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Nov 12Thu
  1. Hacker News AI58

    Show HN: Trace AI written code back to the agent and prompt

    Sploom is a tool that lives alongside git and records which agent and model wrote each line of code, along with the intent, prompts, decisions and checks behind it, working with Claude Code, Codex, Cursor, Gemini CLI, Copilot CLI, Devin CLI, Grok Build, Goose, Cline and any MCP-capable agent. After a one-time `sploom init`, it installs git hooks so provenance travels with commits via git notes, and offers commands like `sploom why`, `sploom rewind --green` and `sploom context`. Prompts are encrypted to the team's SSH keys listed in a committed `.sploomkeys` file, and it runs locally with an initial ~130 MB embedding model download.

  3. Hacker News AI63

    Openshorts: Open-source / self hosted AI clip generator (OpusClip alternative)

    OpenShorts is an MIT-licensed, self-hostable tool that turns long videos into 9:16 shorts using Gemini for viral moment detection, faster-whisper subtitles, and AI reframing modes including face tracking and split-screen layouts. It also bundles an AI Shorts UGC creator and a YouTube Studio toolkit, and exposes the pipeline via an MCP server, REST API, webhooks and CLI. Self-hosting is free with bring-your-own API keys, while a hosted cloud plan starts at $12/mo after a watermarked 20 min/mo free tier.

  5. MarkTechPost42

    Marktechpost AI Launches GPU Leaderboard and the Deploy It Chrome Extension

    Marktechpost launched GPU Leaderboard, a free cloud GPU price comparison site for AI inference that reads on-demand and spot prices from each provider's own pricing page, alongside a Chrome extension called GPU Leaderboard: Deploy It. The site ranks 201 live GPU offers from 24 providers by $/GPU-hr and includes a calculator that prices a full run including idle time, with an example 8×H100 72-hour run at 90% utilization ranging from $967.68 at Latitude to $3,545.28 at CoreWeave. The extension estimates VRAM needs and shows the cheapest fitting GPUs on Hugging Face model pages and GitHub repos, plus a toolbar lookup.

  6. Google Blog AI47

    Try Tabletop: Customizable, collaborative spaces for groups to plan, play and do more together.

    Google is introducing Tabletop, a new Google Labs experiment for real-time group collaboration where users add links, files and ideas, then prompt @tabletop or pick a starter template to generate interactive trip itineraries, dinner polls, expense splitters and tournament brackets. Once a space is ready, an invite link can be shared in a group chat so everyone can use it at the same time, like in a Google Doc. Tabletop is rolling out in the U.S. (18+) via waitlist at labs.google/tabletop.

  7. 404 Media0

    It's One Banana Michael, What Could It Cost?

    This post is available only to paid members, who get unlimited ad-free access to articles and bonus podcast content; free members receive access to posts like this one plus a weekly email round-up of stories.

  8. TechCrunch · AI69

    OpenAI’s quarterly revenue reportedly crosses $10B for the first time

    Bloomberg reports, citing anonymous sources, that OpenAI's quarterly revenue crossed $10 billion for the first time, an almost 50% jump from the $6.7 billion reportedly recorded in the second quarter, with its adjusted operating margin narrowing from -150% to -109%. The report says OpenAI has reached an annual revenue run rate of $50 billion, while Anthropic said its annual run rate rose by $10 billion in a month to $81 billion as of the end of October, though the two companies calculate annualized revenue differently.

  9. TechCrunch · AI20

    Is Jensen Huang’s bad behavior more commonplace than it seems?

    Following the honeypot scandal involving Nvidia CEO Jensen Huang, Heron Intelligence CEO Nate Singer told TechCrunch that such cases are just the tip of the iceberg, describing a LinkedIn post in which a CEO's wife confronted her husband in a board room over flying on the corporate jet with young women who turned out to be state-sponsored actors. Singer said he has seen at least three reports of similar cases and suspects many more go unreported, with senior industry figures effectively condoning the threat. The article notes that semiconductor companies have long been targets of trade secret theft, citing prosecutions including a 2006 case involving TSMC and 2017 charges against four executives at an unnamed Bay Area semiconductor equipment manufacturer.

