Skip to content
Only First-party#Agent
October 19Monday
  1. MiniMax Blog83

    MiniMax releases M3.1 Flash, claiming 70% lower cost and nearly 3x faster inference

    MiniMax officially rolled out MiniMax M3.1 Flash, a next-generation Flash model that it says cuts usage cost by 70% versus its predecessor and makes online inference nearly three times faster. The company reports 73.7% on DeepSWE v1.1, 76.2 on BankerToolBench, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam and 82% on CyberGym, with output speeds of 200+ TPS and a median of 170+ TPS. Pricing is $0.15 per 1M input tokens, $0.60 per 1M output tokens and $0.015 per 1M cached input tokens, and the model is available through the MiniMax API, M Plan and MiniMax Code, with weights to be released later.

    Why it matters: MiniMax's Flash model claims top-tier agentic and coding scores at $0.15 per 1M input tokens, offering a concrete data point on the cost side of agent workloads.

  2. Modal Blog38

    How Legora runs 2,000 parallel agent evals on Modal Sandboxes

    Legora now runs each of its legal agent evals inside a separate Modal Sandbox, booting its full application pinned to a specific commit so every run reflects the exact product version. A typical experiment is 100 cases, 4 harness variants, and 5 trials each — 2,000 runs in parallel — with Modal Volumes snapshotting document, database, and permission state. Legora also moved from gVisor to Modal VM Sandboxes, removing its Docker networking and FUSE workarounds, and cut agent cost by 30% via smarter compaction and custom tool search.

October 17Saturday
  1. Google Research71

    Google Research outlines VERID and closed-loop agent workflows for autonomous wet labs

    Google Research describes three projects aimed at grounding AI agents in real wet-lab experimentation, including the VERID framework, a semantic digital twin of a lab workcell built on the open-source PyLabRobot framework that tracks deck layout, well volumes and tip inventory and dry-runs protocols before execution.

    Why it matters: Google Research outlines three projects that connect AI agents to real wet-lab hardware, showing how simulated dry runs and closed-loop experiments let agents correct their own design rules.

October 16Friday
  1. Modal Blog36

    Modal explains how it built a fast filesystem stack for VM Sandboxes

    Modal detailed how it evolved its filesystem stack to back VM Sandboxes, routing guest requests through virtio-fs and virtiofsd onto its ImageFS lazy-loading filesystem. The first version booted fast but ran slow: recursive chown went from four seconds to over a minute, and pnpm and bun installs were about three times slower. Microbenchmarks showed chown at 9.87 s over virtio-fs versus 2.31 s on gVisor and 1.58 s on ImageFS.

  2. Replit Blog40

    Replit Brings Your Own Cloud (BYOC) to Its Platform

    Replit is introducing Bring Your Own Cloud (BYOC) options that let enterprises run the Replit platform and published apps inside their own Microsoft Azure, Google Cloud, or Amazon Web Services tenant. Full BYOC, in private preview, deploys Replit's cells and agent harness in the customer's cloud, while BYOC for Published Apps, in early access, hosts published projects there with Replit managing the platform. Image Exports instead give teams a Docker image, Terraform file, and SBOM to self-host Replit-built apps.

  3. OpenClaw Blog22

    How PJ Helps Small Businesses Adopt and Own OpenClaw

    PJ, an engineer-turned-pest-control owner in Ocala, Florida, now consults for small businesses—roofing, pool and automotive contractors among them—setting up OpenClaw and teaching them to run it themselves. His clients want a tool they own outright rather than feeding company data into rented software, and one client, 66-year-old admin Donna, uses her OpenClaw daily.

  4. LiveKit Blog38

    How to Build a Production-Ready AI Voice Agent

    LiveKit's guide distills lessons from solution architects and forward deployed engineers on taking AI voice agents from demo to production, organized around three questions: whether the agent is smart and natural enough, whether its performance is observable, and whether it scales reliably and cost-efficiently. It recommends measuring the first turn separately, since one company found roughly 3 out of 4 calls ended at the greeting step, and notes clients moving from GPT-4.1 to Gemma 4 31B on LiveKit Inference saved 300–500ms at p50.

