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Oct 23Fri
  1. AWS Machine Learning Blog31

    How IskayPet unified corporate knowledge with Amazon Bedrock Managed Knowledge Bases

    IskayPet, an Iberian pet care company with over 330 stores and 4,500 employees, built a corporate knowledge assistant on Amazon Bedrock Managed Knowledge Bases that answers employee questions across retail operations, veterinary care, people and onboarding, and procurement in four languages and three countries without managing RAG infrastructure. The solution uses the native SharePoint connector with advanced indexing, a dedicated knowledge base per assistant scoped to specific sites or libraries, document-level access control tied to existing SharePoint permissions, and hybrid search with reranking. Built with Kiro and Claude Haiku 4.5, it reportedly cut time to find an operational answer from 3–5 minutes to about 7 seconds in roughly the first two months in production.

  2. AWS Machine Learning Blog58

    Announcing new ways to customize models with SageMaker AI, without managing GPUs

    AWS announced the general availability of training sessions in Amazon SageMaker AI serverless model customization, letting customers write and coordinate their own post-training loop, including custom loss functions and reward signals, while SageMaker AI handles GPU allocation, distributed execution, fault recovery, and checkpoint storage. The sessions support techniques such as SFT, DPO, RLVR, RLAIF, and multi-turn RL, with LoRA or full fine-tuning, and are available with support for gpt-oss-20b, gemma-4-31B-it, Qwen3.6-27B, and Qwen3.8-27B. Customers can start via the SageMaker Python SDK and cookbook or in SageMaker Studio, with data kept in their own S3 buckets and IAM, KMS, and VPC controls.

  3. AWS Machine Learning Blog34

    Deploy AI in a HIPAA-eligible environment with Amazon Quick

    This AWS post explains how to deploy Amazon Quick, a HIPAA-eligible service, for healthcare workloads that touch protected health information, covering covered entities and business associates, the distinction between HIPAA-eligible and HIPAA-compliant, and the unstructured (knowledge base) and structured (Amazon Quick Sight) PHI data paths. It details the controls Amazon Quick provides—managed infrastructure, encryption, RBAC and SSO, CloudTrail and CloudWatch vended logs—alongside what it does not do, such as de-identification and lake-level governance handled by AWS Glue, Glue DataBrew, and Lake Formation. It closes with a fictional health plan walkthrough, irreversible day-zero decisions (customer managed key, identity model, ACL-aware knowledge bases), and a step-by-step implementation guide.

  4. AWS Machine Learning Blog38

    Stream low-latency speech from AI agents using Kokoro TTS on Amazon SageMaker AI

    This AWS post describes streaming each sentence an agent produces to a hosted Kokoro-82M text-to-speech model on a SageMaker AI real-time endpoint, so synthesis starts on the first sentence instead of waiting for the full response. It covers bidirectional WebSocket handling, an incremental sentence chunker, and two transports for returning audio to the browser, reporting that incremental sending cut mean time to first audio byte from 329.3 ms to 156.7 ms in isolation and made bidi mode 2.4 to 3.5 times faster than batch in a deployed agent.

  5. AWS Machine Learning Blog38

    How Rescale built an AI triage agent with Strands Agents and AI-DLC

    Rescale used the open source AI-Driven Development Lifecycle (AI-DLC) framework on AWS to build an agent that classifies and routes nightly end-to-end test failures, going from problem definition to staging in five days and to production within weeks. The agent runs in Rescale's Argo Workflows CI/CD pipeline on Amazon EKS, uses Claude on Amazon Bedrock for classification, retrieves historical failure patterns from a knowledge base, and queries pod logs via MCP, producing structured remediation reports. In production, classification agreement with human triage rose from 44% to 68%, and time blocking production deployment while failures are analyzed fell 20%.

  6. Microsoft Research58

    Flint-interactive: A language for creating interactive data visualizations

    Microsoft Research introduces Flint-interactive, a language built on Flint that lets agents and developers create interactive data visualizations using high-level semantic events and update operators rather than low-level visualization code. Flint's compiler resolves raw triggers like clicks and drags into semantic events and translates update operators into rendered canvas changes, aiming to improve reliability and reduce generation cost. It supports presets such as linked-brush, a programming API for bespoke interactions, and connections between charts, tables, text, and application state.