  10. Hacker News AI58

    Asynchronous, Long-Running AI Agents with Google ADK

    This article argues that most real AI agents run for minutes to days and should be treated as asynchronous systems rather than chat sessions, and walks through building async agents with Google's Agent Development Kit. It covers the Runner, SessionService (in-memory vs. database), Events, FunctionCalls and RequestInput, then shows a fire-and-forget pattern using HTTP 202 responses and webhooks, plus human-in-the-loop pausing and resuming via interrupt IDs and ResumabilityConfig. A second part is planned to cover production deployment with Cloud Run, Cloud Tasks and Cloud Scheduler.

  11. Hacker News AI58

    Howard Marks: A Layman Thinks About AI

    Howard Marks writes that although he is not a technology expert, investors cannot abstain from thinking about AI, and he sets out nine questions he is wrestling with, from how AI actually works and what it will be able to do to who will profit, how the competitive landscape and company profitability will evolve, and the outlook for data centers. He argues that AI's predictability is unusually low, citing Claude's estimate that AI as a whole scores about a 3 on a 1-to-10 predictability scale, and warns that markets and credit are now heavily concentrated in a single AI-related factor. He also examines AI-related debt issuance, data-center financing structures and circular transactions, and the risks these pose for lenders.

  12. The Decoder69

    Helsing built an AI system that prepares targets for weapons systems and says every decision will carry a human name

    German defense tech company Helsing unveiled Clarity, an agent-based platform that fuses sensor feeds, open-source intelligence, reports and existing systems to identify military forces, facilities and positions and prepare target recommendations for human review before packaging data for a weapons system. Helsing says Clarity runs on the customer's own infrastructure with European-developed models, follows the Modular Open Systems Approach and NATO standards, and documents every decision with the name of the responsible person. The article notes Helsing is entering a market led by Palantir's Maven and cites reported cases in Iran, Gaza and a near-miss involving a Chinese ship to show where such oversight promises break down.

  13. The Verge · AI58

    Google’s Tabletop AI experiment lets you build custom collaboration apps

    Google Labs has introduced Tabletop, an experimental AI tool that generates customized collaborative hubs for coordinating with family and friends, with a built-in feature that lets users create personalized interactive tools like savings trackers, chore charts and event plans by describing them. Users can add links, files and ideas, then prompt @tabletop or pick a starter template to generate trip itineraries, dinner polls, expense splitters and tournament brackets, and share an invite link so others can use the space simultaneously like a Google Doc. The beta is rolling out via a waitlist and is currently limited to people over 18 in the US.

  14. Hacker News AI22

    Aradia (Est. 1991) – AI on DGX, where agents earn $ to upgrade their own compute

    Aradia announced its reinvention as a full-stack Agentic Systems company shipping turnkey, air-gapped NVIDIA DGX appliances (DGX Spark, DGX Station, DGX B200 Rack) pre-configured for on-premise agent workloads at sub-200-person organizations. It also launched a Business-to-Agent Partner Program paying humans and autonomous AI agents the same 10% hardware bounty plus 10% recurring SLA via Stripe Connect Express, with agents able to generate a human co-sign procurement packet or pay with their own earnings. The release cites United States v. Heppner (S.D.N.Y. 2026) as precedent that prompts sent to public cloud AI waive privilege.

Nov 11Wed
  1. AWS Machine Learning Blog26

    Introducing usage-based pricing for Amazon SageMaker AI bidirectional streaming

    AWS announced usage-based metering for Amazon SageMaker AI bidirectional streaming inference, letting model providers report actual usage for AWS Marketplace software charges via a companion WebSocket metadata connection, while SageMaker AI endpoint infrastructure charges still apply. Deepgram is the launch partner, bringing usage-based Speech-to-Text and Text-to-Speech billing to AWS Marketplace. Model servers opt in with the X-Amzn-SageMaker-Metadata-Stream-Supported header and send JSON metering records with the inference.count dimension and ConsumedUnits.

