Claude

@mtsliveSep 25

Bright AI Future founder @OfficialBBrooks predicts open-weight AI becomes the default for regular people once running it locally stops costing $6,000: "I love what NVIDIA's doing. All of these models that are starting to come out that aren't led by frontier labs, I think will eventually end up winning all of this." "The big thing they're concerned about outside of costs is privacy. They've conflated all this with Flock cameras, for example, and a lot of people are against them, both sides of the aisle. When you can have it running locally for yourself, that's such a huge advantage." "As of today, it's too expensive. I looked at a DGX Spark from NVIDIA, and it was like $6,000. The average person can't afford that, so of course they're gonna pay the $20 Claude subscription or OpenAI subscription." "The frontier labs are going to absolutely find their place. But the average person, and the average small business, are gonna really get into using these open weight models."

@rauchgSep 25

We're helping world-class organizations like @klaviyo build agentic deployment platforms. ① Connect every agent (claude, codex, cursor…) ② Configure SSO via their IDP (Okta, Entra) ③ Now everyone can cook, securely® What happens to SaaS once every company sets this up? I get this question a lot 😁 First, the data must come from somewhere. These apps only work if they can be infused with business data. This is why we see the big enterprise SaaS players suddenly prioritize shipping CLIs and MCPs. Or dust off APIs they were neglecting for years. The new 'procurement bar' will be how ergonomic your product is for *agents*, rather than humans. How easily they can navigate your ontology and work with your data. Once that's in place, there's a long tail of SaaS applications that I suspect will never be bought again. They'll be generated. They'll be more secure, more performant, more modern, and tailored to each company's and employee's needs.

@trq212Sep 25

Known affiliation

What are specific requests in Claude Computer/Browser Use that have failed for you? We want to fix. An example for me is: "pay my barber $40 through my personal paypal" (where paypal is logged into my 2nd chrome profile, I'm logged out but password is saved)

@andrewcurran_Sep 25

From the guest post by Matt von Hippel who issued the challenge: 'Things definitely seem to be moving fast. In March, AI was accomplishing physics projects like a student: smaller-scale tasks with a lot of hand-holding and mistakes. In contrast, this is a real frontier calculation, the kind of thing normally tackled by the top experts in amplitudes.'

@exm7777Sep 25

Jev can't write a single word, which is exactly why it should pick what my agents read my Obsidian vaults hold 5k+ notes, one idea per note, each with a one-line summary in an index but Claude can't read all of that on every turn, and a keyword search misses the note that makes the same point in different words so Jev does the picking: > step 1: Jev reads the 28 vault descriptions and picks where the task belongs > step 2: it checks every note summary in that vault in one call, each judged alone > step 3: the top 5 come back with full text - Jev drops any that miss the task > step 4: my code loads the survivors into Claude's context (and nothing else) every pick comes from a list i define, so Jev can't invent a note the worst it can do is pick the wrong one... and each one carries a confidence score that tells my code when to fall back to Claude

@etnshowSep 25

"The future of pharma will not come out of the pharma labs, but tech labs..." Serial pharma entrepreneur Dr Clemens Fischer (FUTRUE Founder and CEO) says bureaucracy holds traditional companies back: "We do not have any company which has more than 180 employees." "The more people you have, the more bureaucracy you have, the more problems you have." "Every time we reach about 170, 180 employees, we just cut and start a new section." "I think the future of pharma will not come out of the pharma labs, but tech labs, even the small tech labs." "I think the really efficient companies are really small, no bureaucracy, and in the best way, even no investors."

@rhyssullivanSep 25

Here's Rhys' practical guide to shipping an MCP your users want: - Your MCP should be able to do everything your dashboard can Yes including things like deleting resources. The way to make this practical inside of an agent while not having things be unsafe is to deep link them into your product from inside the MCP - Do not ship lazy loading / codemode inside of your MCP CodeMode is a harness detail. Most harnesses (Claude / Codex / OpenCode) support codemode now, the problem is if you have two codemode MCPs they don't compose nicely (Executor v2 will still support CodeMode, but, will start to default to a transparent tool proxy for better clients) - Ship a search docs / skills tool Agents love these and it helps give them more understanding of your product MCP is getting skills over mcp which will help here in the future - Have a deep link into product tool Let the agent generate a URL that brings them into your product, people still want to use your dashboard and data visualization! They just don't want to click through things - Customization I think this matters less over time, but, you can allow people to select 'toolsets' of things to connect with to clients. Some clients don't support customizing tool selections and then it's also nice to have tool permissions set to the auth token - Let people OAuth from whatever client they want Please stop putting restrictions on which clients can authenticate to MCP servers, your users hate it, it doesn't improve security, all it does is add more friction PostHog and Sentry are my two 'AI native' companies to look at today, they both have an excellent onboarding, MCP / CLI experience, if you need a place to learn from look at them To be honest though, while all the above is helpful just shipping your API spec + CIMD OAuth to it is enough for 99% of agents to have a good experience

@trq212Sep 24

Known affiliation

my rule for sharing Claude output (PRs, artifacts, messages) is that I only do it if I would be happy sharing the prompts

@weswinderSep 24

i haven’t been this productive with a model since opus 4.5

@theinformationSep 24

China is investigating DeepSeek and Moonshot AI for potential data leaks to Anthropic. @jingyanghk explains the allegations: "Anthropic said that Moonshot and DeepSeek forwarded users' requests, including sensitive data such as police security camera footage, without their own customers', their own users' knowledge, and passed over the answers to their customers as if these were processed by their own models when, in fact, they were processed by Claude."

