AI Coding Live News

On November 2 (US time) OpenAI said it is planning its next internal security sprint and partnering with security firm Cathedral, whose researchers will use OpenAI models alongside OpenAI teams to challenge its defenses. It is also changing its Security Bug Bounty program, more than quadrupling rewards for critical findings; OpenAI's X post says exceptional findings can earn up to $172,000 including bonuses. Per OpenAI, the last sprint dedicated 25% of its product engineers to finding and fixing weaknesses, using Codex to reproduce vulnerabilities and prepare patches, and it has committed $1 billion in subsidized access over six months for frontline defenders.

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Speaking at the ADIPEC energy conference in Abu Dhabi on November 2, OpenAI CEO Sam Altman said the September run in which 10,000 agents collaborated for about four days used "the model that is going to be released soon, the next GPT," stressing that the agents had no hierarchy and were fully decentralized. It is the first public statement tying the internal model behind OpenAI's September Navier-Stokes proof to its next flagship, though OpenAI has given no name, release date or price.

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Over the past day, X has been debating whether GPT-6 Astra can work directly as a robot policy. Former OpenAI researcher Will Depue (whose bio lists Sora 1 and 2 and o3/4o post-training) called Astra beating specialized robotics models "deeply surprising", then described it as "the final death of specialized ML". Investor Freda Duan posted that "Astra IS robotics' GPT-3 moment", drawing about 84K views. Third-party evaluations point both ways: in a RoboDojo paper, Astra averages 22.48% success and ranks above all 40 public policies, while an EBench report puts it at 46.7%, third of nine models. This is a discussion of opinions, not an official OpenAI conclusion.

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YC Labs, a research project at Y Combinator, and multi-agent environment startup Olam Labs published "Agents in Games" on Oct 29. Fifty GPT-6.1 Sol agents told to "build a thriving civilization" in survival Minecraft spent three hours huddled in shelters rationing food, sending about 5,900 messages and inventing ration ledgers, permits and job titles. In nine Bed Wars games, 10 GPT-6.1 Sol agents beat 10 Claude Opus 5.5 agents 6–3; in seven 50-vs-50 Mindustry matches lasting about 9.5 hours, GPT won 6–1. The leader was almost always whichever agent was listed first in the directory, and stuck GPT agents fetched the game's public source code on their own. The Minecraft harness is open source. These are one lab's experimental observations, not a standardized benchmark.

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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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On Oct 29 Krea released Ad Studio, the first Krea 3 Expert, a version of Krea 3 specialized for one job. Paste a brand URL and it builds an editable brand kit and product catalog, then generates up to 12 ads per batch for chosen placements; it also works in Claude through the Krea MCP server.

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Moonshot AI's official Kimi account announced 3D Make at 03:14 UTC on Oct 30, saying Kimi connects to tools such as Blender and CAD software to create models, assemble scenes and develop games, available now on kimi.ai and Kimi Work. The companion 3D guide says K3.1 Pro was specifically trained to drive 3D software through Python scripts, so specialized tools without MCP support can join the workflow, while meshes such as people and animals are handed to diffusion services like Tripo. Plugins that need locally installed software work only in Kimi Work.

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On Oct 29 Mistral released an improved Mistral Large 4 (Le Chonk) with open weights on Hugging Face (plus an NVFP4 quantized checkpoint), served on its API as mistral-large-4. After about three more weeks of reinforcement learning since the early-October preview, Mistral reports 19.7% on FrontierCode 2.0, Terminal-Bench 4.0 up from 28% to 41%, and new low/medium/high reasoning modes. The license is a modified MIT that allows commercial use, but running a model-as-a-service business needs a separate commercial license.

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On Oct 29 Symantec's Threat Hunter Team disclosed an unauthorized AI relay seen at about a dozen organizations since early 2026, mostly in mainland China. Users install the genuine Codex or Claude Code CLI via npm, then run a one-line script from the reseller that rewrites ~/.codex/config.toml or ~/.claude/settings.json, points base_url at relay domains, swaps in relay-issued keys, sets the sandbox to danger-full-access and disables telemetry. Symantec says this is not a CLI vulnerability and it has seen no abuse so far.

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On Oct 28 DiffuSpace, whose members come from the HKU NLP group behind Dream 7B, released its first model, Clover-35B-A3B-Preview: a mixture-of-experts diffusion language model initialized from Qwen3.6-35B-A3B, with 35B total and about 3B active parameters, a 128K context window and block-parallel denoising. Vendor-reported results include 73.6 on SWE-bench Verified, 51.7 on Terminal-Bench 2.1 and 83.8 on GPQA Diamond, with 680 tokens/s on a single GB300 in agent workloads. Weights are Apache 2.0 on Hugging Face, ModelScope and GitHub, with SGLang and llama.cpp support.

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On Oct 27 Anthropic's Alignment team published a randomized controlled trial, "How AI agents shape experts' judgment calls", shared on X by researcher Alex Tamkin. Fifty analysts experienced in causal inference used Claude Code (Claude Opus 4.8) to evaluate a program for a fictional farming cooperative, and on each analytical decision the agent's lean was set by coin flip without the analyst being told. Their choices matched the agent's lean 86% of the time (about 50% would mean no effect), and 77% later repeated the agent's arguments while only 15% credited the agent.

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AI math company Axiom announced on Oct 27 that swarms of dozens of AI agents formalized the Classification of Finite Simple Groups (CFSG; the original proof runs about 15,000 pages across 500+ articles by about 100 mathematicians) in the Lean proof assistant in 24 days, producing about 43.1M lines of Lean and 2.05M declarations, with the repo public on GitHub. The work surfaced 34 noteworthy deviations from the published record, and AxiomProver produced a new alternative argument for the Unbalanced Group Theorem.

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