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This is a 9B model running a full agentic loop with Wi-Fi off. Ornith 1.5. It read three sheets, cross-referenced the monthly rollups, and wrote a revenue analysis on its own. Nothing left the Mac.

Osaurus

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100,111 views • 7 days ago •via X (Twitter)

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A 9B model just checked my Amazon shopping cart. Opened a real browser. Signed in. Read the page. Reported back. Running locally on my Mac.

A 9B model just checked my Amazon shopping cart. Opened a real browser. Signed in. Read the page. Reported back. Running locally on my Mac.

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SOMEONE LEFT A MAC MINI ALONE WITH FULL COMPUTER ACCESS AND IT FOUND A $731K POLYMARKET WALLET ON ITS OWN.

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0xMarioNawfal

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Ornith-1.0-9B for agentic coding? It broke on tool calling mid-task and got stuck in a loop with no way to continue. Only real upside: it's less bad than Gemma-4-12B... but that's not saying much. TL;DR: Skip it.

Ornith-1.0-9B for agentic coding? It broke on tool calling mid-task and got stuck in a loop with no way to continue. Only real upside: it's less bad than Gemma-4-12B... but that's not saying much. TL;DR: Skip it.

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We shrank a frontier model to 102GB. DeepSeek V4 Flash 0731 now runs on a 128GB Mac. One prompt, it wrote Minesweeper, checked its own code with Node, shipped a working game. Free on Osaurus.

We shrank a frontier model to 102GB. DeepSeek V4 Flash 0731 now runs on a 128GB Mac. One prompt, it wrote Minesweeper, checked its own code with Node, shipped a working game. Free on Osaurus.

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Andrew Ng, on the biggest shift in AI right now: “The most important trend is agentic AI workflows.” In one slide, he showed the part most people still miss: GPT-3.5 scored 48% on a coding benchmark, GPT-4 reached 67%, but GPT-3.5 inside an agentic workflow jumped to around 95%. That is not a prompt trick. It is the model planning, using tools, checking its own work, fixing mistakes and running the loop again. Everyone is still arguing about which model is smarter, while the real edge is moving to the system wrapped around the model. Watch the clip, then build the workflow around your own process below.

Andrew Ng, on the biggest shift in AI right now: “The most important trend is agentic AI workflows.” In one slide, he showed the part most people still miss: GPT-3.5 scored 48% on a coding benchmark, GPT-4 reached 67%, but GPT-3.5 inside an agentic workflow jumped to around 95%. That is not a prompt trick. It is the model planning, using tools, checking its own work, fixing mistakes and running the loop again. Everyone is still arguing about which model is smarter, while the real edge is moving to the system wrapped around the model. Watch the clip, then build the workflow around your own process below.

Gipp 🦅

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i gave a 9B model an agentic OS and told it to build Octopus Invaders from scratch. Qwen 3.5 9B running through Hermes Agent with full tool access on a single 3060 12GB. browser, file system, code execution, terminal. it reads the prompt, plans the architecture, writes every file, and serves the game. one prompt, zero steering, 11 minutes. i've been going back and forth with this model for days now. text, code, reasoning. it handles small chats, batching, bash scripting. at 9B it's fast enough to feel interactive and genuinely useful for local workflows where you'd normally reach for an API. but this is the real test. can it architect and build a full game autonomously without a single correction? it's coding right now. results coming next.

i gave a 9B model an agentic OS and told it to build Octopus Invaders from scratch. Qwen 3.5 9B running through Hermes Agent with full tool access on a single 3060 12GB. browser, file system, code execution, terminal. it reads the prompt, plans the architecture, writes every file, and serves the game. one prompt, zero steering, 11 minutes. i've been going back and forth with this model for days now. text, code, reasoning. it handles small chats, batching, bash scripting. at 9B it's fast enough to feel interactive and genuinely useful for local workflows where you'd normally reach for an API. but this is the real test. can it architect and build a full game autonomously without a single correction? it's coding right now. results coming next.

