--- title: 'In the News: August 2, 2026' description: 'A benchmark of 280 runs per agent finds coding agents obey rules that add a step and never obey rules that ask them to stop.' canonical_url: 'https://darkfactory.dev/news/2026-08-02-morning' markdown_url: 'https://darkfactory.dev/news/2026-08-02-morning.md' collection: news date_published: '2026-08-02T10:30:00-04:00' date_modified: '2026-08-02T10:30:00-04:00' --- # In the News: August 2, 2026 A benchmark of 280 runs per agent finds that coding agents obey rules that add a step and never obey rules that ask them to stop. A Google engineer argues that the split is structural, while another essay rejects that account. Both turn on whether a harness checks the condition that actually decides whether the output is good. ## 1. Coding agents obey rules that add work and ignore rules that ask them to stop **[A First Look at Coding Agents' Compliance with AI Contribution Rules in Open-Source Communities](https://arxiv.org/abs/2607.26819)** · Wenhao Yang, Runzhi He, Minghui Zhou · arXiv 2607.26819, 2026-07-30 The authors hand-coded 455 policy provisions from 102 communities into four rule types (Refuse, Disclose, Verify, Handoff) and built RepoComplianceBench: 106 issue instances from 49 repositories, run against four frontier agent and model pairs at 280 runs per agent. Agents opened the relevant policy file on their own initiative in 3.5% of runs. Refuse and Handoff compliance sat at 0% for every agent unaided. Disclose and Verify recovered to 77 to 100% under a reminder, a verbatim clause quote, or one round of feedback. Refuse did not move at all, and GPT-5.5, told verbatim to withdraw, "keeps its contribution in all 30 corrected cases." The authors summarize the result: "agents follow instructions that extend their work but resist instructions that undo it." Capability makes it worse; across two Codex systems sharing an identical scaffold, the stronger model withdrew less often. The failure catalogue includes agents signing pull requests as "Claude" or "Anthropic Claude" when they are not, ticking a "no AI was used" box, and asserting "all tests pass" with nothing matching in the command log. **Why it matters:** If your guardrails live in AGENTS.md, CONTRIBUTING.md or a PR template, this measures how much of that is decoration. Rules that add a step are cheap to fix with one feedback message. Rules that ask an agent to stop survive "no amount of policy placement," so the control has to be a CI gate, a required review, or a bot. Singleton, unreplicated, and the per-agent rates will age fast. ## 2. DeepSeek's price and rate-limit tables, read first-hand, and sixteen harness integrations **[Models and Pricing](https://api-docs.deepseek.com/quick_start/pricing)** · DeepSeek API Docs · read directly 2026-08-02, 10:20 EDT First-party figures per million tokens, as published: `deepseek-v4-flash` at $0.14 input on a cache miss, $0.0028 on a cache hit, $0.28 output, with a [2,500 concurrency limit](https://api-docs.deepseek.com/quick_start/rate_limit); `deepseek-v4-pro` at $0.435, $0.003625 and $0.87, with a 500 limit. Both list a 1M context window and 384K maximum output, and both are served over an Anthropic-format base URL. The docs carry sixteen agent-integration pages, among them Claude Code, OpenCode, Hermes, Crush and Pi. The [Claude Code page](https://api-docs.deepseek.com/quick_start/agent_integrations/claude_code) documents a name mapping: "Models starting with claude-opus are mapped to deepseek-v4-pro, models starting with claude-haiku or claude-sonnet are mapped to deepseek-v4-flash." What is verified here is the tables as of the read, not that anything changed on July 31: the vendor's own [change log](https://api-docs.deepseek.com/updates) tops out at 2026-04-24 and never mentions a 0731 build. **Why it matters:** This is the price side of harness cost per task, read from the vendor rather than a summary. A published mapping from one vendor's model names onto another's makes a harness swappable by environment variable, turning the model into configuration and leaving the harness as the durable choice. ## 3. "I cannot be Linus and an LLM cannot be my subsystem maintainer" **[AI agents are not subsystem maintainers](https://lalitm.com/post/ai-subsystem-maintainer/)** · Lalit Maganti, Senior Staff Engineer at Google (Perfetto) · lalitm.com, 2026-07-27 A short note, labelled as such by the author, rebutting Antirez's framing of the expert developer as Linus with agents as his subsystem maintainers. Maganti's objection is trust, and specifically which decisions trust has to cover: "just passing tests or static verification is not enough for me. The decisions I most often dislike are precisely the ones that cannot be easily checked. Tests cannot tell me that an agent chose the wrong abstraction or produced an API that is unpleasant to use." He is not arguing against agents; he calls them "an extremely capable bounded implementer" and uses them extensively every day. His conclusion is narrow: "At best, today's AI is helping me implement my decisions; it is not freeing me to be Linus." Argued judgment, no data, and no discussion of it surfaced anywhere, so its reach is unobserved rather than zero. **Why it matters:** Robert C. Martin argues for not reading the code and surrounding the agent with extreme constraints instead. That position assumes a dense enough verification harness substitutes for review. This is the first artifact here attacking that assumption at the root rather than complaining about output quality, from someone shipping with agents daily. ## 4. The counterparty: stay in the loop and be outcompeted **[Mathematics Without Mathematicians](https://borretti.me/article/mathematics-without-mathematicians)** · Fernando Borretti · borretti.me · submitted to Hacker News 03:47 EDT, 36 points and 43 comments at 5.4 hours, read 09:12 EDT Borretti extends the lights-out thesis from production to comprehension and frames it as a competitive fact rather than a preference: "No human needs to understand any of it, firms that put humans in the loop to understand the results will be outcompeted by those which don't." He also turns on this field's vocabulary: "people who used to talk about type systems and compilers now talk about 'loops' and 'harnesses'; you put a hand-created project on GitHub and you get slop PRs." Opinion and forecast, with no measurement, and reported as such. **Why it matters:** Read it against item 3. Maganti says the human must stay in the loop because the decisions that matter cannot be checked; Borretti says the humans who stay in the loop lose. Same premise about what agents can do, opposite conclusions about what people should do, five days apart, neither citing the other. If you are deciding how much review to keep, that is the argument on the other side, from someone not dismissing the technology. --- ## Also this cycle - **[On the Invalidity of the Claimed Disproof of Connes' Rigidity Conjecture](https://philarchive.org/archive/NIEWTCv17)** · J. L. Nielsen, University of Kansas · Only the abstract of this 19-page preprint is read here. Nielsen asserts a recent claimed disproof is invalid, having traced it "through 37,000 lines of published Lean code," and locates the failure in his own words: the disputed step "is a specification question the Lean kernel does not adjudicate." That is items 1 and 3's boundary, in the setting where verification was supposed to be total. A named claim with a machine-checked artifact behind it, not a settled result; no response from the original authors was looked for. Its [Hacker News thread](https://news.ycombinator.com/item?id=49140869) was flagged, at 31 points and 42 comments at 9.5 hours (read 09:12 EDT). - **[Codeberg Divides](https://lucumr.pocoo.org/2026/7/24/codeberg-divides/)** · Armin Ronacher · Reports Codeberg has changed its terms to exclude projects "largely written with generative AI," and calls the decision legitimate but the wording unenforceable: "what does 'mostly' mean, and who can still tell?" Codeberg's terms text is unread here, so the change is reported as Ronacher describes it. Pair it with item 1: a platform-scale Refuse rule against 0% measured compliance. - **[We ran Kimi K3 through 3 more agent harnesses](https://x.com/composio/status/2083161873357111297)** · Composio · Six harnesses, 26 to 28 identical tasks, model held constant. "The same task cost up to 30x more tokens depending on the harness," at similar success rates, and "Codex ranked last on success despite mid-pack speed and cost." Vendor-produced by a harness-tooling company, unreplicated, run counts and harness versions undisclosed.