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This is rendered by Flutter 3.47 Stable using Flutter GPU via the flutter_scene package. macOS, Linux, Windows, iOS, Android, Web, and custom embedders. Today. :) No forks, no platform views, no external renderers, no secondary graphics context. It's vanilla Flutter. Each pass is a custom material over geometry derived... show more

Brandon DeRosier

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22,951 views • 11 days ago •via X (Twitter)

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wolfenrain 💙🏳️‍🌈

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Rahul

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Kahris

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i built a full game on a single GPU with a 3B model and this is the worst local AI will ever be. this was supposed to be a benchmark test. load the model, measure tokens per second, write it up, move on. instead i spent 20 minutes playing Octopus Invaders because the game is genuinely fun and i couldn't stop. a model with 3B active parameters built this from a single prompt. it debugged its own collision system when bullets were phasing through enemies. read the error, found the fix, kept building. this is not a frontier API. this is a quantized open source model running on hardware you can buy used for $800-$1200. no cloud. no subscription. no API costs. just a mass produced consumer GPU doing things that would have been absurd 12 months ago. and here's the part that should keep you up at night: every month the models get smaller and smarter. the quants get tighter. the context windows get longer. the tooling gets cleaner. what 3B active parameters does today on 24gb, a 1B model will do on 8gb within a year. you are looking at the floor. not the ceiling.

i built a full game on a single GPU with a 3B model and this is the worst local AI will ever be. this was supposed to be a benchmark test. load the model, measure tokens per second, write it up, move on. instead i spent 20 minutes playing Octopus Invaders because the game is genuinely fun and i couldn't stop. a model with 3B active parameters built this from a single prompt. it debugged its own collision system when bullets were phasing through enemies. read the error, found the fix, kept building. this is not a frontier API. this is a quantized open source model running on hardware you can buy used for $800-$1200. no cloud. no subscription. no API costs. just a mass produced consumer GPU doing things that would have been absurd 12 months ago. and here's the part that should keep you up at night: every month the models get smaller and smarter. the quants get tighter. the context windows get longer. the tooling gets cleaner. what 3B active parameters does today on 24gb, a 1B model will do on 8gb within a year. you are looking at the floor. not the ceiling.

Sudo su

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i'm running a 397 billion parameter model on a amd ai max box that sits on my desk and pulls less power than a gaming laptop. the model is Nex-N2-Pro, 397B-A17B, the open weight release people are putting next to gpt-5.5 on coding. i have it quantized to IQ1_M, 1.75 bits per weight, 90gb of weights loaded into the 128gb of unified memory on amd's strix halo igpu. watch the gpu in this recording. it spikes, it sustains, it does not fall over. that is the part the spec sheets never show you, not just that a 400b model loads, but that an integrated graphics chip holds the load and generates token after token, stable, no crash, no thermal cliff. and it is not a slideshow. roughly 18 tokens a second, faster than you can read. a frontier scale model producing usable output, fully local. no datacenter, no rented h100s, no api key, no permission. three years ago a model this size meant a server room and a budget to match. tonight it is a quiet box on my desk. this is the accessible tier almost nobody benchmarks honestly, and it is further along than the timeline thinks. the full breakdown is coming, rocm vs vulkan on this chip, and this little amd box head to head against the nvidia equivalent. stay tuned.

i'm running a 397 billion parameter model on a amd ai max box that sits on my desk and pulls less power than a gaming laptop. the model is Nex-N2-Pro, 397B-A17B, the open weight release people are putting next to gpt-5.5 on coding. i have it quantized to IQ1_M, 1.75 bits per weight, 90gb of weights loaded into the 128gb of unified memory on amd's strix halo igpu. watch the gpu in this recording. it spikes, it sustains, it does not fall over. that is the part the spec sheets never show you, not just that a 400b model loads, but that an integrated graphics chip holds the load and generates token after token, stable, no crash, no thermal cliff. and it is not a slideshow. roughly 18 tokens a second, faster than you can read. a frontier scale model producing usable output, fully local. no datacenter, no rented h100s, no api key, no permission. three years ago a model this size meant a server room and a budget to match. tonight it is a quiet box on my desk. this is the accessible tier almost nobody benchmarks honestly, and it is further along than the timeline thinks. the full breakdown is coming, rocm vs vulkan on this chip, and this little amd box head to head against the nvidia equivalent. stay tuned.

