Search: “labs”

After 15 months of incremental updates, leaks, and rumored leaks, DeepSeek released version 4. It arrived without the fanfare R1 and R1-preview commanded in early 2025. That quiet reception is the mos

Welcome to The Blueprint, a regular feature where we highlight how Google Cloud customers are tackling unique and common challenges across industries using the latest AI and cloud technologies. We hop

The unit of AI compute has shifted from single hosts to rack-scale systems that integrate NVIDIA GPUs, CPUs, scale-up networking fabrics, and liquid cooling, such as the NVIDIA GB300 NVL72 and NVIDIA

arXiv:2605.19677v1 Announce Type: new Abstract: Cryomicroneedles offer a route to minimally invasive intradermal delivery of living cells, but their cryogenic formulations must reconcile cell protecti

arXiv:2605.18840v1 Announce Type: cross Abstract: Leaderboards rank frontier models on independent axes but do not reveal whether capabilities reinforce or trade off across releases -- and at the fron

What the numbers mean for financial services Executive summary Lambda is the first to publish an audited STAC-AI™ LANG6 result on NVIDIA HGX 8xB200, with independently verified performance data that F

This post discusses how traditional caching mechanisms assume deterministic behavior (identical inputs producing identical outputs), an assumption that breaks down with large language models and espec

4/5 The fix: stop encoding execution order, encode graph position instead. Now you get two independent controls: experiment name (controls the key) and component code (controls the prompt). Change eit

5/5 What this unlocks: - Change one prompt, serve cached results for all other components. - A/B test any component against identical upstream outputs. - Run best-of-N inference without N branches col

4/5 Best-of-N: Run one agent config N times in parallel → select the best trajectory. Leverages LLMs’ non-determinism - but hinges on a good eval mechanism (we use an LLM-as-a-Judge). Can increase acc

5/5 Ensemble: Run multiple distinct agent configs in parallel → select the best trajectory. Leverages success of the portfolio vs single variants (see also: @/karpathy’s LLM Council). Can outperform b

A startup wants to pull magnesium from seawater without torching the environment. Another wants to take small language models to the podium for enterprise customers. Today @jason and @alex sat down wi

New #YAAP episode out now 🎙️ @yuvalinthedeep sits down with @mikegchambers from @awsdevelopers to unpack harness engineering and why it's the reason most agents never make it to production. 🎧 Listen/W

Take: stop searching for the “best model' or “best harness”. Model release → harness breaks → refind your optimal config. Most teams handle this with manual local search, with poor visibility into acc

@vasuman We've been saying this for months. The best compliment you can give an AI system is that it behaves exactly as expected. Every time. We built a campaign around it. It's called Boring AI: http

I am not sure I would agree with all of this, but the relationship between Anthropic and Claude is quite different than the relationship between other labs and their models. And that shows up in lots

4/5 But then we’re left with a search space problem. The possible agent configurations are infinite. And even if you manually optimize, what happens when a better model drops? Or your use case data up

5/5 Maestro automatically searches across this space (model ensembles, scaling strategies, execution policies), maps the Pareto frontier, and surfaces the full accuracy–cost–latency tradeoff surface.

Harnesses If you've used Claude Code or Codex, you've used a harness. A harness is the infrastructure layer that wraps an AI coding agent and decides how it operates, what it can touch, and how you me

Day 1 of #AIDevSF is live. 🙌 📍 Find us at booth 121 🎤 Catch our Chief Product Officer Or Dagan's session on Day 2: Efficiently Navigating the Agentic Action Space: Meta-Model Orchestration using AI21

On March 5, 2026, the much-anticipated paper for FlashAttention-4 (FA4) was published. The code was dropped on GitHub months ago; early benchmarks circulated, and preliminary results were presented at

Tune in to Or Dagan on the Life Self Mastery podcast with @rohitmal, unpacking why most enterprise AI projects fail before they ever reach production. You can also catch Or Dagan live at #AIDev26 this

Are you coming to @DeepLearningAI AI's AI Dev 26 x SF next week? Our Chief Product & Strategy Officer, Or Dagan, will be giving a talk diving into what our product and R&D teams are doing to automate

A 7B agent that beats GPT-4o. Lossless weight compression that speeds up inference by 177%. An arena where 23 teams battled across 103,000 adversarial rounds. This year at ICLR, Lambda is presenting t

