Databricks CEO @alighodsi went off on @a16z pod about enterprise AI adoption: "They're just so far behind in the adoption curve of actually automating things and getting value out of this stuff." Ali says most companies are still just using chatbots. There's hardly any agentic transformation. Why is that? "The models are smart enough, but they just don't have the context that exists inside of any organization." "They have not been in every meeting. They don't know what's in everybody's heads. They don't know all the processes." "If you just fused that and gave that context into the AI models...there's so much productivity gains you could get for any organization on the planet." Context Creation is the biggest opportunity in AI right now.

Now is the exact time @spacex and @meta should release an iPhone competitor with no apps — just agents and optimized for local AI and privacy

Hottest thing in inference right now: “we serve model X at Y tok/s.” Yeah cool bud, at what concurrency, on how many GPUs, and at what cost per million tokens? Y tok/s != we are a good provider

Today, 18 months after launch, our annualized revenue crossed $1 billion. The platform now powers organizations across the Fortune 500. Enterprise adoption has grown 10x since June. More than 30 million people worldwide now use Higgsfield. Thank you to the creators and teams building with us.

Money.

If you want to setup @BoundlessHQ as a provider for your harness, all you need is to tell your agent > Setup boundless.md I'm convinced this is the future

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out. It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend. The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans). More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains. In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries. Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology. I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.

When several people talk at once, a transcript can get messy fast. Our new Nemotron 3 Diarization model tracks who spoke when, even when voices overlap. It handles up to eight speakers, has 100M parameters, and is now available on @huggingface 🤗

Respectfully disagree. There are “window shopper” and there are affluent consumers who hire a personal stylist or use Stitch Fix to save time shopping. There are travel point maxxers and there are travelers who book via a travel agent because again time. Personal agents will not be for most people at the beginning. In fact, am guessing Muse retention will look terrible before it smile curves. But life optimizers will use them, assuming agents get privacy, fraud etc right

Opus 5.5 by @claudeai is now live on Boundless inference.boundless.network → 1M token context → $4 input → $20 output

The hardest thing about building @harvey is doing what’s best for our customers despite immense pressure to do what’s easy. The easy thing would have been to force our customers onto consumption pricing before they were ready and serve them worse models to protect our margins. We chose to help our customers transition on a timeline that works for them and give them the best models in the meantime, even though it hurt our margins. This meant optimizing our product through routing, harness improvements, and post-training so we could serve frontier intelligence at an affordable price. It also meant building the infrastructure for customers to monitor and manage spend: usage dashboards, per-matter cost attribution, spend caps, and ROI reporting. As a result, we improved our gross margins from -50% to positive in a single quarter despite usage doubling month over month and continuing to serve the best models. Our philosophy is simple: do what’s best for our customers, even when it’s painful, hurts our margins, or draws criticism from competitors, X, and the press. We believe the most important part of building a company is earning and keeping your customers’ trust. You do that by doing the hard thing for them, even when it costs you.

Your favorite inference provider has made the @Vercel AI Gateway lineup. Boundless is serving DeepSeek V4.1 Flash alongside leading inference providers. → 0.3s latency → $0.20 input → $1.00 output Explore the full model lineup on Vercel AI Gateway.

Working with three Italians on the team has been the most fun I’ve had in a while. 🇮🇹

The number one trending model on HF is an open-source multilingual system 1 decision model, just a few days after Jev started trending. The open-source AI community is awesome!

JUST IN: Startups are increasingly building custom AI models using open-weight alternatives to cut costs & reduce dependence on OpenAI & Anthropic. — Bloomberg

Your favorite inference provider has made the @Vercel AI Gateway lineup. Boundless is serving DeepSeek V4.1 Flash alongside leading inference providers. → 0.3s latency → $0.20 input → $1.00 output Explore the full model lineup on Vercel AI Gateway.