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GitHub Engineering 2026-03-12 16:00 UTC Score 29.0 USR-0062-20260312-ai-specialis-3e2135c9 Full article

Continuous AI for accessibility: How GitHub transforms feedback into inclusion

AI automates triage for accessibility feedback, allowing us to focus on fixing barriers—turning a chaotic backlog into continuous, rapid resolutions. The post Continuous AI for accessibility: How GitHub transforms feedback into inclusion appeared first on The GitHub Blog .

One Useful Thing 2026-03-12 14:10 UTC Score 20.0 USR-0105-20260312-ai-specialis-ceb5b124 Full article

The Shape of the Thing

Where we are right now, and what likely happens next

MongoDB AI Blog 2026-03-12 14:00 UTC Score 37.0 USR-0070-20260312-ai-specialis-05c9b488 Full article

Observability and OpenTelemetry: Introducing MongoDB Atlas Log Integration

In high-stakes enterprise environments, outages do not wait for business hours, and neither do IT/Network Operators. A latency spike hits the dashboard, and metrics signal that the database is under pressure. The cause? Indeterminate. Meanwhile, the business impact is immediate: orders fail to process, customers can’t access accounts, transactions stall, and critical records become temporarily unavailable. Every minute of uncertainty translates into lost revenue, frustrated users, and escalating pressure. Teams often fall back on a familiar—yet time-consuming—ritual: logging into their data platform, exporting large log files, extracting compressed archives, and manually searching through thousands of lines of entries to identify the issue. What should be a quick diagnosis becomes a manual context-switching investigation. By the time the problematic query, configuration issue, or audit event is identified, users have already experienced the disruption—and the business has absorbed the cost. MongoDB believes the database should be the heartbeat of a digital business. So we’re introducing a new log integration that brings MongoDB Atlas system and audit logs directly into external observability and storage platforms. This enhancement helps bridge the gap between metrics and meaning when it matters most. Flexible log delivery for modern observability workflows Now database operators, DevOps pros, and IT Operations teams alike can send MongoDB system and audit logs—including mong…

Sebastian Raschka Blog 2026-03-12 08:07 UTC Score 28.0 USR-0116-20260312-ai-specialis-1dc2ce2c Full article

Nemotron 3 Super Throughput Notes

Short note on NVIDIA Nemotron 3 Super 120B-A12B, a hybrid Mamba-Transformer MoE model with latent experts and shared-weight MTP.

METR 2026-03-12 07:00 UTC Score 33.0 USR-0147-20260312-research-aca-f0d1f33a Full article

Review of the Anthropic Sabotage Risk Report: Claude Opus 4.6

We reviewed two versions of Anthropic’s Sabotage Risk Report for Claude Opus 4.6, producing two corresponding review documents: our review of the February 11 version and our review of the March 3 version . We recommend that readers refer to our review of the February 11 version, which represents our review of the report as originally received. We expect the public version of the Sabotage Risk Report to be updated to resemble the document we received on March 3, 2026 in content, though not necessarily in exact wording. We expect our second review to cover those changes, but if the updated public version includes any changes that materially affect our opinions, we will publish an updated review. Both documents include an appendix detailing our review process and the differences between the two versions of our review. The following is the executive summary of our review of the February 11 version. The full documents are available as PDFs ( February 11 , March 3 ). Executive summary This document is METR’s external review of the February 11, 2026 version of Anthropic’s Sabotage Risk Report: Claude Opus 4.6. Anthropic shared an unredacted version of their Sabotage Risk Report and other materials with us for our review. We further detail this process in an appendix. We lay out our findings in two sections: Synopsis of Anthropic’s case and redactions for the public version Our assessment: We give substantive feedback on the report in a few key areas: Adequacy of information: We thi…

Lex Fridman Podcast 2026-03-11 20:37 UTC Score 22.0 AI-137-20260311-podcasts-and-b2a5ff58 Full article

#493 – Jeff Kaplan: World of Warcraft, Overwatch, Blizzard, and Future of Gaming

Jeff Kaplan is a legendary Blizzard game designer of World of Warcraft and Overwatch, now preparing to launch a new game, The Legend of California, from his new studio Kintsugiyama – available to wishlist on Steam today, with alpha later in March. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep493-sc See below for timestamps, and to give feedback, submit questions, contact Lex, etc. CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, videos or call-in: https://lexfridman.com/ama Hiring – join our team: https://lexfridman.com/hiring Other – other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: The

