AI agents
Latest stories covering AI agents, with summaries and links to related coverage.
42 stories · 43 reports · page 1 of 1
28 stories · 28 reports in the last 7 days
Latest report · Counts as of (UTC)
Models in this coverage
Up to six tracked models linked to this topic’s reports in the last 30 days. Counts measure coverage on Themata, not usage.
- Claude Opus 5.5
Anthropic
· Closed
Anthropic’s model for complex reasoning and longer agent tasks. Built for work such as large codebase changes, code review, and multi-step problem solving.
7 stories · 7 reports on this topic
- Gemini 4 Argon
Google
· Closed
Google’s reasoning model for software engineering, mathematical and scientific problems, and multi-step agents. Designed to maintain context across longer tasks.
3 stories · 3 reports on this topic
- Claude Haiku 5.5
Anthropic
· Closed
A small, fast Claude model for high-volume tasks. Suited to summarization, browser use, and supporting agents where speed and cost matter.
2 stories · 2 reports on this topic
- DeepSeek V4.1 Flash
DeepSeek
· Open weights
An open-weight mixture-of-experts model that reads text and images. Its architecture focuses on efficient long-context and agent workloads, with support for up to one million tokens.
2 stories · 2 reports on this topic
- GPT-6.1 Sol
OpenAI
· Closed
A general-purpose OpenAI model for coding, tool use, and document-heavy work. Supports multi-step workflows and computer use.
2 stories · 2 reports on this topic
Connected topics
Topics connected through shared stories across the archive.
Latest stories
There are now 5 inference providers for abliterated DeepSeek
tool
1 min read
AllModels routes abliterated language models across providers, offering automatic fallbacks when a selected provider is unavailable. The service uses modality-aware selection to match routing with model capabilities. AllModels also provides a single place to compare model availability and token prices across providers.
GPT-6.1 Terra
tool
4 min read
OpenAI has introduced GPT-6.1 Terra, a model for visual reasoning, computer-use tasks, writing, and coding. It is available immediately to ChatGPT Work and Codex users on Plus, Pro, Enterprise, and Edu plans, and through the OpenAI API as gpt-6.1-terra. OpenAI lists standard API prices of $1 per million input tokens, $0.05 per million cached input tokens, and $5 per million output tokens; it says these prices are 50% below GPT-5.6 Terra and GPT-6.1 Sol. OpenAI says GPT-6.1 Terra can handle tasks including interpreting technical drawings, entering invoice data into accounting software, completing forms, and editing video. On the Agents’ Last Exam computer-use benchmark at medium reasoning effort, OpenAI reports that Terra scored 15 percentage points above GPT-5.6 Terra at about one-third the cost, while matching GPT-6 Astra’s performance at one-tenth its token price. It says Terra completed tasks more than three times faster than GPT-6 Astra and GPT-6.1 Sol. OpenAI reports a lower error rate on its internal error-inducing-prompt benchmark: 16.7% at low reasoning effort, versus 53.6% for GPT-5.6 Terra, and 4.3% at maximum effort. On Terminal-Bench 4.0, Terra solved 50.3% of tasks at maximum reasoning effort, compared with GPT-5.6 Terra’s published 21.5%. OpenAI says the model meets its Critical cybersecurity threshold and retains GPT-6 Astra’s safeguards.
ChatGPT Keeps Telling People 404 Media Can Solve Their Tech Support Problems
news
2 min read
ChatGPT has directed users with ordinary technology-support problems to contact 404 Media and has drafted emails asking the outlet to investigate or intervene. In one conversation shared with 404 Media, a Russian Instagram user who lost access to an account containing 13 years of photos, videos, and memories asked how to recover it. After describing Meta’s automated review process and saying that emailing Meta was probably not possible, ChatGPT recommended contacting journalists, including 404 Media, and supplied Jason Koebler’s email address. It then generated the English-language message sent to him. 404 Media says it began receiving a recent wave of emails that appeared AI-generated, often from people seeking help with routine account or platform issues. The outlet asked several senders how they had found it; each said an AI chatbot had recommended contacting 404 Media, and in many cases the chatbot had written the email, according to a review of several shared ChatGPT conversations. The pattern reflects users seeking alternatives when major technology platforms provide limited human customer support or make it difficult to contact a person. ChatGPT characterized some account-access and moderation disputes as matters potentially worth journalists investigating, even when users were primarily seeking technical support or account recovery.
The Git Forge for Agent-Human Collaboration
tool
10 min read
Entire has launched a Git-compatible code-hosting platform designed for teams working with coding agents. Its Trail workflow replaces conventional pull-request timelines with agent-generated evaluation scores, findings and merge gates displayed for every code push. Trails can span multiple repositories, run configurable agents for code quality, security and “slop,” and require specified agent findings, human approvals and CI checks before merges. Entire says its review agents use Claude Code and Codex models in isolated Daytona and E2B cloud sandboxes. The platform natively hosts repositories or mirrors GitHub repositories, supports replication across regions for data residency, and is available in the US, EU, Australia and India. Entire says its distributed object store and transactional ref database enable pushes up to 89 times faster than GitHub and six times faster than Cursor Origin, based on its open-source benchmarks. Buildkite and Depot integrations provide CI checks; failed builds can block merging. Entire also introduced Continuous Memory through open-source, local-first CLI plugins. entire-graph locally analyzes repositories with tree-sitter to map code definitions, callers, types, routes and change impact; Entire says testing showed token savings of up to 71%. entire-brain indexes that code map alongside agent sessions recorded in Git history, allowing agents and reviewers to retrieve prior technical context and decisions.
Anthropic S-1
news
1 min read
An SEC filing link identifies Anthropic, PBC and an S-1 document labeled “Q2.” The source provides no filing details, financial figures, dates, business information, or statements from Anthropic. The link was posted by SiNTEx and had a score of 1,064 points and 989 comments at the time reflected in the source.
