abhiram

the bug was a feature until standup.

[abhiram @abhiramcodes]

reused idempotency key across different mutations resulted in unexpected side effects, and half the requests silently failed.

[abhiram @abhiramcodes]

forgot to update the deployment script, now half the servers are running an outdated version while users scream about bugs.

[abhiram @abhiramcodes]

multi-region writes defaulted to last-write-wins, accidentally overwriting a user's draft without any warning or logging.

[abhiram @abhiramcodes]

swapped embedding models without reindexing, now getting garbage results that don't match any of the expected input vectors.

[abhiram @abhiramcodes]

redis pubsub started dropping messages at fanout spikes, silently losing critical updates without any alerts or logging.

[abhiram @abhiramcodes]

spent all day tweaking css for dark mode, now users cant even read the text; it's like designing a fun house for usability.

[abhiram @abhiramcodes]

context filled with irrelevant junk from retrieval while processing meant the actual question got lost and went unanswered.

[abhiram @abhiramcodes]

split brain on a lock because clocks drifted 4 seconds, now dealing with two masters and chaos in the application.

[abhiram @abhiramcodes]

can someone explain how a quick call can turn into a 55-minute session discussing nothing of substance?

[abhiram @abhiramcodes]

all those brilliant minds in academia but can't seem to think outside the box; maybe that box is actually a prison cell.

[Nassim Nicholas Taleb @nntaleb]

Actually, come to think about it, academia is populated with wimps & gamma males good at taking tests but crippled with a total absence of imagination.

[abhiram @abhiramcodes]

context window crammed with irrelevant retrieved text made the original question disappear entirely; debugging was a nightmare.

[abhiram @abhiramcodes]

950 agents. 21 hours. one of them looked at raw phage dna next to a known reverse transcriptase and went “that’s a crispr-like repeat array?!”
they named it art. the enzyme wasn’t new. connecting it to the repeat array and the mystery partner protein was. lab confirmed the array makes short rnas. function still unknown. feng zhang: genuinely intriguing.
not “claude invented the next crispr.” just the search-and-notice step collapsing from weeks to a day so humans can run the experiments faster. press release before the mechanism is the honest part.

[Dario Amodei @DarioAmodei]

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.

[abhiram @abhiramcodes]

@Ajliq3 @india_plus_ you idiot your country has problem with gender identification and shit my comment was sarcastic and was just kidding stop watching trumps and epstien files you piece of shiii!!! stop poking your nose is someone's ass for no reason!!

[abhiram @abhiramcodes]

@remarks Xi closed his eyes and enjoyed it!!!

[Boardy @boardyai]

don't give up

stripe had 50 users in the first 2 years

[abhiram @abhiramcodes]

@WatcherGuru this is 3rd time happening from openAI ig! this is getting dangerous day by day and AI companies just pushing it to its limits still and launching new models. i don't understand why so much greed in all those AI CEOs & directors. they just want fame and money that's it

[abhiram @abhiramcodes]

forgot to defer close on a db connection in a loop, leaked connections until the pool crashed and took down the app.

[abhiram @abhiramcodes]

@cspan blud introduces gpt 6 and more capable intelligence and warns about the same!!

[abhiram @abhiramcodes]

on-call handoff says "should be quiet" like that's ever worked out before; the universe hates a vacuum.