TWIML AI Podcast 2026-03-10 23:25 UTC Score 39.0 AI-148-20260310-podcasts-and-a23d20be Full article

Agent Swarms and Knowledge Graphs for Autonomous Software Development with Siddhant Pardeshi - #763

In this episode, Sid Pardeshi, co-founder and CTO of Blitzy, joins us to discuss building autonomous development systems able to deliver production-ready software at enterprise scale. Sid contrasts AI-assisted coding with end-to-end autonomy, arguing that “code is a commodity” and acceptance is the real metric—security, standards, tests, and maintainability included. We explore Blitzy’s hybrid graph-plus-vector approach, which grounds agents and combines semantic signals with keyword search to navigate large repositories efficiently. Sid breaks down context and agent engineering, how effective context windows have plateaued, and why dynamic agent personas, tool selection, and model-specific prompting matter at scale. He details their orchestration of large swarms of AI agents to collaboratively analyze codebases, plan tasks, and execute complex tasks in parallel. We also dig into why Agents.md and flat memories break down, storing feedback in the knowledge graph, and building real-world evals beyond leaderboards to choose the right model for each task. The complete show notes for this episode can be found at https://twimlai.com/go/763.

Carnegie Council AI 2026-03-10 17:30 UTC Score 24.0 USR-0160-20260310-ai-specialis-c5fcd987 Full article

Ethics on Film: Discussion of "One Battle After Another"

This review of Paul Thomas Anderson's Oscar-winning "One Battle After Another" discusses gender roles, white supremacy, and the motivations of revolutionaries.

10 years of AlphaGo: The turning point for AI | Thore Graepel & Pushmeet Kohli
Google DeepMind YouTube 2026-03-10 17:28 UTC Score 25.0 AI-145-20260310-podcasts-and-695e21da Full article

10 years of AlphaGo: The turning point for AI | Thore Graepel & Pushmeet Kohli

Seoul, March 2016. Two players sit hunched over a 19x19 grid covered in a sea of black and white stones. They are playing the ancient game of Go - a game of unimaginable complexity long thought impossible for a machine to master. On one side is Lee Sedol (Sae Dol), a legendary 18-time Go world champion. On the other, AlphaGo, a neural network based AI system built on a powerful technique called reinforcement learning. In the blink of an eye, the world changed. Exactly one decade later, we look back at the match that sparked the modern AI revolution. From algorithmic discovery to the solving of scientific grand challenges like protein folding, the foundation was laid right there on that wooden board. Join Hannah Fry, Pushmeet Kohli (VP, Science) and Thore Graepel (AlphaGo team & Distinguished Research Scientist) as they unpick the legacy of AlphaGo. Further watching: 🎥AlphaGo https://youtu.be/WXuK6gekU1Y 🎥The Thinking Game: https://youtu.be/d95J8yzvjbQ ___ Subscribe to our channel https://www.youtube.com/@googledeepmind Find us on X https://twitter.com/GoogleDeepMind Follow us on Instagram https://instagram.com/googledeepmind Add us on Linkedin https://www.linkedin.com/company/deepmind/

METR 2026-03-10 07:00 UTC Score 49.0 USR-0147-20260310-research-aca-510ce207 Full article

Many SWE-bench-Passing PRs Would Not Be Merged into Main

Summary: We find that roughly half of test-passing SWE-bench Verified PRs written by mid-2024 to mid/late-2025 agents would not be merged into main by repo maintainers, even after adjusting for noise in maintainer merge decisions. Since the agents are not given a chance to iterate on their solution in response to feedback the way a human developer would, we do not claim that this represents a fundamental capability limitation. Rather, our results indicate that a naive interpretation of benchmark scores may lead one to overestimate how useful agents are without more elicitation or human feedback. Introduction It is often unclear how to translate benchmark scores into real-world usefulness. For example, if a model’s SWE-bench Verified score is 60%, does that mean it can resolve 60% of real-world open-source issues? One reason to doubt this is that benchmarks are clean and verifiable in ways the real world is not. To study this quantitatively, we take SWE-bench Verified and zoom in on one such difference — it uses an automated grader rather than the real-world standard of maintainer review. To study how agent success on benchmark tasks relates to real-world usefulness, we had 4 active maintainers from 3 SWE-bench Verified repositories review 296 AI-generated pull requests (PRs). We had maintainers (hypothetically) accept or request changes for patches as well as provide the core reason they were requesting changes: core functionality failure, patch breaks other code or code qua…