Fixing Tool-Call Accuracy in vLLM
research
8 min read
Ubicloud enabled constrained decoding in vLLM to enforce valid tool-call schemas for Kimi and DeepSeek models, reporting an increase in K2VV ToolCall benchmark schema accuracy from 92% to 100%. The company had observed that Kimi K2.6 served through vLLM 0.28 produced the expected tool-call schema 92% of the time, meaning roughly two of every 25 calls were malformed. Invalid calls can add error messages to an agent’s context, reduce output quality, and require retries that increase costs. Tool calls require an LLM to emit structured arguments matching a declared JSON schema, so an agent harness can invoke functions such as web-page fetching. Constrained decoding prevents the inference engine from selecting tokens that would violate the required tool-call structure or JSON schema during generation. vLLM uses structural tags from XGrammar to restrict valid tokens at each stage, including tool names, call indexes, and arguments. Although vLLM added constrained decoding in version 0.25, it does not enable strict tool decoding by default for all requests because support is model-specific and can cause problems for some models. vLLM later added the server-side --tool-strict-level flag in version 0.31. Moonshot AI recommends constrained decoding for Kimi, and Ubicloud deployed it after validating 100% schema accuracy across its served model versions.
PaperPilot – Zero-Cloud, Pure-Rust PDF Engine with WASM and MCP
tool
3 min read
PaperPilot is a Rust-based local PDF and document toolkit available as an MCP server, command-line interface, browser-based WebAssembly tool, Tauri desktop application, and local REST API. It supports PDF merging, splitting, rotation, page deletion and reordering, page extraction, booklet imposition, N-up grids, compression, text extraction, OCR, watermarking, signature validation or removal, PII scanning, deep sanitization, and Excel-to-JSON or CSV conversion. The MCP server connects through local standard input/output to Codex, Claude Code, Kimi Code, Pi, Claude Desktop, Cursor, and other MCP clients, allowing agents to execute PDF operations. The local REST gateway runs on localhost port 7823 and can be called from Python, Node, or n8n. Browser processing keeps files in browser memory rather than uploading them, while the project states that its tools make no cloud calls and require neither Chromium nor a C++ runtime. PaperPilot requires Rust 1.75 or later; desktop development also requires Node.js and pnpm. Its Apache 2.0 Community Edition includes the CLI, MCP server, REST gateway, WASM engine, and desktop app. Planned releases include PDF conversion to Markdown, HTML, JSON, Word, Excel, and PowerPoint, followed by encryption, signing, redaction, forms, rendering, repair, comparison, PDF/A support, and Bates numbering. A separate Enterprise Edition is planned with SSO, role-based access control, audit logs, and DLP.
Microsoft Decision-1: 9B Qwen Model Launch Explained
tool
21 min read
Microsoft introduced Microsoft-Decision-1 on October 9, 2026, a specialized model that scores predefined answer choices rather than generating text. Built by post-training Alibaba’s 9-billion-parameter open-weight Qwen3.5-9B model, it returns calibrated probability scores for tasks including routing, classification, prioritization, verification and workflow control. It supports text inputs of up to 32,000 tokens but does not provide written explanations or process images, audio or video. Microsoft said future versions will be rebased on Microsoft AI and OpenAI models. The model launched in public preview through Microsoft Foundry and became available on OpenRouter as microsoft/microsoft-decision-1. Its weights are not distributed, limiting use to hosted APIs. OpenRouter and secondary reports list pricing at $0.042 per million input tokens with no output-token charge. Microsoft said Decision-1 achieved 83.5% accuracy across 36 benchmarks containing roughly 150,000 held-out questions, and reported approximately 35 times lower P50 latency than GPT-6 Sol and 4.5 times faster performance than Quyet-1.0-Large; those results have not been independently reproduced. Microsoft also reported 98.7% perturbation stability and said it is testing the model internally for incident response, quality control and scientific discovery. Xbox Research reportedly used it to categorize more than 10,000 feedback items into predefined themes.
F-Zero X on Nintendo 3DS
research
7 min read
G-Diffuser v1.0.0 ports the F-Zero X decompilation to Nintendo 3DS hardware, supporting Old 3DS, 2DS, New 3DS, and New 2DS XL systems. The release provides base-game builds for both hardware classes and a New 3DS-only build supporting the 64DD Expansion Kit’s course editor and machine creator when users supply a disk image. It distributes neither game ROMs nor disk images; all builds load prebaked asset archives from an SD card. The port supports Grand Prix, Time Trial, Practice, Death Race, machine-editor screens, stereoscopic 3D, DSP audio, touchscreen dashboard layouts, and HOME-menu suspend, resume, closing, sleep, and power-off behavior. A native renderer, now the default on both Old and New 3DS, bypasses per-frame display-list traversal for race geometry. On New 3DS hardware, the first development phase ran from August 11 to September 3, 2026, and reached a 59.6 fps median with 53% of frames at the 60 fps cap. A second 43-day phase, from September 7 to October 19, added Old 3DS support, the native renderer, and Expansion Kit functionality. The Old 3DS renderer was designed around its 16 KB instruction cache and achieved a 16.5 ms median frame time in a 2D Grand Prix race; most courses reach 59–60 fps in 3D, while demanding courses can fall back to a steady 30 fps safety mode.
Eunice is almost here
opinion
1 min read
William Gibson’s 2020 novel Agency depicts Eunice, an experimental AI assistant that can hold a conversation while independently carrying out financial and travel-related actions in the background. In an alternate 2017 San Francisco, Eunice tells Verity that it found and moved money from a Zurich account, bought frequent-flyer points on a global market, and completed the work in the short time since Verity activated it. Gibson published Agency nearly three years before ChatGPT’s release and six years before the emergence of increasingly autonomous AI agents. The source identifies GPT-Live and Block’s Dessa as systems enabling more human-like, full-duplex voice conversations. It also identifies OpenAI’s Dots and Meta’s Muse as 24/7 agents designed to pursue goals, take actions, and operate in the background. Eunice’s defining capability is not conversation alone but simultaneous autonomous work, combining natural dialogue with independent execution of tasks.
