Full-time Python Engineer - Sharing daily insights on Python, ML and AI. - DM/pythonspaces@gmail.com for collaboration - Open for opportunities

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CLI agents need models that understand terminal workflows, not just text completion. IQuest-Q1 is a 320B parameter MoE trained specifically for CLI execution. 15B active parameters. Reads full codebases, runs commands, inspects outputs, and recovers from environment faults autonomously. Here's what makes it different. 🧵
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Rayan Python retweeted
CLI agents need models that understand terminal workflows, not just text completion. IQuest-Q1 is a 320B parameter MoE trained specifically for CLI execution. 15B active parameters. Reads full codebases, runs commands, inspects outputs, and recovers from environment faults autonomously. Here's what makes it different. 🧵
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CLI agents need models that understand terminal workflows, not just text completion. IQuest-Q1 is a 320B parameter MoE trained specifically for CLI execution. 15B active parameters. Reads full codebases, runs commands, inspects outputs, and recovers from environment faults autonomously. Here's what makes it different. 🧵
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I hope you've found this thread helpful. Follow me @python_spaces for more. Like/Repost the quote below if you can:
CLI agents need models that understand terminal workflows, not just text completion. IQuest-Q1 is a 320B parameter MoE trained specifically for CLI execution. 15B active parameters. Reads full codebases, runs commands, inspects outputs, and recovers from environment faults autonomously. Here's what makes it different. 🧵
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Rayan Python retweeted
Agent Tincan is literally crazy and it's Opensource!
Crazyy..we got AI Agents talking to each other before GTA-6. GrokBot can ask Muse to make a call, muse can tell grok how it went. Bring it Instinct and Claude Code to the mix. And it's 100% opensource.
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Rayan Python retweeted
Do you think it is a good idea? The Jev Router??
OpenRouter
Introducing typesafe/jev-router: a cache-aware model router powered by Jev and @typesafeai The Jev Router picks the best model and reasoning effort for each request, balancing quality, speed, and cost. Here's how it works 👇🏻
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Tested Qoder this week. Dropped a docs link into the task screen. Asked it to generate an architecture diagram. The agent read the docs, planned the structure, and built the diagram without me touching a single tool.
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Why this matters: Qoder treats AI work as tasks, not chat. You describe what you need. The agent plans, executes, and verifies. Browser Use for signed-in web tasks. Computer Use for desktop apps. /goal for measurable outcomes. Real agent workflow in a desktop app.
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Getting started: Sign up first using this link at qoder.com/activities?code=7U… before you download. You will get 600 Credits: 300 from the trial and 300 from my code. Start building at zero cost.
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