Oct 22Thu
  1. NVIDIA Blog60

    NVIDIA Unveils Minerva to Bring End-to-End Observability for AI Factories

    NVIDIA launched Minerva, an AIOps platform that combines telemetry from infrastructure and AI workloads and applies purpose-built AI models to surface timely, actionable insight. It builds a dynamic knowledge graph mapping GPUs, SuperNICs, switches, cables and the jobs running on them, analyzes billions of data points per hour to detect anomalies and predict failures, and includes a native AI assistant plus connections to external agentic AI tools. IREN is among the partners using Minerva, and Aolani, Bitdeer AI, IBM, Jio, Lambda, Quake AI and Tesla plan to deploy it, with Dell Technologies, Lenovo and Supermicro offering it in their AI factory operations portfolios.

    Why it matters: It details a new NVIDIA observability platform and its named deployment partners, useful for readers tracking AI data center operations tooling.

  2. NVIDIA Blog60

    NVIDIA Cosmos Opens New Tools to Train, Evaluate and Deploy World Models

    NVIDIA expanded its Cosmos platform with new training tools, simulation checkpoints and Cosmos Data Factory blueprints, including five cloud-native blueprints for tasks such as autonomous vehicle dataset generation and defect image generation. Cosmos Framework now extends training, optimization and deployment tools as agent skills to open models like GR00T, Alpamayo, QWEN and Pi0.5, while new Cosmos 3 Nano simulation variants and updated NIM microservices offer up to 2x faster inference. The post cites Porsche, ANYbotics, Ultralytics and ADNOC as users of the platform.

    Why it matters: The post details concrete tooling, model support and named customer deployments, useful for readers tracking physical AI and world-model infrastructure.

  3. NVIDIA Blog25

    NVIDIA Transforms European Retail, From Digital Discovery to Physical Operations

    European retailers are using NVIDIA-accelerated open models and physical AI across commerce and fulfillment, including Shopify post-training NVIDIA Nemotron 3.5 Lightning for its Sidekick GraphQL subagent, which it says surpasses a leading frontier model in accuracy on an internal benchmark while cutting serving costs by 90% and delivering responses 7x faster. Otto Group extended its autonomous warehouse initiative, built with Reply on NVIDIA Omniverse and running on Google Cloud, to its largest fulfillment center in Haldensleben, Germany, and Nebius is showcasing three early-access NVIDIA retail blueprints at GTC Berlin. Other cited deployments include Zooplus with Infosys, Photoroom with Vestiaire Collective, L'Oréal with Omi and NVIDIA ALCHEMI, Mirakl, ALLSIDES, and CATCHES with AMIRI.

  4. Microsoft Research78

    Solving a 31-year-old grand challenge in mathematics – the KLS conjecture

    Microsoft researcher Xinzhi Zhang and a collaborator proved the Kannan–Lovász–Simonovits (KLS) conjecture, proposed in 1995, showing that for log-concave distributions the best hyperplane cut is within a universal constant of the best cut of any shape regardless of dimension. The proof emerged from a human-directed agentic process exploring more than 100 candidate approaches, and was followed by independent parallel proofs and a Lean 4 formalization. The authors say unguided attempts with leading AI models using a similar token budget fell short, while the success depended on both AI capabilities and researchers' mathematical judgment.

    Why it matters: It documents a major open problem's resolution and offers a concrete account of how human-guided AI search contributed to a mathematical proof.

Oct 21Wed
  1. NVIDIA Blog60

    Bringing the Spark to Europe: Local Intelligence Powers Next Wave of Agents

    At GTC Berlin, NVIDIA showcased its local AI hardware lineup: RTX Spark for personal agents on Windows PCs, DGX Spark for Linux developers, and DGX Station for enterprise workloads, with a Windows version due in December. Announcements included Underdog Desktop Agent for RTX Spark, DGX Spark 64GB systems rolling out Oct. 23, support for clusters of up to eight DGX Sparks, NVIDIA Sync Model Launcher, and the new Recursive Flint local coding model. NVIDIA also added day-zero support for new Nemotron, GLM, DeepSeek, Qwen, MiniMax and FLUX.3 models, plus accelerations for llama.cpp and vLLM.

