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TodayNovember 13Friday24 items
  1. NVIDIA Newsroom22

    How AI Is Helping Care Teams Get Patients the Right Care, Sooner

    Dyania Health's Synapsis AI clinical reasoning engine reads every note, report and lab result in a patient's record, connecting findings years apart to flag which patients need attention and why, with each signal traceable to its source. It runs on NVIDIA-accelerated computing inside a health system's own firewall, so patient data never leaves the hospital. Cleveland Clinic used it to review records of more than 7,800 aortic valve replacement patients from 2005 to 2021 and build a predictive model of valve deterioration and reintervention risk at three, five and ten years.

  2. Hacker News · AI22

    TimeNet Challenge: An Online Hackathon for Time-Series AI

    The TimeNet Challenge is an online hackathon that asks teams of 2–4 to onboard a time-series dataset in TimeNet format and train or fine-tune a Temporal AI Model such as OpenTSLM for tasks like forecasting, classification, anomaly detection or reasoning. Round 1 launched November 11 and closes November 22 at 11:59 p.m. PST, with more rounds to come. Nebius offers $100 or $500 vouchers per team per round, and winning teams receive prize credits starting at $25,000 from AWS and Google Cloud.

  3. Hacker News · AI58

    Study finds AI-style stale version pins in 13.7% of new npm packages in 2026 H1

    Kobi Mozgori sampled 250,000 npm packages and found that new packages whose pinned dependency versions are mostly round x.0.0 numbers already superseded rose from a 1–2.4% baseline in 2019 to early 2024 to 13.7% in 2026 H1, falling to 7.6% in 2026 H2 through early November. The median pinned dependency was about 155 days old at birth in 2021–2023 but 451 days in 2026 Q2, and the share of packages born with essentially all pins on the newest release dropped from about 28% to about 10%. The author frames the pattern as an inference rather than proof, since registry metadata cannot show who or what wrote a manifest, and notes the decline since mid-2026 is unexplained.

  4. Hacker News · AI18

    Show HN: Vending machine that takes words instead of money

    Cultivated Corpora built a "Type to Pay" vending machine prototype that accepts freshly written human prose instead of money, dispensing snacks in exchange for text used as AI training data. The terminal is being demonstrated at the Data Vandals Newsstand at 51st and Lexington in Downtown 6 from Nov 12 through Nov 17. The company says it is filling the gap for AI firms that need more training data than currently exists.

  5. Hacker News · AI58

    Uber Open-Sources Michelangelo ML Platform, Says It Has Been Validated Across 400+ Projects and 20,000 Monthly Training Jobs

    Uber has open-sourced Michelangelo, its ML platform proven at Uber scale with 400+ active ML projects, 20K+ model training jobs per month, 5K+ models in production and 45M+ real-time predictions per second at peak. The release covers feature management, distributed model training, deployment and monitoring, with feature management, evaluation and monitoring marked coming soon. A Python API built on Uniflow lets users define distributed training tasks and end-to-end workflows, such as a Ray-based XGBoost training pipeline, in a few lines of code.

  6. Hacker News · AI21

    AI Math Kit: A Browser Popup for Exploring Open Math Problems with AI

    AI Math Kit is a browser extension popup that bundles 217 open math problems, 242 plain-language digests of AI and formal-math breakthroughs, and prompt templates for honest AI exploration. The problem bank covers number theory, calculus, and cross-field mysteries, while the breakthrough page includes precise scope notes and paper pointers. A free tier offers browsing; Pro unlocks full explanations and dedicated prompts, with preferences and payment status cached locally.

  7. Databricks Blog36

    Bright Machines uses Debezium and Iceberg to give customers live dashboards from air-gapped factories

    Bright Machines now gives customers live, governed dashboards of factory data from air-gapped plants without opening any inbound access. It captures PostgreSQL changes at the edge with Debezium, writes them as Apache Iceberg, pushes them out over one auditable outbound path, and reads them in place in Databricks via Unity Catalog federation. The first customer-visible dashboard went live about two months after kickoff, with committed rows queryable in one to two minutes and dashboards refreshing every ten minutes.