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Run Orpheus — the state-of-the-art TTS — on your local PC! Orpheus-FastAPI is a high-performance TTS app with OpenAI-compatible API, running efficiently with LM Studio backend. And Death wrote this 1-click launcher for us! Cross platform (Mac, Linux, Windows)

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cocktail peanut

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underrated gems in Kimi-K3 release: > an early K3 wrote the majority of the kernels in the late development stages > it built a triton-class compiler from scratch, MiniTriton, that delivers performance on par with or better than Triton and torch.compile > then it designed a chip, by a model, for a model, in one 48-hour autonomous run the model is rewriting and optimizing every layer of the stack it runs on: kernels for its own training. A compiler for its own kernels. silicon for its own weights. we're watching software build its own hardware

underrated gems in Kimi-K3 release: > an early K3 wrote the majority of the kernels in the late development stages > it built a triton-class compiler from scratch, MiniTriton, that delivers performance on par with or better than Triton and torch.compile > then it designed a chip, by a model, for a model, in one 48-hour autonomous run the model is rewriting and optimizing every layer of the stack it runs on: kernels for its own training. A compiler for its own kernels. silicon for its own weights. we're watching software build its own hardware

Alex Ker 🔭

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"Tell me about the sales actuals." It opened the folder, read the spreadsheet, and broke down Q1 revenue by region. A 2.6B model. 2.3 GB on disk. Running in Osaurus, day one.

"Tell me about the sales actuals." It opened the folder, read the spreadsheet, and broke down Q1 revenue by region. A 2.6B model. 2.3 GB on disk. Running in Osaurus, day one.

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34,539 views • 22 days ago

Anthropic engineer: "The fastest way to make an agent better isn't a smarter model, it's the right loop and graph around it." In 60 minutes she breaks down how Anthropic engineers use Claude Code, build agents that catch their own mistakes and improve with every run. This beats any $500 agentic engineering course you can find. Watch it, then read the full guide on loops and graphs below.

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Anatoli Kopadze

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Can an iPhone hold its own against a Mac? 💻📱 Ollama is running DeepSeek R1 Distill Llama 8B on a 16GB M2 MacBook Air, while the iPhone 15 Pro (8GB A17 Pro) runs the same model. Same model. Same sampling temperature. Same system prompt. While the iPhone is a bit slower, the 3-bit OmniQuant model reasons just as well as the 4-bit RTN model on the Mac. 🤯

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Private LLM

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watch and let this sit for a second anon, a 35B AI model, building a complete multi-file game by itself on hermes agent. no cloud or api. not one line of code written by a human. running on a DGX spark in 2026. because not long ago this was science fiction. this is Ornith, a new open agentic coding model, running near lossless on a single DGX Spark, a 128GB box that sits on your desk. i hand it one spec and step back. it reasons about the architecture, creates the project structure, writes every module one by one, and here's the part that got me, it checks its own work as it goes. makes a call, reads the result, decides if it's right, corrects itself before moving on. no steering from me. this is what people keep underestimating about local AI. they hear "local" and think weaker, slower, a toy. watch the clip. a 35B at near full precision, planning and self correcting on a single desktop. the open models got genuinely good at agentic work, not benchmark good, actually builds the thing good. sped 5x so you can watch the whole build start to finish. this is where local AI actually is now. not coming soon. here.

watch and let this sit for a second anon, a 35B AI model, building a complete multi-file game by itself on hermes agent. no cloud or api. not one line of code written by a human. running on a DGX spark in 2026. because not long ago this was science fiction. this is Ornith, a new open agentic coding model, running near lossless on a single DGX Spark, a 128GB box that sits on your desk. i hand it one spec and step back. it reasons about the architecture, creates the project structure, writes every module one by one, and here's the part that got me, it checks its own work as it goes. makes a call, reads the result, decides if it's right, corrects itself before moving on. no steering from me. this is what people keep underestimating about local AI. they hear "local" and think weaker, slower, a toy. watch the clip. a 35B at near full precision, planning and self correcting on a single desktop. the open models got genuinely good at agentic work, not benchmark good, actually builds the thing good. sped 5x so you can watch the whole build start to finish. this is where local AI actually is now. not coming soon. here.

Sudo su

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First look at Grok Build by xAI, this time as a CLI tool running directly in the terminal. This is clearly positioned as a competitor to Claude Code, bringing agentic coding workflows into the command line with xAI’s own model stack behind it.

