# datasets A dataset is a collection of raw data, often organized as a spreadsheet or CSV file. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Beyond the model: Engineering AI infra with scientific judgement DevFeed: [Beyond the model: Engineering AI infra with scientific judgement]() Original publisher: [Read original article]() Author: AirbnbEng Published: 2026-09-15T17:06:18Z Content type: article Language: en Sources: [The Airbnb Tech Blog - Medium]() Topics: [Agent Harness](), [AI Agent](), [Data Science](), [data](), [datasets](), [Large Language Model]() Tags: [agent-harness](), [ai](), [ai-agent](), [data](), [data-science](), [datasets](), [engineering](), [llms](), [quality](), [science](), [trust]() ### AI overview Airbnb describes an agent harness for data science that embeds scientific methodology around an AI model. The system guides agents through framing questions, selecting evidence, and recording decisions so unstructured-data investigations can be reproduced, audited, challenged, and extended across languages, geographies, and LLM-based products. ### Source excerpt How Airbnb's agent harness transforms unstructured data exploration by encoding scientific methodology into scalable, reproducible, and audit-ready infrastructure. By: Wren Dougherty Ask a coding agent to analyze 100,000 customer support conversations and within minutes you'll have a polished taxonomy, precise prevalence numbers, and an executive-ready summary. What you can't see is the investigation that produced them: the methods it chose, the evidence it weighed, how much to trust it, or whether a second request would agree. All that reaches you is the polish. The model is undeniably intelligent, but intelligence without methodology is not science. LLMs certainly make for confident scientists, but we need them to be responsible ones. Smarter models help, but intelligence has never been the whole of science, in people or in machines. The method is as much the product as the answer. That is the idea behind the agent harness we built for data science: the methodology itself, built as infrastructure around the model. It governs how an AI agent operates, from framing a question to selecting evidence to recording decisions, so results can be reproduced, audited, and challenged, and the method shared, inspected, and built on. The challenge of unstructured data exploration In 2025, Airbnb was preparing to launch an AI customer service assistant. Before it could ship, we needed to understand exactly what kinds of situations it would face in the real world. That included rare events that could be risky for AI to interact with, and involved examining their taxonomy and prevalence to create the datasets that would help us build a more responsible product. The investigative work to do this was rigorous, but the process was deeply artisanal. Months of high-touch iteration went into each investigation, from finding the right data, reviewing samples with experts, and generating representative datasets, and the method was manually curated across notebooks, tables, docs, and indiv ## Your AI coding spend bought 25% more output. Duplication rose 81%. DevFeed: [Your AI coding spend bought 25% more output. Duplication rose 81%.]() Original publisher: [Read original article]() Author: Steve Fenton Published: 2026-09-14T14:39:14Z Content type: article Language: en Sources: [The New Stack]() Topics: [ai-coding](), [Claude Code](), [cursor](), [datasets]() Tags: [agentic](), [ai](), [ai-coding](), [ai-impact](), [ai-operations](), [ai-strategy](), [ai-tools](), [claude-code](), [coding](), [contributed](), [contributed-octopus-deploy](), [cursor](), [dataset](), [software-development]() ### AI overview The article examines the return on investment from AI coding tools. It reports that heavy users gained 25% over their previous velocity, while code duplication rose 81%, and argues that output measures such as lines of code, pull requests, and feature counts do not reliably represent business value. ### Source excerpt Since they arrived on the scene, a great swathe of the software industry has pinned its hopes on AI tools, The post Your AI coding spend bought 25% more output. Duplication rose 81%. appeared first on The New Stack. ## A study of sequence weighting at scale DevFeed: [A study of sequence weighting at scale]() Original publisher: [Read original article]() Author: Alex Renda Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [Jane Street]() Topics: [scaling laws](), [datasets](), [Deep neural networks](), [Chinchilla scaling law]() Tags: [data](), [machine-learning](), [model](), [scaling-laws](), [training]() ### AI