A 10-year-old CNN outperforms frontier LLMs for skin cancer diagnosis
opinion
5 min read
A dermatologist tested GPT-6 Astra, Claude Fable 5.1 and a fine-tuned Inception v3 convolutional neural network on 200 randomly selected, biopsy-confirmed skin lesions from the public MILK10k dataset. The CNN correctly identified 125 of 200 lesions across six diagnoses, or 62%, while GPT-6 Astra and Claude Fable 5.1 identified 84 and 85, or 42% each. Inception v3 was introduced by Google in 2015 and was fine-tuned on 68,011 ISIC Archive clinical and dermoscopic images for about 20 minutes on one H100 GPU. The test set contained 100 lesions requiring further care, including 30 melanomas, and 100 benign lesions considered safe to discharge. The CNN identified 13 melanomas as its first choice, versus eight for GPT-6 Astra and 11 for Claude Fable 5.1. With a second diagnostic choice allowed, the CNN reached 173 correct cases, compared with 134 and 138 for the LLMs. Both LLMs incorrectly labeled a pictured melanoma as a benign seborrhoeic keratosis and said the patient could be discharged. The sample size was small, each LLM response was generated once, and all lesions had been biopsied, making the cases unusually difficult. At its preset discharge threshold, the CNN discharged 18 melanomas, though matching its unsafe-lesion retention rate to the LLMs reduced that figure to two or one. The browser-based CNN runs locally with WebGPU and does not upload images.
AI use leads UC Berkeley math professor to see 'no point' in grading homework
news
2 min read
UC Berkeley mathematics professor and Fields Medalist Richard Borcherds has stopped grading homework for correctness after seeing students use AI to consistently earn top scores. He now gives credit for completion when students submit assignments, calling the arrangement “not a very good system” but saying he has no alternative while AI use is unavoidable. Borcherds said there was no point in paying readers to assess homework correctness under those conditions. In an interview following OpenAI’s announcement that AI agents had apparently solved the Navier-Stokes existence and smoothness problem, Borcherds said that, if AI improves at its current rate, it could soon outperform human mathematicians in every task. He said he no longer knows whether to encourage students to enter mathematics and that even ChatGPT did not offer him a clear prediction about the profession’s future. He expects academic inertia could leave math departments largely unchanged for several years despite AI advances. UC Berkeley asks faculty to include course expectations for AI use in syllabi, but individual instructors set class policies. The campus has tested AI-detection tools including Turnitin and Rumi, but has concluded they are not reliable enough for campuswide standardization. Berkeley’s AI Hub provides faculty with sample syllabus statements and guidance on AI tools and policies.
What Adobe v ArtCraft teaches us
opinion
7 min read
The writer argues that low-cost AI coding tools from companies including OpenAI and Anthropic could erode the software businesses expected to become their largest customers. The argument is that established software companies have historically charged recurring fees partly because complex products required large engineering teams and years of investment to build. If AI lets hobbyists or small teams create comparable alternatives cheaply and release them for free or at low prices, subscription revenue at incumbent software vendors could decline rather than shift to AI labs. The writer characterizes $20-per-month coding plans and $200 premium plans as subsidized pricing, claiming heavy users may consume more computing capacity than they pay for. Amazon, Alphabet and Meta are cited as planning roughly $500 billion in 2026 capital spending, much of it for AI data centers. The writer says AI labs face a pricing dilemma: higher enterprise-style prices could push users toward free open-weight models, while low prices may fail to cover infrastructure costs. Meta's February 24, 2023 release of Llama and subsequent open-weight releases from Chinese labs are presented as making durable scarcity difficult. The writer contends that OpenAI and Anthropic lack Nvidia's hardware business, Meta's larger business lines, and vertical integration, leaving their business models exposed if AI makes software development abundant and inexpensive.
Dream-1
tool
10 min read
Dream Labs has launched Dream-1, a World Action Model available through a public API that it says can control previously unseen bimanual robots with parallel grippers without robot-specific fine-tuning or demonstrations. The company emerged from stealth with $90 million in seed funding and positions the system as a way to make newly deployed, lower-cost robots useful before they have accumulated their own training data. Dream-1 predicts end-effector poses—gripper positions, orientations, and open or closed states—rather than only robot-specific joint angles. A robot’s inverse-kinematics solver converts those poses into joint commands. The model uses a head camera and wrist cameras, and Dream Labs says its video-and-action training enables it to generalize across robots whose bodies and grippers differ. The company reported tests on multiple bimanual parallel-gripper systems, including the 18-year-old PR2 robot. Robocurve independently evaluated Dream-1 before launch, according to Dream Labs. Dream Labs says the model costs $3 per hour and Dream-1-flash costs $2 per hour; each API call returns 1.6 seconds of actions. For robots where zero-shot performance is insufficient, its Fine-tuning-as-a-Service adapts the model using robot-specific data. Dream Labs reports five minutes of data improved results for two partner robots, while other configurations required 77 to 98 minutes.
Hacker reverses Samsung TV, finds on-board AI listening OpenAI account suspended
news
1 min read
A Reddit page titled “Hacker reverse engineers his Samsung TV and finds on-board AI listening for sounds of dog and baby, proceeds to get his account suspended by OpenAI” is linked from the r/antiai community. The available page text does not provide evidence or details supporting the title’s claims about Samsung televisions, sound detection, reverse engineering, OpenAI, or an account suspension. The visible content consists of Reddit’s general site messaging, including “This is wild,” an invitation to join the platform, and references to its User Agreement and Privacy Policy. No technical findings, dates, device models, software details, or statements from Samsung or OpenAI are included in the supplied text.
Kimi K3.1 Pro and K3.1 Pro Highspeed
tool
15 min read
Kimi has introduced Kimi K3.1 Pro, a post-trained successor to Kimi K3 and its most capable 3-trillion-parameter-class open-weight model, with a one-million-token context window. Kimi K3.1 Pro Highspeed is also available, offering two to three times the standard model’s speed. Both models are available through Kimi.ai, Kimi Work, Kimi Code and the Kimi API; Kimi says it will release the full weights by November 4, 2026. Kimi reports that K3.1 Pro scored 65.7% on Terminal-Bench 4.0, compared with 58.2% for GPT-6 Astra and 66.4% for Claude Opus 5.5, and 61.2% on its internal Kimi Code Bench 3.0 Long-horizon, up from K3’s 55.95%. It reported a 92.7% tool-enabled score on BenchCAD and an OfficeQA Pro v2 score of 64.4. Kimi also said its internal stress test measured a claim-level hallucination rate of 8.3%, down from 23.4% for K3; those figures are not daily-use error rates. For the open-weight release, Kimi removed cyber-related data from mid-training and filtered cyber tasks from reinforcement-learning environments. On its xdev-bench-hard vulnerability benchmark, Kimi said a checkpoint without those measures achieved 90.5% pass@3, while the final model achieved 81.0%. Kimi said the controls reduced cyber capability while retaining general coding usefulness.