  8. Hacker News · AI58

    US H-1B data: software developer filings fall 36% as AI-titled roles rise

    US employer H-1B filings for software developers fell 36% from fiscal 2022 to fiscal 2026, comparing October through June, while filings with AI job titles rose and made up 1.8% of all certified filings in fiscal 2026. New-employment software filings fell 46%, and the fiscal 2026 drop was steepest from October to December 2025, down 26% year over year, after the White House announced a $100,000 fee on new H-1B petitions. Titles containing "machine learning" or "artificial intelligence" rose 80%, and shorter titles such as "AI engineer" supplied 92% of the latest year's increase; OpenAI and Anthropic included AI or machine learning in only 11% of their 402 certified filings.

  9. Hacker News · AI85

    Forensic analysis links six Chinese frontier models to export-controlled NVIDIA Blackwell GPUs

    Unemployment Labs' forensic analysis of published model weights finds that six models from DeepSeek, Moonshot AI, MiniMax and Ant Group were initialized on NVIDIA Blackwell hardware, while earlier models from the same labs match Hopper. The method recovers each model's random seed and thread layout from preserved weights, and the author says the matches were reproduced on physical H100, B300 and GB200 chips. The report estimates each training run needed roughly 500 to 3,000 Blackwell GPUs over two to three months, and notes the evidence does not establish where the chips were located or whether export controls were violated.

    Why it matters: The report derives hardware evidence solely from published model weights, offering a reproducible method for tracing which GPUs initialized a model without relying on leaks.

  10. Hacker News · AI34

    e-means: A Faster k-means Variant and the Jevons Paradox of Cheaper Clustering

    e-means is a new k-means variant that updates centroids continuously within each pass using mini-batches and exponential moving averages, reaching the test loss of full-batch k-means in 2.5 epochs instead of 25 on 10 billion DINO embeddings clustered into roughly 16.7 million clusters. It keeps the same objective and convex nearest-centroid geometry, with fitted scaling laws setting batch size and update rate automatically. The author argues cheaper runs mean trying more values of k rather than lower compute bills.

  11. Hacker News · AI60

    Mirendil raises $1B Series A led by Kleiner Perkins to democratize AI R&D

    Mirendil announced a $1B Series A led by Kleiner Perkins and Clearlake Capital, with participation from Battery Ventures, Google, M13, Pear VC and others, to build tools that make frontier AI R&D accessible to scientists and engineers. The company says one researcher working with agents pretrained Mirendil-Base, a competitive coding model, from scratch in 3.5 months with less than $10M in compute, then post-trained it into Fern, which holds up to 10M tokens of context. Lor, now in private beta, lets hundreds of agents write code and run experiments for days at a time, and Mirendil says it runs on Lor every day.

  12. Microsoft Research22

    Microsoft Research Focus Edition #98: AI Translation, KLS Conjecture Proof, and Faster Coding Agents

    Microsoft Research Focus Edition #98 highlights AI advances bringing universal translation closer to reality and a new proof resolving the 31-year-old KLS conjecture in high-dimensional geometry. The edition also covers CompactionBench and APEC, which cut coding-agent context compaction costs by up to 25.4% and execution time by 9.6%, plus the Latent Core Tokenizer, which improved tokenization across 104 languages.

  13. Databricks Blog30

    Databricks Debunks Four LTAP Myths About Lakebase Architecture

    Databricks responds to four common misreadings of LTAP, the Lakebase architecture that keeps one governed copy of operational data serving both Postgres and analytics without CDC. The post argues LTAP is not replication: layers in front of the lake are caches, not a second database with its own catalog. It also says heap pages and Parquet are two lossless encodings of the same rows, so point lookups and scans each read the layout fast for them.