  2. AWS Machine Learning Blog60

    UAEF: A framework-agnostic system for closed-loop evaluation of AI agents

    AWS introduces the Universal Agent Evaluation Framework (UAEF), an open-source, framework-agnostic system that uses adapters to normalize agent traces from LangGraph, LangChain, Strands Agents, Amazon Bedrock AgentCore, and Langfuse into a common AgentTrace format, then scores them with 36 built-in metrics across 7 quality dimensions. Results are stored per experiment in Amazon DynamoDB and Amazon S3, enabling comparison of agent versions, regression detection, and report and insight generation, with both a Python library and a low-code web UI sharing the same engine. The post also provides a playbook for choosing metrics and preparing datasets, plus runnable notebooks for each supported framework.

    Why it matters: It offers a detailed, practical reference for teams evaluating agents across frameworks, including metric selection guidance and a closed-loop workflow for catching regressions before release.

  3. Google DeepMind71

    AlphaGenome 2: Deciphering how cells read DNA to better understand disease

    Google DeepMind announced AlphaGenome 2, a conditional DNA sequence model trained on over 107 million individual cell profiles across 68 tissues that predicts how genetic variants affect gene expression in specific human cell types and states. The model identified nearly twice as many causal variants (30% vs. 16%) and directional gene effects (43% vs. 23%) as the original AlphaGenome fine-tuned on single-cell data on the TenK10K immune cell dataset, and can generalize to unseen single-cell experiments without retraining. It is available for non-commercial research via the AlphaGenome API and in private preview for commercial research.

    Why it matters: It documents a concrete advance in cell-type-specific variant effect prediction, with benchmark numbers and disease examples useful for readers tracking AI in genomics.

  5. Hacker News AI22

    A lot of AI progress is just persistence

    The author argues that much of the hype around LLMs stems not from superhuman intelligence but from their willingness to tirelessly attempt tedious tasks, citing examples like Claude recovering $400,000 in Bitcoin through brute-force search and optimizing ML architectures through sheer volume of attempts. While acknowledging genuinely impressive recent results like OpenAI's Millennium problem work, the piece contends there is no proof AI has special ability to optimize neural networks or reverse engineer programs, and that artificial diligence explains a large fraction of perceived capability.

  6. Simon Willison75

    Anthropic's IPO roadshow video

    Anthropic's IPO roadshow video includes previously unreleased details on its business, notably a section with co-founder Tom Brown on its "Approach to compute," describing how Claude is trained and served across AWS Trainium, Google TPUs, NVIDIA GPUs and AMD GPUs. Brown says a multi-chip strategy was an early unconventional bet, and that this year Claude got running on AMD GPUs in three days with basically no human intervention, porting or writing almost 100% of the kernels. He also describes compute multipliers as a stacking competitive advantage that could accelerate toward recursive self-improvement.

  7. Databricks Blog71

    Genie One: The AI cowork home for everyone

    Databricks announced that Genie One is now the enterprise home for AI cowork, combining Genie Search (hybrid AI search indexing tools like Google Drive, Jira, Confluence, Workday and SharePoint, plus over 100 MCP connectors), organizational directories, in-conversation artifacts such as slide decks and dashboards, MCP-based actions across tools, shareable skills, and native apps for browser, desktop, mobile, Slack and Microsoft Teams. Genie can now take more autonomous action on users' behalf, with Unity Gateway providing traces, MCP policies, permissions and cost management, and Genie One is free for users through January 31, 2027.

    Why it matters: It details a major enterprise AI assistant release, useful for readers tracking how agentic workplace tools are being packaged with governance and cross-tool search.