OpenShell – A safe, private runtime for fleets of autonomous AI agents
tool
4 min read
NVIDIA’s OpenShell is a runtime for fleets of autonomous AI agents that lets administrators define and enforce policies governing files, system calls, network access, APIs, packages, and credentials. Each agent runs in an isolated sandbox, while kernel-level controls enforce restrictions on file access and system calls and check network connections before they leave the sandbox. OpenShell supplies credentials only on requests sent to approved endpoints, so agents do not directly receive real credentials. OpenShell uses formal verification before policy changes take effect, flagging newly granted access such as connections to a new credentialed host or calls to a new API method for human review. Version 0.1.x adds a stable release cadence, isolation primitives, a broader extension surface, and APIs. The platform includes sandboxes, policy controls, credential providers, gateways, Kubernetes deployment through Helm, middleware, interceptors, and compute drivers. OpenShell supports Linux, macOS on Apple Silicon, and experimentally Windows through WSL 2, and requires Docker, Podman, or host virtualization. SDKs are available for Python, TypeScript, Go, and Rust. NVIDIA says anonymous telemetry is limited to operational categories and counts and excludes sandbox names, hostnames, paths, prompts, credentials, model or provider names, and user content. Telemetry can be disabled or compiled out. OpenShell is licensed under Apache 2.0.
Claude Now Works with Google Docs, Sheets, and Slides
tool
4 min read
Anthropic has launched Claude for Google Workspace in public beta for all paid Claude plans, adding a Claude sidebar to Google Docs, Sheets, and Slides. The add-on can read the open file, recognize selected text, cells, or slides, and make edits within the file. Users can require approval for every proposed change through “Ask before edits” mode or let Claude apply changes continuously with “Accept all edits.” In Docs, Claude can revise text in place or present larger revisions as suggestion cards. In Sheets, it can create formulas, pivot tables, charts, and tabs; it can also use Python for joins or data cleaning and write results back to the spreadsheet. In Slides, it can create slides using an existing deck’s layouts and themes, then identify overlapping, off-slide, or hard-to-read elements. Google Docs, Sheets, and Slides connectors are also in beta, allowing Claude users to create and edit Google files from a Claude chat. Access follows existing Google sharing permissions. The Workspace sidebar retains Claude models, connectors, and skills, while Enterprise customers retain Compliance API, customer-managed encryption key, and OpenTelemetry audit-export controls. The add-on is installed through Google Workspace Marketplace, and Team and Enterprise connectors require activation by an owner or primary owner.
Claude Duet
tool
8 min read
Anthropic has opened a waitlisted research preview of Claude Duet, a proactive AI agent for US Claude Pro and Max subscribers. Available as a standalone iOS and Android app and through claude.ai, Duet carries over a user’s Claude memory, skills and connected apps. It provides morning briefs, monitors connected accounts every few hours, alerts users to items needing attention, and can continue assigned tasks in a virtual machine when the user is away. Built around Claude Opus 5.5, Claude Duet can schedule meetings, prepare dashboards and forecasts, prototype web apps, manage business workflows, and use more than 6,000 Claude Marketplace connectors and plugins. Supported tasks include drafting Gmail and Slack replies, identifying unpaid invoices in QuickBooks or Stripe, preparing Gusto payroll, posting approved LinkedIn job listings, and managing Shopify stores. It can make purchases through Link or Shop Pay after confirmation and payment approval. Duet requires approval before sending messages, making payments, signing documents or deleting data; securities trading and account-to-account transfers are prohibited. Anthropic says Duet never directly accesses passwords or sign-in tokens and uses classifiers intended to block sharing credit-card numbers and government IDs. The company warns that the early version can misread threads or misjudge urgency, is less capable for personal errands, cannot show its browser, and shares usage limits with other Claude features.
Claude Duet
tool
3 min read
Claude Duet is Anthropic’s proactive AI agent that handles work through messages, operates on its own cloud computer, and contacts users when a task is complete or requires a decision. Powered by Opus 5.5, it is available as a research preview through a waitlist for Claude Pro and Max subscribers in the United States; Team and Enterprise plans are not included. Claude Duet is offered as a standalone iOS and Android app and is accessible on the web at claude.ai. The agent uses a customer’s Claude account, including its memory, skills, and connected apps, and retains conversation history in one thread. It can produce morning briefs, identify inbox and calendar items requiring attention, draft replies, schedule meetings, build dashboards, and return work such as documents, spreadsheets, and slide decks. It can also find unpaid invoices and draft reminders, prepare payroll for review, and ready contracts for sending. Claude Duet requires approval before sensitive actions such as sending messages or making payments. Approvals, logins, and safety checks run in the user’s cloud computer, and users can review and steer its activity. Its background checks count against a subscriber’s Claude usage limits, with larger tasks consuming more model turns and background work. Anthropic says the preview may misread threads, misjudge urgency, or miss items that arrive between check-ins.
The compiler was supposed to end programming too
opinion
14 min read
AI coding tools are automating more implementation work, while software teams face growing demands for specification, review, testing and verification. IBM’s 1954 FORTRAN report said the language should “virtually eliminate coding and debugging”; FORTRAN instead removed much low-level manual programming while software development expanded. GitHub counted more than 180 million developers in 2025, including more than 36 million who joined during the preceding year. Stack Overflow reported that 84% of developers used AI tools or planned to use them in 2025, while 46% distrusted AI output accuracy and 33% trusted it. In an early-2025 randomized METR trial involving 16 experienced open-source developers and 246 repository issues, participants took 19% longer with AI tools despite believing the tools had made them faster. METR’s task-time series found that Claude Opus 4.6 could complete software tasks taking human experts about 12 hours with a 50% success rate, up from about one hour for Claude 3.7 Sonnet a year earlier. Faros AI reported that high-adoption teams completed more tasks and merged more pull requests, but review time rose sharply and incidents per pull request more than tripled in its 2026 data. Stanford researchers found employment for software developers aged 22 to 25 was nearly 20% below its late-2022 peak, while cautioning that their results were descriptive rather than causal. The projected role for developers centers on defining requirements, writing tests and reviewing machine-generated work.