We just hit #1 on the @huggingface BrowseComp-Plus leaderboard. Best accuracy: 92.53%. Best recall: 88.79%. Lowest calibration error across all submissions. Built with @AI21Labs Maestro. https://huggi

I have found 4.7 great for design, reverted back to 4.6 extended for everything else Anyone else like this? Introducing Claude Design by Anthropic Labs: make prototypes, slides, and one-pagers by talk

A Reddit thread on r/StableDiffusion discussing the capabilities and limitations of Anima Preview 3, a 2-billion-parameter text-to-image model by CircleStone Labs and Comfy Org, focused mainly on anim

1/5 When we saw our Reducer (=LLM judge component in Maestro, our agentic framework, that selects the best output from parallel agent runs) consistently picking gold patches, we were sure Claude Opus

3/5 An example: In instance psf__requests-1724, the gold fix is 2 lines. Our agent’s functional fix was 8 lines. The LLM judge rejected the correct 8-liner as 'messy' and 'redundant,' choosing a clean

4/5 We upgraded our original 3-line “be correct” prompt → a much more detailed prompt that enforced a hierarchy of constraints for correctness, regression safety, and minimality. Basically, get the LL

5/5 The takeaway: If your agent relies on an LLM judge for selection accuracy, measuring code quality isn’t enough; you need a measure of the model's inductive bias toward the 'fingerprint' of a gold

Catch Or Dagan, AI21's Chief Product & Strategy Officer, at #AIDev26 on April 29: 'An End to Manual Tinkering: Optimizing Accuracy, Cost & Latency in Real-World Agents'. We'll be at Booth 121. See you

arXiv:2604.09554v1 Announce Type: new Abstract: Optimism for accelerating scientific discovery with AI continues to grow. Current applications of AI in scientific research range from training dedicate

AnimaYume is a text-to-image model fine-tuned from Anima, a 2-billion-parameter anime-focused image generation model developed by CircleStone Labs in collaboration with Comfy Org, which is itself buil

'Both the utopia and apocalypse scenarios ignore AI's limitations.' Prof. Yoav Shoham, one of the world's leading artificial intelligence researchers and founder of @AI21Labs, discusses hype cycles, u

Scaling model size is hitting diminishing returns. The real gains are in orchestration. Our Co-Founder & Co-CEO @yshoham makes the case in a rare long-form profile by @Calcalistech today. The man tryi

AI Dev 26 brings the builders together. We'll be among them. Meet us there 👇 @DeepLearningAI @AndrewYNg 3,000+ developers. Two days. AI Dev 26 x San Francisco is where the people building AI come toge

If you're an AI/agent builder, it's so important that you don't overbuild and overcommit on a specific toolset and infrastructure. Frontier labs are shipping not just the models, but the harnesses and

Meta is making up for lost time in the AI race. After launching Muse Spark, the first model from its revamped Superintelligence Labs division, and buzzy AI agent Muse, it's now launching two new model

PrismML's Ternary Bonsai 2 27B fits a 24 GB card or Mac, but decodes at ~30 tok/s on an L4 and ~21 on an M4 Pro. z-lab's DFlash 2 drafter was trained on bf16 Qwen3.8-27B, so it guesses worse on the te

NVIDIA’s Seattle Robotics Lab and Isaac engineering team created reusable robotic systems to automate GB300 tester‑tray assembly, achieving over 95 % success on busbar tasks with a 160‑second cycle ti

Across the public sector, the question has shifted from “what’s possible?” to “how can we drive mission impact?” We’ll be answering this question at the Google Public Sector Summit on October 20th at

This past year, OpenAI, Anthropic, and other labs have announced breakthroughs on numerous long-standing mathematical problems, in some cases pushing well beyond what researchers expected current syst

@abuchanlife - training frontier models is expensive - distilling a frontier model is cheap - this market force alone tells us that frontier foundation models will be free/open. - there are others. ht

Indeed OpenAI was incompetent (and arrogant in thinking they knew it all); new cybersecurity software that they should have written themselves may help. But whether it’s enough to stop all of this rem

This week we kicked off the DevFest season in North America at Google Hudson Square in New York City with 80 engineers packed into the room. Typical technical workshops hand you a finished repo, tell