October 15Thursday
  1. Anthropic · Claude.dev62

    How the Claude Code team does code review

    The Claude Code team describes its layered code review flow, in which most reading is done by machines and human attention is reserved for risk-based approval. From June to September, merged pull requests to the Claude Code codebase more than doubled while the share that had to be reverted fell by more than 40 percent. The process stacks verification before the PR, required CI, Claude review, security review, risk-tiered human approval and a merge queue with flags and fast rollback, and the team turned past incidents into a daily eval set for the reviewer.

    Why it matters: Anthropic's Claude Code team details how it moved most code reading to automated layers while merged PRs doubled and reverts fell over 40 percent, offering a transferable review model for agent-heavy teams.

  2. Meta AI Research Blog88

    Meta releases Muse Spark Open, an 816B open-weight multimodal model that runs on two Mac Studios

    Meta released the weights for Muse Spark Open, an open-weight natively multimodal model with 816B total parameters (42B active) and a 1M-token context window, positioned for coding and agentic work. It is the first model in its class to run on a desktop using two Apple M5 Ultra Mac Studios with the MLX framework, and it is available through API partners including Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI, with weights on Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden and Microsoft Foundry Models. Meta says the model ships with auto-tunable Helion GPU kernels in SGLang, delivering 1.03-3.95x kernel-level speedups on NVIDIA and AMD GPUs and a 3% end-to-end speedup on AMD MI350.

    Why it matters: Meta releases open weights for an 816B-parameter multimodal model that runs on two Mac Studios, giving developers a local option for long-horizon coding and agentic tasks.

  3. Modal Blog78

    Meta's Muse Spark Open weights released and available on Modal Auto Endpoints

    Meta released the weights for Muse Spark Open, and Modal now serves the model through its Auto Endpoints with per-token billing. The model targets agentic coding and long-horizon tasks, with native multimodal understanding, a 1M-token context window, multi-step planning, sequential tool use and failure recovery. Meta reports 76.4 on Terminal-Bench 2.1 with the Terminus 2 agent and 60 on DeepSWE 1.1 using its preferred harness at xhigh reasoning effort.

    Why it matters: Meta's open-weight Muse Spark Open is now served on Modal, giving teams a per-token way to try an agentic coding model with a 1M-token context window.

October 13Tuesday
  1. GitHub Blog · AI & ML36

    GitHub Copilot for Beginners: Four Ways to Teach Copilot About Your Codebase

    GitHub's Copilot for Beginners series explains four ways to give Copilot project context: custom instructions in `.github/copilot-instructions.md` plus path-specific files in `.github/instructions`, agent skills in `.github/skills`, custom agents in `.github/agents`, and MCP servers that connect Copilot to external tools such as Playwright. The post walks through each mechanism and how they combine, and points readers to the community Awesome Copilot collection for ready-made examples.

October 12Monday
  2. Meta AI Research Blog82

    Meta releases Muse Wonder and Muse Wonder Full frontier models for personal and professional agents

    Meta introduced the Muse Wonder series, comprising Muse Wonder and Muse Wonder Full, which it says delivers state-of-the-art performance on personal and professional agentic tasks, memory and coding. Muse Wonder is available today through the Meta Model API and Muse Code, while Muse Wonder Full is available today for Muse Code subscribers. The models pair Active Memory, which carries context across conversations and retrieves task-relevant details, with Active Focus, which sustains work on long-running goals and checks in before important actions; Meta says both were trained at its gigawatt-scale Prometheus data center.

    Why it matters: Meta's Muse Wonder series claims state-of-the-art results on agentic, memory and coding benchmarks, with Active Memory and Active Focus as the two capabilities it is built around.

October 9Friday
  1. LiveKit Blog34

    Serve AI uses LiveKit Agent Simulations to rebuild its voice agent on a single prompt

    Serve AI rebuilt its phone agent for a $150M pest control customer on LiveKit, collapsing a 224-node Retell flow graph into a single prompt and validating it with Agent Simulations. About 100 real production calls were converted into simulation scenarios, reaching a 98% pass rate over 10–15 iterations before cutover, with median turn latency of 1.3 seconds against a 1.5 second ceiling. The first migration took one week with no rollback, and containment rose 15%, which Serve estimates saves the customer an additional $320,000 a year.