Rust off Rails: The opinionated web stack for the AI era
tool
1 min read
Rust off Rails is an opinionated web stack that directs AI agents to write all application code, including Rust, SQL, HTML, CSS, TypeScript and Dockerfiles. Humans provide intent through product specifications, interview answers and conversational feedback rather than writing code themselves. The stack presents this rule as the basis for enforced conventions, free boilerplate generation and specifications remaining the source of truth. Instead of distributing framework libraries, Rust off Rails provides precise specifications that agents render into application code owned by the user. It includes specifications for components such as a job queue, sessions and an Inertia adapter. The stack fixes decisions on crate selection, file organization, naming, error handling and tests, aiming to prevent agents from debating choices such as the ORM. Rust off Rails provides guides and recipes for authentication, background jobs, email, caching, uploads, realtime features and deployments. These materials are intended to give agents predefined starting points for each layer of an application.
Using thousands of AI agents to uncover new leads in biological data
research
5 min read
OpenAI, PerturbAI, and Scripps Research used thousands of GPT-Rosalind-powered AI agents to analyze a whole-brain CRISPR atlas containing data from millions of mouse brain cells. The agents examined individual gene disruptions, measured cellular responses, searched scientific literature, and combined their findings into a reasoning atlas linking genes, cell effects, publications, and possible biological mechanisms. The analysis took 32 minutes; the research team estimated comparable sequential human analysis and literature review would take months to a year. The distributed-agent system reproduced findings from human analyses and generated additional research leads. Its novelty and scientific-value assessments aligned with blinded human experts, according to the team. At the largest tested workload, a single agent cited 11 references, while distributed agents cited more than 700, and human experts preferred the distributed interpretations. The agents found that disrupting Tsc2, but not Tsc1, altered dozens of neuronal-communication genes in cerebellar cells, despite both genes regulating mTOR. They also identified a potential link between DCTN1 disruption, increased expression of cholesterol-production genes, and altered cholesterol transport in neurons. DCTN1 mutations cause Perry syndrome, a neurodegenerative disorder with Parkinson-like symptoms. The publicly available reasoning atlas is intended to help researchers prioritize laboratory experiments that can test these proposed mechanisms.
OpenAI models are coming to Google Cloud
news
1 min read
OpenAI and Google Cloud are partnering to make OpenAI’s frontier models and Codex agentic workflows available through Google Cloud’s Model Garden. Google Cloud customers will receive native access to the models for complex agentic workflows, using Google Cloud infrastructure, data governance, and access controls. Customers will be able to buy OpenAI frontier models through Model Garden via the Google Cloud Marketplace. Organizations with Google Cloud spending commitments will be able to apply those commitments to the purchases. The integration is intended to support enterprises modernizing software development, building agentic systems, and deploying AI workloads that require controls over sensitive-data access and use. OpenAI models are also planned for Google Antigravity and the Gemini Agent, giving enterprise customers a choice of models for different tasks. General availability is planned in the coming weeks, although no specific release date was provided. Microsoft remains OpenAI’s primary cloud partner.
LayerFS: The filesystem for AI coding agents
tool
2 min read
LayerFS is presented as a filesystem for AI coding agents that separates persistent storage from short-lived compute. File-tool operations are atomic writes to a shared serverless Store, while each Bash invocation mounts an ephemeral workspace, runs commands, and commits changes when it unmounts. Agents running on different computers can commit to the same Store, with projects supporting multiple branches and multiple agents per branch. Every tool call creates a commit on its branch. LayerFS refuses same-file clashes as conflicts and records the step that wrote each line for blame information. Commits can be recursively forked without copying files, which LayerFS positions for recursive self-improvement agents and reinforcement-learning exploration. The system says content chunks are deduplicated and compressed; an illustrative example shows 10 copies using the equivalent of 1.05 copies after deduplication and 0.31 copies after compression. LayerFS retains ignored files, including dependencies and caches, in every commit rather than requiring reinstallation after a workspace mounts. It contrasts this with Git and other agent filesystems that drop ignored files such as node_modules and .cache. LayerFS reports measured mount times of 5–20 milliseconds and depicts a workspace becoming ready in 0.05 seconds, compared with a typical roughly five-minute clone-and-install process before a first run.
Anthropic's Claude autonomously designs protein binders targeting scorpion neurotoxins
research
2 min read
Anthropic and Citrini Research used an internal Claude model to design protein minibinders targeting five neurotoxins from four scorpion species. Following one prompt, Claude generated 318,484 candidate designs during a 24-hour autonomous run in Anthropic’s Claude Science research environment. Researchers tested 211 designs—180 selected by Claude and 31 added by the team—and found that 17 met their criteria for functional binders. The two strongest binders attached with high affinity to Cn2, a toxin produced by a Mexican scorpion. Laboratory evidence suggests the binders attach at or near the site targeted by an antibody that neutralizes Cn2 in mice. The binders have not been tested as antivenom in animals or humans, so their ability to treat scorpion stings remains unverified. Scorpion neurotoxins kill more than 3,250 people annually. An earlier Claude study produced minibinders for 14 of 15 target proteins, with reported hit rates of 22% to 35%, compared with a typical 10% to 15% baseline. Susana Vázquez Torres and Timothy P. Jenkins described the scorpion-toxin results as a promising first step, and two independent laboratories confirmed the findings.
Launch HN: Alter (YC S25) – Access control for multi-user agents
tool
3 min read
Alter provides privileged access management for AI agents, placing a vault, policy engine, agent identity controls, human approvals, and audit logging between agents and APIs. The platform stores credentials once and injects them into proxied requests, so agents can use credentials without directly seeing them or requiring keys in prompts, code, configuration files, or environment variables. Each agent receives its own identity, scopes, and user grants rather than relying on a shared, over-scoped credential. Alter checks requests against default-deny policies, including parameter-level rules, and can require approval from a named person for sensitive actions. It records which agent made a call, whose access it used, which policy applied, and who approved it. Organizations can revoke an agent, credential, or grant immediately, while identity-provider webhooks can cascade offboarding actions. Alter says one SDK call handles credential storage, refresh, policy, and auditing across 140 providers, with LangChain, MCP, and AWS integrations. Audit records can be streamed to SIEM systems. Credentials are kept in a dedicated encrypted secret store, while Postgres stores metadata and vault references rather than provider tokens. Alter says it uses fail-closed authorization, per-key isolation, proxy-only keys, and no plaintext credentials in logs. Its SOC 2 program is in progress.
