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SOMEONE OPEN SOURCED A SMALL MODEL TRAINED SPECIFICALLY AS A PERSONAL AGENT ROUTER DECIDES WHAT RUNS LOCAL VS CLOUD AUTOMATICALLY ROUTES TASKS TO THE RIGHT MODEL BASED ON COMPLEXITY SMARTER AGENT ORCHESTRATION WITHOUT THE OVERHEAD

0xMarioNawfal

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157,598 次观看 • 4 个月前 •via X (Twitter)

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ANTHROPIC AND OPENAI BOTH MISSED THIS. Someone just open-sourced one SDK that gives AI agents a mouse, keyboard, and screen across macOS, Linux, and Windows. It's called CUA. Same code drives a local VM or a cloud sandbox regardless of the OS. Your agent sees the screen, clicks, types, and runs shell commands like a real user. > Benchmark against OSWorld and ScreenSpot > Export trajectories for model training > Near-native performance on Apple Silicon No separate SDK per OS. No infrastructure headache. 100% Free. Open Source.

ANTHROPIC AND OPENAI BOTH MISSED THIS. Someone just open-sourced one SDK that gives AI agents a mouse, keyboard, and screen across macOS, Linux, and Windows. It's called CUA. Same code drives a local VM or a cloud sandbox regardless of the OS. Your agent sees the screen, clicks, types, and runs shell commands like a real user. > Benchmark against OSWorld and ScreenSpot > Export trajectories for model training > Near-native performance on Apple Silicon No separate SDK per OS. No infrastructure headache. 100% Free. Open Source.

Simplifying AI

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I explored a further possibility with local models: Qwen3.6 35B A3B + NVIDIA LocateAnything-3B as a local Computer Use agent (proof of concept). In the demo, I asked it to switch my Mac to light mode. It did. Then back to dark. Did that too — finding the right toggle in System Settings, clicking it, and verifying the change itself. It's fully screenshot-based, so no Accessibility API needed. If it's on screen, the agent can see it and act on it. This runs entirely on your own hardware — private, local, built from two small open models.

I explored a further possibility with local models: Qwen3.6 35B A3B + NVIDIA LocateAnything-3B as a local Computer Use agent (proof of concept). In the demo, I asked it to switch my Mac to light mode. It did. Then back to dark. Did that too — finding the right toggle in System Settings, clicking it, and verifying the change itself. It's fully screenshot-based, so no Accessibility API needed. If it's on screen, the agent can see it and act on it. This runs entirely on your own hardware — private, local, built from two small open models.

stevibe

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Claude Code's new dynamic workflow is the real deal. 🤖 Why it matters: It's not just a chain of prompts — it's an autonomous agent swarm. 1. Set model to Opus 4.8 + reasoning effort /ultracode 2. Claude detects complexity, writes orchestration script on the fly 3. Spawns a parallel fleet of coordinated subagents Operator takeaway: This is the first time I've seen an agent actually decompose and delegate autonomously without a rigid framework. Have you tried it yet? 🚀

Claude Code's new dynamic workflow is the real deal. 🤖 Why it matters: It's not just a chain of prompts — it's an autonomous agent swarm. 1. Set model to Opus 4.8 + reasoning effort /ultracode 2. Claude detects complexity, writes orchestration script on the fly 3. Spawns a parallel fleet of coordinated subagents Operator takeaway: This is the first time I've seen an agent actually decompose and delegate autonomously without a rigid framework. Have you tried it yet? 🚀

EK

197,752 次观看 • 2 个月前

Funny how you can do the same thing with a local model without sending all your data to a remote cloud.

Funny how you can do the same thing with a local model without sending all your data to a remote cloud.

Victor M

1,196,385 次观看 • 6 个月前

$The hard part of multi-agent systems is getting agents to stay quiet. Put five agents on one task, and they duplicate work and burn tokens talking to each other. Offloop trained a dispatcher model called D1 that decides which agent moves next and when the right move is to do nothing. They achieve state-of-the-art performance on GDPval at a fraction of the usual cost. You can bring your own AI subscription.$

The hard part of multi-agent systems is getting agents to stay quiet. Put five agents on one task, and they duplicate work and burn tokens talking to each other. Offloop trained a dispatcher model called D1 that decides which agent moves next and when the right move is to do nothing. They achieve state-of-the-art performance on GDPval at a fraction of the usual cost. You can bring your own AI subscription.

