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Claude agents reportedly flagged a clue to a previously unknown system in bacteria-infecting viruses

A user says roughly 950 Claude agents searched DNA for 21 hours. One spotted repeating DNA beside an enzyme gene; human analysis and lab experiments then identified a system called ART.

BuBBliKBU
Krunal SoniKS
Tamara NewsTN
8 Sources, 12d ago, first seen 12d ago

TLDR

A user says roughly 950 Claude agents searched DNA for unusual reverse transcriptases, enzymes that copy RNA into DNA. After one agent spotted repeating DNA beside an enzyme gene, scientists pursued the clue in the lab and identified a previously unknown system called ART in viruses that infect bacteria. Its function is still being studied.

Combined views

411K

8 Sources, first seen 12d ago

3.7K likes207 comments2.8K saves260 reposts

Combined views

411K

8 Sources, first seen 12d ago

3.7K likes207 comments2.8K saves260 reposts

Sentiment

Positive76.3%23.7%Negative

Summary

Positive accounts welcomed the AI-driven discovery of unknown biology in drug development as a groundbreaking aid to research and human-AI collaboration, while negative replies worried it could reduce scientists to lab assistants.

Based on 37 sentiment-bearing replies from 36 accounts across 4 conversations.

Featured Source

Sentiment

Positive76.3%23.7%Negative

Summary

Positive accounts welcomed the AI-driven discovery of unknown biology in drug development as a groundbreaking aid to research and human-AI collaboration, while negative replies worried it could reduce scientists to lab assistants.

Based on 37 sentiment-bearing replies from 36 accounts across 4 conversations.

Related

Claude reportedly solves a nine-loop particle physics challenge

Dataconomy reports that Anthropic says Claude computed a nine-loop, six-particle scattering amplitude in planar N=4 super-Yang-Mills theory.

Claude agents reportedly identified a CRISPR-like defense system in bacteriophage DNA

A post claims Anthropic ran 950 Claude agents for 21 hours to identify a defense system involving ART enzymes and accessory proteins. It says lab tests confirmed the agents’ core predictions.

Claude reportedly finds a previously unknown enzyme system resembling CRISPR

A user citing an Anthropic announcement says 950 agents searched DNA for 21 hours, spotting a pattern no one had seen before.

