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Vibe Coding2026-08-06 · source-backed
arXiv 2608.05144 runs Manager, Planner, Engineer and Reviewer roles over persistent project state with fixed model weights, self-evolving through runtime state and control policies rather than training. 76.8% on AARRI-Bench, and mature waves use 21% fewer solve-input tokens and 15% less active workflow time than startup waves. The reliability machinery is worth copying on its own: 34 verifier recoveries and 22 strict review-loop rescues across 254 missions in six real paper pipelines, with 16 stage rollbacks. Rollback as a first-class primitive is what most home-rolled loops lack.
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Cohere launched North Mini Code on June 9 under Apache 2.0, its first developer-focused model. The shape is the pitch: 30B parameters, mixture-of-experts, only ~3B active, and it runs on a single H100. It scores 33.4 on the Artificial Analysis Coding Index, competes on SWE-Ben...
Moonshot AI dropped Kimi K2.6 today and the numbers are hard to ignore. One trillion parameters total, 32 billion active per token across 384 experts, 256K context window, and native multimodal input. It scores 58.6 on SWE-Bench Pro versus GPT-5.4's 57.7 and Claude Opus 4.6's...
I don't care that Grok 4.5 ranks #4. I care that it resolves a SWE-Bench Pro task with an average of 15,954 output tokens where Opus 4.8 spends 67,020. That's a 4.2x efficiency gap, and it lands straight in my monthly bill. SpaceXAI launched Grok 4.5 on July 8, a roughly 1.5T-...
Same weights. Same tasks. Same time budget. One change to the harness, and fail-to-pass on SWE-bench Verified went from 28% to 49%. The setup in arXiv 2608.26218 is clean enough to be worth describing precisely. 169 SWE-bench Verified tasks, a 20,480-token context window, a fi...
CodeGrep measures a 30B OpenHands agent averaging 23 rounds and 631K tokens per resolved SWE-Bench Verified issue, much of it grep, glob and view_file. A 14B retrieval agent trained end-to-end with GRPO raises resolve rate to 27.0% from 25.8% while cutting 15% of rounds and 19...
MAI-Code-1-Flash, a 5B-parameter coding model, is in GitHub Copilot and VS Code, and Microsoft says it beats Claude Haiku 4.5 across core coding benchmarks, +16 points on SWE-Bench Pro at 51.2% versus 35.2%, using up to 60% fewer tokens. MAI-Thinking-1, a 35B-active MoE with a...
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