---
title: "AI"
description: "490 posts about AI, summarised, each linking to the original."
---

# AI
> 490 posts about AI, summarised, each linking to the original.

## Articles

### [Running open-Jev in SQL on Databricks](https://yomu.fyi/post/running-open-jev-in-sql-on-databricks.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Cyrus Cowley, Tejas Sundaresan, Hanlin Tang, Anshul Gupta, Julia Powell
- Published: Sep 24, 2026
- Type: Tutorial

System One decision models such as Jev produce well-calibrated decisions from discrete options and are fast and cheap for large-scale applications, with potential use cases ranging from analyzing customer support transcripts to applying complex decisions at scale. Open-weight versions like SemIf-OpenJev are available, and Databricks enables serving them on governed data using serverless GPUs via AI Runtime, which provides on-demand GPU compute without managing infrastructure. The workflow is packaged in an importable notebook that downloads the model files, logs and registers the model using Express Deployments, and creates a GPU Model Serving endpoint automatically. The endpoint can be called from SQL via ai\_query, which supports arbitrary custom model APIs, allowing structured request formats directly from SQL to return selected classifications and option probabilities as structured columns. The example classifies hotel reviews as good or bad, and users can import the notebook, select Serverless GPU, and run all to get started.


### [Hitting a billion tokens per minute on one GPU by combining a query planner and an inference engine](https://yomu.fyi/post/hitting-a-billion-tokens-per-minute-on-one-gpu-by-combining-a-query-pl.md)
- Company: [Modal](https://yomu.fyi/company/modal.md)
- Published: Sep 24, 2026
- Type: Problem & solution

Modal and CMU built Quail, a query-aware inference layer for AI-SQL that combines a query planner with an inference engine to process structured LLM transformations of database entries. On a multi-join query, Quail achieves over a billion tokens per minute per H100 GPU, over 10x faster than vLLM, costing under 6 cents per billion tokens on Modal. The gain comes from using the query plan to order requests for better KV cache management, including a revised Hydragen-style cascade attention and batching small requests to reduce host overhead. On a new AI-SQL benchmark, Quail runs 1.84x faster than vLLM, but falls behind on agent trace processing. The authors see this as part of a trend toward custom inference engines for specific workloads, with a reusable core like databases post-DataFusion, and note that much work was done by prompting coding agents.


### [Bringing Private Processing to Meta AI Glasses](https://yomu.fyi/post/bringing-private-processing-to-meta-ai-glasses.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Author: By Pritam Shah, Oskar Linde
- Published: Sep 24, 2026
- Type: Announcement

Meta AI glasses require cloud processing for advanced AI features like streaming transcription and long-term recall, but traditional cloud architectures expose data during use. Private Processing uses confidential virtual machines with Trusted Execution Environments (TEEs) on CPUs and GPUs to keep data encrypted even while being computed. Before sending context, glasses perform remote attestation, verifying the server’s TEE certificate against a public transparency ledger that records all deployed binary images. Non-targetable routing via blind-signed tokens and third-party OHTTP relays prevents tying requests to specific users, while encrypted storage ensures data is only accessible with user-provided keys. This infrastructure, already proven for WhatsApp and Meta AI, extends to glasses, with security audits by firms like NCC Group and a bug bounty program for independent research.


### [Introducing NV-Reason-CT Open 3D CT VLM for Radiologist Chain-of-Thought Reasoning](https://yomu.fyi/post/introducing-nv-reason-ct-open-3d-ct-vlm-for-radiologist-chain-of-thoug.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Sep 23, 2026
- Type: Announcement

NVIDIA released NV-Reason-CT, a vision language model for 3D CT analysis that processes volumes as true 3D input rather than independent 2D slices. The model generates structured diagnostic reports covering 30 chest and 29 abdominal abnormalities and emulates radiologist chain-of-thought reasoning with systematic anatomical reviews and uncertainty articulation. Its architecture pairs a Qwen3.5-4B language model with a full 3D ViT encoder that processes volumes resampled to 192³ voxels at 2mm isotropic resolution using 8x8x8 patch tokens and 3D MRoPE for spatial token relationships. Training uses supervised fine-tuning on roughly 550,000 QA examples from CT-RATE, NIH CT, and CancerVerse followed by Group Relative Policy Optimization that refines reasoning quality based on diagnostic accuracy. NV-Reason-CT is an open research foundation not a cleared clinical product and sits within NVIDIA’s Medical AI ecosystem alongside NV-Reason-CXR, NV-Segment-CTMR, and NV-Generate-CTMR.