overview The article studies how sequence weighting affects language-model training across model scales. It reports non-monotonic behavior: small-to-medium models increasingly learn data-specific patterns in proportion to sequence weights, while large models can learn all patterns in the data more independently of those weights. ### Source excerpt TL;DR: We study the scaling laws of data weighting across in-house and open-weight LMs, finding non-monotonic behavior across scales. We vary the weight assigned to sequences during training and measure how strongly the model's loss reduction on a sequence depends on the sequence's weight. Taken together, our results are consistent with a general trend: as models transition from small to medium scale, they transition from learning general patterns independent of data weight to learning data-specific patterns proportional to the data weights. As models then transition from medium to large scale they are able to learn all patterns present in the data, once again independent of data weight. ## Interpreting Pangram DevFeed: [Interpreting Pangram]() Original publisher: [Read original article]() Author: Armin Ronacher Published: 2026-09-14T00:00:00Z Content type: opinion Language: en Sources: [Armin Ronacher]() Topics: [Artificial Intelligence](), [Large Language Model](), [datasets](), [Training AI Models]() Tags: [ai](), [llm](), [model](), [openai](), [training](), [training-data]() ### AI overview The article discusses Pangram, an AI-text detector that classifies passages as human-written, AI-generated, or mixed. It explains that Pangram manufactures training data from human-authored text and LLM-generated rewrites and edits, then describes an experiment using Opus 5 to generate text intended to read as entirely AI-generated. ### Source excerpt Yesterday David Sacks wrote a tweet and within a few minutes people did, what they usually do, and they asked Pangram if it was AI. And Pangram said it's entirely AI generated. To which David replied that these AI detectors are bogus. Now Pangram has a pretty low false positive rate, but if you have ever used an LLM as a writing assitant, you will have probably noticed that it claims your posts 100% AI, even though you don't feel like they are. Pangram itself is a trained model, that attempts to detect segments of text as being definitely human, definitely AI and a mixture of the two. If you want to know how it works, they published a paper. The short summary is that they are manufacturing its own training data by starting from collections of known human authored text. An LLM is then tasked to understand the text and write a fresh new text on the same topic. They also let the LLM perform partial edits on that original human text and through that they can pick up on these co-authored details. Pangram claims their model to have rates of 0.0041% false AI accusations and 0.34% missed AI text. So now that we know this I figured it might be fun to have an LLM re-create David's tweet. I first came up with a prompt. And when I say I came up with that prompt I in fact used an LLM to propose to me from that tweet what I might want to say for the structure. I'm sure if you ask Pangram about if the above text is AI, it will probably say so, but that's not really the point. The point is that I then used Opus 5 to generate a text which reads entirely AI generated. If you are curious, this is the prompt I used: Original prompt to generate a David Sacks like tweet Please write an 8 paragraph tweet about "Pacing the Frontier", not longer than 350 words. Context of this tweet is this post by Dario: https://darioamodei.com/post/we-must-pace-the-frontier And this tweet by Sam Altman: "I agree with Dario that we need to pace the frontier. This has been a primary topic of discussions we' ## SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign DevFeed: [SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign]() Original publisher: [Read original article]() Published: 2026-09-11T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [multimodal](), [Transformer](), [datasets]() Tags: [benchmarks](), [drug-discovery](), [generation](), [model](), [multimodal](), [training]() ### AI overview SimpleDesign is a single-stage, end-to-end multimodal generative model for jointly designing protein sequences and three-dimensional structures. It uses Transformer-based multimodal backbones, trains directly in data space on more than 2 million sequence-structure pairs, and achieves competitive results on co-design and unconditional generation benchmarks. ### Source excerpt Proteins are