Practical AI Podcast 2026-03-09 13:27 UTC Score 31.0 AI-143-20260309-podcasts-and-cd457338 Full article

AI policy and the battle for computing power

AI is reshaping global power, from chip manufacturing and computing power to AI governance and US-China relations. In this episode, Ben Buchanan, Assistant Professor at The Johns Hopkins University and former White House Special Advisor for AI, explores how AI policy, geopolitics, and international cooperation intersect with AI innovation and AI safety. We discuss the strategic importance of computing power, the future of AI governance, and what it will take for democracies to lead responsibly in the age of AI. Featuring: Ben Buchanan – LinkedIn Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Links: The AI Grand Bargain Upcoming Events: Register for upcoming webinars here !

Consultancy.lat AI & GenAI 2026-03-09 11:21 UTC Score 15.0 AI-177-20260309-regional-ai--f480a8a7

SLR acquires Chilean environmental consulting firm Geobiota

SLR, a global sustainability consultancy, has acquired Geobiota, a 200-person Chilean environmental consulting firm specializing in the mining and energy sectors. Founded in 1995, Geobiota provides consulting services and solutions in environmental engineering and natural resources.

I BUILT A FULLY AUTOMATIC MANSPLAINER
Yannic Kilcher 2026-03-06 22:07 UTC Score 17.0 AI-140-20260306-podcasts-and-3ba304b2 Full article

I BUILT A FULLY AUTOMATIC MANSPLAINER

All information about GTC and the DGX Spark Raffle is here: https://www.ykilcher.com/gtc Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Future of Life Institute AI 2026-03-04 22:12 UTC Score 27.0 USR-0145-20260304-ai-specialis-1d70063d Full article

“This is What it Means to be Pro-Human” Declares Broad Coalition of Conservative, Progressive, and Civil Society Groups in Statement of Shared Principles on AI

Amid a rising backlash to Silicon Valley overreach, a remarkably diverse group from across the political spectrum announced a set of AI principles to clearly define the goals of the emerging pro-human movement.

AI Expo Africa 2026-03-04 07:06 UTC Score 20.0 USR-0194-20260304-regional-new-196b17e1 Full article

South African AI Association Enters 4th Year After Busy 2025

JOHANNESBURG, SOUTH AFRICA 3rd March – South African AI Association (SAAIA) SAAIA operated on a funded basis for the second time from 1st March 2025 to 28th February 2026. This was achieved via the amazing sponsorship from Google, Huawei and other vendors who contributed to the years funding tranche for which we are very grateful. […]

OpenMined Blog 2026-03-03 19:33 UTC Score 27.0 USR-0156-20260303-ai-specialis-c7e84c6b Full article

Two Years In: OpenMined Deep Partnerships under the NAIRR

This month, OpenMined is heading back to Arlington, VA, for the NAIRR’s second annual meeting. A lot has changed since last year’s inaugural gathering. The program is transitioning from a proof-of-concept to permanent national infrastructure, NSF has put $35 million on the table to establish a permanent operations center, and OpenMined just received approval to […] The post Two Years In: OpenMined Deep Partnerships under the NAIRR appeared first on OpenMined .

The Dangerous Illusion of AI Coding? - Jeremy Howard
Machine Learning Street Talk 2026-03-03 14:50 UTC Score 62.0 AI-141-20260303-podcasts-and-aa1fcba5 Full article

