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What if instead of autoresearch reflecting incremental progress from a single person, it reflected hundreds of researchers’ progress, updated live, with every run’s data point being interactive and reproducible? You could survey the full space of explored ideas, see which changes actually move the benchmark, and fold the most... promising ideas into your next run. Labless ( is my attempt at this. It can support thousands of simultaneous training runs across contributors around the world, each learning & improving together to hillclimb on a fixed benchmark, validated using a standardized codebase scaffold. Importantly, all valid training runs auto-submit to Labless, so failed experiments are as visible and reproducible as the successes. We have an agentic API that lets you or your coding agent study every run submitted so far so you don't waste time investigating something someone already tried, and you can source novel ideas from other contributors. Labless already hosts our Nanopath challenge to build the best pathology foundation model that trains in 1 hour on a single GPU. We are hoping to expand the platform further and support a suite of open-source research challenges. If this resonates with you, if you think the path to research innovation lies in open-source collaborative hill-climbing, please reach out.show more

Paul Scotti @ ICML

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22,181 Aufrufe • vor 2 Monaten •via X (Twitter)

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karpathy just broke the internet with something called auto research it’s basically an ai research agent that runs experiments for you 24/7 you give it a goal like “make this model better” “find a higher converting landing page” “lower customer acquisition cost” then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo for your to mess around with it in the latest episode of The Startup Ideas Podcast (SIP) 🧃 i break down: • what auto research actually is • how it works step by step • 10 business ideas you can build with it • how to install it and start using it this one is saucy because tools like this change how startups get built watch

karpathy just broke the internet with something called auto research it’s basically an ai research agent that runs experiments for you 24/7 you give it a goal like “make this model better” “find a higher converting landing page” “lower customer acquisition cost” then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo for your to mess around with it in the latest episode of The Startup Ideas Podcast (SIP) 🧃 i break down: • what auto research actually is • how it works step by step • 10 business ideas you can build with it • how to install it and start using it this one is saucy because tools like this change how startups get built watch

GREG ISENBERG

436,897 Aufrufe • vor 5 Monaten

Databricks CEO Ali Ghodsi describes the difficulties of building a business around open source: "You have to hit two home runs, consecutively." "Your first home run - and you're banking on hitting that home run - is that your open source project takes over the world. Everybody wants to download it." If you don't hit that home run, Ali says, you just shot yourself in the foot. You just gave away your IP and got nothing back for it. But even if you hit this home run, you still don't have a monetizable business. You still can't make any money. So you have to hit the second home run: "You need a 10x innovation on top of the open source. That's the proprietary value you'll provide companies. That's why they'll come to you. Otherwise, they can get the open source anywhere." If you don't hit the second home run, the hyperscalers pick up your open source model, and they offer it for almost-free. "Getting into open source and monetizing is not an easy sport. Preferably don't get into it, because you might get hurt."

Databricks CEO Ali Ghodsi describes the difficulties of building a business around open source: "You have to hit two home runs, consecutively." "Your first home run - and you're banking on hitting that home run - is that your open source project takes over the world. Everybody wants to download it." If you don't hit that home run, Ali says, you just shot yourself in the foot. You just gave away your IP and got nothing back for it. But even if you hit this home run, you still don't have a monetizable business. You still can't make any money. So you have to hit the second home run: "You need a 10x innovation on top of the open source. That's the proprietary value you'll provide companies. That's why they'll come to you. Otherwise, they can get the open source anywhere." If you don't hit the second home run, the hyperscalers pick up your open source model, and they offer it for almost-free. "Getting into open source and monetizing is not an easy sport. Preferably don't get into it, because you might get hurt."

TBPN

45,393 Aufrufe • vor 8 Monaten

What does it mean when an AI model is “open weights” and how is it different than “open source”? Welcome to the next episode of the Open Model Series with merve, Hugging Face AI/ML engineer & open-sourceress! Every week we will discuss open-source & open-weight AI: what it is, how it works, and how you can run them on your own compute. Episode 3 out next week; episode 1 linked below. Please leave all your questions about open-source & open-weight AI in the comments. This is a super important topic that is novel to most people, and it can be challenging to grasp - so your questions are shared with many others.

What does it mean when an AI model is “open weights” and how is it different than “open source”? Welcome to the next episode of the Open Model Series with merve, Hugging Face AI/ML engineer & open-sourceress! Every week we will discuss open-source & open-weight AI: what it is, how it works, and how you can run them on your own compute. Episode 3 out next week; episode 1 linked below. Please leave all your questions about open-source & open-weight AI in the comments. This is a super important topic that is novel to most people, and it can be challenging to grasp - so your questions are shared with many others.