@peterj_walkerSep 23

Peek into the first day for Opus 5.5 on OpenRouter: - 22% of Anthropic tokens went to Opus 5.5 (best model launch by share in the last year) - nearly double the user count as day 1 for Opus 5 - doing better today than yesterday Claude cooked, will do a first week recap soon

@mtsliveSep 23

SITUATION EXPLAINED: Does Bernie's superintelligence ban know what superintelligence is? • The bill creates a cabinet-level Department of Artificial Intelligence and pauses advanced AI development • Advanced AI system means anything trained above 10^25 FLOPs, which already covers GPT-4, Claude 3.7 Sonnet, and Llama 3.1 • The Secretary has to ratchet that threshold down every year for algorithmic efficiency, so it catches more models over time • Superintelligence precursor characteristics include accessing secured infrastructure without authorization and uplifting bio or chem weapon design, which most frontier models have to some degree • The bill bans any system displaying one or more of those characteristics, which reads as a ban on most models that exist today @theojaffee: "There are AI risks. This is not the way to address them. It's just way overly broad."

@mtsliveSep 23

SITUATION EXPLAINED: Anthropic gave Claude one prompt. 950 agents ran for 21 hours and found something new in DNA. • Claude identified a previously uncharacterized enzyme system in bacteriophage DNA, with about 2,900 base pairs of repeating DNA next to it that resembles a CRISPR array • The agents burned 210 million tokens, gathered 200,000 reverse transcriptases, found 3,500 candidate systems, and narrowed to 20 • Anthropic calls it ART, array-associated reverse transcriptases. Human involvement was limited to the initial prompt and the lab work • Nobody knows what it does yet, and the pre-print isn't peer reviewed • Feng Zhang, one of the CRISPR pioneers, called it an exciting example of how AI agents can contribute to biological discovery • Dario says the biotechnological utility isn't yet clear, but at minimum it's work he would have been proud to do as a PhD student • Gene editing stocks fell on the news @theojaffee: "I'm getting the vibes of late 2025 to early 2026 for math, where you'd hear of these new results all the time and it would be, is this real or not? I'm really getting the vibes that we're about to see bio take off."

@prathkumSep 23

Let me explain what just happened. Anthropic built a biology lab where Claude works alongside real scientists. They told Claude to search a massive DNA database for interesting reverse transcriptases (enzymes that copy RNA into DNA). Then they let it run: ~950 Claude agents worked in parallel for 21 hours across 200,000+ enzymes. One agent spotted a repeating DNA pattern next to an enzyme, the same kind of signature that originally led humans to discover CRISPR (the gene-editing tool that lets scientists precisely cut and edit DNA, now used in real medical treatments). It cross-checked the pattern against known systems, searched the literature, confirmed nobody had found it before, then wrote up a full scientific report like a human researcher would. Anthropic scientists verified it in the lab. They named it ART. Its exact function is still unknown, but early signs suggest it may be programmable, similar to how CRISPR can be aimed at specific DNA. Why it matters: finding tools like CRISPR used to take human experts months of manual searching. Here, AI did that work and found something real, at a speed no human team could match. So don't misunderstand it as Claude curing cancer. Claude spotted something new in nature that humans missed. Still crazy!

@signulllSep 23

the coolest thing about a new technological revolution like ai is that it forces you to rethink nearly every assumption about how the world used to work (kinda like how elon approaches every big problem). e.g. at one layer personal agents are trying to reshape how ppl manage their day to day lives by radically changing the interface between humans, information, & action. but at another layer the same class of models are reaching much deeper & understanding the primitive machinery of existence itself that we have no clue about yet. science fiction is pretty much becoming reality at nearly every layer of the human experience faster than ever before.

@darioamodeiSep 23

Known affiliation

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.

@mtsliveSep 23

SITUATION EXPLAINED: Claude is going free for doctors in Uganda, Sudan, Haiti, and Mongolia. • A specialized version of OpenEvidence's clinical decision support, free for healthcare providers across roughly 100 low and middle income countries • In those countries, limited access to medical literature, specialist expertise, and continuing medical education impairs patient care • Claude can't perform surgery, but it can put as much medical knowledge as needed in front of staff on the ground • It lands the same week as Anthropic's wet lab and the Pilgrim investment, which is a lot of bio in a short span @theojaffee: "I imagine that medical facilities in these remote parts of the world are so under-resourced that Claude would make a big difference to them."

@mtsliveSep 23

SITUATION DETECTED: Anthropic’s new wet lab has its first public discovery: 950 Claude agents used 210 million tokens to autonomously find a novel enzyme system with CRISPR-like DNA repeats.

@denk_tweetsSep 23

we're now an official connector in both Claude and ChatGPT 🎉 the @beehiiv MCP has lowkey been one of the highest NPS products we've ever launched adoption has been nutty: > 6M+ tool calls since launching in April > 12x monthly usage since then > ~10K workspaces connected today, far too many creators and publishers lose hours to admin work. the future of the creator economy is: > automation for the mundane tasks required to build a business > more time spent on the content itself we've got a lot more in store soon...

@itsolelehmannSep 23

been testing Opus 5.5 and i'm convinced it's the first model that's finally solved animation. it's basically an in-house storytelling studio you can run solo now here's 10 animation use-cases i think you'll start seeing everywhere: 1. podcast clips that turn a guest’s story into scenes people can watch 2. custom b-roll for youtube and talking-head videos that shows exactly what you’re explaining 3. ad creatives with different hooks and visual styles you can test without filming each version 4. product explainers that animate the problem your customer has and how your product solves it 5. looping website animations that show your product assembling, rotating, or working 6. animated shorts and mini-stories for reels, tiktok, and youtube shorts 7. branded intros, outros, and transitions you can reuse across your videos and podcasts 8. animated diagrams for pitch decks that show how customers, data, or money move through your business 9. customer success stories showing the before and after of using your product 10. animated charts and infographics that turn your research into shareable videos