The Dangerous Illusion of AI Coding? - Jeremy Howard

Dive into the realities of AI-assisted coding, the origins of modern fine-tuning, and the cognitive science behind machine learning with fast.ai founder Jeremy Howard. In this episode, we unpack why AI might be turning software engineering into a slot machine and how to maintain true technical intuition in the age of large language models. GTC is coming, the premier AI conference, great opportunity to learn about AI. NVIDIA and partners will showcase breakthroughs in physical AI, AI factories, agentic AI, and inference, exploring the next wave of AI innovation for developers and researchers. Register for virtual GTC for free, using my link and win NVIDIA DGX Spark (https://nvda.ws/4qQ0LMg) Jeremy Howard is a renowned data scientist, researcher, entrepreneur, and educator. As the co-founder of fast.ai, former President of Kaggle, and the creator of ULMFiT, Jeremy has spent decades democratizing deep learning. His pioneering work laid the foundation for modern transfer learning and the pre-training and fine-tuning paradigm that powers today's language models. Key Topics and Main Insights Discussed: - The Origins of ULMFiT and Fine-Tuning - The Vibe Coding Illusion and Software Engineering - Cognitive Science, Friction, and Learning - The Future of Developers RESCRIPT: https://app.rescript.info/public/share/BhX5zP3b0m63srLOQDKBTFTooSzEMh_ARwmDG_h_izk https://app.rescript.info/api/public/sessions/62d06c0336c567d6/pdf Jeremy Howard: https://x.com/jeremyphoward https://www.answer.…

METR 2026-03-03 08:00 UTC Score 44.0 USR-0147-20260303-research-aca-7bd4bcdb Full article

Observations from two CLI game reimplementation runs with Opus 4.6

Update 7/27/2026: I recently investigated the Slay the Spire deliverable deeper. This revealed some moderate problems that weren’t obvious back when I initially scored it. The problems I found are mostly the type that might take a while to surface, or might take close examination or an experienced player to notice. This has been generally in-line with my impression that models often create deliverables that look good initially, but look worse and worse upon deeper examination (unlike human deliverables, which often leave much more informative first impressions). The rest of the post remains the same as it was on March 3 2026. Summary: Opus 4.6 can, with a simple agent scaffold, create mostly-playable but somewhat broken CLI versions of Slay the Spire and Balatro 1 . Intro Last weekend I was trying to think of really difficult tasks we could give to AI agents to upper-bound their capabilities. I thought of two examples: Recreating a basic version of the video game Slay the Spire in the CLI Recreating a basic version of the video game Balatro in the CLI Both of these video games have a few properties that make it especially easy for AI systems to implement them: They already exist, so the AI doesn’t have to come up with new game ideas and do the enormous amount of work necessary to make it a fun game to play. Most player-relevant information is conveyed through text. They have well-defined rules and interactions between game mechanics. They are turn-based and don’t rely on rea…

OpenMined Blog 2026-03-03 00:30 UTC Score 30.0 USR-0156-20260303-ai-specialis-5e90423e Full article

Why Blocking, Licensing & Pay-to-Access Are Insufficient

TL;DR: The three dominant responses to this problem – blocking scrapers, licensing content, charging for scraper access – share the same flaw: once content is copied to a model server, control and attribution are lost. Each treats distribution and control as mutually exclusive. They are not. A different architecture exists, one in which publishers retain […] The post Why Blocking, Licensing & Pay-to-Access Are Insufficient appeared first on OpenMined .

Weaviate Blog 2026-03-03 00:00 UTC Score 30.0 USR-0073-20260303-ai-specialis-5b76ad39 Full article

Weaviate 1.36 Release

This release introduces HFresh vector index (Preview), and brings Server-side Batching, Object TTL, Async Replication Improvements, Drop Inverted Indices, and Backup Restoration Cancellation to general availability.

OpenMined Blog 2026-03-02 20:30 UTC Score 32.0 USR-0156-20260302-ai-specialis-5d9542bc Full article

Reflections on the 2026 India AI Impact Summit

OpenMined participated in the 2026 India AI Impact Summit in New Delhi, demonstrating BioVault for privacy-preserving genomics research and contributing to policy discussions on data sovereignty, conditional openness, and the "Visit, Don't Move" paradigm for cross-border AI collaboration. The post Reflections on the 2026 India AI Impact Summit appeared first on OpenMined .

Amoral American Power, with Professor Matias Spektor
Carnegie Council AI 2026-03-02 15:30 UTC Score 24.0 USR-0160-20260302-ai-specialis-0de8e9b3 Full article

Amoral American Power, with Professor Matias Spektor

From Caracas to Tehran, U.S. power is no longer justified through a narrative of liberal internationalism. Matias Spektor examines the consequences of this shift.