  2. LiveKit Blog58

    LiveKit launches Agent Simulations for testing voice agents

    LiveKit has made Agent Simulations generally available for all LiveKit Cloud projects, a repeatable way to test voice agents at the task level rather than turn by turn. An LLM-driven simulated user plays out each scenario, including backchannels and interruptions in audio mode, and an LLM judge returns a pass/fail verdict with a reason; a CLI can generate scenarios from the agent's own prompts and tools. Text runs are the default and cheap enough for every pull request, while audio runs cover the full pipeline and are better suited to nightly or pre-release schedules. Simulations are free through October, though they call the real agent's LLM, STT and TTS at normal rates.

October 8Thursday
  1. Anthropic · Claude.dev62

    Anthropic walks through building scheduled agent automations with Claude Managed Agents

    Anthropic published a reference implementation for Claude Managed Agents (beta) that reads Slack channels and GitHub pull requests on a cron schedule, tracks changes since the last run via per-source bookmarks, and posts a brief to Slack. The walkthrough covers vault-held credentials, read-only access and a per-run spending cap, plus rules for reporting a failed source as unreadable rather than a quiet day and confirming a Slack post before updating bookmarks and the ledger. A Claude Code command with the claude-api skill configures the agent from the article's guidance.

    Why it matters: Anthropic shares a reference implementation for scheduled Claude Managed Agents that read Slack and GitHub, with concrete rules for bookmarks, failed reads and post confirmation.

  2. Goodfire Research71

    Goodfire trains and deploys probe-based cyber misuse monitors on Kimi K3 and GLM 5.3

    3 and deployed them on a production inference stack, where a cheap probe filters turns and only suspicious exchanges reach an LLM judge. 5% benign-session interruption rate, costs $65 per million turns without judge reasoning and $185 with it, and adds no measurable throughput loss inside SGLang.

    Why it matters: Goodfire shows a probe-plus-judge cascade can monitor agentic cyber misuse at roughly 50x lower cost with no measurable throughput loss, a concrete recipe for serving open models safely.

October 7Wednesday
  1. Vapi Blog36

    Vapi launches Personal Agent Calling beta for AI agents that call businesses on customers' behalf

    Vapi has launched Personal Agent Calling in beta, letting users sign in with Vapi and make calls through a personal agent without setting up an API key. The company argues businesses must verify who an agent represents, what it is allowed to do, and whether that permission still applies, building on the A2A open standard and OAuth. Forcing customers to start over risks losing their business.

October 6Tuesday
  1. Mistral AI82

    Mistral launches Mistral Large 4 preview, a 1T-parameter multimodal model with weights due this month

    Mistral has launched a public preview of Mistral Large 4, a 1 trillion-parameter natively multimodal model with 52 billion active parameters, available now through the Mistral Studio API. Mistral says the model is competitive with the strongest open-source models and outperforms other open-weight models from the US and Europe, scoring 82% on an Artificial Analysis Cyber Index vulnerability reproduction and patching test and 49.8% on its combined Coding Agent Index. The weights are set to be released by the end of the month, after red-teaming with cybersecurity partners and authorities; Mistral says the model was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in its own European datacenters.

    Why it matters: Mistral's largest model yet claims open-weight leadership in cybersecurity and agentic coding, with weights promised by month's end.

  2. Anthropic · Claude.dev72

    Claude Code cloud sessions: a field guide to running tasks on separate VMs

    Addy Osmani walks through Claude Code cloud sessions, where each task runs on a fresh Anthropic-managed VM with the repository cloned onto its own branch, and cloud sessions are included in Pro, Max, Team and Enterprise plans at no extra compute charge. In a four-session test on a sample Node repository, three sessions started within 16 seconds of each other finished 87 seconds after the first began, and the flaky-test session ran the suite 40 times with zero failures. The guide covers setup scripts, network levels, GitHub connection paths and seven workflows suited to cloud sessions, while noting that local sessions remain the better fit for tasks needing local data, VPN services, GPUs or Zero Data Retention.

    Why it matters: A hands-on field guide to Claude Code cloud sessions, showing how per-task VMs enable parallel work and where local sessions still fit better.

