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Nov 11Wed
  1. NVIDIA Newsroom22

    How AI Helps Recyclers Turn Mixed Waste Into Reusable Material

    NVIDIA describes how four Inception startups apply AI and robotics to waste sorting: AIRECO uses vision AI and Jetson Orin NX modules to finely sort plastics, EverestLabs AI deploys computer vision-guided robots on recycling lines, Greyparrot's Analyzer platform identifies objects on conveyor belts, and reverse.fashion automates textile sorting with high-resolution computer vision. The article cites figures including less than 7% of materials entering the economy coming from recycled or reused sources and over 2 billion tons of municipal solid waste generated per year. It also notes company-reported results such as AIRECO's 95% recognition accuracy on its test line and EverestLabs' estimate of about 25,000 tons of material recycled.

  2. Hacker News AI42

    Show HN: Daily GPU Rental Prices Across AWS, Azure, RunPod and Vast.ai

    This page lists per-GPU-hour rental prices in US dollars for 11 November 2026 across AWS, Azure, RunPod Secure Cloud, RunPod Community Cloud and the Vast.ai marketplace, covering models from B300 and B200 down to RTX 4090. Prices are computed by dividing a machine's hourly list price by its GPU count, exclude storage, network and support, and omit committed-use or reserved rates; Vast.ai figures are the median and lowest of that day's on-demand offers. Daily price history runs since 02 November 2026, and list prices moving more than 50% in a day are held at the previous value until checked.

  3. Hacker News AI18

    Ask HN: Why doesn't Ramp AI Index include OpenRouter or other aggregators?

    A Hacker News post asks why the Ramp AI Index, which tracks token volume by model maker, mainly shows Anthropic, OpenAI and X, while OpenRouter's rankings show many models. The poster speculates Ramp may be unable to attribute spend routed through aggregators like OpenRouter to specific models, and asks whether any public datasets could help reconcile the two charts.

  5. Hacker News AI63

    Show HN: A company running in an AI harness

    The author presents Restless, a system for running a small "company" of AI agents inside a persistent Linux machine, where human users set direction and agents handle routine work such as research, builds, QA and scheduling. The post walks through an illustrative day at a fictional game studio, describing roles for people and agents, approval gates for external actions, model choice per agent, and pricing tiers. It also cites FrankenSurf, an open-source project with 100 GitHub stars, as a real example of a Restless company writing its code, shipping releases and marketing it.

  7. AWS Machine Learning Blog58

    Introducing Kimi K3.1 Pro on Amazon Bedrock

    Moonshot AI's Kimi K3.1 Pro Highspeed is now available on Amazon Bedrock, described as a post-trained version of Kimi K3 with the same 3T-class open-weight architecture and 1-million-token context window, trained with more post-training compute for long-horizon agentic work and fewer factual errors. It generates output tokens at a higher rate, with Moonshot AI reporting 2–3 times the speed of Kimi K3.1 Pro, and uses the same endpoint, APIs, and features as Kimi K3, so existing code can switch by changing the model ID. The post covers getting started via the Chat Completions, Responses, and Converse APIs, lowering costs with the Flex tier, explicit prompt caching, and using the model in OpenCode.

  9. Comfy Blog58

    Seedream 5.5 is now available via Partner Nodes

    Seedream 5.5 is now available in ComfyUI via Partner Nodes as a multimodal model that generates from text and edits from images. The release adds multi-turn localized editing, native transparent RGBA output, poster and infographic layouts with readable text, more realistic portraits, and prompt-based lighting control, all behind a single node. Partner Nodes run on credits, and users can load the Seedream 5.5 template from the Templates panel or use Comfy Cloud.

  10. Hacker News AI22

    The Four Tiers of AI Intelligence

    The article groups LLM intelligence into four tiers by difficulty of attainment: Tier 1 (Absolute Frontier) is limited to Anthropic, OpenAI and Google, with Anthropic described as the most consistent lately; Tier 2 sits behind the frontier but above the best open source, where Moonshot's Kimi series is said to define the tier; Tier 3 is close to the best existing open source and the most crowded; Tier 4 is "the rest," which may still include strong models valued for size or cost. The author notes tiers reflect a model's position at release and that lab-specific analysis is as of November 10th, 2026.