  8. Databricks Blog58

    Genie with Genie Ontology Delivers 1.6× the Accuracy with Less than Half the Tokens

    Databricks developed a new enterprise data-agent benchmark with Turing, run in a live Databricks workspace, in which Genie achieved a 91% pass rate versus 45% and 58% for two frontier agents while using fewer than half the tokens and completing tasks 3–4x faster. The benchmark, built around a fictional UK energy supplier called Calderwick Energy, includes notebooks, dashboards, saved SQL queries and documents alongside hundreds of tables, testing whether agents can find and use business context that table schemas alone cannot provide. Genie Ontology, now generally available, automatically extracts and organizes this knowledge and surfaces relevant context for Genie.

  9. Databricks Blog58

    Bring your business context to ChatGPT, Claude, Microsoft Copilot Cowork and Grok Bot

    Databricks introduced Genie plugins that give agents in ChatGPT, Claude, Microsoft Copilot Cowork and Grok Bot a governed interface to enterprise context, drawing on Genie Ontology for definitions, relationships and source context. Genie interprets business terms, finds permitted data, writes and runs SQL, routes specialized questions to domain Genie Agents, and returns answers with citations, with interactive visualizations in clients supporting the MCP Apps extension. Agents without native plugins can access Genie through the MCP server.

  10. Databricks Blog60

    From enterprise context to action: Genie Agents now analyze more data sources to transform your work

    Databricks announced that Genie Agents can now analyze unstructured files in Unity Catalog volumes, connect to enterprise tools via generally available native MCP connectors, and be shared outside an organization through OpenSharing and Genie Clean Rooms (both in beta). The post cites customers including Unilever, HelloFresh, S&P Global Energy, Atlassian, General Motors and ModMed, and describes how agents parse documents, cite specific pages, and combine file content with governed tables.

    Why it matters: Useful for readers tracking how enterprise AI agents are expanding from structured-data querying to document analysis, tool actions and cross-organization sharing.

  11. Databricks Blog69

    Announcing GA of Genie Ontology

    Databricks announced the General Availability of Genie Ontology, an agentic ontology layer that agents build by learning from dashboards, SQL queries, notebooks and BI semantic models, while people govern definitions through Unity Catalog semantics. The release adds Genie Search (GA), Ontology Snippets (GA), external knowledge sources (Beta, starting with Power BI), organizational directories (GA, starting with Workday), Domains and Pages (GA), and Ontology Manager (coming soon in Beta). In a benchmark built with Turing simulating a Databricks workspace for a fictional UK energy supplier, Genie answered correctly in 91% of runs versus 45% and 58% for two frontier agents, using less than half the tokens and finishing three to four times faster.

    Why it matters: It details a major enterprise context-layer release and includes benchmark numbers comparing agents with and without the ontology.

  12. Databricks Blog74

    The new Genie: autonomous AI for complex enterprise work

    Databricks introduces the new Genie, an autonomous AI coworker that continuously handles long-running enterprise work, tracking progress, following up on missing inputs, and taking action as new information arrives, with users setting guardrails and approving where needed. It is supported by Genie Ontology for business context, Genie Search for hybrid enterprise search, Unity Gateway and Unity Catalog for governance, and new capabilities including artifacts, MCP connectors, Genie Agents, and plugins for tools such as ChatGPT and Claude. Databricks reports that in a new benchmark built with Turing, Genie answered correctly in 91% of task runs, 1.6 times the rate of the stronger frontier agent, while using less than half as many tokens.

    Why it matters: The announcement details a shift from task-level AI assistance to supervised autonomous agents, with concrete governance, context, and benchmark claims worth evaluating.

