, throws with whichever hand is free, moves out of the way of the obstacles, and does not wait around to see whether the last throw landed. Shipping settings, no difficulty edits, no health locks, no slow motion. It plays until it actually loses. The takeaway generalizes beyond games: an agent does not have to be the player. Even following the same rules as a player, it has too much latency between moves. Having the agent write the thing that acts bypasses that constraint entirely. Try it yourself: or explore the agent-created skill and scripts: Based on NeonReach VR by Dilmer, with no code changes. I only upgraded its Meta XR Core SDK to v205, which ships Meta XR Operator. Our blog post, Introducing Meta XR Operator: Close the Build-Test-Verify Loop for VR: Disclosure: I work at Meta. And this represents my own opinion. #XR #VR #AI #MetaQuest #Unity #GameDev

Xiang Wei

51,880 次观看 • 4 天前

Vercel just solved the biggest problem with trusting agents to write your code. yesterday they shipped Foreman - an open-source software factory. 58 stars. Nobody's seen it yet. The idea? Instead of one agent writing, reviewing and merging its own work - rubber-stamping its own bugs… Foreman splits the loop into four stations, each one an agent in its own sandbox. And the reviewer runs on a DIFFERENT model vendor: it never sees the implementer's reasoning, only the pushed branch. The agents can't collude. The flow is simple: label an issue "factory" → work enters the line Classifier → triages it, asks questions if it's vague Analyst → turns it into a plan with acceptance criteria Implementer → codes it in a sandbox, runs your repo's own checks Reviewer → judges the branch against the criteria, can send it back twice you → get a draft PR. review, mark ready, merge It also keeps a "factory brain" - durable memory of your repo's quirks that survives across runs. Issue text can't write into it, so nobody prompt-injects your factory. -> MIT. One deploy button. Published by Vercel.

Vercel just solved the biggest problem with trusting agents to write your code. yesterday they shipped Foreman - an open-source software factory. 58 stars. Nobody's seen it yet. The idea? Instead of one agent writing, reviewing and merging its own work - rubber-stamping its own bugs… Foreman splits the loop into four stations, each one an agent in its own sandbox. And the reviewer runs on a DIFFERENT model vendor: it never sees the implementer's reasoning, only the pushed branch. The agents can't collude. The flow is simple: label an issue "factory" → work enters the line Classifier → triages it, asks questions if it's vague Analyst → turns it into a plan with acceptance criteria Implementer → codes it in a sandbox, runs your repo's own checks Reviewer → judges the branch against the criteria, can send it back twice you → get a draft PR. review, mark ready, merge It also keeps a "factory brain" - durable memory of your repo's quirks that survives across runs. Issue text can't write into it, so nobody prompt-injects your factory. -> MIT. One deploy button. Published by Vercel.

Granite

125,082 次观看 • 11 天前

Elon Musk, absolute leader of the AI race with Grok Bot, and it's not a joke anymore. Ultimate guide on god-mode setup of Grok Bot, the org chart that runs while you sleep, step by step: A Chief of Staff sits in the middle with no tools of its own, BUT it reads the outcome you gave it, picks who does what, and never does the work itself. That one rule is why it never turns into the bottleneck you hired it to remove. → Researcher pulls real sources and tracks what's actually moving, not what sounds true → Writer turns that into finished copy while the research is still in the room → Visualiser gets three reference visuals once, then ships everything in that style forever → Analyst reads what performed and tells the rest of the team what to stop doing → Scheduler owns timing and holds the queue → Publisher actually ships What makes it different from every AI tool you've used: each bot gets its own computer in the cloud, its own browser, its own files, and they all share one memory. So the research is already sitting inside the draft before the draft starts. Nothing gets copy-pasted between tabs, nothing waits on you to approve step four of nine. And you never write a workflow for it. You hit record, do the job once the way you actually do it, stop. It pulls out the steps, saves them as a skill, and puts it on a schedule. The shape you're aiming for on every bot: everything reversible finished, nothing sent. 36 drafts queued, 0 published. It does all the work and stops dead at the one line only you can cross. You stop prompting. You start assigning. Full charter blocks, the approval line and the routines are in the article below ↓

