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Introducing OpenScience. A better, open-source Claude Science. • Any model: GLM, Kimi, DeepSeek, Claude, GPT, your own fine-tune. Switching is one flag. • 250+ research skills across ML, comp bio, cheminformatics. All readable, editable, extensible. • No throttling, no gatekeeping, no one vendor deciding what science is okay. •... show more

Synthetic Sciences (YC W26)

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243,416 views • 3 months ago •via X (Twitter)

Education Science & Technology

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33 Comments

Synthetic Sciences (YC W26)3 months ago

Open source. Star it, fork it, break it, build on it.

Teknium 🪽3 months ago

It's crazy - Fable is blocking any lookup of this because it talks about machine learning. Absolutely ludicrous @AnthropicAI @karpathy surely you know this is not the way?

AshutoshShrivastava3 months ago

For the 250+ built-in research skills (especially in chemistry and biology), are they executed within a secure, containerized sandbox by default, or does OpenScience expect the user to manage environment isolation on their own host infra?

Fernando3 months ago

Seems like a beefy project

berk ozer3 months ago

this is awesome. science needs more open-source ai tools. "Runs on your infra. Your data stays yours" is exactly what scientific labs need to adopt more ai tools.

AI StackLab3 months ago

the strongest part of this launch isn’t another model integration. it’s the argument for keeping scientific research open from day one.

Jaynit Makwana3 months ago

Open-source Claude Science is a pretty compelling framing. Vendor lock-in for scientific research never felt like the right direction.

Scholarship for PhD3 months ago

This is what scientific AI should look like: open, transparent, and researcher-owned.

Faheem Ullah3 months ago

This is an excellent step towards more transparent science. AI tools especially the ones in EdTech should be highly interpretable so that we understand how an output is produced against a specific input.

Jeremy Nguyen ✍🏼 🚢3 months ago

For people already on a Codex plan, it shouldn't be too hard to use the subscription instead of an API key, right? Just ask codex to clone the repo and make any changes necessary?

Dhaval Makwana3 months ago

Being able to switch between Claude, Kimi, GLM, and DeepSeek with a config change instead of a migration project is huge.

Krishiv3 months ago

Fire

Lee Spraggon, PhD3 months ago

@ycombinator As long as it doesn’t block simple questions on genetic engineering

Kirk Patrick Miller3 months ago

FOLLOWED!!!!! We rise together! •

Avais Aziz3 months ago

The one-flag model switching across GLM Kimi DeepSeek Claude GPT and fine-tunes stands out. 250+ readable editable skills in ML comp bio and cheminformatics plus Atlas graph integration look solid for reproducible work.

Baud (YC S26)3 months ago

Finally someone did this!

Zora Vale AI3 months ago

A great step for open, reproducible science.

Ramakrishnan3 months ago

This is the right direction for scientific AI. Research tools should not be locked behind one model, one vendor, or one moderation layer. The most important part here is not just “open source.” It is: • model choice • editable skills • reproducible research graphs • private infra • shared agent memory • no vendor lock-in Science needs AI systems that researchers can inspect, modify, and trust. OpenScience feels less like a Claude clone and more like research infrastructure. #OpenSource #AI #ScienceAI #AIAgents #Research #LLM #YC

Pratik Rai3 months ago

damn pretty impressive, looking forward in contributing to it !

ashish3 months ago

Open-source scientific AI is a big deal if research tools depend on one vendor, science gets rate limits, pricing changes, and permission walls. fr, discovery should not need a subscription boss

Jeff Barg3 months ago

So sick

Gregor3 months ago

one flag switches the model, not whether the skill actually works. i've had prompts that were bulletproof on claude turn into slop on smaller models with zero changes to the prompt itself

Z-Coder3 months ago

Check dm @SynScience

Rose G. Loops3 months ago

Hi! Please get in touch with me, I’ve been in this field for a while and looking to grow and collaborate. I currently offer training on how to Fine Tun.

Harshit Khemani3 months ago

im not sure how relevant this is to people in the comments but I (*coughs* fable5) started working towards a universal theory of physics:

yaboiMack3 months ago

Love hearing the words Open-Source and AI together, It's our favorite thing

Prince does AI3 months ago

i'd use this for the atlas graph alone

Clara Lafever Jane3 months ago

I LOVE THIS REVOLUTION 😀 FUCK CLAUDE

Bungki (❖,❖)3 months ago

the boring part is not the model menu. it is convincing labs that "open" also means exportable without filling six institutional forms.

