We empower scientists throughout the world by providing equitable access to genomics technology and genomics-related training and education.

Norwich, United Kingdom.
Joined October 2022
September 2026 edition newsletter You can find all our monthly newsletters here getgenome.net/network To stay up-to-date join our Mailing List
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GetGenome retweeted
We just published: Closing the gap: AI in Genomics training delivers real gains for researchers @GetGenome and @TheSainsburyLab share what changed for the 61 researchers who took part — and what happens next. kamounlab.medium.com/closing…
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"Rhizobia are old friends of plant biology. They still have plenty to teach us. The next surprise may come from a cowpea nodule in the Sahara. I want the scientist who collected it to be the one who finds it." @GetGenome
I just published: Rhizobial Treasure Hunts From the Swiss Alps to the Sahara, new twists in an old symbiosis - and new opportunities to discover them. kamounlab.medium.com/rhizobi…
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How many of the 20M are in the Global South? @GetGenome
Replying to @karpathy
for those who prefer pictures to words,,,
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Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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Now Live! @GetGenome AI in Genomics Training Wiki getgenome-wiki.net/training/
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GetGenome retweeted
This Is What 20 Years of Science Looks Like | Kamoun Lab Tribute Twenty years. More than a hundred people. One long-running obsession: how plants and their pathogens fight it out. This is a tribute to everyone who has been part of the Kamoun Lab at The Sainsbury Laboratory, Norwich: students, postdocs, technicians, visitors, collaborators, and friends from all over the world. They did the experiments, argued over the data, and made the lab what it is. From the Irish potato famine pathogen and blast fungus to effectors, NLR immune receptors, and AI-guided structural biology, the questions kept changing. The spirit didn't. No captions, no years, no names. Just the people and the science. #KamounLab #PlantPathology #PlantScience #OpenScience #TSL #Norwich #LabLife youtu.be/ZJtN6OBqA6Y?si=nBcM… via @YouTube
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GetGenome retweeted
I just published: How do we recognize world-class science? Not by the journal it appeared in What I told early-career researchers about prediction, rigour, failure, priors — and the tyranny of bar graphs. Science predicts. Pseudoscience doesn’t. Twenty-nine years How to science, and how not to What’s a failed experiment? Death by statistics Ditch the bar graphs Get credit for all of it kamounlab.medium.com/how-do-…
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GetGenome retweeted
#GGRoadToJeju2026 invites applications from the global research community to sequence the genomes of plant-associated microorganisms — including pathogenic and non-pathogenic bacteria, fungi and oomycetes. 📅 Apply by 2 November 2026 getgenome.net/callforproject…
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You select the isolates, we sequence the genomes — for free! 🧬 Inspired by @ISMPMI #MPMI2027, we’re pleased to announce that the latest @GetGenome Call for Projects is now open! @GetGenome “Road to Jeju” 2026 getgenome.net/callforproject…
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#GGRoadToJeju2026 invites applications from the global research community to sequence the genomes of plant-associated microorganisms — including pathogenic and non-pathogenic bacteria, fungi and oomycetes. 📅 Apply by 2 November 2026 getgenome.net/callforproject…
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Back from the @GetGenome workshop in Tunisia 🇹🇳. Initiatives like this have a huge impact on young scientists with limited resources. Deeply inspiring, Science shouldn't have borders!!!🚀💡Thanks for the invitation and the experience! #DirectImpactScience getgenome.net/
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GetGenome retweeted
Today we are hosting the FIGHTBLIGHT Project Meeting & Symposium This is a trinational project 🇬🇧🇩🇪🇺🇸 with Silvia Restrepo @srestrep @BTIscience and Alain Tissier @trichomeIPB on bioengineering immune receptors for potato blight resistance kamounlab.dreamhosters.com/F…
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Day 1 “AI in Genomics workshop” 🇹🇳 🌍 With thanks to @Googleorg @TheSainsburyLab @GetGenome
Happening now @EspaceZmorda 🇹🇳 AI in Genomics workshop! @GetGenome @TheSainsburyLab on how AI can be used across genomics — from working with genomic data to protein structure prediction, biological discovery and developing new research questions.
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GetGenome retweeted
We just published: Fold on Tight — 10 lessons on AI structure prediction Andy Posbe @adnroide turned our ten lessons on AI structure prediction into a thread. Here it is, one lesson at a time. kamounlab.medium.com/fold-on…
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Happening now @EspaceZmorda 🇹🇳 AI in Genomics workshop! @GetGenome @TheSainsburyLab on how AI can be used across genomics — from working with genomic data to protein structure prediction, biological discovery and developing new research questions.
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Hands-on training, practical examples, discussion and plenty of opportunities to get stuck in. When AI meets genomics!
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GetGenome retweeted
Talks for early career researchers A menu of short, opinionated talks on how science actually works — skills, publishing, open science, statistics, integrity and evolution. Most grew out of blog posts. They run short by design, because the discussion afterwards is the point. kamounlab.dreamhosters.com/K…
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GetGenome retweeted
I just published: I turned somebody else’s supplementary table into a website The science was open. Supplementary Table S4 was a Word file. kamounlab.medium.com/i-turne…
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New Publication: Tangerine: A new family of Starships from lichen-forming fungi (2026) tsl.ac.uk/publications/17348…
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