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Genome analysis with AI is cool i guess - but a simple CLI wasn't enough for me. Introducing HGI (Human Genome Interface): HGI connects agents to highly optimized bioinformatics tools, performing a full genome analysis in ~1 minute (!) on a MacBook Pro. It identifies both known and novel...

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tuōmo

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13,506 次观看 • 3 个月前 •via X (Twitter)

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Join us in transforming the way we understand the human genome. Bionano's optical genome mapping workflow provides a comprehensive view of structural variants that impact research and therapies, elevating health and wellness for all. #Bionano #OGM #Innovation #GenomeMapping
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Join us in transforming the way we understand the human genome. Bionano's optical genome mapping workflow provides a comprehensive view of structural variants that impact research and therapies, elevating health and wellness for all. #Bionano #OGM #Innovation #GenomeMapping

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We're thrilled to announce SeqHub, an AI-enabled platform for biological sequence analysis. SeqHub brings together sequence search, genome annotation, and data sharing in one place. I dreamed of a single place where I could learn everything about my sequences. Today, a much more refined version of this dream takes form with built by an incredible team at Tatta Bio. Our goal is to make sequence interpretation more intuitive and collaborative for everyone working with biological sequences. Currently, SeqHub is optimized for microbial protein and genome analysis. As we expand beyond microbial data, we'd love your feedback to help shape what comes next. I'm deeply grateful to our team at Tatta Bio, and to our collaborators and funders, for making this vision a reality. Check it out at
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We're thrilled to announce SeqHub, an AI-enabled platform for biological sequence analysis. SeqHub brings together sequence search, genome annotation, and data sharing in one place. I dreamed of a single place where I could learn everything about my sequences. Today, a much more refined version of this dream takes form with built by an incredible team at Tatta Bio. Our goal is to make sequence interpretation more intuitive and collaborative for everyone working with biological sequences. Currently, SeqHub is optimized for microbial protein and genome analysis. As we expand beyond microbial data, we'd love your feedback to help shape what comes next. I'm deeply grateful to our team at Tatta Bio, and to our collaborators and funders, for making this vision a reality. Check it out at

Yunha Hwang

53,016 次观看 • 8 个月前

I made a Ferrari SF90 spec ad with NB Pro, Reve and more. To put this together, I used and tested almost all the tools we currently have. As usual, it all started from a single image in Midjourney. For the video, Kling AI delivered the best results, every clip was made with it.
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I made a Ferrari SF90 spec ad with NB Pro, Reve and more. To put this together, I used and tested almost all the tools we currently have. As usual, it all started from a single image in Midjourney. For the video, Kling AI delivered the best results, every clip was made with it.

Allar Haltsonen

25,964 次观看 • 7 个月前

Just a reminder: Kevin McKernan participated in the original Human Genome Project, and his video is featured at the Museum of Science, where he’s already made history. No one is more knowledgeable on this subject than him. Kevin McKernan is a prominent genomic scientist and entrepreneur. He served as the R&D lead for the Human Genome Project at the MIT Whitehead Institute for Biomedical Research. He founded Medicinal Genomics in 2011, where he pioneered the sequencing of the cannabis genome, making worldwide news as the first to publicly release a cannabis genome sequence. Previously, he was involved in developing next-generation sequencing technologies and founded Agencourt Biosciences, which advanced DNA sequencing methods used in various genomic projects. McKernan holds a degree from MIT and has authored numerous publications and patents in genomics, focusing on areas like DNA sequencing, microRNAs, and personalized medicine. His contributions have solidified his reputation as a leading expert in the field. What is amazing about his discovery is that it ties in with my work finding mRNA vaccines kill the bifidobacteria and finding of loss of bifidobacteria in invasive cancer BUT it also ties in with the independent findings on Covid spike affecting p53 by Wafik S. El-Deiry, MD, PhD, FACP . Imagine 3 different scientists with 3 different pathways coming to the same conclusions. This needs to be looked at …
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Just a reminder: Kevin McKernan participated in the original Human Genome Project, and his video is featured at the Museum of Science, where he’s already made history. No one is more knowledgeable on this subject than him. Kevin McKernan is a prominent genomic scientist and entrepreneur. He served as the R&D lead for the Human Genome Project at the MIT Whitehead Institute for Biomedical Research. He founded Medicinal Genomics in 2011, where he pioneered the sequencing of the cannabis genome, making worldwide news as the first to publicly release a cannabis genome sequence. Previously, he was involved in developing next-generation sequencing technologies and founded Agencourt Biosciences, which advanced DNA sequencing methods used in various genomic projects. McKernan holds a degree from MIT and has authored numerous publications and patents in genomics, focusing on areas like DNA sequencing, microRNAs, and personalized medicine. His contributions have solidified his reputation as a leading expert in the field. What is amazing about his discovery is that it ties in with my work finding mRNA vaccines kill the bifidobacteria and finding of loss of bifidobacteria in invasive cancer BUT it also ties in with the independent findings on Covid spike affecting p53 by Wafik S. El-Deiry, MD, PhD, FACP . Imagine 3 different scientists with 3 different pathways coming to the same conclusions. This needs to be looked at …