    Why it matters: It offers a detailed roundup of NVIDIA's local AI hardware and software announcements for developers and enterprises.

  2. NVIDIA Blog58

    NVIDIA Cloud Partners Scale Across Europe — Sovereign, Open and Built for Safe AI Agents

    NVIDIA Cloud Partners across Europe, the Nordics, Africa and Central Asia are expanding regional compute capacity for model training and inference, with Nscale reporting that NVIDIA DSX MaxLPS enabled 37% more GPUs and nearly 50% higher aggregate inference throughput within the same power budget on GB300 NVL72 systems. Partners including Nebius, Baseten, Fireworks, Mistral and Verda are expanding open-model training and inference services, while the NVIDIA OpenShell Reference Architecture, supported by partners such as Aleria, Baseten, Cassava, CoreWeave, Crusoe and Nebius, is designed to give organizations full-stack control over what AI agents can access and do.

  3. NVIDIA Blog50

    Europe’s Industrial Leaders Reinvent Engineering and Manufacturing With NVIDIA

    NVIDIA announced that European industrial companies are using its CUDA-X and Omniverse libraries and Cosmos and Nemotron open models across engineering and manufacturing, and that the Factory Operations Blueprint (FOX) is now in early access on GitHub for building factory manager and specialist agents. Siemens is integrating FOX-built agents into Intelligence Center X for an estimated 20% cost savings and used Cosmos Data Factory tools at its Erlangen electronics factory to cut inspection calibration time by 40%. The announcement also cites Mercedes-Benz, Audi, Volkswagen Sachsen, Schaeffler, imec, Infineon, Leonardo and others applying NVIDIA simulation and digital twin technologies.

  4. NVIDIA Blog62

    NVIDIA Agent Toolkit Expands, Grounding Enterprise Agents in Company Data

    NVIDIA has expanded its open source Agent Toolkit, adding the Agent Knowledge Foundation with CUDA-X libraries and an Auto-Ontology library, Nemotron Retriever and Kumo open models, the OpenShell secure agent runtime, NemoClaw blueprints and tools such as NeMo Relay and NeMo Compass. The company says enterprises can use Agent Knowledge Foundation without moving data, rebuilding workflows or changing governance controls, and cites adoption by Palantir, Databricks, Snowflake, SAP, ServiceNow, Zoom, AWS and others.

    Why it matters: Useful for readers tracking how vendors are packaging data grounding, runtime security and governance for enterprise agents.

  5. NVIDIA Blog62

    GTC Berlin 2026: Live Updates From NVIDIA CEO Jensen Huang’s Keynote

    At GTC Berlin, Jensen Huang said European companies including L'Oréal, Revolut, Spotify, Merck, Vodafone, Zara, Porsche, Nestlé and HSBC are working with NVIDIA on AI, following a weeklong European road trip in which the company committed nearly $10 billion to the region's technology ecosystem and worked with 12,000+ partners. He announced the NVIDIA DSX POD reference design for Vera Rubin AI factories in modular containers, claiming onsite deployment can shrink from six months to 14 days, along with the OpenShell Reference Architecture for safe AI agents, the Agent Knowledge Foundation, and Agile ONE SN, a 31-degree-of-freedom reference humanoid built on Isaac GR00T expected next year. He also described RTX Spark laptops with 1 petaflop of performance and 128GB of memory, and said Volkswagen, JLR, Volvo and Mercedes-Benz next-generation vehicles will be Hyperion-compatible.

    Why it matters: The piece is a detailed first-hand account of NVIDIA's European AI strategy and product announcements, useful for readers tracking agent infrastructure, AI factories and physical AI.

