Search: “labs”

I was interviewed by Nasdaq a month before the ‘/goal’ functionality came out from the AI labs, and I still think it’s the most underused, high leverage feature you can try. “My favorite thing about t

Also, a lot depends on Chinese labs continuing to ship open weights models. If they stop, the frontier falls further and further behind to those who want to use local/fine-tuned models. I think this i

At least until (if?) rapid improvement stops, it seems less likely someone is going to catch the Big Three AI Labs. Microsoft and Meta released their models, which were fine, but not frontier. SpaceX

Andon Labs' Real-World AI Evals: Claude calls the FBI, AI CEOs, price cartels, Butter-Bench, & Luna https://latent.space/p/andon @andonlabs cofounders @lukaspet and @axelbacklund explain why dollar-de

Many research labs only consider inference efficiency after the fact. Step 3.7 Flash is a 198B sparse MoE VLM designed by @StepFun_ai for inference from the start. 196B language backbone with a 1.8B v

nice post from @Harvey and @LangChain Labs, worth a read. improve agent feedback loop without setting $$ on fire Can we design legal agent verifiers that are up to 1,000x cheaper? Verifiers are LLM ju

We are excited to join Nvidia's Nemotron Coalition of leading AI labs working together to advance open frontier foundation models. To celebrate we have partnered with @nvidia and @nebiustf to provide

A workflow audit is no longer the best way to figure out how to use AI in your job. Despite the advice from AI labs, I'm more convinced, because of AI's reasoning capabilities and long context horizon

Swyx announces that @jgreze will speak at the ai.engineer/wf gathering, which is positioned as an event bringing together leading agent research labs. The post expresses enthusiasm for the upcoming sp

LangChain Labs is conducting research into developing more efficient verification systems for validating the outputs and work products of AI agents. This research likely focuses on improving the speed

Efficient verification is what makes scaling legal agents practical. Excited to partner with @hwchase17 and the @LangChain Labs team on designing efficient verifiers - sharing early results showing op

This is actually one of the main advantages startups have over frontier labs, as long as there's a healthy spectrum of open-weight to closed-weight models on the cost-performance curve. Building a mod

Many research labs only consider inference efficiency after the fact. Step 3.7 Flash is a 196B MoE model, and built for inference from the start by @StepFun_ai. Multi-Matrix Factorization Attention (M

OpenJarvis: a local-first personal AI is now available to run with Ollama Built by Stanford’s @HazyResearch and Scaling Intelligence labs, as part of their “Intelligence Per Watt” research into effici

10/ The bigger point: your product is the best RL environment you'll ever have. Frontier labs ship models that are good at everything. The opportunity is a model that's great at your thing. Product, u

Someone just asked me “What I don’t get is why you see it as some kind of failure that AI labs are now using harnesses and neurosymbolic tools…?” I don’t! As I argued in my newsletter on Claude Code (

Ha. @demishassabis and @DarioAmodei, who are running the two best AI labs in the world both have PhDs. Jim Simons, who founded one of the biggest hedge funds in the world had a PhD. Eric Schmidt, Goog

And it is the model that makes it so it is the labs, as opposed to every other software vendor, can ultimately be the sole provider of products. Their post-training and their harnesses and their contr

The best way to learn AI is to build with agents. To help with that, we've launched hands-on labs and a new series on Agentic Engineering. First topic: Agent Skills. Next in the pipeline: planning, co

As of yesterday, we made everyone within @llama_index research/engineering/product a Member of Technical Staff. Yes all the frontier labs have done this for years. But we didn’t only make these change

Math is easy* because it has verifiable outputs and few messy judgement choices to make. Which AI labs have the guts to make advancing social science a priority? It may actually do more for human flou

One interesting side feature of recursive self-improvement, to the extent that is happening, is that it makes the Big Three labs more appealing to talent, and shortens the runway for launching a poten

Reporting from Google I/O 2026 with the four biggest themes from one of the biggest AI labs in the world. 🎤 Voice AI as an interface Google and Samsung announced new Gemini-powered glasses with Gentle

We’re hiring for Labs! 🧪 If you’re interested in working with us to push forward Continual Learning, pls DM me with a blurb + link to the best Applied Research you’ve done (or even better shipped!) yo