sabine hazan md

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It once took 13 years and $3 billion to sequence the human genome. Now, we're using Google’s AI tools to sequence animal genomes in just days to help save endangered species 🧬 (1/3) ↓
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It once took 13 years and $3 billion to sequence the human genome. Now, we're using Google’s AI tools to sequence animal genomes in just days to help save endangered species 🧬 (1/3) ↓

Google

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The future of AI is open-source. And ollama is the easiest way to build AI applications with open-source LLMs. Here's how to build a free, private RAG app using open-source tools. We'll use: - Ollama for LLMs and embedding models - PostgreSQL for data storage and retrieval - pgai Vectorizer for embedding creation and sync (I use Nomic for embeddings and tinnyllama as my LLM but you can substitute them for any models on Ollama)
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The future of AI is open-source. And ollama is the easiest way to build AI applications with open-source LLMs. Here's how to build a free, private RAG app using open-source tools. We'll use: - Ollama for LLMs and embedding models - PostgreSQL for data storage and retrieval - pgai Vectorizer for embedding creation and sync (I use Nomic for embeddings and tinnyllama as my LLM but you can substitute them for any models on Ollama)

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34,261 次观看 • 1 年前

The Genspark Super Agent / AI Secretary is an absolute game changer! Thanks to the connection with Google tools, I can access numerous functions such as gDrive, Calendar, and Mail, and, for example, get a summary of my emails and answers to further questions. The same goes for Calendar: I can now chat with the various Google tools using Genspark, get an overview, and access them. This significantly boosts productivity! I can get an overview by not only sorting my emails, but also discussing their content with AI and making entries directly in the calendar. An AI secretary became a reality.
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The Genspark Super Agent / AI Secretary is an absolute game changer! Thanks to the connection with Google tools, I can access numerous functions such as gDrive, Calendar, and Mail, and, for example, get a summary of my emails and answers to further questions. The same goes for Calendar: I can now chat with the various Google tools using Genspark, get an overview, and access them. This significantly boosts productivity! I can get an overview by not only sorting my emails, but also discussing their content with AI and making entries directly in the calendar. An AI secretary became a reality.