  6. GitHub Blog · AI & ML22

    How 3 Java Champions are using GitHub Copilot for increased productivity

    GitHub interviews three Java Champions—Vlad Mihalcea, Patrick Baumgartner, and Frank Delporte—about using GitHub Copilot in production and open source work, including a Hypersistence Optimizer app built in two days, a 900-slide Reveal.js to Marp migration, and diagnosing a Pi4J bug where virtual threads pinned in native poll() calls left a fifth button unresponsive. They emphasize reviewing generated code, defining verification gates, and keeping humans accountable for what ships.

Oct 20Tue
  1. Mistral AI80

    Voxtral 3

    Mistral introduces Voxtral 3, a family of speech-to-text and text-to-speech models plus a LiveKit-based voice agent SDK that combines listening, reasoning, tool calling and speaking in one real-time streaming pipeline. It includes Voxtral Small Transcribe 3 for multilingual transcription with diarization and context biasing, Voxtral Mini TTS 3 for expressive speech in 35+ languages and 50+ dialects, and Pro Voice adaptation from 30 minutes of audio. It is available via Mistral's EU-hosted API or self-deployment, with pricing from $0.0035/min for batch transcription to $13 per 1M characters for TTS.

    Why it matters: The announcement details concrete model capabilities, benchmarks and pricing for teams evaluating real-time voice agents and sovereign speech deployment.

  2. GitHub Blog · AI & ML34

    How to continue a GitHub Copilot app session in VS Code

    This GitHub tutorial explains how to open a GitHub Copilot app session in VS Code so its conversation, diff, and worktree carry over, letting you review changes line by line, edit by hand, run tests locally, and push updates to the session's pull request. It lists prerequisites and walks through each step, including prompting Copilot further from the sidebar and committing with normal Git commands.

Oct 17Sat
  1. Google Research71

    Grounding AI agents in biological reality: Building blocks for autonomous wet labs

    Google researchers describe three projects aimed at connecting AI agents to real wet-lab experimentation: the VERID framework, a semantic digital twin of a lab workcell that tracks 3D state, runs fast virtual dry runs, blocks unsafe commands and logs synchronized video and actions; closed-loop aptamer design over three design-test cycles that raised on-target hit rates to 33.5% for myoglobin and 42.1% for α-thrombin; and agent-guided epitope excision for de novo binder design against FAP, which accelerated each folding cycle more than eightfold and, in a human–agent comparison at equal compute, nearly doubled the in-silico hit rate of a scientist working alone.

    Why it matters: It offers a concrete, example-rich account of how agents are being tied to physical lab hardware and closed experimental loops, including failure modes and safety safeguards.

Oct 16Fri
  1. Google DeepMind58

    Accelerating the search for antimicrobial peptides with Google Antigravity

    Google describes how its agentic development platform, Google Antigravity, lets biologists discover, structurally evaluate and prioritize antimicrobial peptide candidates in a single session, using ProtNLM to scan millions of uncharacterized UniProt proteins. A technical walkthrough shows a researcher and agent identifying a novel antimicrobial peptide candidate with predicted activity against respiratory tract infection pathogens, now undergoing wet-lab validation. The post also introduces the Biomolecular Structure Viewer, a 3D visualization tool embedded in Antigravity that shares visual state between researcher and agent.

Oct 15Thu
  1. Google Research75

    Analyzing technical documents with Deep Review

    Google announced the public release of Deep Review, an agentic AI system that provides feedback and technical verification on complex documents, initially for computer science papers and now extended to legal, finance, and other domains. It evolved from the Paper Assistant Tool, which was piloted with STOC 2026, ICML 2026, NeurIPS 2026, and ICLR 2027, offering opt-in pre-submission feedback to authors of more than 16,000 manuscripts. Every user gets two complimentary reviews per week, with higher limits via a Gemini API key, and uploaded documents are not used to train Google models.

    Why it matters: It documents a concrete agentic review pipeline and pilot results, useful for readers weighing AI-assisted verification of technical documents.