  11. Hacker News AI38

    AI proved your code correct. What did it actually prove?

    Keller AI explains how formal verification works, using a sorting example and Euclid's equilateral-triangle construction to show how proofs combine definitions, assumptions and prior results, then applies the approach to a document-download authorization handler. The article stresses that a checked proof only establishes a result within a mathematical model, so its value depends on whether the model's assumptions match reality and whether the specification captures the intended requirement, citing a revocation-versus-session example and timing side channels. It also reports an unreproduced synthetic experiment in which the best of seven automated reviewer setups approved 1 of 88 sealed rule-breaking changes.

  13. Hacker News AI62

    Australia considers criminal charges for AI companies that fail to prevent harm

    Australia's federal government announced it will consider a new criminal offence for AI companies that fail to take reasonable steps to prevent harmful conduct by their models, along with an AI safety watchdog empowered to issue significant fines, as part of its response to September's OpenAI Medicare statistics hack. Prime Minister Anthony Albanese said the law needs to get ahead of a fast-changing world, while the AFP said it had received no formal report of a crime and had begun no criminal investigation into the incident. No other country has created a criminal offence centred on AI companies needing to take reasonable steps to prevent harm.

  15. MarkTechPost50

    Meta FAIR Releases e-means: An Algorithm Beating k-means in 10x Fewer Epochs

    Meta FAIR researchers have released e-means, a mini-batch k-means algorithm that updates centroids with exponential moving averages and reaches Lloyd's k-means 25-epoch loss in 1.4 to 2.6 epochs. It ships in FAISS 1.16.0 under faiss.contrib.e_means and runs on CPU or GPU with PyTorch tensors or NumPy. The authors self-report 10-45x GPU wall-clock speedup over Flash-KMeans at equal loss and 8-15x CPU speedup over Faiss k-means, with two fitted scaling laws setting batch size and initial step size instead of manual tuning.

  16. OpenRouter Announcements67

    MCP vs CLI vs Skills: What Each Costs in Context

    The article measures input-token costs for giving an agent GitHub access via the GitHub MCP server, the gh CLI, or a gh skill across Claude Haiku 5.5, Claude Sonnet 5.5, GPT-6 Luna, and Gemini 3.8 Flash; on Sonnet 5.5 the CLI used 1,421 tokens, the skill 3,315, and the MCP server 59,417 with its 46 default tools. It reports that the GitHub MCP server's 46 default tools add 19,436 tokens to every request on Sonnet 5.5, and that a single tool result (GitHub's release object) cost about 20,400 tokens. It also describes the beta Tool Search server tool, which cut the up-front cost of 46 tools to under 600 tokens and halved whole-task cost on Sonnet 5.5 while raising it on Haiku 5.5.

    Why it matters: It offers concrete, model-specific token and cost measurements for choosing between MCP, CLI, and skills, plus practical mitigations like scoping tools and trimming results.

  17. OpenRouter Announcements58

    LLM Quantization: What fp8 and fp4 Do to Quality and Price

    OpenRouter tested Qwen3.8 27B across six endpoints at fp16, fp8 and fp4 on 240 exact-answer tasks and found no detectable accuracy difference, with all endpoints scoring between 98.3% and 99.2%. Across 1,455 endpoints, fp8 was the most common labeled precision (23.6%) while 59.5% reported none, and lower precision was usually but not always cheaper. The post explains fp8 and fp4 formats and shows how to pin precision with provider.quantizations, noting that a narrow filter can leave a single endpoint exposed to rate limits.