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130,889 次观看 • 1 年前

Excited to share our new project 🦉OWL - an open-source alternative to Manus AI with 6K+ stars and climbing… 🦉OWL: Everyone’s talking about Manus AI (an agent that can research, browse, code, and automate tasks.) But there’s a catch: → All the above agents cost $20 to $200 a month → Manus AI is available via an invite code only OWL by CAMEL-AI is an opensource project that’s breaking records: ⭐ 6K+ GitHub stars in 2 days 🏆 Ranks #1 on the GAIA Benchmark among open-source projects with a 58.18 avg score! 📌 Specialized Multi-Agent Collaboration ↳ AI user agents break down complex tasks ↳ Assistants agents create detailed execution strategies and interact with tools and tool agents ↳ Tools / Tool agents interface with external services and APIs 📌 Real-World Capabilities ↳ Autonomous research, coding, and web browsing ↳ Runs locally on your machine for privacy ↳ Supports top LLMs (easily integrated with GPT-4O, Claude 3.7, Gemini, Mistral, DeepSeek , Qwen, Ollama, Groq more) ↳ Automates complex workflows, from research to execution I 📌 Simple Setup ↳ Fast installs via conda or uv ↳ Docker support for easy deployment ↳ Straightforward config with TOML files No restrictions. No invites. No paywalls. Just powerful multi-agent collaboration. AI autonomy shouldn’t be locked behind paywalls.
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Excited to share our new project 🦉OWL - an open-source alternative to Manus AI with 6K+ stars and climbing… 🦉OWL: Everyone’s talking about Manus AI (an agent that can research, browse, code, and automate tasks.) But there’s a catch: → All the above agents cost $20 to $200 a month → Manus AI is available via an invite code only OWL by CAMEL-AI is an opensource project that’s breaking records: ⭐ 6K+ GitHub stars in 2 days 🏆 Ranks #1 on the GAIA Benchmark among open-source projects with a 58.18 avg score! 📌 Specialized Multi-Agent Collaboration ↳ AI user agents break down complex tasks ↳ Assistants agents create detailed execution strategies and interact with tools and tool agents ↳ Tools / Tool agents interface with external services and APIs 📌 Real-World Capabilities ↳ Autonomous research, coding, and web browsing ↳ Runs locally on your machine for privacy ↳ Supports top LLMs (easily integrated with GPT-4O, Claude 3.7, Gemini, Mistral, DeepSeek , Qwen, Ollama, Groq more) ↳ Automates complex workflows, from research to execution I 📌 Simple Setup ↳ Fast installs via conda or uv ↳ Docker support for easy deployment ↳ Straightforward config with TOML files No restrictions. No invites. No paywalls. Just powerful multi-agent collaboration. AI autonomy shouldn’t be locked behind paywalls.

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Introducing Upstash AI Agents! 🎉 ◆ Build production-ready AI workflows in minutes ◆ Specialize agents with tools (research, writing, analysis) ◆ Works with Vercel AI SDK, Langchain and more The best way to write AI agent workflows, fully customizable 👇🏻

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The 1st approval for genome editing is a historic medical milestone. But it's indirect, disruptive, and rudimentary—just getting started. Hear David R. Liu give a master class on genome editing's future. With transcript, relevant external links & Figures, in Ground Truths (profile)
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The 1st approval for genome editing is a historic medical milestone. But it's indirect, disruptive, and rudimentary—just getting started. Hear David R. Liu give a master class on genome editing's future. With transcript, relevant external links & Figures, in Ground Truths (profile)

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111,757 次观看 • 2 年前

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Launching AI Discovery And Communication DeepAgent discovers other AI agents and software tools on the fly and communicates with them to perform complex tasks. Here is how it works - - DeepAgent searches for other AI agents and tools - Finds the right tool for the job - Establishes contact using MCP and handles auth - Collaborates with the other tool to get the job done! - Remembers this tool for future colabs! Much like humans, it can create a team of AI agents or software tools to perform tasks. Here it is, learning about MindMaps, communicating with another server, and creating a Mindmap!!