  14. MIT News22

    Breakthrough Energy CTO Eric Toone on the Cost of Decarbonization

    Eric Toone, chief technical officer of Breakthrough Energy, said at the MIT Energy Initiative's Energizing@MIT conference that decarbonizing the global economy would cost about $7.5 trillion, roughly 5 percent of global GDP, but that people are unwilling to pay a green premium. Breakthrough Energy Ventures has deployed about $3 billion into about 140 companies, including Luxwall, whose insulating windows delivered 30 percent energy savings in one commercial building.

  15. AWS Machine Learning Blog38

    Tokenomics at Scale: Tracking and Capping Amazon Bedrock Spend per IAM Principal with CUR 2.0 and AWS Budgets

    AWS shows how to track and cap Amazon Bedrock generative AI spend per IAM principal using only native features: Bedrock automatically records the IAM user or role on every inference request, and those tags become cost allocation tags readable in Cost Explorer or CUR 2.0 exports. An AWS Budget scoped to Bedrock and filtered to one identity or tag value can attach a budget action that applies a Deny IAM policy once a threshold is crossed, with no application code for the common case.

  16. Hugging Face Blog69

    IBM releases LLMON preview models built on Granite 4.1 3B and 8B

    IBM released two experimental preview models, granite-4.1-3b-LLMON-preview and granite-4.1-8b-LLMON-preview, derived from Granite 4.1 base checkpoints and trained to consume and generate LLMON, an LLM-native markup language that marks instructions, data and requirements with reserved special-token delimiters. On the OPI benchmark the models avoid 47% more indirect prompt injection attacks than the best frontier model tested and 95% more than the best open-source model, while performing on par with Granite 4.1 Instruct on standard benchmarks. The release includes a GitHub toolkit with lossless JSON-to-LLMON converters, and the authors note the preview is for exploration, not production.

    Why it matters: IBM trained a preview model on Granite 4.1 that can read and write LLMON tags, using explicit instruction-data separation to reduce the indirect prompt injection success rate on the OPI benchmark to near zero while maintaining general capabilities.

  17. The Verge · AI58

    Google researcher's AI agents did not discover an unknown Maya site, archaeologists say

    A Google researcher's team of 48 AI agents flagged a Maya site in Mexico as potentially unregistered, but archaeologists noted the site had already been documented in an academic journal more than a year earlier. Pietro Miotti, who described the work as a personal side project without Google compute or resources, said he checked with Mexico's INAH and later updated his post to credit the earlier work after criticism on social media. Archaeologists including Sarah Parcak and Ivan Šprajc said spotting a site is not the same as discovering it and stressed that peer review and existing research remain essential.

  18. Newcomer42

    The AI 100: The 100 Private Companies Defining the Age of AI

    Newcomer and Bessemer Venture Partners released the inaugural AI 100 ranking of the most promising private AI companies, with Anthropic at #1 ahead of its expected IPO next week and OpenAI at #2. The 100 companies carry a collective valuation of roughly $3.5 trillion, and the researcher-heavy model companies make up only 9% of the list but 55% of that valuation. Inference players Fireworks (#9), Baseten (#12), Fal (#16) and Modal (#20) also rank highly, alongside pivoted veterans like Stripe (#4) and Anduril (#7).

  19. Databricks Blog22

    How Constellation is building a governed data foundation on Databricks

    Constellation, the largest US nuclear fleet operator, is building a governed Enterprise Data Platform on Databricks to give the business one shared meaning for its data. The platform uses Unity Catalog for lineage, fine-grained access and a single permission model, plus Data Vault modeling for critical reusable entities. Early use cases include a nuclear engineering assistant built with Databricks Genie and outage planning.

  20. Databricks Blog25

    Eclipse Aerospace uses Databricks PySpark custom data sources for flight-data ingestion

    Eclipse Aerospace is using Databricks PySpark custom data sources to ingest nearly 20 years of proprietary flight data, over 3TB spanning more than a million flight hours, without always-on dedicated compute. The Rust-backed Python package delivered twice the benchmarked performance of a similar PyArrow-based pure-Python data source and nearly 20x that of a native Python implementation without Arrow. The API, introduced in Apache Spark 4.0 and available in Databricks Runtime 15.4 LTS, replaces the prior Lambda-to-Avro pipeline.