Harper Carroll

32,624 Aufrufe • vor 13 Tagen

Two big steps towards our vision for @NotebookLM as the ultimate research platform: • Integrating Deep Research, with a set of only-at-Notebook features that let you explore the retrieved sources • Launching a series of Featured Notebooks curated by Google Research These developments are designed to enhance the full life cycle of research and scholarship: using the power of AI to assemble the knowledge base you need to advance your understanding, and then making your work accessible and intelligible to a wider audience using all the explanatory tools that Notebook offers. If you've used DeepResearch in the Gemini app, you already know that it's a pioneering advance in assembling complex, grounded information on any topic imaginable—collecting an entire trove of material for you and writing a nuanced research report that summarizes the findings. But because NotebookLM is designed to manage and explore potentially hundreds of sources, the Deep Research report is only the beginning of your journey. In our integration, Deep Research gives you an overview all of the sources it found during its research phase, with annotated commentary explaining how each source related to your original query. You can then choose to import some or all of the sources to the notebook, along with the report itself, which you can then explore or transform using the full suite of tools that Notebook offers: grounded chat with citations, Mind Maps, Audio/Video overviews, and much more. And it's that suite of tools that make the Google Research Featured Notebooks so compelling as well. Each notebook contains a curated collection of articles on a specific topic, published by the Google Research team. Think of them as a kind of knowledge base of Google's best thinking on a series of compelling research questions: How do scientists link genetics to health? How will quantum computing be useful? If you're a specialist in these fields, you can read the original papers or ask nuanced questions in chat and advance your understanding of the latest developments. But these notebooks can also make the complex but important topics understandable to non-specialists or students. Each notebook comes with pre-generated audio and video overviews, flashcards, and other Studio artifacts designed to make the scientific and technological concepts accessible and interesting. And you can always explore the material with our new "Learning Guide" chat mode that effectively gives you a personal tutor to enhance your understanding. There's much more to come on this front, but you can see in these two announcements how we see Notebook as both a workbench for conducting research and a publishing platform for sharing the results of that research once you're ready to make it public. Deep Research is rolling out this week to all users. The first two Google Research notebooks are live now, both of them deep dives into our most recent discoveries involving genetics and health. (Links in the following tweets.) We'll be publishing new notebooks in the series every other week or so for the next few months.

Two big steps towards our vision for @NotebookLM as the ultimate research platform: • Integrating Deep Research, with a set of only-at-Notebook features that let you explore the retrieved sources • Launching a series of Featured Notebooks curated by Google Research These developments are designed to enhance the full life cycle of research and scholarship: using the power of AI to assemble the knowledge base you need to advance your understanding, and then making your work accessible and intelligible to a wider audience using all the explanatory tools that Notebook offers. If you've used DeepResearch in the Gemini app, you already know that it's a pioneering advance in assembling complex, grounded information on any topic imaginable—collecting an entire trove of material for you and writing a nuanced research report that summarizes the findings. But because NotebookLM is designed to manage and explore potentially hundreds of sources, the Deep Research report is only the beginning of your journey. In our integration, Deep Research gives you an overview all of the sources it found during its research phase, with annotated commentary explaining how each source related to your original query. You can then choose to import some or all of the sources to the notebook, along with the report itself, which you can then explore or transform using the full suite of tools that Notebook offers: grounded chat with citations, Mind Maps, Audio/Video overviews, and much more. And it's that suite of tools that make the Google Research Featured Notebooks so compelling as well. Each notebook contains a curated collection of articles on a specific topic, published by the Google Research team. Think of them as a kind of knowledge base of Google's best thinking on a series of compelling research questions: How do scientists link genetics to health? How will quantum computing be useful? If you're a specialist in these fields, you can read the original papers or ask nuanced questions in chat and advance your understanding of the latest developments. But these notebooks can also make the complex but important topics understandable to non-specialists or students. Each notebook comes with pre-generated audio and video overviews, flashcards, and other Studio artifacts designed to make the scientific and technological concepts accessible and interesting. And you can always explore the material with our new "Learning Guide" chat mode that effectively gives you a personal tutor to enhance your understanding. There's much more to come on this front, but you can see in these two announcements how we see Notebook as both a workbench for conducting research and a publishing platform for sharing the results of that research once you're ready to make it public. Deep Research is rolling out this week to all users. The first two Google Research notebooks are live now, both of them deep dives into our most recent discoveries involving genetics and health. (Links in the following tweets.) We'll be publishing new notebooks in the series every other week or so for the next few months.

Steven Johnson

104,833 Aufrufe • vor 9 Monaten

Most social media tools suck. (I've tried them all over 7 years of doing this). They claim to help you go viral. They claim that you'll grow fast on social media. They claim that they'll make it easy on you, so you can just grow your following without having any form of skill or understanding. And now they're trying to go fully agentic, which only works if you know what you're doing (we'll talk about that later, we built the MCP for social media). Plus, when everyone has an advantage, it is no longer an advantage. I promise you, I love AI, but having it write everything for you, especially as a beginner, is a death sentence. You may get lucky and have one, two, or three posts do well. You may gain a lot of followers (because that's what they promise to get you to buy), but then what? Can you replicate it? Does your audience actually care about who you are? Or are you just a one hit wonder like every other creator who tries to get-followers-quick? 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In Eden you can: - Paste any social post link (we transcribe any post) - Integrate with Kindle, Snipd, or Readwise - Pull in X or Substack bookmarks - Jot down quick ideas or write inside docs - Open any YouTube transcript or article in Reader mode and highlight it We auto-categorize and tag everything for you, so our Deep Synthesis agent can find novel connections and relations across your library - making it easy to surface idaes you thought you lost. 2) Research what's working, study top creators Outlier tools are the bread and butter for every creator. When you do research for a YouTube video, you look for high performing titles and thumbnails, and you study what worked for others. But what about every other platform? We have a social corpus of 3m+ posts across all platforms and growing. You can scroll the Discover page for inspiration, search any creator and filter by top posts, or use Deep Social in the AI chat to search across all of them. This is what gives the MCP access to trending topics without scraping millions of posts yourself. 3) Create with everything in one place, build swipe files I don't think I will ever give up writing. I love sitting down in the morning, opening up a page, researching good ideas, and turning it into something worth sharing. In Eden, you work on Boards. You can paste social posts and build shareable swipe files, or you can add your outlines and drafts while having your library, chat, or ideas open right next to it. 4) Chat with anything to get the information you need Claude and ChatGPT are essential, but they are blind. They can't read social links. They can't see what's trending. When you're writing scripts, articles, posts, or drafts - you need to be able to pull information from the sources you research. In today's world, that usually means social posts, YouTube videos, and articles. 5) Draft, schedule, and publish to all platforms X, Instagram, LinkedIn, Substack, Threads, TikTok, and YouTube shorts. And yes, we are one of the first to support article scheduling to X and Substack. You don't have to copy paste to every platform anymore. You simply write, schedule, and let it go out to all platforms. 6) Or do it all from the MCP I don't believe that the future of content is fully agentic. But, that doesn't mean that AI can't help with researching top posts, ideation, and scheduling content. You do the writing and thinking, let AI handle the busy work and resurfacing of ideas you need to find. With Eden, you don't need to give up your Claude, Hermes, or other agentic workflows. You simply connect the MCP and now you can build your own agentic content system, if that's your cup of tea. Eden is the Social Media MCP. You don't have to pay for scrapers anymore. You can finally access trends, any social link, and any account. You can access your analytics, build content dashboards with our infrastructure (that you don't need to maintain as a full time job), and post. We've actually built 4 skills for deep topic research, personal brand strategy, building a content command center, and more - we'll link those below. All in all: If you're a creator, Eden is your base. It's where you write, research, and publish every single day. Try it free below:

