Thursday’s top story: Gemini 3.7 Flash: our most intelligent workhorse model — Google’s new coding/agents workhorse, three weeks after 3.6 Flash. Scoring FrontierCode 1.1 Main 43.6% (vs 34.4%), DeepSWE v1.1 65.3% (vs 49.0%), WebDev Arena Elo 1588, AutomationBench 30.4% (vs 17.0%). Introductory pricing at $0.75/$3.75 per 1M tokens runs to Dec 31, then doubles — worth noting if you’re building agents that will run into next year. Also today: DeepSeek shipped V4-Pro, an open-source agent harness, and raised prices; Qwen3.8-Max weights are runnable locally; OpenAI previewed a Cerebras-powered Ultrafast tier.

Agent frameworks & tooling

  • DeepSeek Harness developer preview — DeepSeek’s open-source agent harness where every capability (models, tools, skills, sessions, sandboxes, storage, loops, scheduling, UI) is a swappable Cordis plugin, and every run is an append-only session log with full trajectory replay. npx @deepseek-ai/dsh web to try; source at github.com/deepseek-ai/deepseek-harness. Worth a look if you build or customize harnesses, not just use them. (HN 457pts)

  • MCP Memory — OKF-backed agent memory server — Show HN, MIT: MCP server for persistent agent memory using Google’s Open Knowledge Format (OKF v0.2) + SQLite FTS5; sub-20ms key lookups, namespace isolation, zero-boilerplate setup for Claude Desktop/Cursor/Codex/Windsurf. (HN · Show HN 47pts)

  • Continued: Codex in ChatGPT desktop app for Linux is now in preview — day 2 of coverage (base specs in yesterday’s digest). The Codex agent itself is now available in preview inside the Linux desktop app (OpenAI developer-community announcement, 408 HN pts). (HN 408pts)

Models & research

  • DeepSeek launches V4-Pro — DeepSeek’s most advanced model, rivaling Kimi K3 on some benchmarks at $0.435/1M input and $0.87/1M output — well below K3 pricing. (Techmeme · The Information)

  • Qwen3.8-2.4T-A95B can now run locally — Open weights of the 2.4T-parameter MoE are out; Unsloth’s Dynamic 1-bit quant shrinks 4.9TB → 397GB GGUF, runnable on ~410GB+ RAM/VRAM (single Mac Studio per Unsloth). Note: quant quality and “rivals GPT-5.6 Sol” are Unsloth’s claims, not independently verified. (r/unsloth · 633pts)

  • OpenAI previews Ultrafast, a Cerebras-powered API tier for GPT-5.6 Sol — Up to 750 output tok/s with “no quality compromise” per Cerebras; limited preview, expanding. Cerebras-benchmarked numbers (11× vs Fable 5, HLE in 11h11m) are vendor claims, but this is a genuinely new way to put frontier inference on the critical path of agent loops. (HN 104pts · Techmeme)

  • Harness-IF: Evaluating Instruction Following Across Instruction Surfaces in Coding Agents — Scores 256 operational rules across the five surfaces a deployed agent reads (system prompt, project files, user instructions, tool/skill descriptions) and introduces Against-Prior Accuracy to separate compliance from coincidence. Every one of 12 frontier models drops 3.6–7.4pts on against-prior rules. Directly useful if you write harnesses or system prompts. (arXiv · submitted Aug 12)

  • Agent Skills Can Be Harmful — Differential analysis attributing failures to loaded skills: 307 skill-induced failures (125 functional, 182 cost regressions) on SkillsBench + SWE-Skills-Bench. Seemingly-relevant skills make agents mis-implement tasks, and validation checklists become mandatory work. Ships SkillTriage attribution tooling — relevant to anyone running skill-based agents. (arXiv · submitted Aug 12)

Industry

All gathered items - what was cut and why (9)