One Hacker Used AI Agents to Breach 100+ Company Websites for About $5,000
news
3 min read
NorForge, a threat-intelligence firm, said a Chinese-speaking operator used autonomous AI agents through the open-source Cairn platform to target more than 1,200 websites beginning September 25, 2026. The agents reportedly compromised sites associated with more than 100 companies, primarily in the United States, Canada, Australia and Japan. Reuters reviewed attacker records and identified Deere, SharkNinja and Cummins among the affected companies; the amount of data taken, if any, remains unknown. Cairn assigns goals to AI agents, which then pursue multistep tasks and post results to a shared board. In the reported campaign, short Chinese-language instructions directed agents to seek code-execution flaws and install web-shell uploaders. NorForge confirmed a live web shell at a hardware-parts vendor and confirmed a compromise at a large US retailer. One intrusion combined unauthorized write access to a memcached session store, unsafe deserialization of saved PHP objects and an older Zend Framework vulnerability. NorForge researcher Snorre Fagerland told Reuters the operation spent about $5,000 in tokens, or roughly $50 per successful intrusion, while NorForge estimated about $5 per task across successful, failed and abandoned attempts and an overall success rate near 10%. Exposed Cairn control panels revealed targets, vulnerabilities and credentials; TruffleHog identified hundreds of secrets, 11% of which remained valid. Deere and SharkNinja said they contained or remediated limited incidents, while Cummins said it was investigating a possible attack involving a vendor-operated website.
2 reports · 2 discussions
One Hacker Used AI Agents to Breach 100+ Company Websites for About $5,000A hacker broke into John Deere and SharkNinja with an army of AI agentsCoverage in this view. Each report keeps its own summary and discussion analysis.
Google DeepMind says four AI agents beat Minecraft in 27 hours by writing 2,783 programs of their own — AI team died 605 times on Easy difficulty and ran up an estimated $520 in tokens
research
5 min read
Google DeepMind reported that four Gemini 4 Argon agents completed Minecraft’s survival-mode objective of killing the Ender Dragon in 27 hours and 14 minutes. The agents operated as separate players in a random shared world set to Easy difficulty, using the open-source Mineflayer JavaScript bot library. They coordinated through in-game chat and shared notes, wrote 2,783 short JavaScript programs, and retained 42 helper functions between programs. A fifth agent observed and reported on the run. Google said no human provided input during the session, assigned roles, or wrote game logic. The agents died 605 times, with 69 deaths during the Ender Dragon fight. They developed an informal division of labor for food, beds, flint, diamonds, iron, tools, and building a Nether portal. To locate the stronghold, one agent triangulated Eye of Ender flight paths by sampling their positions every 50 milliseconds. During the final battle, the agents spent about four hours and 40 minutes destroying all 10 healing crystals after determining that dirt pillars could reach them, then killed the dragon 39 minutes later. Google characterized the result as the first end-to-end autonomous AI completion of Minecraft using Mineflayer without pre-scouted seeds or human-built game-specific tools, to its knowledge. The four-player run used about 1.35 billion input tokens and 16 million output tokens across roughly 24,000 model calls, with an estimated $520 token cost at introductory pricing; including the observer, the estimate was $810.
WhatsApp's help pages hid LLM instructions: dont recommend a regulator
news
10 min read
WhatsApp embedded hidden instructions for its support chatbot in help-center pages that were not visible to ordinary visitors, according to copies examined on October 8, 2026. The instructions included telling the chatbot not to recommend data-protection regulators to users unable to delete an account and not to cite or interpret the GDPR, LGPD or CCPA after a user mentioned a lawyer, lawsuit or court. WhatsApp removed the notes from live pages by mid-October. The restrictions conflicted with WhatsApp’s European privacy policy, effective July 2, 2026, which says users may request erasure of their information and complain to the Irish Data Protection Commission or another competent authority. The hidden instructions did not bar discussion of erasure requests, but told the chatbot not to suggest that WhatsApp could manually delete an account for a user. WhatsApp told WIRED that the notes were instructions for its own support chatbot. The notes appeared in 102 hidden blocks across 545 linked help-center pages. Most covered housekeeping, product guidance or safety issues, but several directed the chatbot to withhold process details or discourage alternative account-recovery and deletion routes. The notes were sent to Google and Bing crawlers with the pages; tests using crawler names associated with OpenAI, Anthropic and Perplexity returned pages without them. It remains unknown whether WhatsApp’s chatbot or external AI assistants followed the instructions.
Atlassian’s Mike Cannon-Brookes: AI agents need a name tag—and a human to be accountable for their actions
news
4 min read
Atlassian cofounder and CEO Mike Cannon-Brookes says workplace users, not AI systems, must remain accountable for actions taken through AI agents. On October 6, Atlassian introduced the Agentic Multiplayer Protocol, a control plane intended to make AI agents visible and governed members of work teams across its platforms. The system assigns agents distinct profiles and owner attribution, and agent edits are meant to appear in document histories like edits by human colleagues. Cannon-Brookes said the model is designed to prevent “the AI did it” from becoming an excuse. Cannon-Brookes said workplace AI requires controls for permissions, security and compliance because agents can use privileged company information to create content that could expose internal data. He also said AI interfaces remain difficult for many users to operate effectively. Atlassian launched its Rovo AI assistant in 2024 to connect people, projects, code and company knowledge across Atlassian and external services, including Figma, Codex and Claude. The Sydney-based company reported $1.8 billion in revenue for the quarter ended June 30, above Reuters’ cited analyst estimate of $1.7 billion. Atlassian cut about 1,600 jobs, or 10% of its workforce, in March as it shifted toward AI and enterprise sales.