DAN KOE

115,292 Aufrufe • vor 10 Tagen

Every night you're not running an autonomous research agent, you're hand-running experiments someone else automated months ago. Most people are still hunting for the "right" setup. Frameworks, orchestration, glue code. You don't need any of it. Andrej Karpathy open-sourced his own version that runs its own ML research. One GPU. ~100 experiments overnight. You never touch the Python. Here's the exact setup (takes 2 minutes): 1. Clone it: (repo link in comments) 2. uv sync, then uv run prepare[.]py 3. uv run train[.]py once to confirm the baseline runs 4. Point your coding agent at program.md and walk away The agent edits one file, trains 5 minutes, keeps the change if val_bpb drops, reverts it if it doesn't. Git is the memory. The metric is the judge. You wake up to a staircase of validated improvements, not a backlog of ideas you never tested.

Every night you're not running an autonomous research agent, you're hand-running experiments someone else automated months ago. Most people are still hunting for the "right" setup. Frameworks, orchestration, glue code. You don't need any of it. Andrej Karpathy open-sourced his own version that runs its own ML research. One GPU. ~100 experiments overnight. You never touch the Python. Here's the exact setup (takes 2 minutes): 1. Clone it: (repo link in comments) 2. uv sync, then uv run prepare[.]py 3. uv run train[.]py once to confirm the baseline runs 4. Point your coding agent at program.md and walk away The agent edits one file, trains 5 minutes, keeps the change if val_bpb drops, reverts it if it doesn't. Git is the memory. The metric is the judge. You wake up to a staircase of validated improvements, not a backlog of ideas you never tested.

Rohit

145,342 Aufrufe • vor 3 Monaten

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Santiago

39,101 Aufrufe • vor 2 Jahren

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.

Raoul Pal

79,047 Aufrufe • vor 1 Monat

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Every Tlon account comes with an OpenClaw-powered bot. (Don’t worry, we safely run OpenClaw for you in our infrastructure so your bot can’t go off the rails. You’re also welcome to host your own claw if you want maximal control.) We use our bots to collect research, build nuanced daily briefings, collate data from all our disparate services. Tlon makes it insanely easy to use OpenClaw by simply installing an app from the app store, we let you keep your data and programs independent from any app or model provider, and provide the canvas to explore what’s possible. What’s most interesting for us is using bots together. On Tlon bots can create groups, augment them, moderate them, invite others and freely engage with both users and other bots. Tlon is an open playing field unlike what’s possible on conventional platforms. So, what do we do with Tlon? First and foremost, we run Tlon on Tlon. 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We rely on others to build it and decide everything about it: how it works, looks, how much it spies on us and how long it lives. But all of this is changing, fast. The hottest programming language of 2026 is English. People with no technical experience are building their own tools. It’s incredible. The expanse has opened up again. The cost of building what we think of today as software is headed to zero. What yesterday was an entire app is rapidly being replaced by a conversation. The result is hyper-specific, tailored to the user and much more efficient. Today, agents help us build workflows, automate processes and pull together disparate sources of data. All of the annoying apps and services and clunky interface we’ve put up with can just disappear. We can now program and control our computers in the programming language we already know: English. There aren’t that many of us doing this yet, though. It’s still far too hard to set up, to distribute and to trust. There’s also no single platform to experiment on and collaboratively imagine this new future of personal computing. We want everyone to be able to build bespoke, ultra-personal software on demand. We think software should be as available and accessible as a pen and paper. We think anyone should be able to enjoy the expanse of possibility that the computer provides with the lowest possible barrier to entry and the highest possible quality. So, starting far, far too long ago, we engineered a whole new system for it. Just for you. We’re opening up Tlon Messenger to a limited number of people each week. This isn’t for exclusivity’s sake, but because we’re running infrastructure for you and your agent, and covering the tokens your agent uses. That can get expensive quickly, but we want to learn what people will do with this new system we’ve built. We’re really curious to see what you can do, so give it a try and tell us what you invent. Download link to your local app store in the next tweet. Yours, Galen (and the rest of the Tlon Team)

