AI Coding Live News

At BoxWorks 2026 on November 5, Box announced an expanded NVIDIA collaboration: Box Context Retrieval, a new engine built on NVIDIA Nemotron Retriever embedding models, combines keyword and semantic search to return the most relevant passages to AI agents while honoring existing file permissions, with general availability for Enterprise Advanced customers planned for early next year. Box's press release claims 20% better search accuracy (Recall@5), 90% fewer tokens and 4x faster answers. Open-weight Nemotron models, starting with Nemotron 3 Ultra, are available in Box AI now. Box also unveiled Box Architect, which designs workflows from a stated goal; Box Mount, which mounts Box folders inside agent sandboxes and works with NVIDIA OpenShell; and agent security controls including MCP Guardrails.

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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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At Universe 2026 on Oct 28, GitHub made GitHub Agentic Workflows, an open source GitHub Next project, generally available on GitHub Free, Team and Enterprise. Developers describe tasks such as issue triage, CI failure analysis, documentation updates and test writing in natural-language Markdown, and gh aw compiles them into standard Actions YAML run by Copilot or a coding agent of their choice. GA adds self-hosted ARC runners with Docker-in-Docker, a non-experimental gh aw forecast cost estimate, per-workflow model providers and model routing, and Cloud Hypervisor or NVX micro-VM isolation.

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In an Oct 26 post on its blog, Runway launched App Studio: describe the tool you want in plain language and App Studio writes, tests and deploys an app with its own interface that generates images, video and audio with Runway's models, using your team's Brand Kits, Workflows and Assets. An existing Agent session or Workflow can be turned into an app in one click. Apps are private by default and can be shared with a team or published as a public link that visitors run on their own credits. Built-in apps at launch include Character Swap, AutoComp and 3D Camera Control. It is available now on every paid plan at no extra charge, drawing on existing Runway credits.

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At Oracle AI World in Las Vegas on Oct 26, Oracle announced Oracle Fusion Claw, a new execution runtime for agents, and 25 Claw-powered Agentic Applications now available across ERP, HCM, supply chain (SCM) and customer experience (CX). It also introduced Fusion Companion and Fusion Agentic Home: employees describe a goal in natural language in supported AI tools such as ChatGPT or in the native Fusion interface, and the system picks and coordinates the transactions, workflows, approvals and AI agents needed, within existing identity, permission and approval controls. Oracle has not published pricing, benchmarks or a detailed rollout schedule.

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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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Perplexity officially unveiled that Perplexity Computer now operates 100% locally on macOS through the native desktop application. Breaking away from cloud-dependent computer-use paradigms that ship continuous video feeds across the internet, Perplexity Computer executes both the underlying inference model and the agent orchestration runtime directly on local Mac silicon. By interfacing natively with macOS Accessibility APIs, the system eliminates network latency, guarantees complete screen privacy, and empowers autonomous desktop copilots to orchestrate multi-application workflows entirely offline.

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Can AI agents recursively self-improve while keeping underlying model weights completely fixed? Researchers introduce the Self-Evolving Agent Harness (Harness-RSI) framework, formalizing feedback-driven revisions across prompts, memory, workflows, executable tools, and repository-level control logic. Backed by continual PAC-learning generalization bounds, historical regression budgets, and high-probability admission rules, the 66-page treatise and codebase are open-sourced on GitHub.

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While deep research (DR) agents excel on synthetic and academic queries, their reliability in realistic personal life and professional workflows remains uncharted. Tencent Hunyuan introduces LifeSearch, a rigorous benchmark featuring 203 real-user tasks across 7 domains backed by 4,500+ hours of expert annotation and 4,000+ fine-grained rubrics. Evaluating 12 leading DR agents reveals that the strongest frontier agent scores only 63.4%, unmasking persistent bottlenecks in multi-step information synthesis and constraint verification.

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While AI coding agents and 'vibe coding' workflows synthesize an unprecedented volume of production software, code security remains highly fragile. Researchers from NUS and Monash University introduced the Last Secure Code Benchmark, evaluating 450 tasks derived from 150 real CVE vulnerabilities across 7 languages (Java, C, C++, JS, Python, PHP, Go) and 24 CWE categories (including 21 of the 2025 CWE Top 25 and 8 of the Top 10 KEVs). While frontier agents achieve 73.6% to 91.3% functional completion rates, even the strongest model (Claude Fable 5) produces code that is both functional and secure on only 25.1% of tasks, exposing acute supply-chain risks in autonomous vibe coding.

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As autonomous software engineering agents and command-line assistants evolve from single-turn tool invocation toward complex, multi-stage delivery, the scarcity of high-quality, long-horizon terminal interaction data has emerged as a fundamental bottleneck. Researchers from USTC, Alibaba Group (AMAP), and SUSTech introduce TerminalHorizon (arXiv:2610.13166), a fully automated data synthesis engine for training long-horizon terminal agents. Addressing standard pitfalls where synthetic environments oversimplify core challenges and eliminate the need for exploration, TerminalHorizon extracts workflows from real-world tasks, pinpoints omitted steps through independent execution loops, dynamically reconstructs environments based on observed evidence, and organically scales task complexity across multiple stages under unified objectives. Synthesizing 1.5K environments and 2,852 trajectories averaging 52.8 turns, the authors train TerminalHorizon-27B (fine-tuned on Qwen3.5-27B). The model achieves 55.8% on Terminal-Bench 2.0 (+14.2 points over base Qwen3.5-27B), sweeping 12 benchmarks across coding agents, production environments, and reasoning with project datasets open-sourced.

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Mainstream coding agent benchmarks like SWE-bench frame evaluation around issue-derived tasks: feeding an agent a bug description and expecting an end-to-end patch. But does this reflect genuine developer practices? JetBrains Research analyzes 4,782 production sessions of professional software engineers inside JetBrains IDEs (arXiv:2610.09633). For long interactive sessions (>=3 turns, comprising 33% of production traffic), user requests span a diverse mix of intent types—project codebase Q&A, strategic planning, code review, refactoring, and test execution—with users frequently transitioning between types mid-session. To align benchmarks with real developer task flows, JetBrains introduces SWE-TaskFlow, an approach that transforms issue-based benchmarks via prompt-splitting and verifiable repository QA into calibrated multi-stage workflows guided by a TaskFlow Alignment Score (TFAS). A pilot study across 700 SWE-Bench Pro tasks demonstrates that sequential multi-step interaction protocols nearly double agent inference expenditure without stable improvements in resolve rates, proving that the interaction protocol itself is a critical, previously unmeasured dimension of agent evaluation.

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