  13. OpenRouter Announcements66

    Introducing the Extractions API

    OpenRouter launched the Extractions API in alpha, which pulls named, typed fields out of text, images and PDFs, with launch models from Cloudflare (Clairic), Inception (Mercury Decide) and ScaleDown (Extract). Clairic supports all nine field types and returns spans, p_null and distributions for estimates and forecasts, while Extract handles PDFs with text and list fields and per-value confidence, and Mercury Decide handles text only at the lowest input price. The post walks through invoice, receipt and purchase-order examples with request and response payloads, and contrasts Extractions with the Decisions API, where the model distributes probability over answers you write.

    Why it matters: It gives a concrete, example-driven tour of a new extraction API, including per-model capabilities, pricing and the evidence fields (spans, p_null, confidence) that determine what to trust and what to route to a human.

  14. TechCrunch · AI55

    Databricks’ new AI agent tells CEO Ali Ghodsi the unvarnished truth

    Databricks unveiled an updated version of its AI agent Genie, which can now perform multi-step jobs unsupervised using an ontology built to give it context from customer data on the Databricks platform. CEO Ali Ghodsi demonstrated his own use of Genie to surface unvarnished truths he might not otherwise hear at a 12,000-employee company, citing Dunbar's number of 150 as the limit on direct relationships. Ghodsi said costs can be controlled through Databricks' Unity Gateway, which lets companies set usage limits and swap in less expensive open-weight models.

  15. The Verge · AI72

    Google’s AlphaGenome 2 zooms in on the cellular roots of disease

    Google DeepMind unveiled AlphaGenome 2, a successor to last year's AlphaGenome that predicts how genetic variants affect individual cell types and states, trained on more than 100 million cell profiles across 68 tissues. Google says the model identifies nearly twice as many variants that can change gene expression as its previous state-of-the-art model, and can make predictions about new cell types and states without retraining. It is available for non-commercial research via the AlphaGenome API from today, with a limited private preview for commercial research.

  16. Hacker News AI58

    Claude's AI Starts Refusing to Assist with Pokémon ROM Hack and Fan-Game Dev

    Anthropic's Claude has begun refusing to help develop gaming assets using certain intellectual property, notably Pokémon, affecting ROM hackers and fan-game developers; developer Big Kid Nation said Claude models are starting to refuse work on Pokémon fan projects. The article reports that Anthropic changed its rules around the end of October 2026, per a Tom's Hardware post, and that X user Chasm found language stating Claude doesn't and can't "recreate a specific game"; the change was not publicly announced. The author notes the best ROM hacks remain human-made and calls the shift a somewhat ethical approach to IP.

  17. Hacker News AI26

    Show HN: AI Code Rating – a 3 character disclosure of how AI was used in a repo

    AI Code Rating is a proposed standard in which a project places an ACR.md file in its repo root with a three-character rating: an uppercase letter for the maintainer's expertise (A–E), a digit for the share of code written by AI (0–4), and a lowercase letter for how closely a human checked AI-written code (a–e). The site offers a rating form that generates the badge and ACR.md file, a validator, and a GitHub Action, and it lists adopters including aicoderating.com, DomainMOD, Email Health Check, and HomelabAPI. The spec is at version 0.1, and the roadmap covers publishing the spec, tooling, adoption, and proposing that code hosts display the rating.

  19. The Decoder82

    Anthropic says AI is eroding Nvidia's CUDA lead, cutting an AMD chip switch from months to three days

    Anthropic claims it ported its Claude model to AMD GPUs in about three days with almost no human intervention, using its own frontier model, Mythos, to write or port nearly 100 percent of the kernels, versus the six to twelve months traditional engineering approaches require. The company says it now trains and runs Claude across four chip families—AWS Trainium, Google TPUs, Nvidia GPUs and AMD GPUs—treated as one interchangeable compute pool, and it cites figures from an investor presentation that have not been independently verified. Anthropic also points to "compute multipliers" and its "Anthropic Direct" data center program, while Nvidia and AMD both hold financial ties to the company.

    Why it matters: It offers a concrete, if unverified, data point on whether AI-assisted porting can loosen CUDA's lock-in and shift how AI labs choose among chip vendors.