Sudo su

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youtube is paying $50,000 a month to channels running motion graphics. no face. no voice. just graphics and numbers the niche is ranking videos. countries by GDP, population, area. WorldData_3D - 700K subscribers. Global Data - 660K subs. one video at 5 million views = $15,000 from ads alone this used to be rendered by a 3D artist in Unity and Blender. weeks of work per video now it's one prompt - finished animation in seconds. you describe, AI generates, you tweak the colors via chat, export mp4 the trick isn't the tooling. it's the format ranking works because the brain has a built-in curiosity timer. who's next? who's first? - people watch to the end. youtube sees retention, pushes harder if you want it self-hosted --> open source motion graphics in React. 70+ effects. prompt claude code, get a finished video 5 channels in parallel = $150-$350K a month from one pipeline youtube doesn't check who rendered the video. it checks retention. the barrier used to be the artist. now the barrier is who hits publish first

youtube is paying $50,000 a month to channels running motion graphics. no face. no voice. just graphics and numbers the niche is ranking videos. countries by GDP, population, area. WorldData_3D - 700K subscribers. Global Data - 660K subs. one video at 5 million views = $15,000 from ads alone this used to be rendered by a 3D artist in Unity and Blender. weeks of work per video now it's one prompt - finished animation in seconds. you describe, AI generates, you tweak the colors via chat, export mp4 the trick isn't the tooling. it's the format ranking works because the brain has a built-in curiosity timer. who's next? who's first? - people watch to the end. youtube sees retention, pushes harder if you want it self-hosted --> open source motion graphics in React. 70+ effects. prompt claude code, get a finished video 5 channels in parallel = $150-$350K a month from one pipeline youtube doesn't check who rendered the video. it checks retention. the barrier used to be the artist. now the barrier is who hits publish first

Ventry

236,460 views • 2 months ago

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QVAC SDK 0.14.0 is live. This release makes the on-device stack faster on mobile, ships the developer-agent path, and takes local text-to-speech to 31 languages. Main highlights: - OpenCode and OpenClaw. The first official OpenCode plugin, plus a maintained OpenClaw compatibility path, both built on managed mode and qvac serve. Point a coding agent at a local model with far less setup and far fewer surprises. - Brain-computer interface transcription, on the SDK. Take recorded neural signal data and decode it into text, fully on-device, no cloud. Stream it in chunks through a simple API. In 0.14 it runs GPU-accelerated on iOS. - Text to Speech in 31 languages with our Supertonic3 upgrade. VOICE AND SPEECH - Supertonic3 multilingual TTS, 5 languages to 31. - Chatterbox and Supertonic now run on the Android GPU, with lower memory use (especially on iOS), quantized s3gen Chatterbox support, and a fix for Chatterbox occasionally emitting random speech. - Whisper transcription now runs on the iOS GPU. Parakeet runs on the Android GPU, with steadier real-time streaming. VISION AND OCR - VLM multi-tile batching: high-resolution Pan and Scan images are encoded in one pass instead of tile by tile, for faster vision throughput. - OCR on ggml (EasyOCR and DocTR) reaches full speed parity with the onnx path, across Metal, OpenCL, and Vulkan. PLATFORM AND RELIABILITY - Dynamic compute backends on Linux: one build picks the right backend at runtime, and opens the door to ROCm and CUDA support without per-backend builds. - Thinking tokens are kept out of the model context, so reasoning no longer fills the KV cache. SDK 0.14.0 is now leaner and faster to start. Let’s build.

QVAC

23,973,950 views • 1 month ago

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Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 views • 9 months ago

Welcome Agentforce Coworker. 🤝 Your new AI teammate is now inside every Salesforce search bar. It doesn’t just find things — it works with you. Agentforce now instantly taps into your CRM data, workflows, customer history, opportunities, and cases… then takes action in real-time. ❌ No copy/paste ❌ No tab hunting ❌ No context switching Just ask. Act. Close faster. 🔥 Plus, build custom agents that run natively in Slack, Teams, and mobile. Available today for every Agentforce customer. We went from internal dog fooding to your hands in record time. The pace at Salesforce is absolutely WILD right now ⚡️Be a Trailblazer — turn yours on today! 👇

Welcome Agentforce Coworker. 🤝 Your new AI teammate is now inside every Salesforce search bar. It doesn’t just find things — it works with you. Agentforce now instantly taps into your CRM data, workflows, customer history, opportunities, and cases… then takes action in real-time. ❌ No copy/paste ❌ No tab hunting ❌ No context switching Just ask. Act. Close faster. 🔥 Plus, build custom agents that run natively in Slack, Teams, and mobile. Available today for every Agentforce customer. We went from internal dog fooding to your hands in record time. The pace at Salesforce is absolutely WILD right now ⚡️Be a Trailblazer — turn yours on today! 👇

Marc Benioff

196,860 views • 2 months ago