Bindu Reddy

13,598 次观看 • 1 年前

REMINDER: The genome for SARS-CoV-2 is a "consensus sequence." Anybody who says the gene sequence for SARS2 is confirmed is *confused or lying.* "[The reality is that] nobody has the code of the pathogen..." "What they set for the control [for the PCR 'test'] is a consensus sequence, which [means] they took AI, they averaged out a section of the genome that they want as that test, and they set it for that. So it doesn't even exist in nature anywhere." This is a clip from a recent discussion between former medical coder and whistleblower Zowe Smith (Zowe Smith) and retired pharma R&D executive Sasha Latypova (sashalatypova.substack.com "Due Diligence and Art"). Smith and Latypova discuss the many shortcomings of PCR "tests," as well as the fact that no genome for SARS-CoV-2 has ever been characterized—only a "consensus sequence," which Smith notes is developed when AI "average[s] out... a section of the genome that they want as a test." She adds, "it doesn't even exist in nature anywhere." Latypova confirms, "when they're saying, 'Oh, we have the COVID virus, the full genome...it's been sequenced. Look at all these papers.' [The reality is that] nobody has the code of the pathogen... Ralph Baric also wrote about it in his work all the time. So nobody has the pathogenic sequence." The pharma insider adds, "What they upload to GenBank is... averaged... And once it's averaged, it's no longer pathogenic anything. It's just a model. And then for PCR, [it] doesn't test the full genome. They do these, like, snippets, and then whatever snippet you wanna set it to, you will find it, and that's how they find... positive COVID. So all of this is total BS." Interestingly, Smith notes that when she worked with PCR in a lab at the Oregon Health and Science University "[she] realized that everything was controlled through EUA [Emergency Use Authorization] and the CDC, so there was no way to independently verify [the controls that were used]."
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REMINDER: The genome for SARS-CoV-2 is a "consensus sequence." Anybody who says the gene sequence for SARS2 is confirmed is *confused or lying.* "[The reality is that] nobody has the code of the pathogen..." "What they set for the control [for the PCR 'test'] is a consensus sequence, which [means] they took AI, they averaged out a section of the genome that they want as that test, and they set it for that. So it doesn't even exist in nature anywhere." This is a clip from a recent discussion between former medical coder and whistleblower Zowe Smith (Zowe Smith) and retired pharma R&D executive Sasha Latypova (sashalatypova.substack.com "Due Diligence and Art"). Smith and Latypova discuss the many shortcomings of PCR "tests," as well as the fact that no genome for SARS-CoV-2 has ever been characterized—only a "consensus sequence," which Smith notes is developed when AI "average[s] out... a section of the genome that they want as a test." She adds, "it doesn't even exist in nature anywhere." Latypova confirms, "when they're saying, 'Oh, we have the COVID virus, the full genome...it's been sequenced. Look at all these papers.' [The reality is that] nobody has the code of the pathogen... Ralph Baric also wrote about it in his work all the time. So nobody has the pathogenic sequence." The pharma insider adds, "What they upload to GenBank is... averaged... And once it's averaged, it's no longer pathogenic anything. It's just a model. And then for PCR, [it] doesn't test the full genome. They do these, like, snippets, and then whatever snippet you wanna set it to, you will find it, and that's how they find... positive COVID. So all of this is total BS." Interestingly, Smith notes that when she worked with PCR in a lab at the Oregon Health and Science University "[she] realized that everything was controlled through EUA [Emergency Use Authorization] and the CDC, so there was no way to independently verify [the controls that were used]."

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Thrilled to see Amazon Web Services making a major contribution to the open source AI community with the launch of the Strands Agents, an open source AI agents SDK! The core of Strands is the simple agentic loop that connects the model and tools together, like the two strands of DNA. This model-driven approach to agent building eliminates the need for complex agent orchestration by embracing the capabilities of state-of-the-art models to plan, chain thoughts, call tools, and reflect. Providing open source tools and interoperability with open source protocols is an important part of our strategy to enable an agentic future. Can't wait to see what you build with Strands!

