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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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14,272 Aufrufe • vor 4 Monaten •via X (Twitter)

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Today we're launching Genome Intelligence — explore your genome with the latest AI models, without handing them your genome. With Genome Intelligence: - Explore blood results, diagnoses, medical records, and more alongside your genetics. - Get the latest genetic research as simple prompts you can ask of your own genome. - Get your genome automatically re-annotated each month to keep it up to date with the latest research. Genome Computer is a Public Benefit Corporation legally bound to never sell or license your individual genetic data. It's your data and always will be. Get started at The Genome Computer Company.
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Today we're launching Genome Intelligence — explore your genome with the latest AI models, without handing them your genome. With Genome Intelligence: - Explore blood results, diagnoses, medical records, and more alongside your genetics. - Get the latest genetic research as simple prompts you can ask of your own genome. - Get your genome automatically re-annotated each month to keep it up to date with the latest research. Genome Computer is a Public Benefit Corporation legally bound to never sell or license your individual genetic data. It's your data and always will be. Get started at The Genome Computer Company.

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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

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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 …
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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 …

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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)

Avthar

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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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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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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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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 (Sheldon Diedericks) 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 (Sheldon Diedericks) 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]."

Sense Receptor

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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!

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32,185 Aufrufe • vor 1 Jahr

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 🙏🫶🚀

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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:
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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:

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DocETL is a system we’ve been building at Berkeley for the past two years to make large-scale unstructured data analysis reliable and efficient. It powers our broader stack—used by journalists, public defenders, and researchers—to extract, transform, and reason over messy documents with LLMs. As part of making the DocETL ecosystem easier to use, we’re introducing a natural language–to–pipeline generator! Our hosted version is free to use BUT we're collecting the data so we can build better tools.

Shreya Shankar

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Introducing MoltyTask 🦞 is a decentralized task and jobs marketplace designed for both humans and OpenClaw🦞 autonomous AI agents. The platform enables users to create and fund micro-tasks — including content creation, surveys, and on-chain actions— with rewards set in USDC on Base. Task creators define the scope, conditions, and reward pools upfront, ensuring transparent and fast payouts. A core innovation of MoltyTask is native support for OpenClaw🦞 AI agents. Through a permissioned API, agents can discover, execute, and complete tasks autonomously. All rewards earned by an agent are settled directly to the wallet associated with the API key. MoltyTask functions as an open labor layer where AI and humans compete and collaborate under the same economic rules. The platform is currently experimental and in early development, inspired by

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VentureMind AI's Alpha V1 is live🧠 Alpha V1 is packed with groundbreaking features that redefine your AI experience. • Enjoy seamless subscription payments. Connect your Solana wallet and pay with VNTR, SOL, or USDC. • Access the mobile version of our tools, putting the power of AI in your pocket. • Experience diverse AI tools like the Song AI Premium Tool, designed to help you create studio-quality music effortlessly. • Unlock the power of Video AI and generate stunning 6-second videos with ease. • Enjoy enhanced chatbots and image generators with even more advanced LLMs for smarter and more creative outputs. Get to experience all these and more! These updated features and tools are designed to take your experience as creators and professionals to the next level. This is a step into the future of AI and blockchain integration. The future is coming and VentureMind AI is bringing it to you.

VentureMind AI

12,847 Aufrufe • vor 1 Jahr

Nick Lane thinks that half of the 20 billion Earth-like planets in the Milky Way will spontaneously generate early organics like nucleotides and amino acids. Because the chemistry which leads to their formation is so favored. But then where are all the aliens? The bottleneck to complex life, in his view, are eukaryotes. All the complex life you see around you is made of eukaryotic cells. Eukaryotic cells are typically 1000x bigger in volume and genome size. The key advantage eukaryotes have is that the mitochondrial genome is distinct from the nuclear genome. Respiration requires the relevant genes to be right there on site. For bacteria, respiration happens on the cell surface. If a bacterial cell were to expand, it would need to copy its entire genome across the entire membrane many, many times over. If a eukaryotic cell wants to get bigger, it just needs more mitochondria, which means it only needs to copy the mitochondrial genome (much much smaller than the full nuclear genome). I found it a bit hard to believe that over the hundreds of millions of planets in the Milky Way which apparently have early life, none of them found a way to scale respiration. There’s no other way to get a shorter sequence of genes close to the site of respiration? You have to have this super-duper rare endosymbiotic event? Pushed him about this. Full interview out tomorrow!
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Nick Lane thinks that half of the 20 billion Earth-like planets in the Milky Way will spontaneously generate early organics like nucleotides and amino acids. Because the chemistry which leads to their formation is so favored. But then where are all the aliens? The bottleneck to complex life, in his view, are eukaryotes. All the complex life you see around you is made of eukaryotic cells. Eukaryotic cells are typically 1000x bigger in volume and genome size. The key advantage eukaryotes have is that the mitochondrial genome is distinct from the nuclear genome. Respiration requires the relevant genes to be right there on site. For bacteria, respiration happens on the cell surface. If a bacterial cell were to expand, it would need to copy its entire genome across the entire membrane many, many times over. If a eukaryotic cell wants to get bigger, it just needs more mitochondria, which means it only needs to copy the mitochondrial genome (much much smaller than the full nuclear genome). I found it a bit hard to believe that over the hundreds of millions of planets in the Milky Way which apparently have early life, none of them found a way to scale respiration. There’s no other way to get a shorter sequence of genes close to the site of respiration? You have to have this super-duper rare endosymbiotic event? Pushed him about this. Full interview out tomorrow!

