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been building an agent-native, node-based, real-time environment. as a test, I gave gpt-5.6 sol a prompt to plan out its own setup and go into a loop of creative exploration until it felt that it was done. it invented every node, every data type, every connection and all of... show more

Spencer Sterling

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22,761 görüntüleme • 1 ay önce •via X (Twitter)

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i wrote a brainstorming skill to get AI to walk a human through a creative process when stuck. then i told it to just use it itself, gave it a simplistic generative art piece, and let it run till it felt it was done. here's a video of every image it generated.

i wrote a brainstorming skill to get AI to walk a human through a creative process when stuck. then i told it to just use it itself, gave it a simplistic generative art piece, and let it run till it felt it was done. here's a video of every image it generated.

joshua schachter

15,399 görüntüleme • 20 gün önce

i gave 5.6 sol access to my camera roll and had it extract pictures of every piece of clothing i own from my photos then, told it to find new outfits for me and render them on me with gpt-image! its kinda cool to see your entire wardrobe in a collection like this

i gave 5.6 sol access to my camera roll and had it extract pictures of every piece of clothing i own from my photos then, told it to find new outfits for me and render them on me with gpt-image! its kinda cool to see your entire wardrobe in a collection like this

thijs

6,440,069 görüntüleme • 1 ay önce

I don't think i can go back to regular software anymore exploring a crazy combo of real-time depth estimation, raymarched SDFs, StreamDiffusion and BiRefNet. The entire setup was authored by GPT 5.6 Sol and Luna in Sentinel over MCP while I steered it in real-time.

I don't think i can go back to regular software anymore exploring a crazy combo of real-time depth estimation, raymarched SDFs, StreamDiffusion and BiRefNet. The entire setup was authored by GPT 5.6 Sol and Luna in Sentinel over MCP while I steered it in real-time.

Spencer Sterling

41,573 görüntüleme • 1 ay önce

We gave an AI agent a real Coinbase wallet — then put Sentinel in front of it. Every payment it makes is gated by policy: allow, block, or escalate. Every decision signed and tamper-evident. Watch it block a prompt-injected payment in real time.

We gave an AI agent a real Coinbase wallet — then put Sentinel in front of it. Every payment it makes is gated by policy: allow, block, or escalate. Every decision signed and tamper-evident. Watch it block a prompt-injected payment in real time.

Montana Labs

15,558 görüntüleme • 2 ay önce

OptimAI Core Node v2 is live. And with it, the introduction of OptimAI Claw. From nodes as infrastructure → to nodes as execution environments. With Core Node v2, every device becomes a runtime for native personal agents. 👉 Get your node up and running. No complex setup. No fragmented tooling. Just a few clicks... and your agent is live. OptimAI Claw brings persistent, node-resident agents directly into your system: • Multi-source research across web & social • Real-time summarization and signal tracking • Autonomous workflow execution • Continuous learning through reinforcement All running locally. All under your control. Supported on Windows & macOS. This is beyond prompt-based AI. Beyond isolated tools. This is personal agent infrastructure. Where every node thinks, acts, and evolves. Claw is now part of the network. And this is only the beginning. Happy Building, Making Impact! 🔥

OptimAI Core Node v2 is live. And with it, the introduction of OptimAI Claw. From nodes as infrastructure → to nodes as execution environments. With Core Node v2, every device becomes a runtime for native personal agents. 👉 Get your node up and running. No complex setup. No fragmented tooling. Just a few clicks... and your agent is live. OptimAI Claw brings persistent, node-resident agents directly into your system: • Multi-source research across web & social • Real-time summarization and signal tracking • Autonomous workflow execution • Continuous learning through reinforcement All running locally. All under your control. Supported on Windows & macOS. This is beyond prompt-based AI. Beyond isolated tools. This is personal agent infrastructure. Where every node thinks, acts, and evolves. Claw is now part of the network. And this is only the beginning. Happy Building, Making Impact! 🔥

OptimAI Network

368,154 görüntüleme • 5 ay önce

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.

OptimAI Network

50,478 görüntüleme • 1 yıl önce

THIS CHINESE DEVELOPER’S NEURAL NETWORK VISUALIZATION IS EXACTLY HOW YOUR OBSIDIAN VAULT SHOULD WORK every node connects to every other node and the whole thing gets smarter the more data flows through it, same way a real knowledge system should work but almost nobody builds it like that most people throw everything into folders and tags and call it a second brain but what they actually built is an archive that gets harder to use every month claude reads across everything you ever captured and finds the connection you need right when you need it here’s how to build the version that actually compounds ↓

THIS CHINESE DEVELOPER’S NEURAL NETWORK VISUALIZATION IS EXACTLY HOW YOUR OBSIDIAN VAULT SHOULD WORK every node connects to every other node and the whole thing gets smarter the more data flows through it, same way a real knowledge system should work but almost nobody builds it like that most people throw everything into folders and tags and call it a second brain but what they actually built is an archive that gets harder to use every month claude reads across everything you ever captured and finds the connection you need right when you need it here’s how to build the version that actually compounds ↓

leopardracer

231,513 görüntüleme • 3 ay önce

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.