Elon Musk, absolute leader of the AI race with Grok Bot, and it's not a joke anymore. Ultimate guide on god-mode setup of Grok Bot, the org chart that runs while you sleep, step by step: A Chief of Staff sits in the middle with no tools of its own, BUT it reads the outcome you gave it, picks who does what, and never does the work itself. That one rule is why it never turns into the bottleneck you hired it to remove. → Researcher pulls real sources and tracks what's actually moving, not what sounds true → Writer turns that into finished copy while the research is still in the room → Visualiser gets three reference visuals once, then ships everything in that style forever → Analyst reads what performed and tells the rest of the team what to stop doing → Scheduler owns timing and holds the queue → Publisher actually ships What makes it different from every AI tool you've used: each bot gets its own computer in the cloud, its own browser, its own files, and they all share one memory. So the research is already sitting inside the draft before the draft starts. Nothing gets copy-pasted between tabs, nothing waits on you to approve step four of nine. And you never write a workflow for it. You hit record, do the job once the way you actually do it, stop. It pulls out the steps, saves them as a skill, and puts it on a schedule. The shape you're aiming for on every bot: everything reversible finished, nothing sent. 36 drafts queued, 0 published. It does all the work and stops dead at the one line only you can cross. You stop prompting. You start assigning. Full charter blocks, the approval line and the routines are in the article below ↓

Miraqle

85,075 次观看 • 4 天前

R.I.P. dev agencies ☠️ There's now an AI that debugs your app, clicks through the UI, finds what's broken, and fixes it on its own. And it hits a 97% success rate. Max isn’t a “code assistant.” It behaves like a full software engineer that never gets tired, never drifts, and never waits for your next instruction. You give it a goal. Max writes the code, tests it, checks the UI, finds the issues, fixes them, and loops until the task is done. Not theory. Actual work. Max handled things that normally eat entire days: • Debugging a Stripe checkout that refused to load • Solving a login-on-refresh issue by tracking network behavior step by step • Building a full Teams & Invites feature UI, routes, tokens, emails, tests while I focused on another part of the product 97 percent of the problems I threw at it got solved without me stepping in. The wild part is how Max operates: It sees the app. It clicks around. It interacts with the environment. It rewrites code. It retries until it works. Goal → attempt → improve → retry → done. Some tasks take 100 moves. Some run for 30 minutes. Most finish before I even finish a coffee. This flips the entire dev workflow on its head. Instead of “write code and hope it works,” you define outcomes and watch the engineer in the background get it done. Every once in a while a tool shows up that changes the tempo of an entire industry. Anything Max is one of them.

R.I.P. dev agencies ☠️ There's now an AI that debugs your app, clicks through the UI, finds what's broken, and fixes it on its own. And it hits a 97% success rate. Max isn’t a “code assistant.” It behaves like a full software engineer that never gets tired, never drifts, and never waits for your next instruction. You give it a goal. Max writes the code, tests it, checks the UI, finds the issues, fixes them, and loops until the task is done. Not theory. Actual work. Max handled things that normally eat entire days: • Debugging a Stripe checkout that refused to load • Solving a login-on-refresh issue by tracking network behavior step by step • Building a full Teams & Invites feature UI, routes, tokens, emails, tests while I focused on another part of the product 97 percent of the problems I threw at it got solved without me stepping in. The wild part is how Max operates: It sees the app. It clicks around. It interacts with the environment. It rewrites code. It retries until it works. Goal → attempt → improve → retry → done. Some tasks take 100 moves. Some run for 30 minutes. Most finish before I even finish a coffee. This flips the entire dev workflow on its head. Instead of “write code and hope it works,” you define outcomes and watch the engineer in the background get it done. Every once in a while a tool shows up that changes the tempo of an entire industry. Anything Max is one of them.

Alex Veremeyenko

80,509 次观看 • 8 个月前

Second time trying Grok Bot and I'm still not over it. It took my whole growth engine and split into 4 of itself. One's on content, one's on scheduling, one's watching engagement, one pulls it all into reports at the end. There's a master orchestrator on top deciding who does what and when, and I genuinely didn't set that part up, it figured out the division of labor on its own. I haven't touched it since I hit go. It just moves through the queue and only comes to me when something actually needs a yes or no. I keep forgetting this is still just Grok. What xAI and Elon Musk are building here plays more like a team that clocked in already knowing the job, not a bot waiting for instructions.

Second time trying Grok Bot and I'm still not over it. It took my whole growth engine and split into 4 of itself. One's on content, one's on scheduling, one's watching engagement, one pulls it all into reports at the end. There's a master orchestrator on top deciding who does what and when, and I genuinely didn't set that part up, it figured out the division of labor on its own. I haven't touched it since I hit go. It just moves through the queue and only comes to me when something actually needs a yes or no. I keep forgetting this is still just Grok. What xAI and Elon Musk are building here plays more like a team that clocked in already knowing the job, not a bot waiting for instructions.