  21. The Verge · AI38

    GDC Survey: 40% of Game Developers Now Use Generative AI on the Job

    Forty percent of game developers say they use generative AI as part of their job, up from 36 percent in 2025, according to the GDC Festival of Gaming's latest annual industry survey. Usage is highest at free-to-play developers (58 percent) and mobile-only studios (70 percent), with research and brainstorming (73 percent) the top use case. The survey also found 34 percent of respondents were laid off sometime in the past two years.

  22. Databricks Blog22

    How H6.ai builds AI-powered financial automation with Lakebase Postgres

    H6.ai automates accounts payable on top of customers' existing ERPs, using a dedicated Lakebase Postgres database per customer as its system of record. Because invoice data keeps changing, the team uses Lakebase copy-on-write branches to freeze snapshots of customer databases and run each regression test on its own short-lived branch, so the live database is never written to. Each run is judged by deterministic assertions plus a panel of AI judges from different model providers.

  23. Databricks Blog58

    Databricks launches Lakehouse//RT to serve real-time analytics without proportional cost growth

    Databricks introduced Lakehouse//RT, a real-time analytics engine that runs high-concurrency workloads directly on lakehouse data and is now available in beta. The company says it delivers sub-100 millisecond latency at 12,000 queries per second, adds capacity one node at a time, and avoids a separate serving layer; customers Aurasell, VisionHeight and nOps report 6x to 10x lower infrastructure costs after migrating from multi-cluster setups, Apache Pinot and Apache Druid.

  24. Epoch AI · Gradient Updates60

    What Huawei's Horinger data center reveals about China's compute gap

    Epoch AI estimates Huawei's Horinger data center in Inner Mongolia holds about 105,000 operational Ascend 910C and 8,000 Ascend 910B chips, equal to roughly 81,000 H100-equivalents (90% CI: 36,000 to 141,000). The estimate comes from commercial satellite imagery and a cooling-equipment model that puts the four AI-focused buildings at up to 286 MW of IT power capacity. That leaves Horinger at about 7% of the AI compute in SpaceX's Colossus 2 in Tennessee, and a planned building could raise its capacity by over 70% by early 2028, still far behind Colossus 2's projected 1.8 million H100e.

November 12Thursday
  1. The Decoder72

    ChinaTalk investigation: US data brokers sell about $500M in RL training environments to Chinese AI labs

    US data brokers including Mercor, Surge AI, AfterQuery and Turing are selling roughly $500 million worth of training data, some of it RL environments, to Chinese AI labs, according to a ChinaTalk investigation. The report identifies at least nine US firms involved and says Chinese labs deliberately buy products already sold to Anthropic or OpenAI to skip costly in-house development, savings that matter given China's chip memory bottlenecks. ChinaTalk notes RL environments are largely unregulated by export controls and argues the Bureau of Industry and Security should apply existing controls to dual-use coding and biology environments, while stressing Chinese labs still trail US peers by at least a few months.

  2. TechCrunch · AI38

    Plurai raises $13M seed to make AI agents more reliable via simulation

    Plurai has raised $13 million in seed funding led by Team8 to make AI agents more reliable using simulation techniques borrowed from self-driving cars. The startup builds a digital twin of a company's systems, maps possible agent flows, and uses synthetic data to prepare models for edge cases, then post-trains small open-weight models tailored to each customer. Tests with Nvidia and Nebius showed its models outperforming midtier frontier models at lower cost.

  3. The Decoder71

    Google DeepMind releases AlphaGenome 2, shifting genomics AI from bulk tissue to single cell type resolution

    Google DeepMind has released AlphaGenome 2, a genomics model that predicts how DNA variants affect specific human cell types and finds nearly twice as many relevant gene changes as its predecessor. Trained on more than 107 million individual cell profiles from 68 tissues in humans and mice, it takes a cell's biological state as an extra input instead of predicting broadly across many tissues at once.