Raoul Pal

79,047 görüntüleme • 1 ay önce

Only if education could be this interactive ❤️‍🔥 I've had a looong wish to build something genuinely useful through vibe coding, and I finally did it. A 3D human anatomy application built with Three.js using GPT 5.6 Sol. It all started with a single design image that I created using GPT Image 2.0. I then used it to generate every 3D organ image, one by one. Next, I converted each of those images into 3D models using Tripo (and no, they didn't sponsor this 😄). After that, I opened Codex, wrote a master prompt based on the design, and gave it the prompt, the design image, and all the 3D models. Codex built the first version beautifully, but there was one big problem. Every single 3D model was nearly 120-150 MB. That obviously wasn't practical for the web and was giving a performance of 16fps. After a few iterations, Codex optimized each model down to roughly 2–5.5 MB while preserving the visual quality, reducing the total asset size from ~900 MB to just 28.6 MB. And each model loads on demand. Along the way, Codex also generated those anatomical illustrations showing where each organ sits in the human body, and even created the interactive hotspot markers that explain different parts of every organ. It handled all of that. The process wasn't exactly one shot, but it also wasn't difficult. You just have to do it step by step. It genuinely felt like building something that could make learning anatomy much more engaging. The inspiration came from Dilum Sanjaya's 3D animal plant cell project. I remember seeing it and thinking, "I want to build something like this one day." And I did it :D Live: Code:

Only if education could be this interactive ❤️‍🔥 I've had a looong wish to build something genuinely useful through vibe coding, and I finally did it. A 3D human anatomy application built with Three.js using GPT 5.6 Sol. It all started with a single design image that I created using GPT Image 2.0. I then used it to generate every 3D organ image, one by one. Next, I converted each of those images into 3D models using Tripo (and no, they didn't sponsor this 😄). After that, I opened Codex, wrote a master prompt based on the design, and gave it the prompt, the design image, and all the 3D models. Codex built the first version beautifully, but there was one big problem. Every single 3D model was nearly 120-150 MB. That obviously wasn't practical for the web and was giving a performance of 16fps. After a few iterations, Codex optimized each model down to roughly 2–5.5 MB while preserving the visual quality, reducing the total asset size from ~900 MB to just 28.6 MB. And each model loads on demand. Along the way, Codex also generated those anatomical illustrations showing where each organ sits in the human body, and even created the interactive hotspot markers that explain different parts of every organ. It handled all of that. The process wasn't exactly one shot, but it also wasn't difficult. You just have to do it step by step. It genuinely felt like building something that could make learning anatomy much more engaging. The inspiration came from Dilum Sanjaya's 3D animal plant cell project. I remember seeing it and thinking, "I want to build something like this one day." And I did it :D Live: Code:

The Bugged Dev

2,094,620 görüntüleme • 1 ay önce

Gave UEFN its own blueprint graph. Python tool running inside the editor. Scans your level and parses your Verse code to draw a full visual map of your game logic. What it shows you: - Every device in your level as a node - Every @ editable reference between devices - Every event subscription and function call - Which Verse functions are actually being used vs sitting there doing nothing What it catches: - Broken connections where an @ editable points to nothing - Orphan devices with zero connections - Unassigned editables you forgot to wire up What you can do with it: - Click a node, and it selects the device in your viewport - Double-click to fly straight to it - Open the Verse file in VS Code with one button - Search and filter by device type - Zoom in and out - Add notes to any device - Export the whole graph as a doc

Gave UEFN its own blueprint graph. Python tool running inside the editor. Scans your level and parses your Verse code to draw a full visual map of your game logic. What it shows you: - Every device in your level as a node - Every @ editable reference between devices - Every event subscription and function call - Which Verse functions are actually being used vs sitting there doing nothing What it catches: - Broken connections where an @ editable points to nothing - Orphan devices with zero connections - Unassigned editables you forgot to wire up What you can do with it: - Click a node, and it selects the device in your viewport - Double-click to fly straight to it - Open the Verse file in VS Code with one button - Search and filter by device type - Zoom in and out - Add notes to any device - Export the whole graph as a doc

Immature

12,119 görüntüleme • 5 ay önce

Demo 2: human + agent design loop inside Codex. Not every workflow needs the agent to drive everything. You design in Open Design. Codex reviews the output. It can inspect files, open pages, test interactions, critique the design, and turn feedback into the next prompt. Design becomes a human + agent feedback loop.