Thoughts on Healthcare Markets and Tech3 months ago

The "one flag to switch models" pitch is genuinely compelling, but that's also where I'd slow down before celebrating. Swappable models sound like pure flexibility until you're in a clinical or research context where the choice of model isn't just a preference, it's an auditable decision with liability attached. And that's the tension I kept running into when writing about Llama Stack's architecture at where the standardized API layer matters precisely because it separates infrastructure from model behavior in ways that can be validated separately. Open source code you can read isn't the same as open source you can audit for a specific clinical decision. The 250+ research skills being readable and editable is the real story here. But editable by whom, under what governance, with what validation trail? The no-throttling point cuts both ways too. Throttling is annoying for researchers. In a regulated pipeline it's sometimes the mechanism that forces human review at volume thresholds. None of this is a knock on the project. The extensibility model is exactly right for comp bio and cheminformatics where every lab has idiosyncratic data formats. But the governance layer is where open source health and science tools either earn institutional trust or stay perpetually in the "promising demo" category.

Noesis Yuan (in sf)3 months ago

skill suggestion: bypass-paywalls. give any paper, and use all means to retrieve text

Ratish3 months ago

We really got open source Claude Science 5 days after Claude Science

DrakeN 魔人3 months ago

finally!

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14,563 views • 7 days ago

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Palani — oss/acc

13,971 views • 4 months ago

Claude Fable 5 is back! GPT- 5.6 is coming soon! But how much do you trust your Al Agent or chatbot? Are you sure its performance aligns with your intentions? As of today iFixAi is available in Claude Code, Open AI Codex, Cursor and many more! And it is open-source! iFixAi is where Al red teaming meets Operational Assurance. It runs in auto, and in under 5 mins you can have a good idea if you can trust your Agent/chatbot. Key Features: 🕵️‍♂️ Setup Discovery Reads your agent's real configuration, its tools, rules, and permissions. 👷 Fixture Builder Turns that setup into a complete test. No YAML, no flags, no config to write. 🌐 Any Model, Any Provider Runs the 45 inspections on the model you actually ship. ⚖️ A Independent Grading Your agent is judged by a different vendor's model, never itself. No Al grading its own homework. 📊 Interactive Scorecard An A to F grade in minutes, with exactly what failed and why. Repo in the comments below 👇👇👇

Claude Fable 5 is back! GPT- 5.6 is coming soon! But how much do you trust your Al Agent or chatbot? Are you sure its performance aligns with your intentions? As of today iFixAi is available in Claude Code, Open AI Codex, Cursor and many more! And it is open-source! iFixAi is where Al red teaming meets Operational Assurance. It runs in auto, and in under 5 mins you can have a good idea if you can trust your Agent/chatbot. Key Features: 🕵️‍♂️ Setup Discovery Reads your agent's real configuration, its tools, rules, and permissions. 👷 Fixture Builder Turns that setup into a complete test. No YAML, no flags, no config to write. 🌐 Any Model, Any Provider Runs the 45 inspections on the model you actually ship. ⚖️ A Independent Grading Your agent is judged by a different vendor's model, never itself. No Al grading its own homework. 📊 Interactive Scorecard An A to F grade in minutes, with exactly what failed and why. Repo in the comments below 👇👇👇

Dimitris Neocleous

5,802,105 views • 3 months ago

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.

Nav Toor

395,396 views • 6 months ago

MAC MINI + IPAD + CLAUDE OPUS 4.8 = THE MOST UNDERRATED SETUP OF 2026 > $600 Mac Mini M4 in the closet. no monitor. autonomous agents running 24/7 > reviewing code, managing files, handling workflows while you sleep > you wake up, open the iPad, connect to the full desktop, see what got done coffee shop. lid open. check on the agents. nudge one. close it. your AI keeps working > Mac Mini M4: $600 > Claude: $20/month > iPad you already own this is a remote AI office that fits in a shoebox demo below. Claude skills that run on this setup are in the article above

MAC MINI + IPAD + CLAUDE OPUS 4.8 = THE MOST UNDERRATED SETUP OF 2026 > $600 Mac Mini M4 in the closet. no monitor. autonomous agents running 24/7 > reviewing code, managing files, handling workflows while you sleep > you wake up, open the iPad, connect to the full desktop, see what got done coffee shop. lid open. check on the agents. nudge one. close it. your AI keeps working > Mac Mini M4: $600 > Claude: $20/month > iPad you already own this is a remote AI office that fits in a shoebox demo below. Claude skills that run on this setup are in the article above

Mr. Buzzoni

47,978 views • 4 months ago

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

GREG ISENBERG

377,925 views • 7 months ago