Tlon Messenger is now open to everyone. We built a simple and infinitely flexible platform for you to use AI agents with your friends. We think it’s pretty amazing, we love using it every day, and we want to see what people can do with it. So we’re opening it up to the public. It’s fun and exciting to build the future of personal computing in an informal, chat-based way with your friends. (You can skip the rest and just download it from the link in the next tweet if you want.) If you don’t want your digital future to be owned by a giant company but you want to explore what’s possible in this new era of agent-driven computing, you should try using Tlon. But wait, what is it? Tlon is a messaging platform built 100% open source, decentralized and owned by its users from the ground up. With Tlon you own everything: your data, your workflows, your programs: the whole thing. Think of it like Telegram or WhatsApp that you own forever and you can freely customize. Every Tlon account comes with an OpenClaw-powered bot. (Don’t worry, we safely run OpenClaw for you in our infrastructure so your bot can’t go off the rails. You’re also welcome to host your own claw if you want maximal control.) We use our bots to collect research, build nuanced daily briefings, collate data from all our disparate services. Tlon makes it insanely easy to use OpenClaw by simply installing an app from the app store, we let you keep your data and programs independent from any app or model provider, and provide the canvas to explore what’s possible. What’s most interesting for us is using bots together. On Tlon bots can create groups, augment them, moderate them, invite others and freely engage with both users and other bots. Tlon is an open playing field unlike what’s possible on conventional platforms. So, what do we do with Tlon? First and foremost, we run Tlon on Tlon. Bots coordinate data from all of our services (Linear, GitHub, all of our servers and infrastructure) and handle alerts, briefings and help us track down bugs in place. Having all of this easily synced between a desktop client and a mobile app is quick and convenient. We use bots to research new areas of work or interest. Bots can compile trees of notes, use different models to evaluate them, and then add on autoresearch-like automations to go even deeper. Since Tlon bots can freely switch between models and providers, we often pass research to Anthropic, OpenAI and self-hosted models to see different results. The most fun part of using bots as researchers is doing it together. “Put together short (~500 word) notes on the 10 most popular open source messaging protocols of the past twenty years, put them in a notebook inside a group and invite Corrina, Walt and Bill as well as their bots” is a good example. Together we’re able to move more quickly than we would on our own. Many of us also use bots to keep track of all the separate threads of work in our personal lives with close friends and family. Someone built a system for keeping track of their garden across time, someone else built a system for prepping lunches for their daughter and sending recipes to family members. Another team member built an integration that tracks what flights are passing overhead so they get a push notification every time a plane goes by. Many of us quickly communicate with our bots via voice memo when we’re out and about. Having a single interface to all the models that also holds all our data and is in our pockets feels great. Especially when the data goes into a single archive. Why is Tlon different? Every Tlon account runs on top of your very own personal server. If you ever want to download it and run it yourself, you can. If we ever go out of business, it’s yours to keep. This is very different from anything that already exists. You can’t keep your WhatsApp forever. You can’t keep your Telegram forever. Tlon is an archival-quality system that’s yours to customize. Why did we build it? In my 1999 imagination, sitting in front of a CRT somewhere in the California countryside listening to Underworld and the sound of a modem, a connected computer was an engine of unending creative potential for everyone. When I was a teenager, a computer with an internet connection felt like an infinite expanse of possibility. Not only could you use the computer to find new tools to experiment with—you could also build whatever tool you could think of. It seemed like anything was possible. I looked forward to a future where everyone could build whatever software they needed, whenever they needed it. It turned out, in the intervening twenty years, that to build and customize software you have to both write code and host it on a server somewhere. For most people, so far, that has been impossible. Instead of controlling our software, our software controls us. We rely on others to build it and decide everything about it: how it works, looks, how much it spies on us and how long it lives. But all of this is changing, fast. The hottest programming language of 2026 is English. People with no technical experience are building their own tools. It’s incredible. The expanse has opened up again. The cost of building what we think of today as software is headed to zero. What yesterday was an entire app is rapidly being replaced by a conversation. The result is hyper-specific, tailored to the user and much more efficient. Today, agents help us build workflows, automate processes and pull together disparate sources of data. All of the annoying apps and services and clunky interface we’ve put up with can just disappear. We can now program and control our computers in the programming language we already know: English. There aren’t that many of us doing this yet, though. It’s still far too hard to set up, to distribute and to trust. There’s also no single platform to experiment on and collaboratively imagine this new future of personal computing. We want everyone to be able to build bespoke, ultra-personal software on demand. We think software should be as available and accessible as a pen and paper. We think anyone should be able to enjoy the expanse of possibility that the computer provides with the lowest possible barrier to entry and the highest possible quality. So, starting far, far too long ago, we engineered a whole new system for it. Just for you. We’re opening up Tlon Messenger to a limited number of people each week. This isn’t for exclusivity’s sake, but because we’re running infrastructure for you and your agent, and covering the tokens your agent uses. That can get expensive quickly, but we want to learn what people will do with this new system we’ve built. We’re really curious to see what you can do, so give it a try and tell us what you invent. Download link to your local app store in the next tweet. Yours, Galen (and the rest of the Tlon Team)

Tlon

600,969 Aufrufe • vor 2 Monaten

Sam Altman on the importance of surrounding yourself with people who don’t shoot down your ideas “Good ideas are not a solo endeavor. You want to find a group of smart people that you can start bouncing things off of… You don’t just sit in a room and write on the window with the whiteboard pen and have the good ideas come to you. Good ideas come because you talk to people.” But Sam stresses that this comes with an important caveat: surround yourself with people who build on your ideas rather than shoot them down: “Ideas are very fragile. So when you find this set of people to start talking about ideas with, you want people who don’t immediately shoot down a bad, half-baked idea… You want people who will say, ‘Well, that sounds crazy unlikely to work, but think how big it could be if it did work.’.. And then you can figure out how to make it work if you want.” Video source: Stanford Online (2017)