Demo 2: human + agent design loop inside Codex. Not every workflow needs the agent to drive everything. You design in Open Design. Codex reviews the output. It can inspect files, open pages, test interactions, critique the design, and turn feedback into the next prompt. Design becomes a human + agent feedback loop.

Open Design

20,103 görüntüleme • 3 ay önce

you let your agent keep notes, because the alternative was explaining yourself every morning it stopped asking, and it started repeating things that had stopped being true every guide on this covers what to save and where to put it, private for one agent, shared for the team not one of them covers how a fact leaves the agent writes its own memory while it works, so a guess it made on a tired tuesday gets stored in the same shape as something you told it, and after that the two are indistinguishable i counted mine this morning: forty files, six of them about decisions we reversed months ago, all forty read into every session since you did not give it a memory you gave it a record with no way to be corrected

you let your agent keep notes, because the alternative was explaining yourself every morning it stopped asking, and it started repeating things that had stopped being true every guide on this covers what to save and where to put it, private for one agent, shared for the team not one of them covers how a fact leaves the agent writes its own memory while it works, so a guess it made on a tired tuesday gets stored in the same shape as something you told it, and after that the two are indistinguishable i counted mine this morning: forty files, six of them about decisions we reversed months ago, all forty read into every session since you did not give it a memory you gave it a record with no way to be corrected

Lagosaur

13,883 görüntüleme • 4 gün önce

🇺🇸 Bill Gates attempts to apologize for his connection to Epstein: "I regret every minute I spent with him, and I apologize for doing it. You know, the facts are that I only went to dinners, I've never been on the island, I've never dated any women, and so the more this comes out, the clearer it becomes that while that time was a mistake, it had nothing to do with this kind of behavior."

🇺🇸 Bill Gates attempts to apologize for his connection to Epstein: "I regret every minute I spent with him, and I apologize for doing it. You know, the facts are that I only went to dinners, I've never been on the island, I've never dated any women, and so the more this comes out, the clearer it becomes that while that time was a mistake, it had nothing to do with this kind of behavior."

Lord Bebo

39,180 görüntüleme • 7 ay önce

Today, I gave the Replit AI Agent a Real test. I built an Employee Vacation tracker app. 100% AI prompting, no code. I hated the process until I mastered it. The app I built is so cool that I bought a domain to let you all use it for free. Every step, with great details 🧵:

Today, I gave the Replit AI Agent a Real test. I built an Employee Vacation tracker app. 100% AI prompting, no code. I hated the process until I mastered it. The app I built is so cool that I bought a domain to let you all use it for free. Every step, with great details 🧵:

John Rush

180,362 görüntüleme • 1 yıl önce

People think Matt faked it when he said one prompt built a whole cod game. So i read the prompt properly and ran it myself. Spent over 30% of my weekly tokens on the 20x plan for 12 hours. It's real. but here's the part nobody's talking about. The prompt says don't stop until you beat call of duty in a blind side by side. It never beat it. It kept making the scores, and judges the game every single round. it failed its own test the entire time. That's why the game got better and better . An impossible high bar means it never stops improving.

People think Matt faked it when he said one prompt built a whole cod game. So i read the prompt properly and ran it myself. Spent over 30% of my weekly tokens on the 20x plan for 12 hours. It's real. but here's the part nobody's talking about. The prompt says don't stop until you beat call of duty in a blind side by side. It never beat it. It kept making the scores, and judges the game every single round. it failed its own test the entire time. That's why the game got better and better . An impossible high bar means it never stops improving.