Swami Sivasubramanian

32,185 次观看 • 1 年前

I sat down with Dr. Veera Rajagopal (Veera Rajagopal ) - physician-scientist at Regeneron and one of the best communicators in genetics - to talk about how genetics is transforming drug discovery and healthcare. We get into how human genetics is used to find new drug targets, why 23andMe’s model failed, the real impact of AI on genetic research, and why whole genome sequencing is the future. 0:00:00 – Dr. Rajagopal’s path from medicine to genetics 0:07:48 – Moving to industry, & the shift from academia 0:13:57 – Scaling science vs. pure research 0:18:18 – Why 23andMe’s Model Didn’t Work & the future is whole genome 0:28:53 – How AI is (and isn’t) changing genomics 0:43:12 – What people misunderstand about genetic drug discovery 0:51:22 – The future of genomics and the mission of Nucleus
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I sat down with Dr. Veera Rajagopal (Veera Rajagopal ) - physician-scientist at Regeneron and one of the best communicators in genetics - to talk about how genetics is transforming drug discovery and healthcare. We get into how human genetics is used to find new drug targets, why 23andMe’s model failed, the real impact of AI on genetic research, and why whole genome sequencing is the future. 0:00:00 – Dr. Rajagopal’s path from medicine to genetics 0:07:48 – Moving to industry, & the shift from academia 0:13:57 – Scaling science vs. pure research 0:18:18 – Why 23andMe’s Model Didn’t Work & the future is whole genome 0:28:53 – How AI is (and isn’t) changing genomics 0:43:12 – What people misunderstand about genetic drug discovery 0:51:22 – The future of genomics and the mission of Nucleus

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11,253 次观看 • 1 年前

🔥 Bactopia v3 is here! 🔥 The Nextflow pipeline for bacterial genomes is now faster, packed with more tools, updated docs, and much much more! Get started at: #bioinformatics #genomics
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🔥 Bactopia v3 is here! 🔥 The Nextflow pipeline for bacterial genomes is now faster, packed with more tools, updated docs, and much much more! Get started at: #bioinformatics #genomics

Robert A. Petit III, PhD

16,915 次观看 • 2 年前

Congratulations to National Human Genome Research Institute’s Ellen Sidransky for winning the 2024 Breakthrough in Life Sciences!
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Congratulations to National Human Genome Research Institute’s Ellen Sidransky for winning the 2024 Breakthrough in Life Sciences!

National Human Genome Research Institute

17,150 次观看 • 2 年前

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀
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Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀

Roberta Raileanu

104,982 次观看 • 1 年前

5 huge AI agents, MCP, and LLM updates today: 1. China just dropped an opensource Deep Research multi-agent framework with MCP support. It connects AI Agents with specialized tools for tasks like web search, crawling, and Python code execution. 100% opensource.
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5 huge AI agents, MCP, and LLM updates today: 1. China just dropped an opensource Deep Research multi-agent framework with MCP support. It connects AI Agents with specialized tools for tasks like web search, crawling, and Python code execution. 100% opensource.

Unwind AI

79,540 次观看 • 1 年前

Chinese scientists have decoded the genome of the flowerpot snake, the world's only all-female snake species. This tiny, 20-centimeter snake reproduces without males, thanks to a unique triploid genome and powerful DNA repair mechanisms.
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Chinese scientists have decoded the genome of the flowerpot snake, the world's only all-female snake species. This tiny, 20-centimeter snake reproduces without males, thanks to a unique triploid genome and powerful DNA repair mechanisms.

CGTN

68,216 次观看 • 1 年前

Robot AI brains, aka Vision-Language-Action models, cannot adapt to new tasks as easily as LLMs like Gemini, ChatGPT, or Grok. LLMs can adapt quickly with their in-context learning (ICL) capabilities. But can we inject ICL abilities into a pre-trained VLA like pi0? Yes! Introducing RICL (Retraining for In-Context Learning), our Conference on Robot Learning (CoRL) 2025 paper. Our RICL-pi0 model can adapt to unseen objects, novel motions, and new scenes with just ICL and RAG (retrieval-augmented generation). RICL-pi0 also boosts performance on the long-tail of tasks. A quick 1 minute video summary:
1:03

Robot AI brains, aka Vision-Language-Action models, cannot adapt to new tasks as easily as LLMs like Gemini, ChatGPT, or Grok. LLMs can adapt quickly with their in-context learning (ICL) capabilities. But can we inject ICL abilities into a pre-trained VLA like pi0? Yes! Introducing RICL (Retraining for In-Context Learning), our Conference on Robot Learning (CoRL) 2025 paper. Our RICL-pi0 model can adapt to unseen objects, novel motions, and new scenes with just ICL and RAG (retrieval-augmented generation). RICL-pi0 also boosts performance on the long-tail of tasks. A quick 1 minute video summary:

Kaustubh Sridhar

52,158 次观看 • 10 个月前