  3. LiveKit Blog38

    Retell vs. Vapi vs. LiveKit: A Developer Comparison for Voice AI Agents

    LiveKit published a developer-focused comparison of Retell, Vapi and LiveKit for building voice AI agents, framing the choice around four questions: who iterates on the agent after launch, what happens when needs fall outside defaults, whether it must run beyond phone calls, and cost at scale. Retell is a managed platform with a visual editor, Vapi a developer-oriented managed platform configured via API and dashboard, and LiveKit an Apache 2.0 open source framework with self-hosting. Estimated all-in cost per minute runs about $0.070–$0.310 for Retell, $0.070–$0.116 for Vapi and $0.037–$0.099 for LiveKit.

  4. Figma Blog50

    Figma agent exits beta with guidelines, cross-platform context, and multiplayer agent following

    The Figma agent is out of beta with better instruction-following and long-task handling, winning over 60% of human-graded evals with professional designers. New capabilities let library owners upload markdown guidelines, pull context from FigJam, Figma Slides, and Figma Design, and follow collaborators' agents on the canvas. Uber's design systems team now publishes documentation that once took multiple people months in a single afternoon.

October 5Monday
  1. GitHub Blog · AI & ML58

    GitHub releases ReviewBench, an open benchmark for AI code review

    GitHub released ReviewBench, an open benchmark for AI code review agents built on 219 public pull requests across 19 languages, with language and repository-size distributions modeled on 103.9 million GitHub pull requests. The golden set draws findings from human reviewers, author follow-up commits, static analysis and multiple frontier LLMs, graded by Claude Sonnet 5 under a published rubric, and senior engineers independently re-labeled every finding with 96.6% agreement. GitHub says its offline evaluation of Copilot code review anticipated the direction of a production A/B test, where addressed rate rose 8.0%, recall rose 13.6% and cost per review fell 8.0%.

  2. Modal Blog35

    TypeSafe AI scales Jev to trillions of tokens in three days on Modal

    TypeSafe AI used Modal's serverless platform to train and deploy Jev, its first System One Model, and served over one trillion tokens within three days of launch. Jev is not an autoregressive LLM: it takes state and questions as input and outputs typed, structured values with probabilities in parallel, targeting low-latency workloads. A week after launching, it topped OpenRouter's leaderboard for 1K-10K token contexts, handling 15.1% of requests.

October 2Friday
  1. GitHub Blog · AI & ML25

    GitHub Blog: Three Skills Developers Need as AI Reshapes Their Work

    GitHub Blog outlines three skills developers should strengthen as AI changes their work: directing AI agents rather than just using them, critically reviewing AI output instead of trusting the first answer, and using time saved by AI to tackle broader problems. It cites GitHub Copilot's built-in Rubber Duck agent, which uses a second model to critique plans, code, and tests before a developer proceeds.

  2. OpenClaw Blog40

    Tencent AIG Joins ClawScan to Strengthen ClawHub Skill and Plugin Security Review

    Tencent's AI-Infra-Guard (AIG) has been integrated into ClawScan, the open-source command-line tool powering security review on ClawHub, so every uploaded skill and plugin now passes through AIG. ClawScan runs AIG and NVIDIA's SkillSpector independently, with an AI judge making the final assessment. On a fixed 556-case subset of SkillTrustBench, ClawScan matched 86.9% of benchmark labels and correctly classified 98.6% of malicious cases.

  3. Vapi Blog58

    Vapi opens GPT-Live beta to all customers, splitting voice agents into speaker and reasoner

    Vapi has opened its GPT-Live beta to every Vapi customer, after a limited beta last month with OpenAI's full-duplex speech-to-speech model. On Vapi the model runs as a speaker that handles the whole call and delegates lookups or actions to a separate reasoner model, with the handoff controlled through two prompts instead of one. Existing phone numbers, tools, tests and observability carry over, the transcriber config is dropped, and the voice switches to one of OpenAI's 22.

