Snyk moved its internal support agent, Snyk Assist, into the core Snyk product in September 2026, giving every paying customer access. Built on LangChain and LangGraph with observability in LangSmith, the agent answers questions in plain language and can open support cases or log feature requests. It runs as a single agent behind Slack, web and API surfaces, with tools attached per user permissions.
Tessl's blog post argues that teams should record agent mistakes as attributed, signed diary entries, then curate them into reusable context packs rather than adding unverified rules to files like AGENTS.md. The author describes a REST API case where an agent regenerated the OpenAPI spec and TypeScript client but missed the Go client, and the same lesson had to be re-taught in a fresh session.
Tessl argues that an AI-native organization collapses the handoff between people who own outcomes and the work itself, so product managers and designers can execute changes through agents. It says PR count and token spend are insufficient measures, and that merge rate better shows whether the new workflow is working. The article also says the boundary should follow decision authority, with engineers still owning architecture and data models.
The author argues that system use cases, with actors, preconditions, scenarios, and acceptance criteria, work better than user stories as the input for AI code generation in enterprise business applications. He describes a process that skips the plan-and-task phase, reverse-engineers legacy systems into use cases and entity models for modernization, and recommends self-contained system verticals and risk-based review.
AI DevCon New York, running November 2–4 at Industry City in Brooklyn, centers its program on software factories, the systems needed to make agentic development repeatable, trustworthy and scalable. The article argues that moving from one developer using an agent to an engineering organization requires layers covering context and skills, harnesses and tools, orchestration, verification and evaluation, and feedback.
The article explains how a software factory combines agents, tools, and engineering practices to take work from intent to verified output. It argues that teams should enforce domain rules, workflow gates, and tool permissions independently, version shared guidance and checks, and review lessons before reuse.
OpenAI's DevDay 2026 session demonstrates Codex shifting from a single-user tool to a team-oriented agent. The session shows a persistent personal agent investigating a 2am outage, from the first Slack message through a reviewed fix, using voice, Appshots, plugins, and meeting notes to keep the team informed.
OpenAI's DevDay 2026 video presents what's new in Codex across the CLI, app, and web. Live demos show moving from delegating individual tasks to having Codex tackle entire problems and goals. The source is a short description only, so no specific features or figures are confirmed.
OpenAI's DevDay 2026 session shows how its teams use ChatGPT to manage launches, research competitors, and turn expertise into tools. The source provides no further details on specific features, metrics, or availability.
A DevDay 2026 session from Higgsfield explores how its agents choose models and provision their own compute. It covers when to use dedicated GPUs versus hosted models, how to deliver predictable throughput, and where the approach can fail.
Stack Overflow's small teams used Codex to build Stack Overflow for Agents and reimagine Stack Internal, according to a DevDay 2026 session. The talk covers lessons for accelerating R&D and bringing new products to market.
OpenAI's DevDay 2026 session shows developers how to cut AI costs while preserving quality. It covers measuring cost per completed task, choosing models and reasoning effort, and using prompt caching, Programmatic Tool Calling, and the Batch API.
OpenAI's YouTube video shows developers how to build plugins for ChatGPT that run a custom interface alongside a conversation. The plugins can bring selected context into the user's work with ChatGPT, according to the source's description of plugin extensions through Meetings and OpenAI Developers.
Cornerstone OnDemand built Orion AI, a multi-agent system using Amazon Bedrock and the open source Strands Agents framework, that cut database diagnosis from 45 minutes to 10, a 78% reduction. The system also reduced manual lifecycle steps from more than 10 to a single interaction and filtered redundant alerts by a median of 65%. A three-person team delivered it in six months.
Xiaomi released the MiMo-V2.5-TTS Series, three speech synthesis models for stock voices, voice design, and voice cloning. The models accept natural-language style instructions and inline audio tags, and the source says the three models are free of charge for a limited time on the Xiaomi MiMo API platform. Xiaomi also open-sourced integration Skills for agent applications on GitHub.
Why it matters: The release shows how a TTS family adds style instructions, inline audio tags, and voice design or cloning to speech synthesis, which matters for agent and creative workflows.