### [Claude discovers a novel enzyme system](https://yomu.fyi/post/claude-discovers-a-novel-enzyme-system.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Sep 23, 2026
- Type: Announcement

Anthropic launched a new life sciences research group to use AI agents for fundamental biology discovery. In one of their first programs, Claude agents autonomously analyzed over 200,000 reverse transcriptase sequences from a DNA database. After 21 hours and 210 million tokens, one agent spotted a repeating DNA pattern next to a reverse transcriptase gene, which it identified as a CRISPR-like system. The system, called ART (array-associated reverse transcriptases), consists of the RT, an accessory protein, and an array of non-coding DNA repeats that are expressed as distinct small RNAs. Its function remains unknown, but the pattern of repeats is only found together in other programmable DNA-cutting systems, suggesting ART may be a new tool for biotechnology.


### [Validate GPU Cluster Readiness Before AI Workloads Land](https://yomu.fyi/post/validate-gpu-cluster-readiness-before-ai-workloads-land.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Sep 23, 2026
- Type: Explainer

GPU clusters can pass standard health checks yet fail under real AI training workloads due to a single degraded GPU, network link, or misrouted traffic. NVIDIA Cluster Readiness Engine (NVCRE) is an open source Kubernetes controller that runs real distributed workloads across topology-aware node groups before production jobs land, measuring results and reporting which nodes failed each test. Its layered API uses Custom Resource Definitions for Certification, Workflow, and Job resources, attributing each failure to a specific node and category such as NCCL bandwidth or training goodput. The WorkloadRun API handles platform detection, NCCL settings, shared-memory volumes, and gang scheduling, so operators do not need to write those manifests by hand. NVCRE integrates with NVSentinel for passive telemetry and is available under Apache 2.0, with a roadmap for inference and automated lifecycle validation.


### [Small Talk With Prasun Kumar, CEO and Founder of Oppex AI](https://yomu.fyi/post/small-talk-with-prasun-kumar-ceo-and-founder-of-oppex-ai.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Mitsal Athaya
- Published: Sep 23, 2026
- Type: Explainer

Oppex AI builds AI agents that help developers resolve production incidents by gathering context before the engineer is paged. The agents collect logs, cloud metrics, database health, affected customers, recent code changes, and past incident history, then present a recommendation. Because developer logs are not plain English, the team constructs a knowledge graph of how the entire system connects. They harden the agent by pitting it against a chaos monkey that breaks test systems without revealing the method, forcing the agent to learn. The 15-person team has launched the product with early enterprise customers and aims to bring mean time to resolve under 10 minutes, eventually moving toward autonomous system healing.


### [\*\*Know Who Spoke When: Build Real-Time, Multi-Speaker AI with NVIDIA Nemotron 3 Diarization\*\*](https://yomu.fyi/post/know-who-spoke-when-build-real-time-multi-speaker-ai-with-nvidia-nemot.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Francesco, Ivan Medennikov, Taejin Park, Jagadeesh Balam, Adi- margolin, Maryam Motamedi
- Published: Sep 23, 2026
- Type: Announcement

NVIDIA Nemotron 3 Diarization is an open-weight, 100M-parameter model that ranks #1 on VoiceArena’s Diarization-Bench leaderboard with a 14.72% Diarization Error Rate (DER). It classifies who spoke when in conversations, handling overlapping speech and supporting up to eight speakers across live and recorded audio. The model uses a Sortformer approach with arrival-order speaker labeling, a 31-layer Transformer encoder, and two memory mechanisms for streaming inference. It accepts 16 kHz single-channel audio and outputs a floating-point tensor of speaker-activity probabilities at a configurable time step. Developers can combine its timestamps with automatic speech recognition to produce speaker-attributed transcripts, with recommended input-buffer latencies ranging from 0.32 to 30.4 seconds.