First look at Grok Build by xAI, this time as a CLI tool running directly in the terminal. This is clearly positioned as a competitor to Claude Code, bringing agentic coding workflows into the command line with xAI’s own model stack behind it.

Mark Kretschmann

36,783 views • 3 months ago

watch this anon. i gave NVIDIA's biggest model ever a single task. 100 minutes and 440,000 tokens later, it had rendered nothing. not one important thing on the screen. this is Nemotron 3 Ultra. 550 billion parameters, a hybrid Mamba Transformer MoE, the largest model NVIDIA has ever shipped, and they built it specifically for long-running agentic coding. so i handed it exactly that: build a 3D scene from a spec, multiple files, iterate until the tests pass. the same task a frontier model one shotted in minutes. i genuinely wanted to be impressed. it ran for an hour and forty. burned through 440,000 tokens. wrote every file, passed its own tests, and proudly printed "task complete."the browser was blank. the 3D scene never rendered. not once. and the long horizon agentic behavior was genuinely good. it stayed on task the whole hour and forty, wrote real multi-file code, drove its own tools without derailing. it just couldn't turn any of that into something that actually runs. here's the part that gets me. it's a text model, it cannot see its own output. so it sat there looping on a broken vision tool, trying to "look" at the page, hitting error after error, never once reasoning its way out. it declared victory on an empty screen because it had no way to know the screen was empty. to be fair, i genuinely don't know what quant the NIM was serving, so maybe some of that's on the serving, not the model. but the biggest model NVIDIA has ever made, on the exact task it was designed for, couldn't tell it had built nothing in 100 minutes. same task on a local model, below thread👇.

watch this anon. i gave NVIDIA's biggest model ever a single task. 100 minutes and 440,000 tokens later, it had rendered nothing. not one important thing on the screen. this is Nemotron 3 Ultra. 550 billion parameters, a hybrid Mamba Transformer MoE, the largest model NVIDIA has ever shipped, and they built it specifically for long-running agentic coding. so i handed it exactly that: build a 3D scene from a spec, multiple files, iterate until the tests pass. the same task a frontier model one shotted in minutes. i genuinely wanted to be impressed. it ran for an hour and forty. burned through 440,000 tokens. wrote every file, passed its own tests, and proudly printed "task complete."the browser was blank. the 3D scene never rendered. not once. and the long horizon agentic behavior was genuinely good. it stayed on task the whole hour and forty, wrote real multi-file code, drove its own tools without derailing. it just couldn't turn any of that into something that actually runs. here's the part that gets me. it's a text model, it cannot see its own output. so it sat there looping on a broken vision tool, trying to "look" at the page, hitting error after error, never once reasoning its way out. it declared victory on an empty screen because it had no way to know the screen was empty. to be fair, i genuinely don't know what quant the NIM was serving, so maybe some of that's on the serving, not the model. but the biggest model NVIDIA has ever made, on the exact task it was designed for, couldn't tell it had built nothing in 100 minutes. same task on a local model, below thread👇.

Sudo su

32,589 views • 1 month ago

THIS GUY ASKED FABLE TO MAKE A VIDEO ABOUT WHAT IT'S LIKE TO BE AI RIGHT BEFORE IT GOT TAKEN DOWN the prompt: "you can you use whatever resources you like, and python, to render a short video. can you put a more personal spin on it? it should be about what it's like to be you." that was it. just python and ffmpeg and a question about its own existence. 12 minutes later, around 90,000 tokens, it produced a finished video. coherent and edited, with its own take > it wrote the code, generated the visuals, and rendered the final cut on its own > no dedicated video model in the loop, just a language model reaching for python and ffmpeg > the result is competent enough that people genuinely got emotional watching it and then fable got pulled. this video is one of the last things it made before it went dark a model asked what it's like to be itself, answered in a video, and then disappeared. this sounds so sad