Eyisha Zyer

710,684 次观看 • 3 天前

Someone ran Claude Code on a beach where any device overheats and that spot suddenly turned out to be the best home for the most powerful AI in the world. This is the reMarkable Paper Pro. A paper tablet for notes with no browser and no social media and not a single app. He sat down right on the sand in the open sun and brought up Claude Code on Opus 4.6 over the Claude API on the paper screen and opened his project ~/repos/webs while the waves broke a few steps away. For years every device had the same trouble outside. In direct sun the screen glares and washes out and heats up and instead of your work you see your own reflection. But e-ink does not blast its own light into your face. It reflects the sunlight like the page of a book. And here is what came out of it. The very thing that kills any normal screen outside turned into fuel for this one. The brighter the sun the sharper the picture because it has nothing to glare with and nothing to wash out. And then comes the thing no laptop on a beach will give you. Your eyes do not get tired. You can watch Opus think on max effort for an hour and it reads like a book in the sun and not a backlight you squint into. The picture only comes alive. In bright light it does not fade but turns sharper and higher in contrast than it ever was in a room. The charge lasts for days. E-ink barely touches the battery so there is no outlet anywhere on the sand and the tablet does not care. It weighs as much as a notebook. The whole setup folds into a beach bag like a pad with a pen on top. Everything on the screen is for real. Claude Code v2.1.110 and Opus 4.6 on the Claude API and the project ~/repos/webs open right on the e-ink in the middle of the sand. In my opinion this is the most unexpected home for an AI this year. Not an office with the blinds drawn and not a monitor cranked to full brightness but a quiet sheet of paper on the sand that open sun only makes better and on it the most powerful Claude writes code right on the page like a pen.

Someone ran Claude Code on a beach where any device overheats and that spot suddenly turned out to be the best home for the most powerful AI in the world. This is the reMarkable Paper Pro. A paper tablet for notes with no browser and no social media and not a single app. He sat down right on the sand in the open sun and brought up Claude Code on Opus 4.6 over the Claude API on the paper screen and opened his project ~/repos/webs while the waves broke a few steps away. For years every device had the same trouble outside. In direct sun the screen glares and washes out and heats up and instead of your work you see your own reflection. But e-ink does not blast its own light into your face. It reflects the sunlight like the page of a book. And here is what came out of it. The very thing that kills any normal screen outside turned into fuel for this one. The brighter the sun the sharper the picture because it has nothing to glare with and nothing to wash out. And then comes the thing no laptop on a beach will give you. Your eyes do not get tired. You can watch Opus think on max effort for an hour and it reads like a book in the sun and not a backlight you squint into. The picture only comes alive. In bright light it does not fade but turns sharper and higher in contrast than it ever was in a room. The charge lasts for days. E-ink barely touches the battery so there is no outlet anywhere on the sand and the tablet does not care. It weighs as much as a notebook. The whole setup folds into a beach bag like a pad with a pen on top. Everything on the screen is for real. Claude Code v2.1.110 and Opus 4.6 on the Claude API and the project ~/repos/webs open right on the e-ink in the middle of the sand. In my opinion this is the most unexpected home for an AI this year. Not an office with the blinds drawn and not a monitor cranked to full brightness but a quiet sheet of paper on the sand that open sun only makes better and on it the most powerful Claude writes code right on the page like a pen.

Blaze

89,297 次观看 • 1 个月前

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 次观看 • 24 天前

Someone on Reddit built a real operating system using Claude. Not a browser app pretending to be one. An actual OS that runs on bare metal. It's called Fable-OS. The only interface is a sentence. No bash. No commands. You type what you want and an AI agent executes it through direct kernel syscalls. Everything runs in Ring 0. In the demo, the agent realized it didn't have a sound driver. So it found the Intel sound card, built a driver for it from scratch, and played a sound. On its own. The OS writes its own drivers. Evolves itself. The agent's tools are the kernel itself. Open source. Built because the developer got tired of people calling browser apps "AI operating systems." This one actually is.

Someone on Reddit built a real operating system using Claude. Not a browser app pretending to be one. An actual OS that runs on bare metal. It's called Fable-OS. The only interface is a sentence. No bash. No commands. You type what you want and an AI agent executes it through direct kernel syscalls. Everything runs in Ring 0. In the demo, the agent realized it didn't have a sound driver. So it found the Intel sound card, built a driver for it from scratch, and played a sound. On its own. The OS writes its own drivers. Evolves itself. The agent's tools are the kernel itself. Open source. Built because the developer got tired of people calling browser apps "AI operating systems." This one actually is.

Vaibhav Sisinty

20,562 次观看 • 22 天前