fundamental to biological processes, with their function determined by the complex interplay between the amino acid sequence and the three-dimensional structure. Developing generative models capable of understanding this intrinsically multi-modal relationship is crucial for fields like drug discovery and protein engineering. Existing models often rely on a multi-stage training process where autoencoders that tokenize data into latent representations are trained in a first stage. Secondly, a generative model is trained on the latent representation of the autoencoder(s), i.e... ## (Re)introducing Developer Story DevFeed: [(Re)introducing Developer Story]() Original publisher: [Read original article]() Author: Philippe Beaudette Published: 2026-09-10T18:01:54Z Content type: news Language: en Sources: [Stack Overflow Blog]() Topics: [Programming](), [Code](), [Large Language Model](), [datasets]() Tags: [career](), [community](), [company](), [cv](), [datasets](), [developer](), [identity](), [llms](), [news](), [privacy](), [profile](), [programming](), [se-stackoverflow](), [se-tech](), [skills](), [work]() ### AI overview Stack Overflow is reintroducing Developer Story, a profile feature designed to showcase developers' careers, specialties, contributions, and technical identity. The article also introduces Stack Identity, a broader vision for verified proof of developer work, with privacy controls and integrations for verified contributions from other sites. ### Source excerpt For the past few years, we've been looking at ways to bring a little more of the individual developer back to Stack. ## Build an end-to-end RFI questionnaire workflow using Amazon Quick Automate DevFeed: [Build an end-to-end RFI questionnaire workflow using Amazon Quick Automate]() Original publisher: [Read original article]() Author: Chaytanya Kumar Published: 2026-09-10T16:08:57Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Automation](), [Amazon S3](), [CSV](), [Amazon Web Services](), [datasets]() Tags: [advanced-300](), [amazon-quick-suite](), [amazon-s3](), [amazon-simple-storage-service-s3](), [automation](), [aws](), [data](), [how-to](), [technical-how-to](), [tutorial]() ### AI overview A tutorial for automating RFI questionnaire processing with Amazon Quick Automate. It reads multi-tab workbooks from Amazon S3, extracts and structures questions, and writes CSV output to Amazon S3. ### Source excerpt Learn how to build an end-to-end RFI questionnaire workflow with Amazon Quick Automate. Read a multi-tab RFI workbook from Amazon S3, use natural-language prompts to extract and structure the questionnaire data, refine the workflow through conversation, and write clean CSV output back to Amazon S3 -- cutting development from days to hours. ## How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules DevFeed: [How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules]() Original publisher: [Read original article]() Published: 2026-09-10T16:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Deep neural networks](), [datasets](), [Code]() Tags: [ai](), [ai-models](), [antibiotics](), [applied-ai](), [chatgpt](), [code](), [codex](), [datasets](), [deep-learning](), [search]() ### AI overview César de la Fuente's lab uses deep-learning models, ChatGPT, and Codex to search genome and protein datasets for antimicrobial candidates that could help fight drug-resistant infections. ### Source excerpt César de la Fuente's lab uses Codex and ChatGPT to search living and extinct genomes for antimicrobial candidates to fight drug-resistant infections. ## Cloudera and Mistral Partner to Bring Specialized, Sovereign Intelligence to Enterprise Data DevFeed: [Cloudera and Mistral Partner to Bring Specialized, Sovereign Intelligence to Enterprise Data]() Original publisher: [Read original article]() Published: 2026-09-10T10:42:55Z Content type: news Language: en Sources: [Mistral AI Blog]() Topics: [model-deployment](), [Inference](), [data](), [datasets]() Tags: [ai](), [ai-models](), [cloud](), [data](), [enterprise](), [inference](), [on-prem](), [partnership](), [train]() ### AI overview Cloudera and Mistral announce a partnership to deploy and train customized AI models on enterprise data across hybrid, on-premises, cloud, and air-gapped environments while retaining data control. ### Source excerpt Cloudera and Mistral join forces to bring specialized, sovereign AI intelligence to enterprise data, helping regulated industries innovate on their own terms. ## Introducing ChatGPT for Financial Services DevFeed: [Introducing ChatGPT for Financial Services]() Original publisher: [Read original