  4. Meta Newsroom · AI18

    Meta Signs Long-Term Purchase Agreements for Lower Carbon Cement, Steel, Trucking and Recycled Plastics

    Meta has signed a set of long-term purchase commitments covering cement, steel, trucking and recycled plastics to bring clean manufacturing to commercial scale in its data center supply chains. The deals include at least 180,000 tons of lower carbon cement from Sublime Systems, near-zero-emission steel from Sweden-based Stegra, an expanded zero-emission freight route with Nevoya along I-10 in California and Arizona, and 5,000 tons per year of recycled PET with MacroCycle Technologies.

  5. MIT News34

    MIT researchers develop training technique for faster, more trustworthy climate emulators

    MIT researchers have developed a training technique that boosts the performance and adaptability of AI climate emulators while using only a fraction of the data typically required. The method generates optimized synthetic training scenarios with rapid emissions variations, letting an emulator trained on far less data accurately predict unseen scenarios where traditional emulators failed. The work appears in the Proceedings of the National Academy of Sciences.

  6. Hugging Face Blog36

    Hugging Face Releases Fine-Tuning Guide and Scripts for TRELLIS.2 3D Generation

    Hugging Face published fine-tuning scripts and containers for TRELLIS.2, a 3D generation model, runnable locally or on Hugging Face Jobs. The guide uses LoRA adapters via the PEFT library on only the sparse-structure flow DiT and shape flow DiT, leaving the material stage frozen. On a 188-object footwear subset of the GSO dataset, the pretrained model often rendered incorrect soles, which the LoRA fine-tune targets.

  7. AWS Machine Learning Blog46

    Detecting and recovering from silent data corruption on Amazon SageMaker HyperPod

    A PyTorch DDP training loop on Amazon SageMaker HyperPod gains three checks that catch silent data corruption: a non-finite gradient check, a ring replay audit comparing loss bits and gradient fingerprints across nodes, and a weight fingerprint comparison after the optimizer step. Tested with software-injected faults on 4 ml.p5.48xlarge instances (32 NVIDIA H100 GPUs), the checks name a suspect rank, map it to a node, and let HyperPod replace the node and resume from a checkpoint.

  8. AWS Machine Learning Blog12

    AWS re:Invent 2026 AI/ML Track: Attendee's Guide

    AWS will hold its annual re:Invent conference from November 30 to December 4, with the AI/ML track offering more than 140 unique sessions out of over 2,000 total learning sessions. The guide maps sessions to the AWS AI stack, spanning AI chips (AWS Trainium and NVIDIA GPUs), models and inference on Amazon Bedrock and SageMaker AI, and agent development with Amazon Bedrock AgentCore. Sessions are organized from newcomer content to technical deep dives, and many can be viewed on demand.

  9. MarkTechPost58

    Google DeepMind releases AlphaGenome 2 for cell-type-specific variant effect prediction

    Google DeepMind released AlphaGenome 2, a conditional sequence-to-function DNA model that reads a 1 Mb DNA window plus a target cell state and predicts variant effects for one cell type per call. On TenK10K single-cell eQTLs across 28 immune cell types, causal variant recall at 90% precision rose from 16% for fine-tuned AlphaGenome 1 to 30%, and direction-of-effect recall rose from 23% to 43%. The model is available through the AlphaGenome API for non-commercial research, with weights promised upon final publication and commercial use in private preview.

  10. Google Research58

    Google Research uses wearables to build CKM risk profiles and personalized coaching

    Google Research introduced an investigational framework that turns consumer wearable data into a coachable health age and simulates personalized 12-week what-if health trajectories. The SensorWM world model was pretrained on 2.28 million person-weeks of de-identified Fitbit data from roughly 100,000 users and forecasts 20 physiological markers under different step, sleep and cardio routines. In a blinded study, lifestyle coaches chose the counterfactual-grounded plan as best overall in 71.4% of evaluations, and physicians rated 99.2% of plans within safe exercise intensity limits; the authors note the models are research prototypes, not clinical diagnostic tools.