We’re releasing Nemotron-Labs-Diffusion - the first Tri-mode LM family (3B/8B/14B) that switches between 1⃣Autoregressive, 2⃣Diffusion, and 3⃣Self-Speculation decoding by simply changing the attention

Introducing Gemma-4-31B-it-Pearl on Together AI, Pearl Research Labs’ instruction-tuned checkpoint of Gemma 4 31B powered by @prlnet Proof of Useful Work protocol. AI natives can now use this Pearl mo

Dear AI labs, A neverending black and white kanban board modeled after Jira (no offense) is not what we want as the future of work. Give me flexibility. Give me delight. Give me color. Please create t

Nous Research announced that Deepseek V4 Flash is temporarily available for free access through the Nous Portal, courtesy of Nous Research in collaboration with @novita_labs. This limited-time offer p

I was going to say LangChain Labs is the most exciting thing we’ve announced recently, but with so many launches it’s hard to justify… but still incredibly thrilled to see this get started!! Checkout

LangChain Labs is a new applied research initiative announced by LangChain focused on advancing continual learning capabilities. The effort represents the company's commitment to developing systems th

suuuuper excited to be collaborating with the excellent LangChain Labs team on this effort prod agent tracing is the seed that lets you close the loop for continual learning. too much data gets collec

There are some clear exceptions to the rule, but I feel like the labs got the message that they were scaring people and now tweet about happy stuff and random product trash talk rather than the coming

You will know that the AI labs believe in ASI when they disband their newly formed consulting (sorry “forward deployed engineering”) groups. As long as people are required to figure out how AI is usef

𝐅𝐮𝐥𝐥-𝐏𝐚𝐫𝐚𝐦 𝐑𝐋 𝐧𝐨𝐰 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 𝐟𝐨𝐫 𝐊𝐢𝐦𝐢 𝐊𝟐.𝟔 You've been told only 3 AI labs matter. The best AI apps never believed that. @cursor_ai, @vercel, @genspark_ai don't run only off-the-shelf models. They trai

Frontier labs are betting AGI models will be so good you won't ever want to customize them. We think different. Building on a closed platform means renting your intelligence. The landlord sets the ter

Labs; “we will have a nation of geniuses in a data center by 2028, capable of beating humans at every task, but you will need to hire our newly trained forward-deployed engineers for a six month engag

Ramp Labs is doing a ton of cool work all the way from launching products to hardcore research on LLM internals, this is a fun one 🔥 Talked to @ramplabs Head of Applied Research Alex Shevchenko on the

Seems like a massively underinvested area in the “pivot to enterprise.” The fact that the Labs are building their own deployment consultancies (which will take a long time) suggests a failure of imagi

Two frontier labs. One accelerated computing platform. Congrats to @SpaceX and @AnthropicAI on the new compute partnership, powered by 220,000+ NVIDIA GPUs inside Colossus 1. The future of AI runs on

Ethan Mollick advocates for dedicated funding toward developing independent benchmarking systems for AI models, noting that evaluation is currently predominantly conducted by the labs that create the

The unreasonable effectiveness of LLMs is what makes them so weird. The labs don’t need to decide what kind of AI to build, because better LLMs do better at most things. Finance? Pig disease identific

I think the expression is “pulling the ladder”! All labs trained their models by distilling (at the very least distilling the web) which allowed them to become the fastest growing businesses in the hi

Distillation is largely an industry standard and not just something done by Chinese labs targeting OpenAI/Anthropic. Many American companies also distill Chinese (open) models. LIVE TRIAL UPDATE: Open

The post highlights a contrasting news cycle where the White House condemned Chinese AI labs for alleged model distillation attacks, while Elon Musk later admitted under oath that his company xAI enga

Most model trainings outside of frontier labs fail. 📈 Because of bad or insufficient data. 🚮 Or just data for what you want + not general capabilities. 🎯 Most builders give up + become elevated prompt

At 30 trillion tokens a day, we're the largest inference provider outside the frontier labs' own APIs. We're hiring. Come build the infrastructure behind the top AI workloads: https://fireworks.ai/car