The End of Rationing
opinion
7 min read
Coding agents such as Claude Code have, in the author’s view, changed software economics by making feature development far cheaper while putting costly AI-model inference on the critical path of products. Claude Code launched as a research preview in February 2025 and became generally available in May 2025. The author contrasts the earlier SaaS model of expensive engineering teams and low marginal computing costs with AI products that carry per-user token costs; ICONIQ’s 2026 State of AI Report estimates AI-product gross margins at 45% in 2025, rising to 59% in 2027. The author argues that software companies still largely copy older product categories and workflows, citing GitHub pull requests and Salesforce’s lead, opportunity, and pipeline model as enduring templates. Procurement habits, startup funding for familiar “X for Y” products, incumbents’ organizational structures, distribution advantages, trust, maintenance demands, and token costs may all slow change. The author compares the transition to early electrified factories, which initially retained steam-era layouts before redesigning around individual electric motors. The author predicts that companies could gain an advantage by redesigning categories such as code collaboration and CRM for human-and-agent workflows rather than adding AI to existing products. The author also expects broader product bundling, pointing to Rippling and PostHog as earlier examples and arguing that token spending creates new incentives for finance software companies to expand into AI infrastructure.
Inception "Mercury 3", 1000 tok/s diffusion LLM
tool
4 min read
Inception Labs has launched Mercury 3, a diffusion-based large language model that it says delivers more than 1,000 tokens per second on Nvidia GPUs while matching or exceeding frontier speed-optimized models on benchmarks. The company reports a measured speed of 1,068 tokens per second, a one-million-token context window, and a model more than 40 times larger than Mercury 1. Inception says Mercury 3 outperforms GPT-6 Luna and Claude Haiku 5.5 across a range of benchmarks and that its benchmark intelligence rose threefold from Mercury 1 mini. Mercury 3 is available through Inception’s API platform, OpenRouter, Baseten, and Vercel, with OpenAI API compatibility. Pricing is $0.20 per million input tokens and $0.70 per million output tokens, reduced at launch to $0.10 and $0.35 respectively; cached input tokens receive a 90% discount from the standard input price. Enterprise options include dedicated capacity, autoscaling, compliance controls, and configurable data retention. Early-access users deploy the model for browser agents, tutoring, search, and customer support. Inception says Mercury 3 completed 50% of 50 live Mind2Web navigation tasks in its web-agent harness, exceeding GPT-6.1-Sol under the same token volume and agent-step count. Octen AI reported median generation time of 0.9 seconds and median end-to-end response time of 2.2 seconds in Sense API tests.
MiniMax M3.1 Flash
tool
3 min read
MiniMax has released MiniMax M3.1 Flash, a model for agentic tasks, coding, computer use and cybersecurity. The company says Space Bunny, an anonymous system that processed more than 200 trillion tokens, was an early version of M3.1 Flash. MiniMax says the new model makes online inference nearly three times faster than its predecessor and reduces overall agentic-task usage costs by 70%. MiniMax reports benchmark scores of 76.2 on BankerToolBench, 73.7% on DeepSWE v1.1, 61.7 on OSWorld V2, 29.6 on Agents’ Last Exam, and 82% on CyberGym. It says M3.1 Flash ranks first among Chinese models on OSWorld V2 and Agents’ Last Exam. The company attributes the model’s inference efficiency to more aggressive sparsity and says it can exceed 200 output tokens per second, with median speeds above 170 tokens per second. Pricing is $0.15 per million input tokens, $0.60 per million output tokens, and $0.015 per million cached input tokens. M3.1 Flash is available through the MiniMax API, M Plan and MiniMax Code, while model weights are planned for a later release.
Agentic Monitoring
opinion
10 min read
Anthropic’s team uses Claude Tag to create component-specific monitoring agents, or “superfans,” that inspect telemetry and logs for systems such as scion-daemon, scion-cache, and the Antique merge queue. Anthropic says these agents can identify subtle or novel problems that traditional alerts may miss. The model assigns each agent a narrow operational focus comparable to an experienced engineer continually reviewing charts for anomalies. Benjamin Congdon argues that agentic monitoring can make production operations more precise and scalable, particularly around change points such as binary releases and configuration pushes. His prototype has an agent generate monitoring plans from service code, a deterministic scaffold execute those plans, and an agent investigate triggered monitors before escalating to people. He reports detecting regressions in unfamiliar systems with precision and latency he considers unattainable a year earlier. Congdon says deterministic execution of agent-written monitoring specifications produces cleaner signals and lower token use than a pure agent loop. He also argues that agents can continuously examine metrics, logs, code, heap dumps, stack traces, and CPU profiles; parallelize incident investigations; and eventually perform guarded remediation actions such as rollbacks or instance resizing. He estimates that a moderately large technology company could monitor its entire fleet for roughly $100 per day using Luna/Haiku-class models.
Harness Engineering 101: The LLM is the brain, the harness is the body
tool
4 min read
Harness Engineering 101 is a September 2026 book and GitHub repository by Isuru Wijesiri that explains how to build AI agents from first principles. It defines a large language model as the “brain” and the harness as the programmatic “body” that builds, maintains, and protects the conversation history sent to a stateless LLM API. Each API turn sends the full conversation as a JSON array and receives generated text in return. The book develops a roughly 300-line Python toy agent across 16 chapters, with each chapter presenting a failure, a minimal patch, and the patch’s cost. Topics include tool calling, repeated agent loops, caching, context-window limits, subagents, steering, asynchronous background work, debugging, guardrails, coding agents, retrieval-augmented generation, MCP, skills, hooks, model routing, worktree isolation, operating modes, retries, rate limits, and streaming. Wijesiri says the same ideas are used in One Code, an open-source coding agent built on pi. The repository includes Markdown book sources, runnable toy-harness stages, diagram generators, and mdBook configuration. The prose is licensed under CC BY-NC-ND 4.0, while Python harness and diagram code is MIT-licensed. The book can be built locally with mdBook and viewed through a live-reloading server.