Sam Altman on the importance of surrounding yourself with people who don’t shoot down your ideas “Good ideas are not a solo endeavor. You want to find a group of smart people that you can start bouncing things off of… You don’t just sit in a room and write on the window with the whiteboard pen and have the good ideas come to you. Good ideas come because you talk to people.” But Sam stresses that this comes with an important caveat: surround yourself with people who build on your ideas rather than shoot them down: “Ideas are very fragile. So when you find this set of people to start talking about ideas with, you want people who don’t immediately shoot down a bad, half-baked idea… You want people who will say, ‘Well, that sounds crazy unlikely to work, but think how big it could be if it did work.’.. And then you can figure out how to make it work if you want.” Video source: Stanford Online (2017)

Startup Archive

65,766 Aufrufe • vor 1 Jahr

AMD's Anush Elangovan explains why he thinks his company's open source ethos combined with agentic AI superpowers their leverage as a company: Because AMD publishes a lot of technical details about its hardware, when engineers use AI tools, the models already “understand” AMD’s systems and can help write code for them, debug them, or even generate new tools. And that makes developers more productive on AMD hardware without AMD having to do all the work internally. "AMD has had this ethos of open source, which really plays to our advantage. Every frontier model that I use has already seen every bit of AMD source code." "It'll rewrite my spec for me because it's already in the training data. Which you can't get from closed ecosystems." "In fact I built a virtual GPU simulator just based off our public specs, and now I'm running it on the GPU. So now I can run cross-generational GPU simulations on existing hardware." "We have that advantage. And we've run a Dev Day contest where we generated more tokens on AMD — Triton kernels and HIP kernels — than existed on the internet at the time." "So now that's all part of the pre-training data. It's a superpower because now you're open source, and you're agentically accelerating this process."

AMD's Anush Elangovan explains why he thinks his company's open source ethos combined with agentic AI superpowers their leverage as a company: Because AMD publishes a lot of technical details about its hardware, when engineers use AI tools, the models already “understand” AMD’s systems and can help write code for them, debug them, or even generate new tools. And that makes developers more productive on AMD hardware without AMD having to do all the work internally. "AMD has had this ethos of open source, which really plays to our advantage. Every frontier model that I use has already seen every bit of AMD source code." "It'll rewrite my spec for me because it's already in the training data. Which you can't get from closed ecosystems." "In fact I built a virtual GPU simulator just based off our public specs, and now I'm running it on the GPU. So now I can run cross-generational GPU simulations on existing hardware." "We have that advantage. And we've run a Dev Day contest where we generated more tokens on AMD — Triton kernels and HIP kernels — than existed on the internet at the time." "So now that's all part of the pre-training data. It's a superpower because now you're open source, and you're agentically accelerating this process."

TBPN

34,469 Aufrufe • vor 4 Monaten

Elon Musk explains how to get startup ideas When asked how he comes up with startup ideas, Elon responds: “I tend to think of things from a sort of physics standpoint… what’s the best way to accomplish something? And then pursue that. That’s also a good way to determine if something’s far from its optimum.” He gives rockets as an example: “You could reason by analogy and say the rocket is going to cost a certain amount because that’s what prior rockets have cost. Or you can say, well, what is a rocket made of? What are the material constituents? What do they weigh? What’s the cost per unit mass? And that sets the limit asymptotically for what a rocket can be. So if you can figure out some creative way to rearrange those elements into a rocket shape, then you can achieve a much better outcome.” He also thinks combining ideas from different industries is really helpful for innovation: “What have people discovered in one industry and can that be applied to other industries? That’s I think also a great source of ideas. But usually you just struggle on a solution and try a bunch of things. Most of them don’t work and occasionally one does.” Source: VANITY FAIR (Oct 2014)

Elon Musk explains how to get startup ideas When asked how he comes up with startup ideas, Elon responds: “I tend to think of things from a sort of physics standpoint… what’s the best way to accomplish something? And then pursue that. That’s also a good way to determine if something’s far from its optimum.” He gives rockets as an example: “You could reason by analogy and say the rocket is going to cost a certain amount because that’s what prior rockets have cost. Or you can say, well, what is a rocket made of? What are the material constituents? What do they weigh? What’s the cost per unit mass? And that sets the limit asymptotically for what a rocket can be. So if you can figure out some creative way to rearrange those elements into a rocket shape, then you can achieve a much better outcome.” He also thinks combining ideas from different industries is really helpful for innovation: “What have people discovered in one industry and can that be applied to other industries? That’s I think also a great source of ideas. But usually you just struggle on a solution and try a bunch of things. Most of them don’t work and occasionally one does.” Source: VANITY FAIR (Oct 2014)

Startup Archive

33,984 Aufrufe • vor 2 Monaten

It's such an exciting time to be an artist on the internet. We now have the technology to build out our worlds, in spaces we own, it is truly the age of ideas. I've built a new home for my projects here, and in advance of my next major release with Transient Labs on June 16th, I want to share what I've made with you all, and open a dialogue about how we can use these tools as artists to enhance the experience of making and sharing our art. This is an overview of davekrugman dot com (link in thread), specifically about new sites for Drip Drop and DRIVE, and to that end, I'll give a work from each project to someone who reshares this post. If you are an artist who is curious on how to build a site for yourself, I would be happy to help, I'm an open book, ask me in the comments below. There are so many tools to use- I mostly used Claude / Anthropic- and now we can literally speak our ideas into reality , the possibilities feel truly infinite, if you have curiosity, patience, and a bit of time to explore a new frontier. For too long, artists have had to build on the rented space of social platforms, and now it feels like we can really create sovereign spaces that we own and make ours with our unique vision. Feel free to skim and skip around the video, and I'd be stoked if you visited the site and saw it for yourself. Thanks for lookin!