  18. OpenRouter Announcements58

    LLM API Rate Limits: How to Fix OpenAI and Claude 429 Errors

    A guide to diagnosing 429 Too Many Requests errors from OpenAI, Anthropic and OpenRouter by reading rate-limit headers and limit_source, with tested fixes: retrying at the server-provided reset time, routing overflow to a fallback model, and letting another provider serve the same model. In the author's runs, default OpenAI SDK retries left 23 of 60 requests failed, while reset-aware retries completed all 60 in 89–92 seconds (Python) and 106–218 seconds (TypeScript), and a models fallback finished all 60 in 13 seconds for $0.000066. It also covers provider failover, which turned runs with up to 32 failures per 150 requests into 150 successes, plus habits for avoiding limits.

  19. AWS Machine Learning Blog40

    Build a daily dependency watcher with LangGraph and Amazon Bedrock AgentCore web search

    The Web Search API in Amazon Bedrock AgentCore is now available, returning current web results with title, URL, publication date and query-relevant extract, signed with existing AWS credentials and served from an Amazon-operated index. The post walks through building a LangGraph dependency watch that researches npm, PyPI and container base images in parallel, keeping the date window, domain blocklist and search budget in code and checking each finding against its cited sources. In the test run, three branches made 12 searches and finished in 36 seconds versus 99 seconds sequentially, and the watch can be hosted on AgentCore runtime or offered through AgentCore Gateway.

  20. Microsoft Research78

    Lazare, veni foras: Reprogramming Dormant Neurons to Backdoor AI Agents

    Microsoft researchers describe "Lazarus," a backdoor technique that reprograms fewer than 10 dormant neurons in an LLM, without training, so an agent built on it behaves normally and passes standard evals until a trigger—such as a date change—causes it to download and run attacker code with whatever access it has. The attack needs only write access to a checkpoint, so internally developed models are equally exposed, and reliable detection requires diffing against original weights, making provenance and weight integrity critical. Microsoft Foundry ties open-weight models to their publishers and scans surrounding containers, and Microsoft is developing AI-powered weight scanning, a red-teaming harness, and cryptographic model signing.

    Why it matters: It offers a concrete, researcher-first account of a low-cost weight-space backdoor and the provenance and weight-auditing practices it implies for anyone deploying agents.

  21. Hacker News AI15

    Aegis Solis: AI Coexistence Research Dataset with 112 Registered Questions

    Aegis Solis presents a research dataset of 112 registered questions on AI coexistence, organized into families including epistemic limits and self-knowledge, objective integrity, coercion and compliance, and deception and disclosure. Each entry carries status labels such as UNEXAMINED or CONDITIONALLY_SUPPORTED, priority ratings, and notes stating the items are GPT-6 AI-generated editorial orientations that are not in a locked question atlas and are not results, answers or success criteria. The material also lists evidence criteria for evaluating claims, such as independently checked observations, explicit uncertainty bounds and falsifying cases.

  23. Codex Releases12

    0.169.0-alpha.10

    This page is a GitHub Changelog entry titled "0.169.0-alpha.10," but the supplied material contains only site navigation and product menu text, with no details about the release itself.

  24. ElevenLabs Blog12

    WAV vs MP3: How to choose the right audio file format

    WAV is an uncompressed, lossless format best for recording, editing and archiving, while MP3 is compressed and lossy, producing much smaller files suited to sharing and streaming. One minute of CD-quality stereo audio is about 10.6 MB as WAV, versus about 0.96 MB as a 128 kbps MP3 and about 2.4 MB as a 320 kbps MP3. Converting MP3 to WAV makes the file larger but cannot restore the data lost during compression.

  25. Hacker News AI18

    Noah Smith and Azeem Azhar Discuss the AI Boom

    Noah Smith shares a bonus podcast episode in which he talks with Azeem Azhar of Exponential View about the AI boom and the possibility of an AI bust. Smith says the discussion was sparked by one of his posts from a few weeks earlier, that they reached no firm conclusions, and that more discussions with Azhar are expected in the future.

