AI Coding Live News

On November 3, OpenAI published "Accelerating engineering for the physical world," describing how its models help with mechanical design, electrical design and simulation. Two employees used GPT-6 Astra to design a device for talking to Codex (enclosure, PCB, firmware and software) in under a week, and a contract manufacturer built a small prototype batch in under a month, though manufacturing readiness still took several rounds of human review. Token usage across OpenAI hardware teams has grown 23-fold since February. On BenchCAD 2.0, a newer internal model averaged 0.875 versus 0.594 for GPT-6 Astra at its highest reasoning setting (OpenAI-reported). OpenAI is also expanding its work with Chip Ganassi Racing and announced a new research collaboration with Mazda.

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Indian AI company Sarvam released Sarvam Prism on November 3, a "System One" decision model for enterprise workflows: given a context and questions with fixed answer options, it returns a probability for every option in a single forward pass instead of generating text. Sarvam says that across the 25 decision models it tested, Prism averages 71.8 over three indices with a median response time of 44 ms on one B200, the highest score under 100 ms, and scores 82.5 on its Indian-language benchmark. Weights are on Hugging Face under Apache 2.0, with a hosted API as well. All scores are Sarvam's own measurements.

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On November 2 voice AI company Hume launched the Audio Decision API, which it calls the first audio-native model that answers questions about speech and sound. Developers upload raw audio, write each question as a sentence with candidate answers, and get back a probability for each answer, with no transcription step. It supports yes/no, multiple-choice, rating-scale and time-span questions, such as how many speakers there are, whether someone laughs, whether a caller sounds frustrated, or whether a siren is in the background. Per Hume, it was trained on more than 10 million human judgments and in an initial evaluation reached 70.7% agreement with a human panel's average judgment, versus 56.5% for an individual rater. Access is currently by request or waitlist.

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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 30, Microsoft's Mage team released Mage-VL-Cowork on Hugging Face under Apache-2.0: a 4B multimodal computer-use agent in which a single model decides at every step whether to act through screenshots and GUI clicks (computer_use) or through bash and Python in a terminal, with no external orchestrator. Microsoft reports a two-run average of 66.0% on OSWorld-Verified (361 tasks, 50-step budget), above Qwen3.6-27B at 63.9% but below the 9B SCALECUA-Qwen3.5-9B at 68.7%.

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On Nov 2, semiconductor and AI research firm SemiAnalysis released GDPx, a benchmark of agentic capability across 11 domains: chip design, cybersecurity, biology, finance, legal, quant trading, recursive self-improvement (RSI), hardware design, materials science, alignment and software engineering. Its 206 tasks were built by 14 specialist data partners, with SemiAnalysis doing final QA. On the official leaderboard Claude Opus 5.5 leads at 12.0%, followed by Gemini 4 Carbon at 10.9% and GPT-6 Astra at 9.6%; nearly every model scores 0 on finance and legal. SemiAnalysis says safety classifiers drag down Opus 5.5 and GPT-6 Astra, and plans a new version every 3–6 months. All scores are as reported by SemiAnalysis.

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On October 31, independent evaluator Artificial Analysis released AA-Omniscience v1.1, its knowledge and hallucination benchmark and part of Intelligence Index v5. The question set shrinks from 6,000 to 3,000, dropping previously public and saturated questions, fixing errors and ambiguities, and adding new ones, and a new 300-question public set on Hugging Face is calibrated to the private set's difficulty, with 0.99 Pearson and Spearman correlation on Net Score. Gemini 4 Carbon (high) leads with a Net Score of 54, ahead of Claude Opus 5.5 at 48 and Claude Fable 5.1 at 46; Fable 5.1 has the highest accuracy (68%) and Gemini 4 Argon the lowest hallucination rate (4%). Models score about 1.9 points lower than on v1 on average, and MiniMax M3.1 Flash drops 22 points. All figures are as reported by Artificial Analysis.

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On October 31, Google DeepMind said PlanAssist, a Gemini-built planning tool developed with the UK government, Google Cloud and Faculty, will be made available to 12 more local planning authorities in England, bringing the total to 15 for a 12-month Beta on live applications. In early pilots at Barnet, Camden and Dorset, DeepMind says the average officer time on householder assessment tasks fell by 58%, and more than 70% of the drafted reports were approved with no edits. Planning officers keep the final decision.

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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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a16z's Charts of the Week for Oct 30 (by Moses Sternstein) looks at the "Rack Spread": GPU rental prices are rising while token prices fall. The official a16z X account summed it up on Oct 31: since May, average token prices are down 67% and token spend is up more than 120%. By the article's napkin math, Ramp data gives roughly 122%, and Ornn's blended price multiplied by OpenRouter volume gives roughly 160%. A separate post says that since mid-July B300 rentals are up 106%, B200 up 66% and H100 up 17%. a16z also stresses that these indexes come from small, biased samples and do not show true total demand.

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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 October 29 Pika released Pika H3 Flex, a joint audio-video generator post-trained on MiniMax H3, with weights on Hugging Face, code on GitHub, and ComfyUI and Diffusers support. Pika says it distilled sampling from 50 denoising steps to six, averages about 1.8x faster DiT denoising than Fal H3 Max at 768p, and can produce a 15-second clip with soundtrack in as little as about 7 seconds; the hosted API adds 1080p to 4K upscaling and five exportable audio stems (dialogue, score, SFX, foley, ambience). All speed and quality figures are vendor-reported.

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