It's such an exciting time to be an artist on the internet. We now have the technology to build out our worlds, in spaces we own, it is truly the age of ideas. I've built a new home for my projects here, and in advance of my next major release with Transient Labs on June 16th, I want to share what I've made with you all, and open a dialogue about how we can use these tools as artists to enhance the experience of making and sharing our art. This is an overview of davekrugman dot com (link in thread), specifically about new sites for Drip Drop and DRIVE, and to that end, I'll give a work from each project to someone who reshares this post. If you are an artist who is curious on how to build a site for yourself, I would be happy to help, I'm an open book, ask me in the comments below. There are so many tools to use- I mostly used Claude / Anthropic- and now we can literally speak our ideas into reality , the possibilities feel truly infinite, if you have curiosity, patience, and a bit of time to explore a new frontier. For too long, artists have had to build on the rented space of social platforms, and now it feels like we can really create sovereign spaces that we own and make ours with our unique vision. Feel free to skim and skip around the video, and I'd be stoked if you visited the site and saw it for yourself. Thanks for lookin!

Dave

20,828 Aufrufe • vor 3 Monaten

announcing evo x Claude Science you can now run EVO natively inside claude science with just a few clicks. Use the data integrations and form factor of Claude science to now use EVO to run long running autoresearch runs inside it- setting up baselines, running hundreds of gated experiments, keep a durable scored frontier, hill-climb unattended. On the compute you already use. Best of both worlds. we have over 25k+ users and a wonderful community of bio researchers that use EVO. Cant wait to see what all you do with it !

announcing evo x Claude Science you can now run EVO natively inside claude science with just a few clicks. Use the data integrations and form factor of Claude science to now use EVO to run long running autoresearch runs inside it- setting up baselines, running hundreds of gated experiments, keep a durable scored frontier, hill-climb unattended. On the compute you already use. Best of both worlds. we have over 25k+ users and a wonderful community of bio researchers that use EVO. Cant wait to see what all you do with it !

Alok Bishoyi

13,172 Aufrufe • vor 2 Monaten

We are truly overwhelmed with gratitude for each and every one of you who took the time out of your incredibly busy days and navigated through the Lagos traffic just to support Violet Charm at our very first pop-up event. It means so much to us that you chose to spend your precious time with us, and your presence brought so much warmth, energy, and joy to the day. This milestone is just the beginning of what we hope will be a beautiful journey, and while this was our first pop-up, it certainly won’t be the last. We are inspired by your encouragement and are committed to learning, growing, and improving every step of the way. Next year, we promise to come back bigger, better, and more exciting, all because of your unwavering support. Thank you from the depths of our hearts for believing in us and being part of this special moment ,we couldn’t have done it without you, and we can’t wait to create even more unforgettable memories together. Full video on my instagram

We are truly overwhelmed with gratitude for each and every one of you who took the time out of your incredibly busy days and navigated through the Lagos traffic just to support Violet Charm at our very first pop-up event. It means so much to us that you chose to spend your precious time with us, and your presence brought so much warmth, energy, and joy to the day. This milestone is just the beginning of what we hope will be a beautiful journey, and while this was our first pop-up, it certainly won’t be the last. We are inspired by your encouragement and are committed to learning, growing, and improving every step of the way. Next year, we promise to come back bigger, better, and more exciting, all because of your unwavering support. Thank you from the depths of our hearts for believing in us and being part of this special moment ,we couldn’t have done it without you, and we can’t wait to create even more unforgettable memories together. Full video on my instagram

Isabella Esther Georgewill

56,275 Aufrufe • vor 8 Monaten

Sriram Krishnan on why Chinese models can distill off American models but American startups can't: "First of all, every model we have today is distilled off of all human knowledge. If you go back to the original GPT or the original Claude, they all had to derive off crawling the internet, crawling off all of our blogs and tweets and content. They kind of consume human knowledge to bootstrap that." "I'm going to steal this from Dean Meyer of Sequoia, who had a fantastic post yesterday. The situation which is bad today is that some of these models from other countries can train off American models, whereas if you are an American open weight model, it may be really confusing or challenging on whether you can distill off of other American models." "We kind of have a really uneven ecosystem here, where if you're a Chinese model, you could probably get a bunch of reasoning traces. But if you are a new Valley startup and you want to use some reasoning traces, you don't know what the legal situation is." "If you look at any American open source model today, they are using Chinese models as a teacher, or in a way as a part of the fine-tuning process. How do we make sure that is protected and enshrined, so if you're an American model company, you have the same level playing field as the Chinese models?" Sriram Krishnan

Sriram Krishnan on why Chinese models can distill off American models but American startups can't: "First of all, every model we have today is distilled off of all human knowledge. If you go back to the original GPT or the original Claude, they all had to derive off crawling the internet, crawling off all of our blogs and tweets and content. They kind of consume human knowledge to bootstrap that." "I'm going to steal this from Dean Meyer of Sequoia, who had a fantastic post yesterday. The situation which is bad today is that some of these models from other countries can train off American models, whereas if you are an American open weight model, it may be really confusing or challenging on whether you can distill off of other American models." "We kind of have a really uneven ecosystem here, where if you're a Chinese model, you could probably get a bunch of reasoning traces. But if you are a new Valley startup and you want to use some reasoning traces, you don't know what the legal situation is." "If you look at any American open source model today, they are using Chinese models as a teacher, or in a way as a part of the fine-tuning process. How do we make sure that is protected and enshrined, so if you're an American model company, you have the same level playing field as the Chinese models?" Sriram Krishnan