Ziwen

86,753 görüntüleme • 1 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

153,908 görüntüleme • 2 ay önce

Introducing Headlong, an open source microharness for persistent agents: self-guided agents that think continuously. Most agent harnesses are reactive: you send a task, the agent completes it, and then it sits frozen until the next request. Cron jobs and heartbeats wake it up to run a checklist and put it back to sleep. A Headlong agent is never asleep. It keeps generating thoughts about whatever it decides is interesting, in a self-guided loop inspired by human inner monologue. Your message doesn't start a session. It's one more observation that lands in the agent's thought stream, and the agent decides if and when to reply. Headlong is built on the idea of persistent agency: continuous inner thought generation between external interactions. The agent sets its own interests and priorities, comes up with its own projects, and sometimes pings you unprompted with progress. To keep our prototype as simple and small as possible, we implemented Headlong as a microharness: a complete agent harness in under 10K lines of Bash, organized as a handful of small executables. It includes a loop that generates the next thought, shellm (a recursive language model written in Bash), a trajectory stored as a DAG of jsonl files, and context as a projection of that trajectory. We've been running one Headlong agent internally at Laude for several weeks. The whole team talks to it over Slack and Telegram, and every conversation lands in its single stream of thought. It works in its own fork of Headlong and we've pulled over 50 of its commits into main. One night, with nobody talking to it, it went back to check whether a recall process it had built was actually wired into its mind, found that it wasn't, diagnosed and fixed the bug, and verified the fix end to end. 48 minutes, no human asked for the fix or was in the loop at any point. Every step is a timestamped line in its log. Things broke too, and we wrote those up. Background thinking costs us $1 to $2 an hour, our agent stopped its own service three times by accident, and self-delegation died on day one. Details in the post. One line installs everything and starts an agent. Use a dedicated sandbox and spend-capped API key; it runs real shell commands and thinks around the clock. Headlong is research software, be careful! curl -fsSL | bash Launch post: Repo: Headlong is a Laude Institute / MIT collaboration.

Introducing Headlong, an open source microharness for persistent agents: self-guided agents that think continuously. Most agent harnesses are reactive: you send a task, the agent completes it, and then it sits frozen until the next request. Cron jobs and heartbeats wake it up to run a checklist and put it back to sleep. A Headlong agent is never asleep. It keeps generating thoughts about whatever it decides is interesting, in a self-guided loop inspired by human inner monologue. Your message doesn't start a session. It's one more observation that lands in the agent's thought stream, and the agent decides if and when to reply. Headlong is built on the idea of persistent agency: continuous inner thought generation between external interactions. The agent sets its own interests and priorities, comes up with its own projects, and sometimes pings you unprompted with progress. To keep our prototype as simple and small as possible, we implemented Headlong as a microharness: a complete agent harness in under 10K lines of Bash, organized as a handful of small executables. It includes a loop that generates the next thought, shellm (a recursive language model written in Bash), a trajectory stored as a DAG of jsonl files, and context as a projection of that trajectory. We've been running one Headlong agent internally at Laude for several weeks. The whole team talks to it over Slack and Telegram, and every conversation lands in its single stream of thought. It works in its own fork of Headlong and we've pulled over 50 of its commits into main. One night, with nobody talking to it, it went back to check whether a recall process it had built was actually wired into its mind, found that it wasn't, diagnosed and fixed the bug, and verified the fix end to end. 48 minutes, no human asked for the fix or was in the loop at any point. Every step is a timestamped line in its log. Things broke too, and we wrote those up. Background thinking costs us $1 to $2 an hour, our agent stopped its own service three times by accident, and self-delegation died on day one. Details in the post. One line installs everything and starts an agent. Use a dedicated sandbox and spend-capped API key; it runs real shell commands and thinks around the clock. Headlong is research software, be careful! curl -fsSL | bash Launch post: Repo: Headlong is a Laude Institute / MIT collaboration.

Andy Konwinski

357,000 görüntüleme • 17 gün önce

A sample of caffeine from prompt to launch it took about 30 mins to launch but I wrote an extremely thorough an elaborate prompt. I wanted to QA test if I could crash it. Nope it did QA and solved its own issues for me. My mind was blown.

A sample of caffeine from prompt to launch it took about 30 mins to launch but I wrote an extremely thorough an elaborate prompt. I wanted to QA test if I could crash it. Nope it did QA and solved its own issues for me. My mind was blown.

stoik.icp

21,586 görüntüleme • 1 yıl önce

July 15th, 2015 was the very first time I used qualitative data to assess my sleep, life, work, and health. It was also the first time I began to see a calendar as a place to store data about myself. Asking how I felt and giving it a number created a deeper awareness that revealed more quantifiable insights over time. When I built Existence, I decided I did not want to do it for just the whole day. I wanted to see the quality of every block of time.

July 15th, 2015 was the very first time I used qualitative data to assess my sleep, life, work, and health. It was also the first time I began to see a calendar as a place to store data about myself. Asking how I felt and giving it a number created a deeper awareness that revealed more quantifiable insights over time. When I built Existence, I decided I did not want to do it for just the whole day. I wanted to see the quality of every block of time.

Rob Dyrdek

45,157 görüntüleme • 9 ay önce