### [What Dropbox has learned from deploying AI at company scale](https://yomu.fyi/post/what-dropbox-has-learned-from-deploying-ai-at-company-scale.md)
- Company: [Dropbox](https://yomu.fyi/company/dropbox.md)
- Author: Dropbox Team
- Published: Sep 23, 2026
- Type: Explainer

Dropbox has learned that deploying AI at scale requires examining workflows end to end, addressing new bottlenecks like too many code reviews or infrastructure strain. The company uses a framework measuring speed, effectiveness, quality, and impact, with proxy metrics including pull request throughput and change failure percentage. Dropbox reports around 70% AI-generated code, comparable to top technology peers, and its pull request throughput ranks in the top 5% of a custom benchmark while change failure percentage is at the 75th percentile. The company connects AI token usage to engineering value through its internal agent service Nova, and it uses ground-up initiatives like boot camps and aligned incentives to drive adoption. Human skills like problem solving, judgment, and leadership become more critical as AI lowers the cost of producing software, and Dropbox aims to extend AI workflows beyond engineering to product, design, and customer experience teams.


### [How to Guide Your Language Flow](https://yomu.fyi/post/how-to-guide-your-language-flow.md)
- Company: [Apple](https://yomu.fyi/company/apple.md)
- Published: Sep 23, 2026
- Type: Announcement

Probe guidance, a novel method for guiding flow matching models, is introduced. It uses the frozen internal states of an existing diffusion model to construct a guidance signal, operating on a similar principle as autoguidance but removing the need for an additional forward pass at inference time. The method provides a reliable path ensuring the weak and strong models share similar dynamics. Probe guidance sets a new state-of-the-art performance on unconditional generation when benchmarked on continuous diffusion language models. Applied to a 1.7B diffusion language model, it consistently improves multiple choice question answering benchmarks.


### [Rollouts and Security Review](https://yomu.fyi/post/rollouts-and-security-review.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Published: Sep 23, 2026
- Type: Announcement

Cursor launched two bots for the last mile of shipping code: Rollouts and Security Review. Rollouts watches every change as it deploys and reports health per environment, with verdicts of verified healthy, regression detected, or inconclusive. It reads the diff and systems a pull request touches, writes a monitoring plan as a PR comment, and tracks deploy events per environment. Security Review reads every pull request in the context of the codebase and posts one review comment reporting exploitable bugs, including injection, authentication bypasses, and secrets. Both bots are available today on Teams and Enterprise plans, with usage credits for the next 10 days.


### [Conversations in the AI Era: Manchester Meetup Recap](https://yomu.fyi/post/conversations-in-the-ai-era-manchester-meetup-recap.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Ryan Britnell
- Published: Sep 23, 2026
- Type: Announcement

Twilio and Ciptex hosted the Conversations in the AI Era meetup in Manchester on September 3rd, 2026, drawing developers, tech leads, and founders from the local scene. Darragh O’Connor demonstrated using a SIP trunk to connect callers directly into a livestream, emphasizing solving the problem with the simplest tool. Matthew Duggan of Ciptex presented an architecture where AI and human agents share conversation state and tools, treating handoff as a change of ownership rather than a recovery path. Paul D’Ambra of PostHog discussed how cheap code generation made reviewing code the bottleneck, leading them to build AI review agents that substantially dropped their revert rate. The next meetup is scheduled for November 4th, 2026.