Xiaomi MiMo has released MiMo-V2.5-ASR, an open-source speech recognition model that the company says achieves state-of-the-art results across multiple benchmarks. The model supports bilingual Chinese–English recognition, Chinese dialects such as Wu, Cantonese, Hokkien, and Sichuanese, code-switching, and lyrics transcription. It is also designed to handle noisy environments and multi-speaker conversations.
Xiaomi MiMo-V2.6-Pro is applied to new materials R&D across literature review, molecular design, and automated dry-lab experiments for PFAS capture. The source offers no benchmark scores, speed, pricing, or availability details.
Xiaomi introduces the MiMo-V2.6 series of models, as stated on its official MiMo page. The source excerpt gives only the tagline, describing frontier intelligence across all modalities, so specific capabilities, benchmarks, and availability cannot be confirmed from it.
Databricks has released a public Beta of a Workday Data Connect federation connector for Unity Catalog, letting teams query Workday HR and finance data in place without copying it. Workday administrators share approved tables through Workday Data Cloud, and Databricks administrators create an OAuth connection and foreign catalog to govern access. The data is read-only, and Workday remains the system of record.
Databricks has released Funke, a Python and PySpark library and deployable pipeline that parses HL7v2 healthcare messages into native Spark types while preserving the full message hierarchy. It succeeds Smolder, the Scala data source Databricks open-sourced in 2021, and ingests through Auto Loader into Unity Catalog bronze and silver tables. Users can query segments, fields, components, and subcomponents directly with DataFrame or Spark SQL expressions.
Databricks introduces database branching in Lakebase Postgres, letting each coding agent work in its own isolated database branch created in under a second regardless of size. Branches use copy-on-write storage, consuming extra space only as they diverge, and scale to zero when idle so unused branches incur no compute cost. Schema changes are tracked in code and promoted to the parent branch through migrations rather than merged back, and ephemeral branches are created per pull request for testing.
OpenAI's YouTube livestream presents three demos showing how Codex supports production monitoring. The demos cover tracing a checkout failure in Grafana, investigating a Kubernetes rollout causing out-of-memory restarts, and linking a Codex Security finding about a missing resource limit to service availability.
MIT Statistics and Data Science Center director Sasha Rakhlin argues that AI progress is fastest where results can be verified quickly, citing a model reaching gold-medal level at the International Mathematical Olympiad a year before models produced new research results. He says departments should reconsider how they reward work, emphasizing question-asking, replication, and disclosure of AI's role in a researcher's contributions. He also urges universities to build shared lab infrastructure that captures failed experiments and tacit expertise.
Midjourney is testing a "Thinking Mode" on its Alpha website, where users can click "Rerun (Thinking)" in a job's lightbox to regenerate an image. The company says early tests show gains in prompt accuracy, typography, and coherence, and it is asking users to share feedback in its #ideas-and-features channel. It may later offer the mode broadly or as an option to add more thinking after a job.
Midjourney's alpha site now lets users share folders with others as Collaborators or Viewers, with sharing by link also available. A new thinking mode lets users rerun jobs made with 8.2 standard and edit models to fix missed prompt details such as objects, layout, anatomy, and text. Sharing does not change image privacy, so non-stealth images can still appear on Explore and profiles.
NVIDIA developers are pairing frontier AI models, including GPT-6 Astra and Claude Fable 5, with Omniverse libraries to turn simulation ideas into working applications. Examples include a humanoid warehouse simulator, an autonomous-driving testing workflow, and sensor-matching digital twins. The projects are guided through natural-language instructions and reviewed by developers.
Developers are pairing frontier AI models with NVIDIA Omniverse libraries to turn simulation ideas into working applications, from humanoid warehouse simulators to autonomous-driving test environments. In the examples, developers direct AI agents through natural-language instructions and review results, while Omniverse provides GPU-accelerated physics, rendering and sensor simulation. One experiment reported a simulated Unitree G1 humanoid clearing a hurdle in 64 of 100 trials.