Lex Fridman Podcast 2026-03-01 04:33 UTC Score 17.0 AI-137-20260301-podcasts-and-d13d94a5 Full article

#492 – Rick Beato: Greatest Guitarists of All Time, History & Future of Music

Rick Beato is a music educator, interviewer, producer, songwriter, and a true multi-instrument musician, playing guitar, bass, cello & piano. His incredible YouTube channel celebrates great musicians & musical ideas, and helps millions of people fall in love with great music all over again. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep492-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/rick-beato-transcript CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, videos or call-in: https://lexfridman.com/ama Hiring – join our team: https://lexfridman.com/hiring Other – other ways to get in

Lex Fridman Podcast 2026-03-01 03:32 UTC Score 17.0 AI-137-20260301-podcasts-and-9757b476 Full article

Transcript for Rick Beato: Greatest Guitarists of All Time, History & Future of Music | Lex Fridman Podcast #492

This is a transcript of Lex Fridman Podcast #492 with Rick Beato. The timestamps in the transcript are clickable links that take you directly to that point in the main video. Please note that the transcript is human generated, and may have errors. Here are some useful links: Go back to this episode’s main page Watch the full YouTube version of the podcast Table of Contents Here are the loose “chapters” in the conversation. Click link to jump approximately to that part in the transcript: 0:00 – Introduction 0:44 – Guitar solos 4:43 – Gypsy jazz and Django Reinhardt 6:14

MongoDB AI Blog 2026-02-27 16:30 UTC Score 29.0 USR-0070-20260227-ai-specialis-8ab3a719 Full article

Inside MongoDB Dublin: The Heart of Our International Growth

Nestled between the Irish Sea and the Wicklow Mountains, MongoDB’s Dublin office brings together people from around the world. It’s a place where you can build a meaningful career, contribute to leading global products, and feel part of a close-knit community. Located in Ballsbridge just south of Dublin city center, the office is a short walk from the Lansdowne DART station and is well-served by multiple bus routes, making it easy to plug into everything the city has to offer. Image of a wall in the MongoDB Dublin office that is painted with Dublin relevant illustrations and text that says "Build together" and "Make it matter" As MongoDB’s international headquarters, Dublin is a key hub where over 300 employees from more than 40 nationalities own critical parts of the company’s products and support customers running mission-critical systems across the globe. Established in 2012, MongoDB Dublin has long played a pivotal role in helping the company achieve its mission of empowering innovators to create, transform, and disrupt industries by unleashing the power of software and data. In this spotlight, you’ll hear from people across MongoDB’s Product & Technology, Sales, and Technical Services teams about what it’s like to build your career—and your life—in Dublin with MongoDB. Image of CEO, CJ Desai, speaking in front of a group of employees in the Dublin office. CEO CJ Desai holds an “Ask Me Anything” session in a recent visit to Dublin. Life at MongoDB Dublin The MongoDB Dubl…

MongoDB AI Blog 2026-02-27 15:30 UTC Score 37.0 USR-0070-20260227-ai-specialis-2ad5a66f Full article

Towards Model-based Verification of a Key-Value Storage Engine

In our previous post, we talked about our process of specifying MongoDB’s distributed transactions protocol and how it enabled novel analysis of its performance characteristics. In this follow-up, we talk about how the modularity of our specification also enabled us to check that the underlying storage engine implementation actually conforms to the abstract behavior defined in our formal specification. That is, we are able to formalize the interface boundary between the sharded transaction protocol and WiredTiger, the underlying key-value storage engine, and develop an automated way to generate tests for checking conformance between the semantics of the underlying storage engine layer and this abstract model. As mentioned in the previous post, a deeper exploration of the concepts covered in this post is covered in our recently published VLDB ’25 paper, Design and Modular Verification of Distributed Transactions in MongoDB. Modular, Model-Based Verification As discussed in Part 1, we had developed a TLA+ specification of MongoDB’s distributed transactions protocol in a compositional manner, describing the high level protocol behavior while also formalizing the boundary between the distributed aspect of the transactions protocol and the underlying single-node WiredTiger storage engine component. As mentioned, the distributed transactions protocol can be viewed as running atop the lower level storage layer. When considering the correctness guarantees of the distributed transact…

TWIML AI Podcast 2026-02-26 23:52 UTC Score 56.0 AI-148-20260226-podcasts-and-b85a484e Full article

AI Trends 2026: OpenClaw Agents, Reasoning LLMs, and More with Sebastian Raschka - #762