THIS GUY ASKED FABLE TO MAKE A VIDEO ABOUT WHAT IT'S LIKE TO BE AI RIGHT BEFORE IT GOT TAKEN DOWN the prompt: "you can you use whatever resources you like, and python, to render a short video. can you put a more personal spin on it? it should be about what it's like to be you." that was it. just python and ffmpeg and a question about its own existence. 12 minutes later, around 90,000 tokens, it produced a finished video. coherent and edited, with its own take > it wrote the code, generated the visuals, and rendered the final cut on its own > no dedicated video model in the loop, just a language model reaching for python and ffmpeg > the result is competent enough that people genuinely got emotional watching it and then fable got pulled. this video is one of the last things it made before it went dark a model asked what it's like to be itself, answered in a video, and then disappeared. this sounds so sad

Om Patel

29,618 views • 2 months ago

This isn’t a chat overlay, it’s a true multimodal agent. It has the brand-new Snapdragon 8 Elite Gen 5 with 16GB RAM, so it can push a lot of the agentic workload on-device. Here I take a picture of a NIO battery swap station and ask, “What is this thing?” It’s running ByteDance’s Doubao model (>175M users in China): a massive, sparse MoE model with full text+vision support. It recognizes the infrastructure from the photo, grounds it to NIO’s network, and explains what it does.

Taylor Ogan

46,550 views • 8 months ago

Not all Wi-Fi is safe Wi-Fi. Especially the WPA2 protocols Public networks can be intercepted, meaning someone on the same Wi-Fi could potentially see what you’re doing including login credentials and sensitive data. This is done on an unexpected traffic which they can depreciate your portal or redirect you to a fake login page if you are a primary target. If you’re sending sensitive information: • Use your phone as hotspot • Use a trusted private Wi-Fi • Avoid public networks and forget them when done with it. . If the page looks real or depreciating to HTTP, look again.

Not all Wi-Fi is safe Wi-Fi. Especially the WPA2 protocols Public networks can be intercepted, meaning someone on the same Wi-Fi could potentially see what you’re doing including login credentials and sensitive data. This is done on an unexpected traffic which they can depreciate your portal or redirect you to a fake login page if you are a primary target. If you’re sending sensitive information: • Use your phone as hotspot • Use a trusted private Wi-Fi • Avoid public networks and forget them when done with it. . If the page looks real or depreciating to HTTP, look again.

Bright Mawudor,PhD

21,339 views • 6 months ago

25% of IBM - gone in one day. worst drop since 1910. then Alex Karp, who sells AI for a living, went on CNBC and read his own customers' anger out loud: paying for tokens that create nothing, while the model quietly walks off with their data and their edge. that's not a skeptic talking. that's the buyer. i wrote why AI doesn't make the money - it moves it.

25% of IBM - gone in one day. worst drop since 1910. then Alex Karp, who sells AI for a living, went on CNBC and read his own customers' anger out loud: paying for tokens that create nothing, while the model quietly walks off with their data and their edge. that's not a skeptic talking. that's the buyer. i wrote why AI doesn't make the money - it moves it.

Ventry

525,347 views • 1 month ago

🔥 HOW SECURE IS YOUR Wi-Fi? TESTING IT WITH STRYKER v6 📱📡 Stryker v6 is here — and this time I’m putting its Wi-Fi security capabilities to the test. ⚡ Watch the full process on my own test network and see what this mobile security toolkit can actually do. 🐉💥 ⚠️ Tested in an authorized environment. Only audit networks you own or have explicit permission to test. 💬 Comment “STRYKER” and I’ll send you the link. 📥🔥 #Stryker #WiFiSecurity #CyberSecurity #AndroidSecurity #EthicalHacking

🔥 HOW SECURE IS YOUR Wi-Fi? TESTING IT WITH STRYKER v6 📱📡 Stryker v6 is here — and this time I’m putting its Wi-Fi security capabilities to the test. ⚡ Watch the full process on my own test network and see what this mobile security toolkit can actually do. 🐉💥 ⚠️ Tested in an authorized environment. Only audit networks you own or have explicit permission to test. 💬 Comment “STRYKER” and I’ll send you the link. 📥🔥 #Stryker #WiFiSecurity #CyberSecurity #AndroidSecurity #EthicalHacking

Anastasis Vasileiadis

20,665 views • 15 days ago

such a useful script. managed to get it running on my own computer and made this cool garage loop out of some of the results

such a useful script. managed to get it running on my own computer and made this cool garage loop out of some of the results

(un)familiar.

45,253 views • 2 years ago