October 1Thursday
  1. Anthropic · Claude.dev60

    Getting started with Claude Code mods: building a context-window forecast

    Claude Code mods are small JavaScript or TypeScript files that run inside a session as hooks shipped in plugins, and can observe events, rewrite behavior or draw custom UI in the terminal or desktop app. The guide builds Token Weather, an roughly 80-line mod that reads $.session.usage() after each turn and draws a weather-style context-window band above the prompt, then tours Blast Radius, which holds risky Bash commands, and Replay Theater, which steps through a turn's edits. Mods require Claude Code 2.1.287 or later and are on by default; finished code for all three is in anthropics/claude-code-playground.

    Why it matters: A hands-on walkthrough of Claude Code mods that builds a working context-window meter in about 80 lines, showing how hooks, state and custom UI fit together.

  2. Modal Blog58

    Modal launches Sidecars, a low-latency trust boundary for Sandboxes

    Modal introduced Sidecars in Beta, isolated containers that run alongside a Sandbox on the same host to separate trusted code such as credentials, proxies and harness logic from untrusted agent-generated code. Modal says communication between a Sandbox and its Sidecars is at least 3x faster than between separate Sandboxes in the same region, with p50 travel per call of 0.58ms versus 2.6ms. Each Sidecar has its own outbound network policy, a single Sandbox can run up to 250 Sidecars, and Ramp already uses the feature to run a custom egress proxy next to its Inspect coding agent.

  3. Modal Blog60

    Modal launches VM Sandboxes, giving agents full Linux VMs with Docker support

    Modal has made VM Sandboxes generally available, giving agents a full Linux VM through the same APIs, modal.Images, sub-second cold starts and CPU/memory bursting as its existing gVisor-based Sandboxes. Users enable it with runtime="vm" when they need Docker, FUSE filesystems or niche Linux kernel features, while gVisor remains the default runtime. Modal says early customers have already launched over 20 million VMs, with Linear running every Coding Session on a VM Sandbox and Legora and Snorkel using them for legal agent evals and agent simulations.

    Why it matters: Modal's VM Sandboxes give agents a full Linux machine with Docker and kernel access while keeping the existing Sandbox APIs and sub-second cold starts.

  4. Modal Blog42

    Modal Runtime Roundup: VM Sandboxes, Modal Clusters, and More

    Modal announced VM Sandboxes, giving agents a full Linux computer for Docker stacks, local databases, and mobile simulators, already used by Linear, Legora, and Snorkel. Modal Clusters are now generally available via the @modal.clustered decorator, supporting trillion-parameter training and serving across coordinated containers. Sticky Sessions and Endpoint Candidates were also introduced, with the latter in private beta.

September 30Wednesday
  1. Factory62

    Factory makes Automations generally available for Droid agents

    Factory has made Automations generally available, letting teams describe a recurring workflow and have Droid run it on a schedule or event trigger. Each automation combines a trigger (schedule, Slack, GitHub, or webhook), instructions, a user-chosen model and reasoning level, and a machine that can be local, connected, or a Factory-managed Droid Computer. Factory says 52% of enterprise users ran automated code review in the past 30 days, 48% used security review, 35% used AutoWiki, and automation session volume grew 51% over the prior 30 days.

    Why it matters: Factory makes its Automations generally available, showing which recurring engineering workflows enterprises actually hand to Droid and how model and machine choice work per task.

  2. Vapi Blog58

    Vapi launches Personal Agent Calling beta to let assistants place phone calls

    Vapi launched Personal Agent Calling in beta, which lets personal assistants dial a business, work through phone menus, hold the conversation and report back a result. The feature works with assistants including Muse, Instinct, GrokBot, ChatGPT Work, Claude Code, OpenClaw and Hermes, and users paste a prompt pointing the assistant to phone.vapi.ai. In a test, Head of Revenue Bryan Parman's assistant waited 14 minutes on hold with an airline and declined a $51.69 upgrade fee, after which the representative waived it and confirmed an Economy Plus aisle seat at no charge.

September 29Tuesday
  1. Vapi Blog55

    Vapi launches CX toolkit for building and improving customer-service voice agents

    Vapi launched Vapi for CX, a toolkit for building, deploying, observing and optimizing customer-experience voice agents. It combines a natural-language Composer for CX teams with the same config object reachable through API, CLI and an MCP server, plus evals, versioning, fallback plans, call scorecards and weekly latency, cost and quality data across transcribers, models and voices. Vapi says Amazon Ring runs 100% of its inbound volume on the platform after evaluating more than 40 vendors, and that Kavak builds new agent versions in about five minutes with NPS up 20 points.