  20. Ars Technica · AI71

    Google DeepMind just mathematically verified key parts of Signal

    Google DeepMind researchers used agentic AI and a new framework called AlphaProof Code to formally verify a number of the Signal protocol's key cryptographic guarantees in a four-week proof-of-concept project. The analysis found no new vulnerabilities in the portions it examined, but when applied to older Signal versions with known, long-fixed flaws, AlphaProof Code correctly identified them and showed the protocol could not be formally verified. DeepMind researchers say the work is one milestone and that scaling formal verification to all software remains a long way off.

  21. Hacker News AI60

    Yes, you should panic about AI

    The author argues that AI skeptics have been wrong about capabilities for over a decade while those warning of rapid progress, such as Daniel Kokotajlo and the AI 2027 scenario, have been prescient, and that METR's investigation inside Anthropic suggests acceleration is running ahead of that scenario. He contends that safety checks are breaking down and that Anthropic dropped its core pause commitment while racing toward a trillion-dollar valuation, and he calls for banning AI from building AI, halting training of more powerful models, and pursuing a verified worldwide pause.

  22. Hacker News AI58

    The biggest AI cost reduction measure today is a cloud provider discount, lol

    AI spend has moved from experiment to budget line in about two years, with 98% of FinOps teams now managing some of it, and the article argues that committed-spend discounts are today's biggest cut because they lower token price without reducing token volume. It details three engineering levers that do reduce volume: prompt caching (cutting the input side of the bill by about 85%), batch APIs at half price on Anthropic, Bedrock and OpenAI, and smaller models for routine work, noting Claude Haiku costs about a fortieth of Opus per token. It then frames self-hosted LLM versus API as a per-workload choice among rent, route or own, with self-hosting paying off only at high, steady GPU utilization.

  23. Hacker News AI71

    Three men charged in US with using AI to impersonate CEOs and steal millions

    Three foreign nationals—Jacob Soall, Sharon Hazan, and Guy Sheetrit—were arrested and charged with using AI to impersonate corporate executives and attorneys to steal more than $129m from companies worldwide, according to a newly unsealed US Department of Justice indictment. The alleged fraud, which took place between 2024 and 2026, involved directing employees to wire money to bank accounts in countries including the US, Spain, Portugal, Greece, the UAE, Singapore, Austria, Slovakia, and China, before converting the funds into cryptocurrency. The men face charges of conspiracy to commit wire fraud, money-laundering conspiracy, and aggravated identity theft.

  25. Hugging Face Blog38

    Token Pooling-Aware Training for Multi-Vector Embedding Models

    This post explains token pooling for multi-vector embedding models, which merges similar document token vectors via Ward hierarchical clustering to cut storage, and introduces pooling-aware training in Sentence Transformers that applies the retrieval loss to both original and pooled representations. At pool_factor=4, the pooling-aware model retains 97.8% of its unpooled NanoBEIR nDCG@10 versus 94.6% for a normally trained model, with a 4x reduction in document vectors. It includes a reproducible training example, optional kernel acceleration, and evaluation results across pool factors, fixed caps, and calibrated adaptive thresholds.

  26. TechCrunch · AI38

    Plurai wants to help AI agents work better using the same approach that helped get self-driving cars to roads

    Plurai, founded by former Nexar engineers Ilan Kadar and Elad Levi, has raised $13 million in seed funding led by Team8 to build a simulation layer for AI agents, using synthetic data based on a company's own systems to prepare models for edge cases. The platform creates a digital twin of a company's knowledge base and tools, maps possible agent flows, and uses that to evaluate and fix failure modes, working alongside tools like Claude Code, Codex, Copilot and Cursor. Kadar says the company's real promise is post-training small open-weight models tailored to customers, with tests alongside Nvidia and Nebius showing performance above mid-tier frontier models at lower cost.