2026.06.26 15:56

I'm blown away. Ornith-1.0 35b is the best local model I've ever run that doesn't require 200GB+ of RAM Incredible at coding According to my tests it's both smarter and faster than Qwen 3.6 Plus it runs like a dream on a DGX Spark Here's how I'm using it: I have it running every hour and looping through different parts of my Henry Intelligent Machines codebase It looks for security vulnerabilities Any vulnerabilities it finds, it writes a report with the fix Then once a day another loop I have running in Codex reads the report with all the security bugs and verifies, then fixes I also have it once a day going through a reviewing all my open PRs Local models running on loops 24/7 is the future. I promise you What's becoming possible today with local models is truly mind blowing

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Ornith@ornith_

2026.06.25 14:15

Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding. Ornith-1.0 spans the full parameter sizes including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. It achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks including: ✅Terminal-Bench 2.1(77.5) ✅SWE-Bench(82.4 on verified, 62.2 on pro, 78.9 on Multilingual) ✅NL2Repo(48.2) ✅SWE Atlas(41.2 on QnA, 42.6 RF, 39.1 TW) ✅ClawEval(77.1) Post-trained on top of gemma4 and qwen3.5, Ornith-1.0 employs a novel self-improving training strategy in which reinforcement learning is used to generate not only solution rollouts, but also the task-specific scaffolds that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model generate higher-quality solutions in agentic coding.😎 All models are released under the MIT license, enabling full commercial and research use. 📖Tech Blog: 🤗Huggingface:

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