AI Coding Live News

On November 5 Perplexity Developers (@perplexitydevs) said every Agent API effort level now costs less per query with equal or better research quality, and added an ultra tier for broad long-running research. Vendor figures: medium rose from ~55% to ~77% on BrowseComp/DSQA with nearly 90% lower cost; xhigh held ~76.5% WANDR at ~25% of August 2026 cost; ultra runs three parallel researchers plus a consolidator at ~85.9% WANDR and about $21.71/task.

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On November 5, Cognition launched Devin Review 2.0. Since August, per-PR review cost fell about 82%, with a typical review at about $0.25–$0.75 and median latency about 94 seconds (down from about 139). It adds automatic review rules, incremental re-reviews, configurable per-PR spend limits, and tighter Devin Autofix loops. New controls include Devin Review Ultra and Custom Reviewers (beta).

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On November 5 OpenAI published a National Community Compact for U.S. compute campuses it owns and operates: no NDAs with public officials except narrow security carve-outs, public construction and impact reporting (noise, water, energy), Ratepayer Protection Pledge to pay full electricity and incremental grid costs with stranded-asset assurances, Grid Protector peak curtailment (up to 1,000 MW in Effingham, Georgia), and community investments already listed at $80M in Effingham and $40M in Pike County, Ohio. A PDF of the Compact is on openai.com.

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Independent evaluator Artificial Analysis published Tencent Hunyuan Hy4 results on the Intelligence Index v5 on November 5 (08:28 UTC): a composite score of 35, tying Grok 4.7 (xhigh) and one point behind Claude Haiku 5.5 (max). Among China-based labs it ranks second only to Kimi K3.1 Pro (46). On agentic coding it scores 31% on FrontierCode 2.0 and 37% on Terminal-Bench 4.0; cost per Intelligence Index task is about $0.78, roughly one-tenth of same-score Grok 4.7.

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On November 4 OpenAI published "A thousand fewer paths to root." Its autonomous pipeline for vulnerability discovery, validation, exploitation and patching scanned the Linux kernel in mid-October with an upcoming cyber model, reproduced 1,724 vulnerabilities and produced exploits and patches for 948 distinct local privilege escalation (LPE) bugs. The run cost $1,213,430 in API credits, about $1,279 per LPE, and hundreds of reports and patches have already gone to maintainers.

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Ricursive Intelligence released its first model, Ripple-65B Preview, on November 4 (18:24 UTC), open-sourcing the instruction-tuned model (BF16 and NVFP4) and a base model on Hugging Face under Apache 2.0. The official model card describes a 65B dense Transformer pretrained on 12T tokens with up to 1M context, and self-reports TerminalBench 2.1 80.5, SWE-bench Pro v2.0 Hard 79.1 and DeepSWE 1.1 44.3, ahead of much larger US open-weight models on agentic coding. It fits on local hardware such as the DGX Spark. CEO Anna Goldie says it cost about a tenth of the prior comparable state of the art.

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On November 2, OpenAI announced the Grid Protectors Program on its official blog: working with utilities and regulators so its future self-built compute campuses (its new term for data centers) cut electricity use when the grid is strained, while paying the full infrastructure and electric-service costs. The pilot is the planned campus in Effingham County, Georgia (Project Camellia), which Georgia Power says needs about 3,200 MW and commits up to 1,000 MW of flexible demand response, equal per OpenAI to the average use of roughly 700,000 households. The campus is still in development.

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On October 31, Scott Alexander published "Against SURA" on LessWrong. He uses SURA (Stop Unilaterally + Raise Awareness) for the view that AI companies, their employees and countries should halt AI work on their own and focus on raising awareness, and argues it would hand the lead to companies that do not prioritize alignment and cost the US its bargaining chip with China. He cites AI Futures Project members' survival estimates for scenarios without a global pause, mostly in the 25%-50% range, to push back on 90%+ doom estimates. Blogger jessicat quoted it and said parts of AI safety now behave like a mass political movement with bad epistemics (about 359 likes, 63K views); Bloomberg Odd Lots co-host Joe Weisenthal summed it up as people competing to out-doom each other (about 287 likes, 38K views). This is an opinion debate, not a policy announcement from any organization.

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HarnessPro (arXiv 2610.26770, submitted Oct 19 and revised Oct 20), from Tsinghua University, Z.AI (Zhipu) and collaborators, is a label-free way to improve an agent harness: specialized agents diagnose execution traces, rewrite instructions and runtime policies, score candidates with LLM-generated tests, and keep Lite, Medium and Heavy harnesses along the performance-cost frontier. Per the paper, with GPT-5.5 and Codex the Heavy mode reaches 80.8% on Terminal-Bench 2, 4.5 points above the prior RHO method, while cost per task drops from $1.09 to $0.72. The code is MIT-licensed and drew wider media coverage this week, including from Pandaily.

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Microsoft Research, Microsoft Security and UC Berkeley open-sourced TokenEconomizer on GitHub (Apache 2.0, v0.1.0 released October 26), a plugin for Claude Code, Codex, GitHub Copilot CLI and Pi, with experimental opencode support. It learns from earlier sessions in the same repository: micro-skills remember repo facts, tool pruning drops tool definitions that go unused, and a loop guard nudges an agent that is stuck. The paper reports 18.4–30.9% lower mean cost across five agent and model combinations on 277 SWE-Bench tasks (paper figures).

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Epoch AI reported on Oct 29 that, in a deliberately basic setup emulating an unsophisticated attacker, Z.ai's open-weight GLM 5.3 read security fixes that were public but not yet shipped and autonomously built exploit chains for all six Firefox releases from Sep 17 to Oct 26, letting a single webpage visit read local files. Marginal cost was about $1,000 per version and the fastest run took 12 hours. Mozilla patched a previously undisclosed bug the model also used in Firefox 159.0 on Oct 27.

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On Oct 29 Amp shipped macOS Satellites: threads keep running on a Linux orb, and when work needs macOS Amp boots a fresh cloud Mac with Xcode and simulators, uses it only to build, run and test, and returns screenshots, logs and screen recordings to the thread. mac.medium costs $2/hour and mac.large $4/hour; the beta is waitlisted. CEO Quinn Slack says agents need a Mac only about 35% of the time even for macOS-heavy work.

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