  2. Google Research65

    Putting Earth AI to work: Geospatial agents for our planet's biggest challenges

    Google shares a technical report on its experimental Geospatial Reasoning agent, which connects Gemini to Earth AI foundation models and planet-scale data, and reports evaluations across four benchmarks, including two newly released ones (GeoKTools and GeoExpertQueries). The post describes early partner testing with organizations including WHO AFRO, People in Need, GiveDirectly, the Milken Institute and WPP, and announces expanded early access via Ask Google Earth and an experimental agent-to-agent API.

    Why it matters: It offers a concrete look at how a planning-based geospatial agent is evaluated and applied to real public health, disaster and infrastructure workflows.

  3. Google Research58

    Research@ 2026: 10 ways we're amplifying human ingenuity

    At its Research@ event in Mountain View, Google Research outlined 10 updates, including expanded early access to Google Earth AI's experimental geospatial capabilities, a multi-agent orchestrator for reproducing prior methods, and public access to its Deep Review verification system. Other highlights include the Synaptics Astra SR200 Series chip with Google's co-designed Coral NPU, upcoming integration of TimesFM and TabFM into Gemini for business, and a Lancet study in which 98 patients consulted AMIE ahead of urgent care visits. Google.org is also providing $10 million in funding for crisis resilience and public health work.

Oct 14Wed
  1. Google DeepMind82

    Developing Gemini 4 Argon responsibly and safely

    Google describes the safety and security approach for Gemini 4 Argon, covering misuse prevention in cyber and biological domains, real-time misalignment monitoring, and prompt injection resistance. It says Argon solved all 12 simulated cyberattack scenarios (up from five previously), reduced adversarial violations nearly twofold, and cut attack success on Gray Swan's Indirect Prompt Injection benchmark to 0.7% across 15 adaptive attempts. The post also notes that penalizing shortcuts in the chain of thought during training dropped openly discussed shortcuts by 61% while silently taken shortcuts more than tripled, from 11.2% to 36.4%.

    Why it matters: Offers a detailed, first-hand account of how frontier-model safeguards are tested and where they still fall short, useful for readers tracking AI safety practice.

  2. Google DeepMind92

    Gemini 4 Argon: Built to solve your hardest problems

    Google announced Gemini 4 Argon, a frontier model now available globally via the Gemini API in AI Studio and the Gemini platform, Google Antigravity, the Gemini app, and AI Mode in Search, priced at $2 per million input tokens and $10 per million output tokens. Google reports Argon ranked #1 on the Vals Index and cites internal and partner uses including a Chrome architectural refactor, a 2.85x inference speedup on Gemma 4 E2B, quantum computing and fusion research, rare disease analysis, enzyme design, and a robotics video-to-sim-to-real pipeline. Google also described safety work including jailbreak defenses, agent misalignment monitoring, and a 0.7% attack success rate on the Gray Swan indirect prompt injection benchmark.

    Why it matters: The post is a detailed launch announcement whose benchmark claims, pricing, and safety figures give readers a concrete picture of a major frontier model release.

Oct 13Tue
  1. GitHub Blog · AI & ML38

    GitHub Copilot app for Beginners: How to teach Copilot about your codebase

    GitHub's beginner guide explains four ways to give Copilot project context: repository custom instructions in .github/copilot-instructions.md plus path-specific files in .github/instructions, agent skills in .github/skills for repeatable procedures, custom agents in .github/agents for broader specialized tasks, and MCP servers that connect Copilot to external tools and data sources such as Playwright. It suggests starting small by adding one convention you keep explaining to .github/copilot-instructions.md, and points to Awesome Copilot for community-shared examples.

  2. Microsoft Research52

    Simulating student learning for better AI tutoring

    Microsoft Research introduces StudentSim, a research system that trains personalized AI student simulators from publicly available, de-identified learner records to evaluate and improve AI tutors before testing with real learners. The work also presents StudentSimEval, a benchmark spanning chess, second-language English writing, and mathematics that measures behavioral fidelity and guidance responsiveness, with StudentSim outperforming baselines such as GPT-5.4 and Maia2. In a chess tutor reinforcement learning study, a tutor trained with StudentSim feedback reached 90.5% accuracy versus 75.7% for the no-RL baseline and 71.6% for the GPT-5.4 reward condition, as rated by blind human chess experts.