8 Sources

BuBBliK@k1rallikthis is f*cking HUGE - AI spotted UNKNOWN biology ~950 Claude agents searched DNA for 21 hours and flagged a biological system nobody had described before. One agent noticed a pattern. Scientists took the clue into the lab. → THE SEARCH: Claude explored genetic data for unusual reverse transcriptases - enzymes that copy RNA into DNA → THE CLUE: An agent spotted repeating DNA beside an enzyme gene, compared known systems, and checked the literature → THE FINDING: Human analysis and lab experiments identified a previously unknown system called ART in viruses that infect bacteria → WHAT’S NEXT: Its function is STILL being studied. The finding opens new questions for researchers The WILD part? The data already existed. AI helped scientists notice something hidden inside it. Imagine a science feed moving as fast as your AI coding feed.12d
Krunal Soni@krunal_soniClaude just discovered a new enzyme system on its own. 950 agents, 21 hours, 210 million tokens. No one told it what to look for. MIT's Feng Zhang called it a real contribution to biological discovery. #AI #Anthropic #Claude #AIResearch12d
Tamara News@Travel_expore_An AI Spent 21 Hours Searching DNA. It May Have Found Something Big. https://tamaranews.com/2026/09/28/claude-crispr-enzyme-discovery-2026/ #ArtificialIntelligence #TechnologyNews12d
NAVION@navionofficialMany antibiotics are made by molecular assembly lines that break if you swap a single part. Researchers used three AI models to design 76 new parts and tested them in 578 variants inside living cells. The best tripled the yield. Published in Nature Communications.7d
levithefirst@levithefirstan AI just ran a biology experiment and found something humans hadn’t published before not only did the AI read 60,000 pieces of yeast data. it studied existing relationships between genes, proteins and other biological factors, then came up with approximately 2,000 predictions that could actually be tested. a robot lab took those predictions and grew the yeast, measured what happened, and sent the results back to the AI. even when an experiment failed, it didn't stop (one would think the AI system will receive a negative result and stop there) but no. it used the failure to go again (AI is now resilient 💀) one of those experiments pointed it to aminoadipate, a substance that helps yeast survive stress caused by formic acid. AI can now read → form a hypothesis → run an experiment → study the result → change the hypothesis → test again at the moment we don't need to panic because humans still set the safety boundaries and controlled parts of the process. but we know nothing big thing starts big, it starts with baby steps.6d
Tekraj Awasthi🧑‍💻🇳🇵@trawasthi_aiWake up, baby! Germany has joined the race of frontier LLMs with its sovereign model "Kolibri". And it's Open-Source, too! Beats Qwen2635B, Nemotron3 Super, and Mistral Small 4 in the Math, Science, and Code arena! 🎉 The underlying architecture: custom MoE Transformer6d
Science Magazine@ScienceMagazineIn a new Science study, researchers present the Virtual Biotech, a multi-agent AI system to inform drug-development decisions. By combining diverse biomedical and clinical evidence in a unified platform, the system may help identify therapeutic opportunities that would otherwise be missed. Learn more: https://scim.ag/46WEiVK6d
Avi@AviFelmanThe megatrends I’m spending the most time on: 1: AI + biotech economics AI-designed drugs are reaching human trials. The bigger opportunity is whether better discovery translates into fewer expensive failures and more approved medicines per dollar of R&D. 2: The AI infrastructure buildout Growing AI usage requires more computing capacity, networking, cooling and power. I’m interested in the companies solving the physical constraints on that expansion. 3: Domestic energy production, especially nuclear Energy security is bringing renewed attention to domestic nuclear capacity. Reactor restarts, upgrades and fuel supply chains are worth watching alongside the development of new reactors. 4: Tokenization and onchain finance Tokenized securities are moving into real transactions, including recent trades using DTC-tokenized assets. The opportunity is making assets easier to transfer, settle and use as collateral, with stablecoins developing alongside them as payment infrastructure. DTCC · BIS 5: Grid modernization and energy storage Generating electricity is only part of the challenge; delivering it when and where it’s needed requires investment. Transmission, transformers, batteries and grid software all deserve attention as electricity demand grows. 6: AI agents and the economics of services Businesses are starting to delegate complete tasks to AI. If that becomes reliable across more workflows, small teams could deliver services at a scale that previously required much larger organizations. Anthropic 7: Robotics and physical automation Factories now operate roughly five million industrial robots worldwide. Better machine intelligence could make a wider range of physical tasks economical to automate. 8: Defense modernization NATO allies have committed more than $40 billion to counter-drone capabilities over five years. I’m watching the growing importance of drones, sensors and the systems needed to detect and defeat them. NATO 8: Critical minerals and secure supply chains Electrification increases demand for copper and other strategic minerals, while concentrated production creates supply risks. Mining, refining and recycling are becoming central to industrial security. 9: Cybersecurity and digital identity AI is helping attackers operate faster and at greater scale while introducing new systems that need protection. Verifying identities, controlling access and securing AI agents should become increasingly valuable. Microsoft 10: Collectibles and Unique assets In a world where production cost trends to zero, the wealthy will allocate to items with provenance and irreproducibility (classic cards, gemstones, gold, etc) 11: GLP-1s and metabolic medicine These medicines are expanding beyond weight management into indications including cardiovascular risk reduction and liver disease. I’m watching how broader clinical uses reshape demand, manufacturing capacity and access to treatment. 12: Water infrastructure and reuse Growing water insecurity is strengthening the case for treatment, recycling and industrial reuse. Reliable water supply could become an increasingly important constraint on where cities and industries can grow. Plan it to make videos on each one6d
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    Claude