article]() Published: 2026-09-10T07:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [data](), [datasets](), [Model Context Protocol](), [Latency]() Tags: [chatgpt](), [data](), [datasets](), [financial-services](), [gpt](), [latency](), [mcp](), [models](), [openai](), [product](), [reasoning](), [retrieval](), [security]() ### AI overview ChatGPT for Financial Services combines GPT-6 Astra with built-in, OpenAI-hosted financial data for research, financial modeling, and client materials. It includes data-provider datasets, citations, enterprise controls, and MCP performance improvements. ### Source excerpt Introducing ChatGPT for Financial Services, combining built-in financial data and GPT-6 Astra for research, modeling, and client-ready materials. ## How BitRobot Crowdsources Real-World Data for Embodied AI, with Jonathan Victor DevFeed: [How BitRobot Crowdsources Real-World Data for Embodied AI, with Jonathan Victor]() Original publisher: [Read original article]() Author: Amira Valliani Published: 2026-09-07T07:00:00Z Content type: article Language: en Sources: [Solana News Feed]() Topics: [datasets](), [Robotics](), [Robot Navigation](), [Solana](), [Artificial Intelligence]() Tags: [ai](), [blockchain](), [blockchain-technology](), [crypto-news](), [cryptocurrency](), [dataset](), [defi](), [depin](), [nfts](), [payments](), [podcasts](), [robot-navigation](), [robotics](), [solana](), [solana-ecosystem](), [training](), [web3]() ### AI overview The article discusses how BitRobot crowdsources real-world interaction data for embodied AI. It describes FrodoBots, which generated roughly 2,000 hours of urban robot navigation data, and explains how BitRobot uses a network model to produce and reward contributors of robotics data. ### Source excerpt BitRobot open-sourced 2,000 hours of robot navigation data and uses Solana to track and reward embodied AI data contributors. ## NVIDIA to Acquire Hugging Face for $12.93B, Pledges the Platform Stays Open and Hardware Neutral DevFeed: [NVIDIA to Acquire Hugging Face for $12.93B, Pledges the Platform Stays Open and Hardware Neutral]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-04T18:01:12Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [hugging face](), [Nvidia](), [AI Development](), [AI Infrastructure](), [Application Development](), [datasets](), [Cloud](), [Deployment](), [model-deployment](), [inference-providers]() Tags: [ai](), [ai-infrastructure](), [application-development](), [cloud](), [creators](), [datasets](), [deployment](), [enterprise](), [hugging-face](), [inference-providers](), [models](), [multi-cloud](), [nvidia](), [platforms]() ### AI overview NVIDIA has agreed to acquire Hugging Face for $12.93 billion, with plans to expand its infrastructure and AI development capabilities. Hugging Face is expected to retain its brand and operate as an open, hardware-neutral platform supporting models, datasets, applications, multiple clouds, accelerators, and inference providers. ### Source excerpt NVIDIA has agreed to acquire Hugging Face for $12.93 billion, a transaction that would extend the company's position from accelerated compute and AI infrastructure into one of the industry's most widely used platforms for open models, datasets, and application development. In an announcement published on the NVIDIA website, CEO Jensen Huang said the company plans The post NVIDIA to Acquire Hugging Face for $12.93B, Pledges the Platform Stays Open and Hardware Neutral appeared first on StorageReview.com. ## Transfer learning for genomic prediction in underrepresented populations DevFeed: [Transfer learning for genomic prediction in underrepresented populations]() Original publisher: [Read original article]() Published: 2026-09-03T18:20:31Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Fine-tuning](), [Model Development](), [Google](), [datasets]() Tags: [datasets](), [evaluation](), [general-science](), [google](), [healthcare](), [learning](), [machine-intelligence](), [research](), [training]() ### AI overview Google Research evaluates transfer learning for polygenic risk-score prediction across populations. European-cohort transfer learning improves prediction for small underrepresented target cohorts but can reduce accuracy as target cohorts grow, particularly for population-specific traits. ### Source excerpt General Science ## A connectomics milestone: Mapping the complete male fruit fly brain DevFeed: [A connectomics milestone: Mapping the complete male fruit fly brain]() Original publisher: [Read original article]() Published: 2026-09-03T16:00:03Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [datasets](), [Google]() Tags: [ai](), [datasets](), [general-science](), [google](), [health-bioscience](), [machine-intelligence](), [open-source](), [open-source-models-datasets](), [research](), [science]() ### AI overview Google Research describes a complete wiring map of the male fruit fly's brain and central nervous system, containing over 166,000 neurons and 125 million synaptic connections. The connectome was produced through a decade-long partnership using computing and AI, and is available to explore and download via Neuroglancer. ### Source excerpt General Science ## GHarchive data has become unreliable for measuring GitHub activity DevFeed: [GHarchive data has become unreliable for measuring GitHub activity]() Original publisher: [Read original article]() Author: KD (noreply@blogger.com) Published: 2026-09-03T16:00:00Z Content type: opinion Language: en Sources: [Google Open Source Blog]() Topics: [data](), [datasets](), [GitHub](), [Crawler](), [GitHub API](), [Open Source](), [GraphQL]() Tags: [collect](), [data](), [dataset](), [datasets](), [github](), [google](), [google-open-source](), [graphql](), [open-data-sets](), [open-source](), [oss](), [pipeline](), [pull-requests](), [retention](), [stream](), [volume]() ### AI overview This article examines the reliability of open source data, focusing on GHarchive's coverage of GitHub events. It argues that GHarchive should not be used for real-time or volume-based metrics because event retention has declined and some activity is omitted by the GitHub Event stream and API limitations. ### Source excerpt by Sophia Vargas, Google Open Source & Andrew Nesbitt, Ecosyste.ms Every second, open source contribution quietly shapes the software we rely on, and yet our view of this open ecosystem is surprisingly opaque. Open source development is performed in public spaces -- we can see the commits, issues and comments, the APIs and endpoints are free to use -- the logs are just sitting there, so why can't we just collect all of the data? ...Said every researcher, everywhere. However in most cases of open source related data, we are only looking at part of the whole. Why am I writing this post? Because many of us (including many business decision-makers) are too comfortable with unsubstantiated data. We've gotten used to it. Our models assume that it's smelly and we adjust the logic and weights to compromise. When it comes to open source, our confidence is even lower, even though our resulting decisions can directly impact individuals whom we collectively depend on. Let's consider one of my favorite datasets: GHarchive. Started as a hobby project in 2011, this crawler has amassed more than 15 years of event data from GitHub. While this source provides a historical record of open source development on GitHub, as a real-time or comprehensive source of metrics, it's unreliable and should not be a source for volume-based metrics. In 2025, GHarchive captured 14% fewer events than in 2024, despite steady growth in platform adoption. Since 2025, we estimate that data retention in GHarchive has fallen to ~50% and in 2026 it may be as low as 20% for some event types (see figure below). Prior to 2025, you could make the general assumption that the majority of events would be represented in this pipeline. Since 2025, we must now assume we may be missing at least half of events and possibly more -- not to mention all of the additional activity that's left out of the event API (see GitHub's GraphQL API.) The crawler logic behind this dataset is simple: give me all the events from the GitHub Ev ## NVIDIA to Acquire Hugging Face DevFeed: [NVIDIA to Acquire Hugging Face]() Original publisher: [Read original article]() Author: 黄仁勋 Published: 2026-09-03T11:56:49Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [hugging face](), [datasets](), [Deployment](), [cloud-infrastructure]() Tags: [ai](), [artificial-intelligence](), [cloud](), [corporate](), [datasets](), [deployment](), [developers](), [high-performance-computing](), [hugging-face](), [models](), [nvidia](), [open-source](), [platform](), [software](), [supercomputing]() ### AI overview NVIDIA says it has agreed to acquire Hugging Face and plans to scale its platform and infrastructure. The announcement says Hugging Face will remain open, supporting model, framework, cloud, inference-provider and hardware choices across the AI ecosystem. ### Source excerpt I'm excited to announce that NVIDIA has agreed to acquire Hugging Face for $12,930,300,000. Together, we will scale Hugging Face's platform, strengthen its infrastructure and expand access to AI for