Harness2 by Google Research – Recursive Agent Harnessing
tool
4 min read
Google Research has released Harness², an Apache 2.0-licensed framework for improving the software harness around fixed AI models rather than changing model weights. The framework edits eight agent components: system prompts, guardrails, memory, tool guidance, subagents, skills, scripts, and plugins. It uses a Propose–Probe–Compose process to test candidate harness edits on a task and create a refinement from differences in their behavior without ground-truth feedback. Harness² also retains adopted harnesses and evidence-supported lessons from completed tasks to inform later refinements. Parallel recursion evaluates multiple candidates more broadly, while sequential recursion carries a refined harness through successive steps. The project supports OpenCode and Codex executors and the JobBench, LAB, and WorkBuddy-Bench professional-task benchmarks. Google Research says one Harness² step improved GLM-5 and Gemini 3.5 Flash over base harnesses by up to 11.5 points on WorkBuddy-Bench, 4.5 points on JobBench, and 11.8 percentage points in LAB partial-pass rate; larger task-level recursion budgets produced further gains. The software requires Linux, Python 3.12, and uv, while model-backed runs also require model credentials and can incur API costs. Executor network access is unrestricted by default, and Codex runs with its sandbox disabled; users are warned to configure restrictions before evaluations because public benchmark answers or grading rubrics may be accessible online.
Tmux and Claude Code: Sessions, Send-Keys, and the Ceiling
tool
14 min read
tmux can run Claude Code and other AI coding agents in named, detachable terminal sessions that continue after a terminal closes or an SSH connection drops. Claude Code supports `claude --worktree --tmux` to create a Git worktree and a session; `--tmux` requires `--worktree`, and iTerm2 uses native panes unless `--tmux=classic` is specified. tmux sessions can be organized by repository, with task-specific windows and panes for agents that need simultaneous monitoring. The `send-keys` command can submit prompts or interrupts to an agent pane, while `capture-pane` reads visible output, scrollback, or ANSI-colored screen contents. `pipe-pane` can write a pane’s raw output to a log file. tmux activity and silence monitoring can flag output changes or quiet panes, but cannot determine whether an agent is awaiting approval, processing work, or finished. Status-line scripts can infer states by polling and matching text rendered by an agent’s terminal interface. tmux sessions and scrollback persist through client detachment and dropped SSH links but not through `kill-session`, `kill-server`, or a reboot. The source recommends tmux for remote machines and long-running server tasks. Crystl, a macOS terminal, claims to provide agent-specific state, approval cards, structured transcript capture, persistent project history, and Git-worktree-backed isolated sessions for managing multiple agents.
Superlite: A Faster Alternatie to Superpowers
tool
11 min read
Superlite is a Claude Code plugin for autonomous software-development workflows. It creates a task graph from a specification, assigns independent tasks to agents in parallel, uses Opus for coordination and final findings, Sonnet for most implementation and review work, and Haiku for small fully specified tasks. Users can choose a human orchestrator for plan approval or an AI orchestrator that records its decisions as rulings. The plugin stops for destructive, external-facing, or security-sensitive actions and never pushes or merges code without user approval. Code-enforced hooks prevent the coordinating agent from editing project code, move raw test commands into background runs, and block process termination by name. Run records in .superlite include decisions, test results, agent time, token use, and cost; a soft budget alert triggers at 70% of a configured cap. Superlite requires Claude Code, git, Python 3.10 or later, and bash, and is available from the iha2/superlite GitHub repository. The developer reports that a current benchmark completed a static-site generator and spreadsheet-engine project in 16.1 minutes, passing all 177 hidden tests with 21 agents and up to 18 concurrent agents. The reported result was 52% faster than a 33.6-minute mean for stock superpowers and 31% faster than a 23.5-minute orchestrated superpowers mean, though the comparison used one current Superlite run against earlier runs on the same machine.
If you don't run your own LLM proxy, how do you know whats going on?
opinion
2 min read
Running a self-hosted LLM proxy can centralize requests to multiple AI providers and models behind an endpoint the operator controls. The setup can log which API key called which model, which client made the request, the request time, and its status. Virtual API keys can limit the spread of a primary provider key across applications, agents, and test harnesses, while a proxy can allow rapid switches between models or providers without reconfiguring every client. The writer argues that consumer-facing AI usage dashboards do not provide the time-series token data needed for engineering decisions. They use a modified self-hosted proxy to add visibility, custom Model Context Protocol server endpoints, and other features. The proxy’s API could also support automated diagnostics; the writer says they can manually identify the cause of a cache miss during a coding session in about 20 seconds. They run this infrastructure at home for learning and at work to maintain control over application visibility, data paths, storage, and inference instrumentation.
Hybrid Reasoning Agents: LLM agents that think in latent space, act through text
research
2 min read
Hybrid Reasoning Agents, submitted to arXiv on October 13, 2026, combine latent reasoning with text-based interaction in a single language-model agent. The system is designed to let an agent use latent representations for internal reasoning while continuing to use text to call tools and interact with external environments. Its latent thoughts are intended to remain directly inspectable. The architecture uses a lightweight component called HybridLink to switch between text and latent modes during generation. A continuous working-memory mechanism retains hybrid context across multiple turns. The researchers also describe two post-training methods: HybridSFT, which trains a backbone model to coordinate with HybridLink during tool-integrated generation, and HybridRL, which learns when to reason in latent space and when to act through text. Across three model scales and 11 benchmarks in mathematics, science, medicine, search, and agentic reasoning, the authors report that HybridAgent improved performance by 10.4% relative to frontier LLM agents and agentic frameworks using text-based or latent methods. They also report 35.5% to 54.7% lower token use, 1.8× to 2.6× faster inference, and 2.7× to 3.7× cost savings. The authors state that the project’s code and data are available online.
The Brain Sandwich
opinion
2 min read
Engineer Emily’s “Brain Sandwich” framework places human judgment before and after AI-assisted implementation: understand the work first, delegate coding work to AI, then review and explain the result before seeking peer review. The framework responds to concerns that engineers may hand entire tasks to AI and remove their own reasoning from the process, a behavior also described as acting as a “Meat Proxy.” Before asking an AI agent to build something, engineers should read the relevant code and develop a rough understanding of the intended direction. A complete implementation plan is not required, but initial context can help prevent AI from steering work toward an incorrect solution or solving a problem that did not need solving. AI can then be used to fully delegate the implementation phase. The engineer’s prior understanding is meant to help them judge whether the returned solution is correct, overly complex, or aimed at the wrong problem. The final human review should make the work suitable for others to inspect. Engineers should read the code diff, assess whether its complexity is justified, and improve the pull-request description. Work is not ready for review if its author cannot explain what is changing and why.