### [What is AI analytics? Why it only works on governed data](https://yomu.fyi/post/what-is-ai-analytics-why-it-only-works-on-governed-data.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Richard Tomlinson
- Published: Sep 22, 2026
- Type: Explainer

AI analytics applies AI and machine learning to data analysis, enabling natural-language queries and automated insights without manual query-building, shifting from retrospective dashboards toward agentic investigations. Unlike traditional BI, which requires users to navigate predefined dashboards and filters, AI analytics systems understand intent, determine relevant data and business context, and can conduct multi-step investigations on behalf of the user. A key challenge is that AI analytics only works when grounded in governed data: without shared metric definitions, authoritative sources, and permission-aware execution, the system produces untrustworthy results. Trust in AI analytics is a system property, not a model property; it requires governed data, shared business context, permission enforcement, and verifiability of queries and calculations. Better AI models do not eliminate the need for business context; as AI becomes more autonomous, semantic definitions and governance become more critical.


### [Resilient Edge AI: Keeping Connected Vehicles Smart Offline](https://yomu.fyi/post/resilient-edge-ai-keeping-connected-vehicles-smart-offline.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 22, 2026
- Type: Explainer

Connected vehicles face intermittent connectivity in tunnels, rural roads, and underground garages, yet drivers expect reliable AI assistance during those moments. The post argues that offline capability is a requirement, not a fallback, for trustworthy in-vehicle AI. It proposes three principles: design for the moment of need, treat vehicle knowledge as an evolving product asset, and separate availability from connectivity. A reference architecture uses ObjectBox for local persistence and synchronization with MongoDB Atlas, which provides the cloud foundation for shared knowledge and updates. The enduring pattern is to keep critical interactions available at the edge while using the cloud for scale, coordination, and continuity when connectivity returns.


### [From 2D Drawings to 3D Models: Designing an AI Reconstruction Pipeline on MongoDB](https://yomu.fyi/post/from-2d-drawings-to-3d-models-designing-an-ai-reconstruction-pipeline.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 22, 2026
- Type: Explainer

BIRD is a research framework that converts five 2D perspective drawings into a proportionate, dimensioned 3D building envelope. It uses a dual-branch CNN to separate architectural line segments from noise, a text recognition model to scale them to real-world inches, and a wrapping algorithm to fold the elevation views around the floor plan. The framework achieved a median accuracy of roughly 93% across complete building plan sets from small residences to a multi-story apartment complex. The proposed deployment on MongoDB Atlas stores each project as a single JSON document containing all drawings’ segments, corrections, and final geometry, with S3 for image storage and Atlas Stream Processing for event-driven workflow coordination. MongoDB Search and Vector Search with Voyage AI embeddings enable full-text and semantic search over past projects, allowing drafters to start from corrected models of similar builds.


### [How UK AISI and EvalEval Are Making Benchmark Results Reproducible](https://yomu.fyi/post/how-uk-aisi-and-evaleval-are-making-benchmark-results-reproducible.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Avijit Ghosh, Jenny Chim, Deep Joshi, Srishti, Matt Kennedy, Irene Solaiman, Jessica McFadyen, Lynn Tan, Coz
- Published: Sep 22, 2026
- Type: Announcement

The UK AI Security Institute (AISI) has begun using EvalEval’s Every Eval Ever (EEE) schema and Evaluation Cards platform to openly share LLM evaluation results. This collaboration, stemming from a joint workshop at NeurIPS 2025, aims to make benchmark results more reproducible by providing standardized configuration and context alongside verified scores. AISI released results for five benchmarks—HealthBench, FrontierMath, Humanity’s Last Exam, SWE-Bench Pro, and Terminal-Bench 2.0—across six frontier models, plus two cyber evaluations using a partially overlapping model set. The release accompanies a paper studying how inference-time compute and evaluation protocol affect performance, revealing that models continued solving tasks when given correctness feedback. By adopting a shared reporting schema, evaluators can compare findings across studies and understand how setup choices influence reported performance, while EvalEval invites model developers, evaluation creators, and researchers to contribute to the shared infrastructure.