In this episode, Sebastian Raschka, independent LLM researcher and author, joins us to break down how the LLM landscape has changed over the past year and what is likely to matter most in 2026. We discuss the shift from raw model scaling to reasoning-focused post-training, inference-time techniques, and better tool integration. Sebastian explains why methods like self-consistency, self-refinement, and verifiable-reward reinforcement learning have become central to progress in domains like math and coding, and where those approaches still fall short. We also explore agentic workflows in practice, including where multi-agent systems add real value and where reliability constraints still dominate system design. The conversation covers architecture trends such as mixture-of-experts, attention efficiency strategies, and the practical impact of long-context models, alongside persistent challenges like continual learning. We close with Sebastian’s perspective on maintaining strong coding fundamentals in the age of AI assistants and a preview of his new book, Build A Reasoning Model (From Scratch). The complete show notes for this episode can be found at https://twimlai.com/go/762.

Instacart Tech Blog 2026-02-26 18:55 UTC Score 24.0 USR-0056-20260226-ai-specialis-590c6078 Full article

Our Early Journey to Transform Instacart’s Discovery Recommendations with LLMs

Key Contributors: Moein Hasani, Hamidreza Shahidi, Trace Levinson, Guanghua Shu Introduction At Instacart, we are laser-focused on improving the user experience by making shopping feel easy, engaging, and personalized. Our discovery surfaces play a central role in bringing this to life. Alongside explicit Search intents, discovery is our opportunity to meet customers’ implicit needs, presenting them with the most relevant and inspiring content we have to offer. The main discovery surface within the Instacart app, referred to here as the “Shopping Hub”, is one of the most critical in this regard. This is the surface a customer lands on within the Instacart app after selecting their desired retailer, guiding them along their entire journey. What users see here shapes not just what they buy, but how intuitive and enjoyable their experience feels. Given its importance, our team runs dozens of Shopping Hub experiments per year, constantly evaluating new ways to enrich the discovery experience. Historically, these experiments have been constrained by static content libraries feeding our recommendation systems. With the rapid advancement of generative AI, a critical opportunity began to emerge: rather than incrementally improving a swath of legacy systems, could we leverage LLMs to rethink how content shows up for a user from the ground up? Which new primitives could we build to uplevel quality, personalization, and cohesion across the page? This blog post walks through our early j…

Weaviate Blog 2026-02-26 00:00 UTC Score 35.0 USR-0073-20260226-ai-specialis-efd829bf Full article

Building A Legal RAG App in 36 Hours

Learn how we built a production-ready, end-to-end RAG application in just 36 hours using the Query Agent and the new Weaviate Agent Skills library.

Vector Institute News 2026-02-25 21:09 UTC Score 35.0 USR-0017-20260225-research-aca-03ba31ef Full article

Remarkable 2026 Poster Session: 60 research projects shaping AI’s future

Vector Institute’s third annual Remarkable 2026 conference brought together over 1,500 researchers and industry leaders in person and online on February 19-20 to explore how AI research translates into real-world […] The post Remarkable 2026 Poster Session: 60 research projects shaping AI’s future appeared first on Vector Institute for Artificial Intelligence .

MongoDB AI Blog 2026-02-25 16:45 UTC Score 45.0 USR-0070-20260225-ai-specialis-4e49681b Full article

Innovating with MongoDB | Customer Successes, February 2026

Who says that winter is when things slow down? MongoDB has had a busy start to the year, with a steady stream of announcements and product features—all against the backdrop of an industry moving at warp speed. It's been a lot, and it's been a blast! For example, the energy at January’s MongoDB.local San Francisco—where we announced capabilities to help teams ship production AI faster—was infectious. MongoDB isn’t just starting a new chapter in AI; we’re rewriting the book in real time. The next generation of AI companies isn't just looking for a temporary place to store data; they’re looking to build on a generational modern data platform. Indeed, the most innovative founders are moving away from rigid, legacy systems and embracing a single, fluid foundation that can grow with them. At MongoDB.local SF, our message was clear: Choose your data platform strategically in order to ship faster. From our new Voyage 4 models to the general availability of our Intelligent Assistant, we are obsessed with anticipating what developers need next. This assistant is particularly impactful because it embeds MongoDB-specific expertise directly into Compass and MongoDB Atlas, allowing developers to troubleshoot performance without the "context-switching" that traditionally slows them down. In this issue, I’m thrilled to spotlight four startups who are building the future on the right foundation. You’ll see how Modelence and Thesys are using our flexible document model to eliminate 'operation…