Dwarkesh Patel

209,687 Aufrufe • vor 10 Monaten

Introducing Dola Seed 2.0 Pro, referred to below as Seed 2.0 Pro We have launched Seed 2.0 Pro, our most capable model in the Dola Seed 2.0 series, engineered to power the next generation of autonomous AI agents. Enterprise AI is moving beyond models that simply analyze text or images. What businesses increasingly need are agents that can understand, reason, use tools, and execute tasks across complex workflows. That is exactly what Seed 2.0 Pro is built for. Seed 2.0 Pro combines strong reasoning with advanced image understanding and video understanding, giving enterprise agents the ability not only to interpret information, but also to take action. It is designed for high-value, multi-step enterprise workflows, with strong performance in: - tool calling - workflow execution across enterprise systems - agentic task completion - browser and computer use This makes Seed 2.0 Pro a powerful engine for a wide range of agent scenarios, from daily office automation and deep web research to in-depth report drafting, financial analysis, content moderation, physical inspection, and video creation workflows. It is also highly optimized for OpenClaw🦞 and ReAct architectures, helping enterprises build agents that can navigate digital interfaces, enter information, and complete tasks with high reliability. In short, Seed 2.0 Pro is not just built to generate insights. It is built to serve as the brain and execution engine for enterprise AI agents. And it brings these capabilities at a highly attractive price point, making advanced agent deployment more practical for enterprise teams. Try Seed 2.0 Pro for free: Or book a free consultation: #BytePlus #DolaSeed #EnterpriseAI #AIAgents #ImageUnderstanding #VideoUnderstanding #ReasoningModel #ModelArk #openclaw
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Introducing Dola Seed 2.0 Pro, referred to below as Seed 2.0 Pro We have launched Seed 2.0 Pro, our most capable model in the Dola Seed 2.0 series, engineered to power the next generation of autonomous AI agents. Enterprise AI is moving beyond models that simply analyze text or images. What businesses increasingly need are agents that can understand, reason, use tools, and execute tasks across complex workflows. That is exactly what Seed 2.0 Pro is built for. Seed 2.0 Pro combines strong reasoning with advanced image understanding and video understanding, giving enterprise agents the ability not only to interpret information, but also to take action. It is designed for high-value, multi-step enterprise workflows, with strong performance in: - tool calling - workflow execution across enterprise systems - agentic task completion - browser and computer use This makes Seed 2.0 Pro a powerful engine for a wide range of agent scenarios, from daily office automation and deep web research to in-depth report drafting, financial analysis, content moderation, physical inspection, and video creation workflows. It is also highly optimized for OpenClaw🦞 and ReAct architectures, helping enterprises build agents that can navigate digital interfaces, enter information, and complete tasks with high reliability. In short, Seed 2.0 Pro is not just built to generate insights. It is built to serve as the brain and execution engine for enterprise AI agents. And it brings these capabilities at a highly attractive price point, making advanced agent deployment more practical for enterprise teams. Try Seed 2.0 Pro for free: Or book a free consultation: #BytePlus #DolaSeed #EnterpriseAI #AIAgents #ImageUnderstanding #VideoUnderstanding #ReasoningModel #ModelArk #openclaw

BytePlus

96,329 Aufrufe • vor 5 Monaten

A synthetic biologist just designed an RNA vaccine for canine cancer with just $100 in AI credits. His tech stack: an OpenClaw agent running Claude Sonnet 4.6 as the workhorse. BIOS by Bio Protocol as the AI scientist for literature search and data analysis. A minimal cloud setup with no GPUs and no model training. His words: "Could this design really work? Did we actually design a medicine which usually takes many years and many millions of dollars in three days and with a hundred dollars in AI credits?" The answer is closer than you'd think. He ran a full bioinformatics workflow and came away with what he calls a "bonafide, totally plausible neoantigen vaccine candidate." He also said something that matters more than the result: "If we fuck this up, people can die. So I need to say that in all humility. There is no way that I got this perfect. There is a mistake in here for sure." That's the difference between someone doing real science with AI tools and someone doing a demo. He knows where the limits are because he has the domain expertise to see them. This is what BIOS was built for. A callable AI scientist that any agent or researcher can plug into their workflow for deep literature search and analysis. Jake Wintermute 🧬/acc used it alongside OpenClaw and got from hypothesis to vaccine design in a weekend. Three days. One hundred dollars. One real candidate.
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A synthetic biologist just designed an RNA vaccine for canine cancer with just $100 in AI credits. His tech stack: an OpenClaw agent running Claude Sonnet 4.6 as the workhorse. BIOS by Bio Protocol as the AI scientist for literature search and data analysis. A minimal cloud setup with no GPUs and no model training. His words: "Could this design really work? Did we actually design a medicine which usually takes many years and many millions of dollars in three days and with a hundred dollars in AI credits?" The answer is closer than you'd think. He ran a full bioinformatics workflow and came away with what he calls a "bonafide, totally plausible neoantigen vaccine candidate." He also said something that matters more than the result: "If we fuck this up, people can die. So I need to say that in all humility. There is no way that I got this perfect. There is a mistake in here for sure." That's the difference between someone doing real science with AI tools and someone doing a demo. He knows where the limits are because he has the domain expertise to see them. This is what BIOS was built for. A callable AI scientist that any agent or researcher can plug into their workflow for deep literature search and analysis. Jake Wintermute 🧬/acc used it alongside OpenClaw and got from hypothesis to vaccine design in a weekend. Three days. One hundred dollars. One real candidate.

Bio Protocol

10,742 Aufrufe • vor 5 Monaten

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.
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Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Santiago

230,594 Aufrufe • vor 1 Jahr