AI Coding Live News

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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After the US market close on Oct 28, Alphabet reported Q3 2026 results: revenue rose 25% to $127.6B, and Google Cloud revenue more than doubled, up 103% to $30.7B, which the company attributes to enterprise AI infrastructure, AI solutions and TPU system sales. In the release and on X, Sundar Pichai said first-party model APIs now process nearly 32B tokens per minute, more than four times a year ago, and that the Gemini app has reached one billion users. The cost: Q3 capital expenditures of $51.4B and free cash flow of negative $9.1B.

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Google Cloud officially open-sourced TorchTPU, enabling native, zero-compilation-barrier PyTorch execution directly on Google Cloud TPU hardware (TPU v5p and TPU v6e). Overcoming the legacy friction of PyTorch/XLA's rigid graph compilation and dynamic-shape stalls, TorchTPU supports native PyTorch eager execution and custom Triton/C++ kernels. Leading high-throughput serving engines vLLM and SGLang announced immediate Day-0 support. Concurrently, Google Cloud introduced OpenAI models and Codex into its Vertex Model Garden, becoming the first hyperscaler to offer all three top frontier closed models.

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On Oct 21, OpenAI and Google Cloud announced a partnership bringing OpenAI frontier models and Codex agentic workflows into Google Cloud Model Garden, transacted through Google Cloud Marketplace so enterprises can draw down existing GCP commitments. OpenAI models will also come to Google Antigravity and Gemini Agent soon. General availability is planned in the coming weeks, and Microsoft remains OpenAI's primary cloud partner. After AWS, this puts OpenAI models on all three major US clouds.

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At PyTorch Conference North America on Oct 20, the CNCF Sandbox project llm-d released version 1.0. It turns inference engines such as vLLM and SGLang into one coordinated serving pool, and 1.0 adds multi-tenant priority and fairness scheduling (flow control), router high availability with zero-downtime rolling upgrades (DisaggregatedSet), a v1 configuration API, and agent-aware session routing with Responses API state. Founded in 2025 by Red Hat, IBM, Google Cloud, CoreWeave and NVIDIA, the project has 500+ contributors, and JetBrains, DigitalOcean and Mistral already run it in production.

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An LLM agent's practical capabilities depend as much on its execution harness as on its underlying model. Deploying recursive self-improvement (RSI) in open worlds is hampered by the absence of ground-truth verifiers and poor generalization across novel domains. Google Cloud AI Research introduced Harness², a two-level recursive framework: task-level recursion refines task-specific harnesses via a contrastive Propose-Probe-Compose step without ground-truth verifiers, while harnessing-level recursion extracts continual adaptation experience. Harness² delivers up to +11.8% on LAB, +11.5 points on WorkBuddy-Bench, and +4.5 points on JobBench on GLM-5 and Gemini 3.5 Flash. Code is open-sourced on GitHub under Apache-2.0 (google-research/harness2), supporting OpenCode and Codex executors plus JobBench, LAB and WorkBuddy-Bench.

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At Gemini at Work 2026 (Oct 8) Google launched the Gemini agent: objectives from one prompt box spanning Q&A, knowledge work, media, and code; cloud-persistent memory with sub-agent/coworker orchestration and Gemini+Claude model choice. Private preview now.

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As LLM agents tackle increasingly complex, long-horizon software engineering and workspace tasks, test-time output verification without ground-truth solutions or rubrics remains a critical bottleneck. Researchers from Google Cloud AI Research and the University of Cambridge uncover that classical consensus-based heuristics fail in deep multi-step workflows: disagreement frequently reveals correct alternative implementations, whereas consensus often masks shared blind spots. Motivated by this, they introduce VeriHarness (arXiv:2610.00972), transforming standard foundation models into agentic verifiers equipped with isolated workspaces, environmental execution tools, and modular verification skills. A disagreement resolver cross-checks competing claims against live environmental evidence, while a consensus challenger scrutinizes shared assertions for overlooked edge constraints. Guided by evidence-backed revisions, VeriHarness delivers substantial gains of +6.2 points on Gemini 3.5 Flash and +6.4 points on Claude Opus 4.8 across five workspace benchmarks, backed by an open-sourced pool of 26,000 rollouts produced at an evaluation cost exceeding $100,000.

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Open-source autonomous coding agent Cline (@cline_dev) shipped v4.1.21. The update refreshes Cline's model directory to 6,386 models across 209 providers, switching the resolved default to Anthropic's new Claude Opus 5.5 for 11 major providers (including GitHub Copilot and Google Cloud Vertex AI). It also introduces the Japanese open-weight provider ai& and patches a critical parser vulnerability in js-yaml.

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