  2. OpenClaw Blog71

    OpenClaw Enterprise launches as an open source platform for managing persistent agents

    OpenClaw announced OpenClaw Enterprise (OCE), an open source, vendor neutral platform for managing persistent agents in sensitive environments, currently in development ahead of its 1.0 release later this year. OCE adds an enterprise control plane with multi-tenancy, hard security boundaries, sandboxing, LLM-based reviews and fine-grained permissions, and lets the harness, model and sandbox be swapped for third party or internal implementations. Organizations can self host it today via docker-compose for local development or kubernetes for internal deployment, and it is being piloted at companies including Red Hat and OpenAI.

    Why it matters: OpenClaw Enterprise is an open source control plane for persistent agents in sensitive environments, with multi-tenancy, hard security boundaries and auditability, now available for internal pilot workloads.

September 28Monday
  1. Anthropic · Claude.dev72

    Anthropic adds build-eval and hillclimb commands to the claude-api skill in Claude Code

    Anthropic added two commands to the claude-api skill inside Claude Code: /claude-api build-eval builds an evaluation set in your codebase, and /claude-api hillclimb iterates on an application against that evaluation with a held-out set to catch overfitting.

    Why it matters: Anthropic adds build-eval and hillclimb commands to its claude-api skill, showing how Claude Code can design evaluations and iterate against them while guarding against overfitting.

  2. Anthropic · Claude.dev82

    Anthropic releases Claude Sonnet 5.5 with unchanged per-token pricing and 30% faster output

    Anthropic released Claude Sonnet 5.5, the second model in the Claude 5.5 family after Opus 5.5, which the company says is smarter, more efficient and 30% faster than Sonnet 5 at the same per-token price. Because it typically needs fewer tokens per task, Anthropic says most work costs up to 30% less, and the model ships with a native 1M-token context window, 128k max output and thinking on by default. The release guide lists breaking changes for Sonnet 5 users, including replacing disabled thinking with the between_tools setting, swapping forced tool_choice for auto plus strict tools, and moving computer use to the computer_toolset_20260801 toolset.

    Why it matters: Anthropic's Sonnet 5.5 keeps Sonnet 5 pricing while cutting per-task token use, and the migration notes show what changes for teams already building on Sonnet 5.

September 25Friday
  1. OpenClaw Blog74

    Microsoft Autopilot is built on OpenClaw, with contributions flowing both ways

    Microsoft introduced Autopilot, a persistent personal agent built on OpenClaw, as part of its new Copilot experience, with a private preview expanding to first customers at the end of this month. Autopilot is the new name for Microsoft Scout, announced earlier this year, and Omar Shahine's team says it is working with OpenClaw and the OpenClaw Foundation on an enterprise-grade runtime. OpenClaw highlights upstream contributions from Microsoft engineers, including policy conformance checks, a native Windows companion, and reliability fixes for background jobs, restarts and duplicate replies.

    Why it matters: Microsoft's Autopilot is built on OpenClaw, and the upstream contributions listed here show how a shared agent runtime improves across platforms.

September 24Thursday
  1. Vapi Blog25

    Vapi Engineers Answer Top Questions on Model Intelligence, Simulations and Campaigns

    Vapi product engineers Mhar Andal, Scott Lowe and Adham El Fawal demoed Model Intelligence, Simulations and Campaigns in a September 22 webinar and answered top builder questions. Model Intelligence recommends transcriber, LLM and voice presets and shows cost, latency and quality metrics, while Simulations run AI testers with pass/fail results and Campaigns dial lists of up to 10,000 contacts.

September 23Wednesday
  1. Vapi Blog38

    Vapi tests Jev as a real-time guardrail for voice AI

    Vapi ran early experiments using TypeSafe AI's early-access "System One" model Jev as a guardrail that scores whether a voice assistant's response violates a developer-defined rule before it is spoken. In one early test, Jev caught at least three out of four violating responses, though Vapi says it is not a 100% compliance guarantee and only an additional check on top of prompt guardrails.