### [Amazon launches research initiative with Stanford University to advance AI and science](https://yomu.fyi/post/amazon-launches-research-initiative-with-stanford-university-to-advanc.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Staff writer
- Published: Sep 21, 2026
- Type: Announcement

Amazon and Stanford University have launched the Stanford and Amazon Research Initiative, a formalized framework to advance research in AI, energy, and healthcare. The initiative builds on a deep relationship where currently more than 10 teams across Amazon fund active research and PhD fellowships at Stanford, spanning humanoid robotics, post-quantum cryptography, causal measurement science, and AI-driven radiology. It aims to accelerate the path from breakthrough research to real-world solutions that benefit society at large and broaden participation from diverse scholars. Focus areas will leverage strengths in artificial intelligence, machine learning, automated reasoning, and health, supported by Amazon’s cloud computing and AI services. The collaboration includes joint research projects, PhD fellowships, and interdisciplinary symposia, and was announced at a Stanford event with AWS CEO Matt Garman and Vice Provost David Studdert discussing the impact of university-industry collaborations.


### [Advancing AI for biology: Teaching models to design and characterize antibodies](https://yomu.fyi/post/advancing-ai-for-biology-teaching-models-to-design-and-characterize-an.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Melih Yilmaz, Luca Giancardo, Yue Zhao, Edward Lee, Chuanyui Teh, Fangda Xu, Gordon Trang
- Published: Sep 21, 2026
- Type: Announcement

Monoclonal antibodies are a mainstay of biopharmaceutical development, with over 100 FDA-approved drugs and established manufacturing and regulatory pathways, yet conventional discovery is costly and slow, typically taking six to twelve months from target to lead candidate. AI promises to make antibody development cheaper, faster, and more flexible by designing and characterizing therapeutic antibodies computationally, but scientific questions remain. Development hinges on three factors: the best binding site on the target, which candidates bind most tightly, and whether candidates can survive manufacturing and the clinic. Predictive models for each factor do well on familiar targets and assays but considerably worse on unfamiliar ones, a gap that in-distribution benchmarks have made difficult to measure. Three papers from Amazon Bio Discovery’s science team address research questions about each factor, with methods integrated into the AI-powered application that connects biological AI models with lab services.


### [Introducing Our New Dropbox API Documentation](https://yomu.fyi/post/introducing-our-new-dropbox-api-documentation.md)
- Company: [Dropbox](https://yomu.fyi/company/dropbox.md)
- Author: Dropbox Developer Support Team
- Published: Sep 21, 2026
- Type: Announcement

Dropbox has launched a modernized API documentation site at https://docs.dropboxapi.com, rebuilt to make integration faster and more intuitive. The new documentation features interactive API testing that lets developers set parameters, issue API calls, and view real responses from the Dropbox API directly in their browser without switching tools. An embedded AI assistant offers instant help with endpoint questions, troubleshooting, and code examples, while the documentation integrates with MCP (Model Context Protocol) so AI tools like Claude Code and Cursor can access up-to-date API information. Through the MCP server at https://docs.dropboxapi.com/\_mcp/server, developers can connect their AI assistants to answer questions, explore the API, and assist with integrations using information directly from the docs. The update delivers a clean, responsive, searchable interface that brings endpoints, types, schemas, and related documentation together, with improved navigation, and provides comprehensive information including detailed error responses, type definitions, and full request/response schemas.


### [Partnering with Accenture on embedded evaluation](https://yomu.fyi/post/partnering-with-accenture-on-embedded-evaluation.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Sep 18, 2026
- Type: Announcement

Anthropic is partnering with Accenture’s specialist AI business, Faculty, on independent evaluation of frontier AI, advancing a commitment to embed evaluators within Anthropic. The work will cover model evaluation and red-teaming, alignment assessments, and testing safeguards, informed by Accenture’s experience deploying AI across industries. Anthropic and Accenture each expect to invest at least $1 billion in building capacity over five years, although operating details, access standards, reporting practices, and funding models remain unsettled. Embedded evaluators would work inside AI companies with employee-comparable access to observe training, follow development and deployment decisions, speak with staff, verify safety commitments, identify blind spots, and report incidents; Anthropic says its partnership is non-exclusive and that model safety remains its responsibility.


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