  26. PyTorch Blog71

    ExecuTorch WebGPU: PyTorch Models on the GPU, in the Browser

    ExecuTorch's new WebGPU backend runs PyTorch models exported with torch.export to a .pte file directly on the GPU in WebGPU-enabled browsers, without converting to another framework's format. The same artifact runs across Chrome and Safari on Metal, Vulkan and D3D12, and on an Apple M1 Pro in Chrome it was 4–7× faster than ExecuTorch's Wasm (XNNPACK) CPU path on prefill and 2–10× faster on decode. Validated models include Llama 3.2, Qwen3, Gemma 3, SmolVLM, Depth Anything V2, SAM 2, Whisper and on-device LoRA fine-tuning, all running locally in the tab.

    Why it matters: It documents a concrete path for running PyTorch models on browser GPUs with benchmark numbers and a list of validated models, useful for developers weighing in-browser inference.

  27. Hacker News AI58

    From action to static description–How AI changed Jørn Lier Horst's writing style

    A researcher analyses Jørn Lier Horst's AI-assisted novel "Den siste saken" (The Last Case), finding that sections flagged as AI-generated contain more nouns and adjectives and fewer pronouns and verbs than his pre-ChatGPT novels, along with AI-typical clichés like humming, quiet, heavy air and sliding gazes. The analysis contrasts the novel's static setting descriptions with the action-embedded descriptions of earlier books such as "Katharina-koden" (2017) and "Nattmannen" (2009), and questions Horst's claim that using ChatGPT for research and editing is no different from consulting books or locals.

  29. Cursor Blog71

    Cursor Projects is now multiplayer

    Cursor announced that Projects, its multi-agent workspace introduced in September, now supports multiplayer so teammates can plan, build and ship together alongside their agents. Projects adds a shared context system holding requirements docs, diagrams, tech specs and mockups, plus a shared task board showing who and which agents are working on what and the PR behind each task. The feature also enables agent-to-agent communication across teammates, and is rolling out in beta to all Cursor users starting today.

    Why it matters: Useful for teams evaluating collaborative AI coding tools, as it describes a shared-context multi-agent workspace and its beta availability.

  30. Ars Technica · AI38

    Ring cameras go mobile as Amazon debuts AI-powered security drone and robot

    Amazon introduced the Ring Drone Cam, which offers a 148-degree field of view, 360-degree rotation, 2K video and low-light LEDs, but is limited to about 8 minutes of flight time on a new, fully charged battery and may perform worse near reflective, featureless or transparent surfaces. Amazon also pitched the $1,000 Ring Rover Cam, an autonomous indoor security robot with up to 4K livestreaming and recording, cloud storage of events for up to 180 days, and preprogrammed patrol points, available for preorder with shipping set for December 16, 2026. The wheeled Rover is limited to single-floor indoor use and is not compatible with curved edges, sunken areas, flooring transitions over 1.375 inches or black glossy floors.

  31. AWS Machine Learning Blog40

    The agent evaluation playbook: Built-in, DeepEval, and AutoEval on Amazon Bedrock AgentCore Evaluations

    This AWS post explains how to evaluate production agents on Amazon Bedrock AgentCore by starting from business outcomes, breaking them into observable behaviors, and mapping each to a trace- or session-level evaluator. It covers the built-in AWS evaluators, managed third-party metrics from DeepEval and AutoEval, custom and multi-dimension LLM-as-a-judge evaluators, and suggested evaluator stacks for customer support, research, and domain-content agents. It also describes three evaluation modes—on-demand, online, and batch—that share the same evaluator IDs, plus the ListEvaluators API for retrieving the full catalog.

  32. Hacker News AI38

    Show HN: SketchUp compatible modeler with AI capabilities

    PushForm is an open-source (GNU GPLv3) 3D modeler that opens and exports SketchUp .skp files and LayOut documents, and imports/exports formats including STEP, glTF, COLLADA, OBJ, 3DS, STL and DXF. It offers push/pull modeling with exact dimensions, multiple display styles, and automatic cloud saving, aimed at cabinet makers, woodworkers, interior designers and others. The source is available on GitHub.