Oct 8Thu
  1. Google Research71

    Does better work always mean better workers?

    A three-month field experiment with 133 patent lawyers found that access to a Google Labs AI patent-writing assistant raised drafting scores by 0.34 SD at 10 days and 0.38 SD at 90 days, with gains concentrated in fewer poor scores rather than more excellent ones. On a later redlining task where AI was not allowed, the benefit appeared only for senior lawyers (0.45 SD), while juniors showed no discernible improvement and their scores bifurcated toward more very low and more good results. The authors argue that improving output and building durable professional judgment are different goals that may be in tension for junior professionals.

    Why it matters: Offers a rare blinded, months-long field test that separates AI-assisted output gains from unassisted skill development, with direct implications for how firms train junior professionals.

  2. Microsoft Research71

    Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

    Microsoft Research Asia introduces Harnessed Agentic RL, a paradigm in which the same agent harness used in deployment participates directly in reinforcement learning, and open-sources Agent Lightning v1.0, a roughly 3,500-line framework with an API Gateway, Rollout Controller, and Customized Trainer built on verl. Agents run as standard Kubernetes jobs without commercial sandbox services, and Collocated Async RL shares GPUs between rollout and updates, achieving about a 2x end-to-end speedup over synchronous RL. An end-to-end coding agent pipeline raised Qwen3.5-9B from 41.8% to 56.4% Pass@1 on SWE-bench Verified using about 6,000 training samples.

    Why it matters: It offers a concrete, reproducible recipe for training deployed agents in place, with code size, infrastructure, and benchmark numbers stated explicitly.

Oct 7Wed
  1. Microsoft Research23

    What AI gets wrong and what failure teaches us

    In a Microsoft Research Podcast conversation with Chad Atalla, Microsoft partner research manager Jennifer Neville discusses how her team uses practical evaluations—multiturn conversations, collaborative environments and long-horizon agentic tasks—to find where current AI systems break down beyond traditional benchmarks. She describes research showing that model performance degrades significantly over multiturn interactions and that errors can accumulate in long workflows, and offers practical guidance such as restarting a chat with a fully specified single turn and checking answers rather than fully delegating work.

Oct 6Tue
  1. Mistral AI83

    Introducing Mistral Large 4

    Mistral launched a public preview of Mistral Large 4, a 1-trillion-parameter natively multimodal model with 52 billion active parameters, trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral's own European datacenters, with weights to be released by the end of the month. Mistral reports competitive performance across coding, agentic workflows, multimodal understanding, cybersecurity, science and knowledge work, claiming it outperforms any open-weight model developed in the US or Europe. The preview API is available on Mistral Studio, and the model will serve as the foundation for a new generation of specialized Mistral models.

    Why it matters: Useful for readers tracking open-weight frontier models, European AI sovereignty, and Mistral's claimed benchmarks and release timeline.

  2. Google Research34

    Open and Emergent Problems in Agentic Privacy and Security: A Contextual Angle

    Google shares a new workshop report, produced with more than 50 academic and industry leaders at the Contextual Agent Privacy and Security (CAPS) Workshop held in late 2025 in New York City, on foundational privacy and security challenges facing autonomous agents. The report grounds agent privacy and security in the theory of Contextual Integrity, extending it to contextual security, and advocates a contextual policy engine within a supervisor layer alongside system-level sandboxing, model-level reasoning, user-centric controls, multi-agent guardrails, and ecosystem governance. It also calls for standardized multi-agent benchmarks and "Agent Gym" sandboxes, and invites the broader research community to collaborate.

Oct 5Mon
  1. GitHub Blog · AI & ML61

    ReviewBench: An open benchmark for AI code review

    GitHub introduces ReviewBench, an open benchmark for AI code review built from analysis of 103.9 million GitHub pull requests, containing 219 public pull requests across 19 languages with a multi-source golden set labeled for severity and category. It reports grounded and augmented precision, recall, and F1 metrics, uses Claude Sonnet 5 as the LLM grader under a published rubric, and had its ground-truth findings independently re-labeled by senior engineers with 96.6% agreement. GitHub says ReviewBench's offline predictions for a Copilot code review multi-model ensemble matched a later A/B test, which showed an 8.0% rise in addressed rate, 13.6% in recall, 61% in comment volume, and an 8.0% drop in cost per review.