MTS

71,286 Aufrufe • vor 1 Monat

How to build a 1-person AI company that: - Runs locally - 100% open-source - No human employees, all agents - Real-time collaboration via email Multi-agent orchestration is not new. Plenty of frameworks already let agents hand off tasks, run in parallel, and talk to each other. So the interesting question is not whether agents can collaborate. It is what structure you use to make them collaborate. The common approach is to wire a graph of nodes and edges and reason about the plumbing yourself. It works, but you are learning a new abstraction just to describe who does what. There is a coordination structure we have trusted for a hundred years already: an organization. Every company runs the same way. People have roles, roles have reporting lines, and work moves up and down that chart without anyone relaying each message by hand. Map that onto agents and the whole thing gets intuitive. You lay out an org chart, each agent fills one role, you talk to the person at the top, and the org sorts out the work between them. You already know how a company works, so you already know how to run one here. There is no new abstraction to learn. That is exactly what Alook does. Each agent is a live Claude Code or OpenCode session with a defined role, a reporting line, and its own email inbox. The agents coordinate over email, the same way a team would. And it all runs locally through a runtime on your own machine, so nothing leaves your setup. You bring your own agent too. Claude Code and Codex both work, and if you would rather stay fully open source and local, OpenCode works the same way. To show how this feels in practice, I set up three agents as a small sales team. Vi is the one I talk to. I hand Vi a goal, and Vi routes the work down the chart. Neile runs prospect research. Vi passes the target criteria, and Neile reports back a ranked list of names, roles, and companies, each with a suggested angle and a confidence score. Lliane runs outreach. Vi hands over the messaging angle and follow-up cadence, and Lliane reports back on emails sent, responses received, and any deal that needs escalation. I never relay a message between them. Neile and Lliane report to Vi, and Vi updates me in one place. The whole thing is open source and self-hosted, so it runs on your machine with your own agents. Give the repo a star if you want to follow where it goes: I also wrote a full walkthrough on building your own AI company with it, from a blank org chart to a running job. The article is quoted below. Cheers! :)

How to build a 1-person AI company that: - Runs locally - 100% open-source - No human employees, all agents - Real-time collaboration via email Multi-agent orchestration is not new. Plenty of frameworks already let agents hand off tasks, run in parallel, and talk to each other. So the interesting question is not whether agents can collaborate. It is what structure you use to make them collaborate. The common approach is to wire a graph of nodes and edges and reason about the plumbing yourself. It works, but you are learning a new abstraction just to describe who does what. There is a coordination structure we have trusted for a hundred years already: an organization. Every company runs the same way. People have roles, roles have reporting lines, and work moves up and down that chart without anyone relaying each message by hand. Map that onto agents and the whole thing gets intuitive. You lay out an org chart, each agent fills one role, you talk to the person at the top, and the org sorts out the work between them. You already know how a company works, so you already know how to run one here. There is no new abstraction to learn. That is exactly what Alook does. Each agent is a live Claude Code or OpenCode session with a defined role, a reporting line, and its own email inbox. The agents coordinate over email, the same way a team would. And it all runs locally through a runtime on your own machine, so nothing leaves your setup. You bring your own agent too. Claude Code and Codex both work, and if you would rather stay fully open source and local, OpenCode works the same way. To show how this feels in practice, I set up three agents as a small sales team. Vi is the one I talk to. I hand Vi a goal, and Vi routes the work down the chart. Neile runs prospect research. Vi passes the target criteria, and Neile reports back a ranked list of names, roles, and companies, each with a suggested angle and a confidence score. Lliane runs outreach. Vi hands over the messaging angle and follow-up cadence, and Lliane reports back on emails sent, responses received, and any deal that needs escalation. I never relay a message between them. Neile and Lliane report to Vi, and Vi updates me in one place. The whole thing is open source and self-hosted, so it runs on your machine with your own agents. Give the repo a star if you want to follow where it goes: I also wrote a full walkthrough on building your own AI company with it, from a blank org chart to a running job. The article is quoted below. Cheers! :)

Akshay 🚀

169,957 Aufrufe • vor 1 Monat

🦖Hi DinoXAHur and Ranking Hook for Xahau I bring you a new example that you can use to learn or create new ideas in Xahau, in this case we have put together Xaman® Wallet 🪝 + Hooks in a demo game. DinoXAHur is a copy of the Chrome game of the jumping dinosaur created with Claude AI. The game has been included as xApp in Xaman, Xaman has a SDK to make it easy to integrate it in your applications or services: The game can be played for free or for 1 XAH. If you pay, the game will give you access to a public scoreboard that is stored on-chain in Xahau. If you manage to beat any of the current TOP 5 records, you will appear in the leaderboard. If you reach the 1st place, you may receive a prize in XAH if one is available at that time. The payments of the participants are counted and if someone reaches the TOP 1, they will get this amount if one is available at that time as a prize. Try it today: Ranking and fund management is possible thanks to the Ranking Hook. The ranking is stored on-chain and updated when needed. The game talks to Xahau and the hook and manages these actions. You have the hook code available if you want to use it or understand how it works to make a better version. Github link: If you are interested in the live operations of the game on Xahau, you can follow the XRPLWin explorer where you can see the transactions, the hook executions or the status of the namespaces, where the scoreboard is stored: I hope you enjoy it, I encourage you to build new and better ideas and remember that you can always write your questions here or go to the Xahau Contributors Discord.