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What if your model handled its own tuning? ⚙️ With NVIDIA TAO 7, just prompt your coding agent what you want in plain language and let it do the rest. • Agent skills plug into your coding agent and improve accuracy. • AutoML removes the guesswork from hyperparameters. LLM-guided tuning finds strong configs up to 2x faster. • Fine-tune any Hugging Face CV or VLM model on local NVIDIA GPUs. • Data enhanced fine-tuning helps your agent identify why the model fails, then fix it. Try TAO skills ➡️

NVIDIA AI

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Turn your Cursor agent window into a deck builder. Composer 2.5 + open-slide is a great combo - the model handles design and generation cleanly, and the browser integration lets you watch the deck come together live as the agent works. $ 𝚗𝚙𝚡 @𝚘𝚙𝚎𝚗-𝚜𝚕𝚒𝚍𝚎/𝚌𝚕𝚒 𝚒𝚗𝚒𝚝

Turn your Cursor agent window into a deck builder. Composer 2.5 + open-slide is a great combo - the model handles design and generation cleanly, and the browser integration lets you watch the deck come together live as the agent works. $ 𝚗𝚙𝚡 @𝚘𝚙𝚎𝚗-𝚜𝚕𝚒𝚍𝚎/𝚌𝚕𝚒 𝚒𝚗𝚒𝚝

Yiwei Ho

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Let me explain the agent loop, simple It's the core of every agentic system, and the part most people overcomplicate It's just this: 1. Send messages to the model 2. Model responds, maybe calls a tool 3. You run the tool 4. Append the result back to messages 5. Repeat until stop_reason is end_turn Step 4 is the whole thing, the write-back is what makes it an agent The model has to see what actually happened before it decides the next move That's the entire loop... understand this cold before you reach for a framework

Let me explain the agent loop, simple It's the core of every agentic system, and the part most people overcomplicate It's just this: 1. Send messages to the model 2. Model responds, maybe calls a tool 3. You run the tool 4. Append the result back to messages 5. Repeat until stop_reason is end_turn Step 4 is the whole thing, the write-back is what makes it an agent The model has to see what actually happened before it decides the next move That's the entire loop... understand this cold before you reach for a framework

Daniel San

12,514 次观看 • 1 个月前

I love this opensource project! Someone just put a 28.9M parameter AI model on an ESP32-S3. A chip that costs only a few dollars. No cloud. No API. No internet. It runs locally at ~9.5 tokens/s and can generate stories on a tiny screen. This changes the way we think about AI hardware. The next generation of AI toys, robots, and IoT devices may not need the cloud at all. Small chips are getting smarter. 🤖

I love this opensource project! Someone just put a 28.9M parameter AI model on an ESP32-S3. A chip that costs only a few dollars. No cloud. No API. No internet. It runs locally at ~9.5 tokens/s and can generate stories on a tiny screen. This changes the way we think about AI hardware. The next generation of AI toys, robots, and IoT devices may not need the cloud at all. Small chips are getting smarter. 🤖

Steven Cheng

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let me save you 3 hours of head scratching. if you're running local models like Qwen3.5-35B-A3B through Claude Code via llama.cpp's Anthropic endpoint, the chain will break every 3 to 5 minutes. tool call fails. flow stops. you reprompt. it recovers. 2 minutes later it stops again. the model is fine. the harness chokes on local inference latency. switch to OpenCode. same localhost endpoint. same model. same GPU. the chain doesn't break. the tradeoff: OpenCode sometimes loops. the model forgets what it already read and repeats the same tool call. but a loop you can interrupt. a broken chain kills your momentum and you start over. watch both side by side. proprietary agent vs open source agent. same 3B model. different failure modes. pick your poison.

let me save you 3 hours of head scratching. if you're running local models like Qwen3.5-35B-A3B through Claude Code via llama.cpp's Anthropic endpoint, the chain will break every 3 to 5 minutes. tool call fails. flow stops. you reprompt. it recovers. 2 minutes later it stops again. the model is fine. the harness chokes on local inference latency. switch to OpenCode. same localhost endpoint. same model. same GPU. the chain doesn't break. the tradeoff: OpenCode sometimes loops. the model forgets what it already read and repeats the same tool call. but a loop you can interrupt. a broken chain kills your momentum and you start over. watch both side by side. proprietary agent vs open source agent. same 3B model. different failure modes. pick your poison.