    Why it matters: It offers a concrete, reproducible methodology and public dataset for comparing AI code reviewers, plus evidence on how offline scores tracked a production experiment.

Oct 2Fri
  1. GitHub Blog · AI & ML22

    AI is changing developer work. Here are three skills to strengthen.

    GitHub outlines three skills developers should strengthen as AI reshapes their work: learning to direct AI agents rather than just use them, not trusting AI's first answer and instead having a second model critique it before review, and using time saved on implementation to tackle broader problems like customer needs and architectural tradeoffs. The post illustrates these with examples such as coordinating multiple agents on an authentication flow and a second model critiquing a SQL query. It notes GitHub Copilot's built-in Rubber Duck agent uses a second model to critique plans, code, and tests.

Sep 26Sat
  1. GitHub Blog · AI & ML55

    GitHub Copilot app for Beginners: How to build custom workflows with canvases

    GitHub explains canvases in the GitHub Copilot app: customizable, bidirectional interfaces such as kanban boards, checklists, or dashboards that you create by entering the /create-canvas skill in an agent session and describing the workflow in plain English. The agent builds the interface in the right-side panel, and both you and the agent can update its shared state immediately without a separate sync step. Canvases are saved as extensions for reuse or team sharing, and ready-made ones are available through Awesome Copilot.

Sep 16Wed
  1. Mistral AI58

    Mistral and Mozilla are bringing open, private and multilingual AI to your web browser

    Mistral announced a partnership with Mozilla under which its models now power Firefox Smart Window (beta), Mozilla's AI browsing assistant, for users in France and North America, with the UK and Germany expected later this year. Mistral says it fine-tunes models on regional languages and dialects, and that Smart Window conversations are not saved on Mozilla's servers by default, with partners agreeing to zero data retention. Mozilla CEO Anthony Enzor-DeMeo and Mistral CEO Arthur Mensch framed the deal as two open source advocates working together on privacy, control and choice in AI-powered browsing.

Sep 9Wed
  1. Mistral AI58

    Modernizing complex legacy code with AI agents.

    Mistral helped a European energy operator migrate 40,000 lines of Fortran 77 to C++ for a physics-intensive reservoir simulator that had no test suite or centralized documentation. The team built a numerical parity harness before migrating, used agents to document the codebase via a caller-callee tree, and settled on a human-supervised workflow of coder, tester and reviewer agents migrating module by module. The first sprint covered 40,000 of 300,000 lines, and the authors note that migrations depending on external systems or undocumented physics would face additional challenges.

Jul 26Sun
  1. Berkeley AI Research38

    Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction

    The post introduces ABBEL, a framework that replaces full interaction history with periodically updated natural-language "belief states" and supervises their contents through belief grading, including an autoencoding-inspired reconstruction objective. On CollabBench collaborative coding, reconstruction-based belief grading roughly halved the gap to full-context models and trained in 50 steps instead of 100, while using fewer peak tokens; on Combination Lock and multi-objective QA, domain-knowledge grading and a peak belief length penalty further improved efficiency. The authors argue that isolating and supervising summaries addresses the performance cost of recursive summarization in long-horizon tasks.

Jul 7Tue
  1. Berkeley AI Research38

    Intelligence is Free, Now What? Data Systems for, of, and by Agents

    A Berkeley EPIC Data Lab perspective argues that as inference costs fall toward near-zero, data systems face three challenges: redesigning for agent workloads (e.g., exploiting the 80-90% duplicate sub-queries in agentic speculation), building a substrate for agent swarms (structured memory, concurrent shared-state edits, coordination), and letting agents synthesize custom data systems that can be trusted. It surveys work including Bespoke OLAP, GenDB, and custom key-value store synthesis, and sketches a future where agents and data systems co-evolve and blur together.