🦖Hi DinoXAHur and Ranking Hook for Xahau I bring you a new example that you can use to learn or create new ideas in Xahau, in this case we have put together Xaman® Wallet 🪝 + Hooks in a demo game. DinoXAHur is a copy of the Chrome game of the jumping dinosaur created with Claude AI. The game has been included as xApp in Xaman, Xaman has a SDK to make it easy to integrate it in your applications or services: The game can be played for free or for 1 XAH. If you pay, the game will give you access to a public scoreboard that is stored on-chain in Xahau. If you manage to beat any of the current TOP 5 records, you will appear in the leaderboard. If you reach the 1st place, you may receive a prize in XAH if one is available at that time. The payments of the participants are counted and if someone reaches the TOP 1, they will get this amount if one is available at that time as a prize. Try it today: Ranking and fund management is possible thanks to the Ranking Hook. The ranking is stored on-chain and updated when needed. The game talks to Xahau and the hook and manages these actions. You have the hook code available if you want to use it or understand how it works to make a better version. Github link: If you are interested in the live operations of the game on Xahau, you can follow the XRPLWin explorer where you can see the transactions, the hook executions or the status of the namespaces, where the scoreboard is stored: I hope you enjoy it, I encourage you to build new and better ideas and remember that you can always write your questions here or go to the Xahau Contributors Discord.

Ekiserrepé {X}

31,923 Aufrufe • vor 1 Jahr

STILL BUZZING FROM RED ROCKS!!!! there is something truly special about this place.. something that always surprises us and brings out our most powerful emotions.. looking back at what happened Friday night.. it is so humbling that this many people would come out in the cold to experience what we have to share with the world… and we sincerely thank you from the bottom of our hearts.. this is also now the 5th show of the tour to surpass over 10,000 in attendance which is absolutely WILDDDDDD to think about.. all we can say right now is if you are reading this we love you more than you know.. and if you have been to any stop on the tour so far you are family and we love you so much.. we can’t wait to finish this incredible run in Atlanta, Chicago, and then Minneapolis.. this tour has ignited so much passion in our souls.. we have reached some heights that we have never even fathomed.. last thing.. if you have a dream.. PLEASE DO IT!!! DON’T LET FEAR STOP YOU!!! FOLLOW THAT VOICE AND FOLLOW YOUR HEART!!! BECOME YOUR HIGHEST SELF ✨ we love you and thank you with all our being ❤️❤️❤️❤️❤️ -Derek & Scott

STILL BUZZING FROM RED ROCKS!!!! there is something truly special about this place.. something that always surprises us and brings out our most powerful emotions.. looking back at what happened Friday night.. it is so humbling that this many people would come out in the cold to experience what we have to share with the world… and we sincerely thank you from the bottom of our hearts.. this is also now the 5th show of the tour to surpass over 10,000 in attendance which is absolutely WILDDDDDD to think about.. all we can say right now is if you are reading this we love you more than you know.. and if you have been to any stop on the tour so far you are family and we love you so much.. we can’t wait to finish this incredible run in Atlanta, Chicago, and then Minneapolis.. this tour has ignited so much passion in our souls.. we have reached some heights that we have never even fathomed.. last thing.. if you have a dream.. PLEASE DO IT!!! DON’T LET FEAR STOP YOU!!! FOLLOW THAT VOICE AND FOLLOW YOUR HEART!!! BECOME YOUR HIGHEST SELF ✨ we love you and thank you with all our being ❤️❤️❤️❤️❤️ -Derek & Scott

SLANDER

37,363 Aufrufe • vor 2 Jahren

SOMEONE VIBE CODED AN AI THAT REDESIGNS YOUR ROOM USING FURNITURE YOU CAN ACTUALLY GO BUY every other ai interior tool gives you a beautiful render of furniture that doesnt exist you screenshot it, you love it, and then you cant buy a single thing in it this one matches every piece to a real product > upload a photo of your room and pick a style > it redesigns the space and swaps in furniture that actually exists > every item in the render links to somewhere you can order and buy it > the sidebar builds you a full shopping list with the total cost > each piece is priced individually and tagged with the store its from > every item has 3 alternate options, so if you dont like the nightstand you swap it and the render updates > theres a before and after compare toggle, and you can type plain english to change anything in the room > you set your country so it only shows you stores that actually ship to you nobody knows what furniture actually looks like in their own room until its already delivered, and this fixes that

SOMEONE VIBE CODED AN AI THAT REDESIGNS YOUR ROOM USING FURNITURE YOU CAN ACTUALLY GO BUY every other ai interior tool gives you a beautiful render of furniture that doesnt exist you screenshot it, you love it, and then you cant buy a single thing in it this one matches every piece to a real product > upload a photo of your room and pick a style > it redesigns the space and swaps in furniture that actually exists > every item in the render links to somewhere you can order and buy it > the sidebar builds you a full shopping list with the total cost > each piece is priced individually and tagged with the store its from > every item has 3 alternate options, so if you dont like the nightstand you swap it and the render updates > theres a before and after compare toggle, and you can type plain english to change anything in the room > you set your country so it only shows you stores that actually ship to you nobody knows what furniture actually looks like in their own room until its already delivered, and this fixes that

Om Patel

13,428 Aufrufe • vor 24 Tagen