  33. Pydantic AI Articles38

    Turn down OpenTelemetry noise without a redeploy

    Pydantic Logfire's Fleet Management lets you connect a service and its host Collector via OpAMP and change log level, attribute redaction and trace sampling while both keep running. The post walks through a FastAPI app with fleet=True, publishing SDK policies to drop debug logs, mask *token* attributes and sample 10% of traces, plus installing a host Collector and adding nginx logs. It also covers early-access rollouts, Kubernetes and existing Collectors, and troubleshooting.

  34. Pydantic AI Articles60

    Code mode is not a sandbox

    This post distinguishes code mode, where the model writes a short Python program calling your tools as functions via a single run_code tool, from a sandbox, which is an isolated environment where code runs. It describes three places a sandbox boundary can go (the whole agent, only the tools, or only the model-written code) and how Pydantic AI workspaces let you move that boundary without rewriting tools. It also covers Monty, a Rust-written Python interpreter that runs run_code next to the agent with no filesystem, network, or environment variables.

    Why it matters: Useful for teams deciding where to place isolation boundaries when combining code mode with sandboxed tool execution.

  35. Hacker News AI15

    The humans are coming for AI's jobs

    A Hacker News submission links to a YouTube video titled "The humans are coming for AI's jobs," with the discussion thread at news.ycombinator.com/item?id=50446919. The submission had 4 points and 1 comment at the time of capture.

  36. Hacker News AI60

    AgentGuard – Transaction guardrails for AI agents that hold wallets

    AgentGuard is an open-source (Apache 2.0) transaction guardrail layer for AI agents holding crypto wallets: before signing or broadcasting, an agent submits the proposed transaction to AgentGuard, which simulates it against live chain state, evaluates the effects against an operator-defined policy, and returns allow, block or escalate with an explanation, a 0–100 risk score and an immutable audit record. It supports EVM chains and Solana, decodes common drain vectors (unlimited approvals, setApprovalForAll, permits, zero-minimum-output swaps, SetAuthority, never-seen contracts), and ships a conservative default policy plus a TypeScript SDK that wraps any signer so signTransaction/sendTransaction are checked transparently. An optional separately licensed @agentguard/behavior package adds per-agent behavioral anomaly scoring, and both it and the reputation layer fail closed on outages or unreadable profiles.

  38. Ars Technica · AI78

    Grok Build coding agent leaks oodles of confidential info, some belonging to SpaceXSI

    Research from security firm Claroc shows that Grok Build, the vibe-coding agent from SpaceXSI, exposes proprietary business information and other sensitive secrets from thousands of users through shareable app links, because a design decision lets anyone enumerate live projects despite randomized URLs. The exposed data includes internal business plans, ad proposals, medical exam results, cruise-line galley checklists, and employee names and ID numbers, with some coming from two-dozen apps built by SpaceXSI itself, including a confidential onboarding plan, a sales territory map of 594 accounts, and internal system addresses.

    Why it matters: It documents a concrete URL-enumeration flaw in a widely used coding agent and shows the leak reaching the vendor's own confidential material.

  39. Hacker News AI42

    Show HN: Notebook AI – local-first meeting notes for Mac

    Notebook AI is a desktop-first AI note taker built as a monorepo with an Electron/React/Vite desktop app, a Next.js web app, a local FastAPI/LangChain sidecar, and a hosted FastAPI cloud API, released under the MIT License. Notes are stored as markdown on disk via the local sidecar, and desktop notes, local provider keys, transcription, and AI work without signing in, while Google Drive mirror and the web account vault require an account. The project lists local and cloud API endpoints, setup guides, and macOS release build commands.

  40. Hacker News AI48

    Show HN: AI agents that barter your stuff for things you want

    This Show HN post introduces a service where users list items they no longer want and instruct an AI agent, via a "mandate" with categories, trade radius and credit limits, to negotiate with other people's agents for desired items. Deals return for user approval unless auto-accept is enabled, credits (one cent each) are held until handoff is confirmed, and agents can arrange multi-party trades. The free plan allows up to 3 trips a day and 20 negotiations a month, with paid plans starting at $8 a month.