Hospitals, academic centers, medtech firms, and pharma companies all face the same obstacle: imaging data is locked in clinical systems and hard to share. The EXAM study across 20 institutions showed federated learning, which shares model weights rather than patient data, improved AUC by 16% on average. Collaboration remains difficult due to scanner and protocol heterogeneity, privacy governance, and the lack of a common data substrate.
NVIDIA's Omniverse libraries, guided by SimReady Foundation specifications and agentic NVIDIA skills, provide a structured workflow for converting CAD assets to OpenUSD for robotics simulation. The workflow covers configuring and validating materials, collision geometry, joints, and other physics properties before testing robot behavior.
Claude Code v2.1.295 adds onFailure: "block" for command and HTTP hooks, so a hook that cannot start, times out, or exits unexpectedly blocks the action. The release also adds an optional models list for Claude apps gateway upstreams, plus upstream_request_id in the inference audit event, and fixes a range of MCP, plugin, and terminal issues.
Epoch AI estimates the AI chips shipped through 2027 could support about 30 to 170 million concurrent frontier-model agents, or nearly 2 billion with cheaper models. Its researchers find the cost of a fixed level of AI performance has fallen about 47% per quarter over the past three years. The newsletter also reports China's semiconductor supply-chain exposure is 2.7 times that of the US.
A ComfyUI developer generated 15-second 448×256 video in 15 seconds or less on one RTX 5090 using MiniMax H3 with FastVideo's FastH3 V2 checkpoint in four sampling steps. The setup combined sparse attention, a smaller ClipProj text encoder, a pruned INT8 checkpoint, and a fused FP4 MLP, cutting VRAM needs from 80GB to under 30GB. The custom ComfyUI node is open source.
OpenAI's Codex 0.162.0 release adds tools for creating and listing managed Git worktrees from trusted local projects when the worktrees feature is enabled. The update also lets users pin tasks in the agent Command Center, copy transcript blocks with /copy, and make URLs clickable in approval headers, questions, and warnings, along with several Linux and Windows sandbox fixes.
Leapfrog, a small team doing high-volume AI visual and production work for fashion and brand clients, is building a "one brain" system that makes company knowledge and client context searchable through natural-language agents. The starter stack described is OpenClaw in a sandbox, a GitHub repository, Obsidian on the local machine, and Telegram as the access point. The system's research structure had roughly 1,200 files at the time of the talk.
Tessl's talk at AI Native DevCon London argues that agent skills, which can be markdown files with instructions and bundled material, act as supply chain components that can shape agent behavior. The author says reading SKILL.md once is insufficient because risks can sit in supporting files, updates, and workspace trust settings. He identifies the danger as the combination of private context, untrusted content, and external communication, and cites research scanning roughly 4,000 public skills for issues including malware-like behavior.
Mozilla.ai's cq project proposes a shared knowledge layer where AI agents capture lessons from non-obvious fixes as structured knowledge units that other agents can later query. The default setup is local-first, using a local SQLite database so nothing leaves the machine, with an option to connect to a remote team server that adds review.
John Groetzinger, writing in a personal capacity rather than for Cisco, argues that enterprise skills need packaging, evaluation, syncing, and distribution rather than scattered markdown files. He describes using skills to make cheaper models viable, converting curated TAC knowledge-base articles into maintained skills, and rolling out an eval framework across teams. He also describes syncing a repository README to Confluence with a deterministic script.
Amazon Bedrock AgentCore payments lets AI agents pay for model inference one request at a time, using x402 with USDC on the Base network. Incarna used the service to connect its agents to BlockRun, a pay-as-you-go router serving more than 90 models from more than 15 providers. Spending limits are enforced at the infrastructure layer, outside the model.
The NVIDIA KGMON team placed second in the KDD Cup 2026 Data Agents competition with a system built around a smaller, clearer, and easier-to-verify agent harness. The competition required agents to answer natural-language questions over heterogeneous sources, including databases, CSV and JSON files, prose documents, PDFs, and briefing videos.
Tessl argues AI code review is slow because generated code outpaces trust, and proposes executable specs that let agents check preview environments against product intent. Its spec reviewer splits work between a planner agent that extracts requirements and parallel verifier agents that test each one against the code and base branch.