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Google has open-sourced GNM Head, its parametric model for generating 3D human heads The model, based on real-world scan data, provides 250 controls for head shape, and 380+ for facial expressions A free add-on integrates it into Blender #B3D

Google has open-sourced GNM Head, its parametric model for generating 3D human heads The model, based on real-world scan data, provides 250 controls for head shape, and 380+ for facial expressions A free add-on integrates it into Blender #B3D

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Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

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i found a way to make UNCENSORED AI AGENT on a RTX 4090 GPU (!!!) with LOCAL 30B model weights this is GLM-4.7-Flash with abliteration, need 24GB VRAM, safety alignment surgically removed from the weights, the model has native tool calling, it actually executes bash, edits files, runs git (1) use ollama to pull weights of GLM > ollama pull huihui_ai/glm-4.7-flash-abliterated:q4_K (2) proxy it to any coding agent via ollama > ollama launch claude --model huihui_ai/glm-4.7-flash-abliterated:q4_K > ollama launch codex --model huihui_ai/glm-4.7-flash-abliterated:q4_K > ollama launch opencode --model huihui_ai/glm-4.7-flash-abliterated:q4_K (3) have fun

i found a way to make UNCENSORED AI AGENT on a RTX 4090 GPU (!!!) with LOCAL 30B model weights this is GLM-4.7-Flash with abliteration, need 24GB VRAM, safety alignment surgically removed from the weights, the model has native tool calling, it actually executes bash, edits files, runs git (1) use ollama to pull weights of GLM > ollama pull huihui_ai/glm-4.7-flash-abliterated:q4_K (2) proxy it to any coding agent via ollama > ollama launch claude --model huihui_ai/glm-4.7-flash-abliterated:q4_K > ollama launch codex --model huihui_ai/glm-4.7-flash-abliterated:q4_K > ollama launch opencode --model huihui_ai/glm-4.7-flash-abliterated:q4_K (3) have fun

chiefofautism

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Open weight models are now available in Kiro. Three open weight models are available in the IDE and CLI, so you don’t have to default to the largest model for every task. Choose the right model based on speed, cost, or long-running agentic workflows. What model would you like to see supported next? #Kiro #BuildwithKiro #AICoding

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Gemma 4 looks at a parking lot. Decides what to ask. Calls SAM 3.1. "Segment all vehicles." 64 found. "Now just the white ones." 23 found. One model reasoning and orchestrating. One model executing. Both running locally on a MacBook. MLX. No cloud. No API.

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Fable 5 + Higgsfield MCP = a full marketing team inside Claude. The most anticipated model in AI runs content pipelines and deep analysis. With /goal, /loop, and a Fable 5 agent council, agent-run marketing is about to take off. A month of campaign work ships in a day.

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Most people see a Mac Mini as a home computer. He saw a $300 invoice waiting to happen. A guy in Shenzhen figured out that every early-stage startup, every founder, every small business owner needs the same thing, someone to tell them what their competitors are doing and where the gaps are. Nobody wants to pay $2,000 for a research firm. Nobody wants to wait a week. He set up Hermes on a laptop. Local model. No API costs. First report took 15 minutes. He charged $300 and delivered same day. Then he bought another machine. Then another. Now there are 65 Mac Minis on metal shelves in his apartment. Each one runs its own agent. Each agent has its own skills folder that grows every time it completes a task. Month one: $3,200. Month three: $9,600. The tool: Hermes Agent. Free on GitHub. The model: Qwen 3.6 27B. Also free. Total monthly cost: $2 in electricity. The hardware paid for itself in week two. The shelves haven't changed. He just keeps adding machines.

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