    8 Sources

    BuBBliK@k1rallikthis is f*cking HUGE - AI spotted UNKNOWN biology ~950 Claude agents searched DNA for 21 hours and flagged a biological system nobody had described before. One agent noticed a pattern. Scientists took the clue into the lab. → THE SEARCH: Claude explored genetic data for unusual reverse transcriptases - enzymes that copy RNA into DNA → THE CLUE: An agent spotted repeating DNA beside an enzyme gene, compared known systems, and checked the literature → THE FINDING: Human analysis and lab experiments identified a previously unknown system called ART in viruses that infect bacteria → WHAT’S NEXT: Its function is STILL being studied. The finding opens new questions for researchers The WILD part? The data already existed. AI helped scientists notice something hidden inside it. Imagine a science feed moving as fast as your AI coding feed.12d
    Krunal Soni@krunal_soniClaude just discovered a new enzyme system on its own. 950 agents, 21 hours, 210 million tokens. No one told it what to look for. MIT's Feng Zhang called it a real contribution to biological discovery. #AI #Anthropic #Claude #AIResearch12d
    Tamara News@Travel_expore_An AI Spent 21 Hours Searching DNA. It May Have Found Something Big. https://tamaranews.com/2026/09/28/claude-crispr-enzyme-discovery-2026/ #ArtificialIntelligence #TechnologyNews12d
    NAVION@navionofficialMany antibiotics are made by molecular assembly lines that break if you swap a single part. Researchers used three AI models to design 76 new parts and tested them in 578 variants inside living cells. The best tripled the yield. Published in Nature Communications.7d
    levithefirst@levithefirstan AI just ran a biology experiment and found something humans hadn’t published before not only did the AI read 60,000 pieces of yeast data. it studied existing relationships between genes, proteins and other biological factors, then came up with approximately 2,000 predictions that could actually be tested. a robot lab took those predictions and grew the yeast, measured what happened, and sent the results back to the AI. even when an experiment failed, it didn't stop (one would think the AI system will receive a negative result and stop there) but no. it used the failure to go again (AI is now resilient 💀) one of those experiments pointed it to aminoadipate, a substance that helps yeast survive stress caused by formic acid. AI can now read → form a hypothesis → run an experiment → study the result → change the hypothesis → test again at the moment we don't need to panic because humans still set the safety boundaries and controlled parts of the process. but we know nothing big thing starts big, it starts with baby steps.6d
    Tekraj Awasthi🧑‍💻🇳🇵@trawasthi_aiWake up, baby! Germany has joined the race of frontier LLMs with its sovereign model "Kolibri". And it's Open-Source, too! Beats Qwen2635B, Nemotron3 Super, and Mistral Small 4 in the Math, Science, and Code arena! 🎉 The underlying architecture: custom MoE Transformer6d
    Science Magazine@ScienceMagazineIn a new Science study, researchers present the Virtual Biotech, a multi-agent AI system to inform drug-development decisions. By combining diverse biomedical and clinical evidence in a unified platform, the system may help identify therapeutic opportunities that would otherwise be missed. Learn more: https://scim.ag/46WEiVK6d
    Avi@AviFelmanThe megatrends I’m spending the most time on: 1: AI + biotech economics AI-designed drugs are reaching human trials. The bigger opportunity is whether better discovery translates into fewer expensive failures and more approved medicines per dollar of R&D. 2: The AI infrastructure buildout Growing AI usage requires more computing capacity, networking, cooling and power. I’m interested in the companies solving the physical constraints on that expansion. 3: Domestic energy production, especially nuclear Energy security is bringing renewed attention to domestic nuclear capacity. Reactor restarts, upgrades and fuel supply chains are worth watching alongside the development of new reactors. 4: Tokenization and onchain finance Tokenized securities are moving into real transactions, including recent trades using DTC-tokenized assets. The opportunity is making assets easier to transfer, settle and use as collateral, with stablecoins developing alongside them as payment infrastructure. DTCC · BIS 5: Grid modernization and energy storage Generating electricity is only part of the challenge; delivering it when and where it’s needed requires investment. Transmission, transformers, batteries and grid software all deserve attention as electricity demand grows. 6: AI agents and the economics of services Businesses are starting to delegate complete tasks to AI. If that becomes reliable across more workflows, small teams could deliver services at a scale that previously required much larger organizations. Anthropic 7: Robotics and physical automation Factories now operate roughly five million industrial robots worldwide. Better machine intelligence could make a wider range of physical tasks economical to automate. 8: Defense modernization NATO allies have committed more than $40 billion to counter-drone capabilities over five years. I’m watching the growing importance of drones, sensors and the systems needed to detect and defeat them. NATO 8: Critical minerals and secure supply chains Electrification increases demand for copper and other strategic minerals, while concentrated production creates supply risks. Mining, refining and recycling are becoming central to industrial security. 9: Cybersecurity and digital identity AI is helping attackers operate faster and at greater scale while introducing new systems that need protection. Verifying identities, controlling access and securing AI agents should become increasingly valuable. Microsoft 10: Collectibles and Unique assets In a world where production cost trends to zero, the wealthy will allocate to items with provenance and irreproducibility (classic cards, gemstones, gold, etc) 11: GLP-1s and metabolic medicine These medicines are expanding beyond weight management into indications including cardiovascular risk reduction and liver disease. I’m watching how broader clinical uses reshape demand, manufacturing capacity and access to treatment. 12: Water infrastructure and reuse Growing water insecurity is strengthening the case for treatment, recycling and industrial reuse. Reliable water supply could become an increasingly important constraint on where cities and industries can grow. Plan it to make videos on each one6d
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