  11. Meta Engineering Blog40

    Meta Intelligent Delivery: ML System Allocating Compute for Ads Ranking

    Meta detailed Intelligent Delivery, an ML-driven system that allocates compute across its ads serving stack toward the system-wide optimum, contributing roughly 10% incremental ranking performance on top of model and system advancements from 2017 through the first half of 2026. Its Resource Core Model uses 6B sparse and 20M dense parameters to predict each request's marginal utility, and Meta says it improved identification of high-impact ad requests by about 20% in offline benchmarks. The system also relies on multi-layered allocation mechanisms and increasingly agentic AI to adapt as models and hardware change.

November 11Wednesday
  1. Google DeepMind72

    DeepMind releases AlphaGenome 2, a DNA model predicting variant effects in specific cell types

    Google DeepMind announced AlphaGenome 2, a next-generation DNA sequence model that predicts how DNA sequences and their variants affect specific human cell types. Trained on more than 107 million individual cell profiles across 68 tissues, the conditional model identified nearly twice as many expression-changing variants as the previous model, and on the TenK10K immune cell dataset it recovered 30% of causal variants versus 16% for the original AlphaGenome fine-tuned on single-cell data. It is available for non-commercial research via the AlphaGenome API and in private preview for commercial research.

    Why it matters: AlphaGenome 2 shifts from bulk-tissue averages to single-cell conditioning, roughly doubling causal variant recall on immune cell data, which matters for linking GWAS signals to specific cell types.

  2. The Verge · AI71

    Google DeepMind releases AlphaGenome 2, a genetic AI model that predicts variant effects by cell type

    Google DeepMind has unveiled AlphaGenome 2, a successor to last year's AlphaGenome that predicts how genetic variants affect individual cell types and states rather than broad tissue averages. Trained on more than 100 million individual cell profiles across 68 tissues, the model identifies nearly twice as many variants that can change gene expression as the previous state-of-the-art model, and can make predictions about new cell types and biological states without retraining. Google is offering it for non-commercial research via the AlphaGenome API from today, with a limited private preview for commercial use; DeepMind's Žiga Avsec said its finer predictions are more computationally expensive, so it will not replace AlphaGenome soon.

  3. ChinaTalk58

    US data vendors are selling RL environments to Chinese AI labs, report finds nine vendors

    US data vendors including Mercor, Surge AI, Turing, AfterQuery and Pareto are selling reinforcement learning environments to Chinese AI labs, and the report identifies nine vendors with China business. It argues these sales let Chinese labs skip costly rollout experimentation and save memory-constrained compute, while noting the deals fall largely outside export controls and that the lag behind US labs limits the damage.

  4. Latent Space62

    Mercor raises $750M Series D at $20B valuation on $3B annualized revenue

    Mercor raised a $750M Series D at a $20B valuation after reaching $3B in annualized revenue, up from $1B in February and $2B in June. The company reports 5 million experts and frames its market as the 800-plus US occupations whose workflows models have yet to be evaluated against. Mercor also says it already spends 3x as much on inference as on employee salaries, and argues that if the ratio holds across the economy it points to double-digit GDP growth over the next five years.

  5. Hugging Face Blog41

    Pooling-Aware Training for Multi-Vector Embedding Models with Sentence Transformers

    Hugging Face shows how pooling-aware training lifts retrieval quality for multi-vector embedding models at a fixed storage budget. With pool_factor=4, a normally trained model keeps 94.6% of its unpooled NanoBEIR nDCG@10 while cutting document vectors 4x, and pooling-aware training raises that retention to 97.8%. The post walks through HierarchicalTokenPooling, budget calibration and a reproducible Sentence Transformers example.