Atomic Agent

@atomicagent_io

Local-First AI Agent. Team: @skinbagwbones @gladkos @sosidudku

California, USA

atomicagent.io

Joined July 2026

Pinned
Atomic Agent
@atomicagent_io
Aug 14
Qwen 3.8 27B on Atomic Agent was 2x faster than on Hermes and Prime agents! Outputs: Atomic: 5,535 lines, 19 modules, 2h35min Hermes: 10,424 lines, 109 modules, 4h15min Prime: 12,226 lines, 129 modules, 4h42min We gave three agents the same tasks, rebuild these games: ✦ Hole
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Qwen
@Alibaba_Qwen
Aug 14
We promised open weights for Qwen3.8. Now, time to meet them! 🎉 ⚡ Qwen3.8-27B: - A native multimodal dense model. With just 27B parameters, it outperforms Qwen3.7-Plus overall and shines in real-world coding & office workflows. - 262K native context, easily extendable to 1M
Atomic Agent
@atomicagent_io
Aug 3
Qwen3.8-Max became the brain of Atomic Agent, Hermes and OpenClaw. We gave the same task: Turn a photo of a hand-drawn floor plan into an interactive 3D walkthrough of that apartment and open it in the browser. Outputs: – Atomic Agent: 66 min, 557K tokens, $2.01 – OpenClaw: 32
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Qwen
@Alibaba_Qwen
Aug 3
📢Meet Qwen3.8-Max — our most capable model to date. Next week, the open weights of Qwen3.8-Max will be released, and Qwen3.8-27B is also going open-weights to meet you all!🎉 Qwen3.8-Max, a new bar for coding and cowork at 2.4T parameters: - Autonomous coding: 10+ days of
Atomic Agent
@atomicagent_io
Jul 28
Open weight Kimi K3 performs at Claude Fable 5 level on 3D arcade games! We gave both models the same prompts: ✦ Pac-Man with four hunting ghosts ✦ Nokia Snake remade in 3D ✦ Retro space pinball table with real physics Outputs (thinking included): ✦ Kimi K3 (local, 8x
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Kimi.ai
@Kimi_Moonshot
Jul 27
Releasing the model weights and technical report of Kimi K3. Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window. New model architecture: 2.5x the intelligence per unit of compute, not just more params. Alongside
Atomic Agent
@atomicagent_io
Jul 24
Atomic Agent beat Hermes on GAIA: 69.8% vs 58.5%, and it was 1.6x faster! We ran both agents through the full GAIA Level 1 benchmark, 53 real-world tasks, same 4-bit qwen-3.6-35b on the same Apple M4 Max. Results: ✦ Atomic Agent: 37 of 53 solved, done in 3h 12m ✦ Hermes
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Atomic Agent
@atomicagent_io
Jul 22
First local agent to beat Hermes on benchmarks! ✦ runs Qwen, Gemma, Llama via llama.cpp ✦ stable-prefix caching keeps sessions cheap ✦ TurboQuant cuts the KV-cache 6.4× smaller ✦ 37 tasks solved vs Hermes' 31 on GAIA Level 1 Open source on macOS, Windows & Linux 👇
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