developers and institutions worldwide. Over the past decade, Clem, Julien, Thomas and the team at Hugging Face have built something remarkable: a vibrant home for [...] ## Healthcare organizations can now connect EHR and additional industry data to ChatGPT DevFeed: [Healthcare organizations can now connect EHR and additional industry data to ChatGPT]() Original publisher: [Read original article]() Published: 2026-09-01T12:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [data](), [datasets]() Tags: [ai](), [chatgpt](), [compliance](), [data](), [datasets](), [healthcare](), [integration](), [product](), [workflow]() ### AI overview ChatGPT for Healthcare can connect authorized Epic patient context and structured public healthcare data, helping healthcare teams review records, research, and official sources within governed workflows. ### Source excerpt ChatGPT can now connect to trusted healthcare data, helping clinicians securely access patient context, medical research, and more. ## TimesFM-3: A zero-shot foundation model for multivariate forecasting DevFeed: [TimesFM-3: A zero-shot foundation model for multivariate forecasting]() Original publisher: [Read original article]() Published: 2026-08-31T17:19:40Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [foundation-models](), [Time Series](), [Google](), [datasets](), [Transformer architecture](), [Fine-tuning](), [generalization in machine learning]() Tags: [data-management](), [features](), [fine-tuning](), [foundation-models](), [google](), [machine-intelligence](), [model](), [product](), [time-series](), [transformer-architecture](), [zero-shot]() ### AI overview Google Research introduces TimesFM-3, a 330-million-parameter time-series foundation model designed for accurate multivariate forecasting in a single forward pass. Pre-trained on more than one trillion real-world and synthetic time points, it jointly models coevolving series and external covariates in zero-shot settings without task-specific fine-tuning. ### Source excerpt Data Management ## GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models DevFeed: [GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models]() Original publisher: [Read original article]() Author: Naoto Usuyama, Jeya Maria Jose Valanarasu, Tristan Naumann Published: 2026-08-31T16:00:00Z Content type: article Language: en Sources: [Microsoft Research]() Topics: [foundation-models](), [datasets](), [data]() Tags: [analysis](), [data](), [datasets](), [foundation-models](), [models](), [performance](), [research](), [research-blog](), [scale](), [validation]() ### AI overview GigaPath-Flash and GigaTIME-Flash are efficient pathology foundation models designed to reduce computational demands while maintaining strong performance. They enable repeated analysis of larger cancer cohorts and support population-scale research into disease biology, biomarkers, and clinical outcomes. ### Source excerpt What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research. ## How Bits Database Optimization proves a query rewrite is faster DevFeed: [How Bits Database Optimization proves a query rewrite is faster]() Original publisher: [Read original article]() Author: Alex Weisberger; Nenad Noveljić; Bowen Chen Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Optimization](), [database monitoring](), [Database](), [synthetic-data](), [datasets](), [cpu](), [IO](), [Security](), [Statistics]() Tags: [cpu](), [data](), [database](), [database-monitoring](), [infrastructure](), [optimization](), [performance](), [security](), [software](), [sql](), [statistics](), [synthetic](), [synthetic-data]() ### AI overview The article explains how Bits Database Optimization validates that a proposed query rewrite is faster. It describes controlled benchmarking with simulated production-like datasets, accounting for cache state, CPU and I/O contention, execution time, and database work. ### Source excerpt Learn how Bits generates synthetic data, measures simulation fidelity, and uses execution time and database work to determine whether an optimization is truly faster. ## Benchmarking vector indexes DevFeed: [Benchmarking vector indexes]() Original publisher: [Read original article]() Author: Evgeniy Patlan Published: 2026-08-27T13:35:32Z Content type: article Language: en Sources: [Blog - Percona]() Topics: [benchmarking](), [Ground truth / benchmark quality](), [Databases](), [ann](), [datasets](), [Sorting]() Tags: [ai-vector](), [ann](), [benchmark](), [benchmarking](), [benchmarks](), [blog](), [blog-post](), [data](), [database-performance](), [database-trends](), [databases](), [datasets](), [embeddings](), [index](), [measurements](), [model](), [open-source](), [percona](), [points](), [report](), [run](), [search](), [sorting](), [vector-search](), [vectorsearch]() ### AI overview This article explains how Percona's vector-bench measures vector indexes by building database engines from pinned versions, running them in identical containers with the same data and hardware, and comparing consistent measurements. It describes embeddings, exact search, approximate nearest neighbour search, and ground truth as the brute-force reference needed to evaluate index accuracy. ### Source excerpt Nearly every database has vector search now, and every one of them has a blog post with a big number in it. Almost none of those numbers can be checked, because the thing that makes them meaningful is usually missing. We built a vector-bench to stop guessing. You name the engines you want, build them ... Continued The post Benchmarking vector indexes appeared first on Percona. ## Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino DevFeed: [Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-08-27T13:18:53Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [Artificial Intelligence](), [Computer vision](), [Neural Network](), [object-detection](), [C](), [datasets]() Tags: [ai](), [ai-powered-livestock-monitoring](), [arduino](), [computer-vision](), [data](), [livestock-monitoring](), [model-training](), [monitoring](), [neural-networks](), [uno-q](), [yolo]() ### AI overview An Arduino Blog article profiles four 17-year-olds in Italy developing an AI-powered livestock monitoring system with the UNO Q. The project uses computer vision, custom-trained neural networks, and diverse image data to identify animals, count them, and detect possible health problems in real time. ### Source excerpt Pietro Maria Piazza, Alessandro Nesci, Davide Santucci, and Matteo Angiolillo are not waiting to finish school before starting to build something real. Based in Forlì, Italy, the four friends behind Blue Proton Initiative strive to develop an AI-powered livestock monitoring system designed to help farmers identify individual animals and detect early signs of health problems [...] The post Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino appeared first on Arduino Blog. ## From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers DevFeed: [From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers]() Original publisher: [Read original article]() Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Large Language Model](), [LLM evaluation / benchmarking](), [Natural language processing](), [datasets]() Tags: [datasets](), [evaluation](), [models](), [post-training](), [research](), [responses](), [search]() ### AI overview The article introduces a rubric-based reward framework for grounded open-domain question answering. It generates query-specific rubrics from retrieved evidence and decomposes them into quality dimensions for fine-grained post-training supervision. The method improves composition, grounding, and instruction-following results over the stated baselines and evaluation datasets. ### Source excerpt Designing effective reward signals for open-domain question answering is challenging because high-quality responses must simultaneously satisfy multiple aspects of answer quality that are difficult to capture with a holistic scalar objective. We introduce a rubric-based reward framework that generates query-specific rubrics grounded in retrieved evidence and decomposed into multiple quality dimensions, providing fine-grained supervision during post-training. Averaged across three evaluation axes (composition, grounding, and instruction-following), our approach improves over the... ## GlucoFM: Foundation model for continuous glucose monitoring DevFeed: [GlucoFM: Foundation model for continuous glucose monitoring]() Original publisher: [Read original article]() Published: 2026-08-26T18:42:43Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [foundation-models](), [Google](), [data](), [datasets]() Tags: [data](), [foundation-models](), [google](), [health-bioscience](), [machine-intelligence](), [model](), [research]() ### AI overview GlucoFM is a lightweight, self-supervised foundation model for continuous glucose monitoring. Its dual-stream design separates slower glycemic trends from short-term deviations while preserving time-of-day and missingness information. Evaluated across four cohorts and seven clinical prediction tasks, it achieved higher average PR-AUC than the evaluated GluFormer variant. ### Source excerpt Health & Bioscience [Next page]()