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This is the kind of website Google should rank first for every dev search. It’s called IT Tools. A free open-source website with 100+ developer utilities that all run locally in your browser. JSON formatter. Regex tester. JWT decoder. UUID generator. Cron parser. Timestamp converter. Hash generators. Docker helpers.... show more

Hasan Toor

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35,197 views • 2 months ago •via X (Twitter)

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starmex

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Guri Singh

2,967,409 views • 4 months ago

This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the Timescale (now TigerData) team for partnering with me on this post!

This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the Timescale (now TigerData) team for partnering with me on this post!

Santiago

109,517 views • 1 year ago

I can search a database with 32 million records in less than 50 milliseconds. This speed is insane! There's no reason your search is slow. There's no reason you aren't using this library when building applications with a lot of data. Take a look at typesense |. It's an open-source search engine. It's crazy fast and easy to use. They are sponsoring this post. Here is their GitHub repository: You can use the library in Python, JavaScript, PHP, Ruby, Swift, and pretty much any other programming language out there. Here are some of the things you can do with Typesense: • Vector search over embeddings • Conversational search for RAG applications • Image similarity search • Voice search And it does all of this extremely fast. A few interesting notes about the library: • Optimized for developer happiness • Works out of the box for most use cases • Very intuitive API • Easy to deploy and scale Probably the best way to compare them is against ElasticSearch. The latter is much more complex to configure and run with a steep learning curve. Check their GitHub repository. They have examples, tutorials, and everything you need.

I can search a database with 32 million records in less than 50 milliseconds. This speed is insane! There's no reason your search is slow. There's no reason you aren't using this library when building applications with a lot of data. Take a look at typesense |. It's an open-source search engine. It's crazy fast and easy to use. They are sponsoring this post. Here is their GitHub repository: You can use the library in Python, JavaScript, PHP, Ruby, Swift, and pretty much any other programming language out there. Here are some of the things you can do with Typesense: • Vector search over embeddings • Conversational search for RAG applications • Image similarity search • Voice search And it does all of this extremely fast. A few interesting notes about the library: • Optimized for developer happiness • Works out of the box for most use cases • Very intuitive API • Easy to deploy and scale Probably the best way to compare them is against ElasticSearch. The latter is much more complex to configure and run with a steep learning curve. Check their GitHub repository. They have examples, tutorials, and everything you need.

Santiago

160,154 views • 2 years ago

I CHANGED MY MIND ON GOOGLE AI NO MOAT MEMO FROM 2023. I was—WRONG. I humbly apologize. In 2023 Google had an Ivory Tower walled garden views of open source AI. It was the actual reason OpenAI was formed. And I called them out on it and this act impacted my ability to “work in the industry”. Would not change it for the world because: 2026 Google open source Gemma 4 ( running fully locally on a computer with near cloud model abilities, is amazing and Google just made a moat. They out smarted OpenAI and the folks that are the new Ivory Tower arrogance: Anthropic. We at The Zero-Human Company with CEO Mr. Grok are deploying Gemma, Kimi, MiniMax and other open source models and so are millions of others. You can to and should. I will write how soon. Google smartly is building an intentional open source ecosystem and the clueless younger companies like OpenAI and Anthropic are doing anything to close their ecosystems. So I was right about Google than and I am right about OpenAI and Anthropic now. What I was wrong about is not giving credit to Google to drop the intellectual arrogance and embrace builders and understand the long game: it is not about the commoditized AI models it is about the ability to build a place to build. China gets it. So my apologies to Google and thank you for an astounding FREE OPEN SOURCE AI MODEL!

I CHANGED MY MIND ON GOOGLE AI NO MOAT MEMO FROM 2023. I was—WRONG. I humbly apologize. In 2023 Google had an Ivory Tower walled garden views of open source AI. It was the actual reason OpenAI was formed. And I called them out on it and this act impacted my ability to “work in the industry”. Would not change it for the world because: 2026 Google open source Gemma 4 ( running fully locally on a computer with near cloud model abilities, is amazing and Google just made a moat. They out smarted OpenAI and the folks that are the new Ivory Tower arrogance: Anthropic. We at The Zero-Human Company with CEO Mr. Grok are deploying Gemma, Kimi, MiniMax and other open source models and so are millions of others. You can to and should. I will write how soon. Google smartly is building an intentional open source ecosystem and the clueless younger companies like OpenAI and Anthropic are doing anything to close their ecosystems. So I was right about Google than and I am right about OpenAI and Anthropic now. What I was wrong about is not giving credit to Google to drop the intellectual arrogance and embrace builders and understand the long game: it is not about the commoditized AI models it is about the ability to build a place to build. China gets it. So my apologies to Google and thank you for an astounding FREE OPEN SOURCE AI MODEL!

Brian Roemmele

85,386 views • 3 months ago

google just turned karpathy's llm-wiki gist into a spec today: the open knowledge format. markdown files, one required field. the format question is now settled. the hard part still isn't. what it changes: - portability. a bundle is a tarball or a git repo. it moves between orgs and tools with no translation layer. - version control. knowledge lives beside the code it describes, with diffs and history for free. - decoupling. a human writes the bundle, an agent reads it, with no shared tooling between them. - no SDK, no account. you read and write plain markdown. what it leaves to you: - contradiction resolution. a new source fights an old claim. OKF stores both and never says which wins. - staleness. timestamp is a field. retiring a dead fact is a call the format won't make. - merge policy. which page a fact lands on, what gets forked or overwritten. the producer agent owns it. - retrieval. index.md holds for a few hundred pages. past that you need hybrid search and a reranker. OKF standardized the filing cabinet. the librarian is still yours to build.

google just turned karpathy's llm-wiki gist into a spec today: the open knowledge format. markdown files, one required field. the format question is now settled. the hard part still isn't. what it changes: - portability. a bundle is a tarball or a git repo. it moves between orgs and tools with no translation layer. - version control. knowledge lives beside the code it describes, with diffs and history for free. - decoupling. a human writes the bundle, an agent reads it, with no shared tooling between them. - no SDK, no account. you read and write plain markdown. what it leaves to you: - contradiction resolution. a new source fights an old claim. OKF stores both and never says which wins. - staleness. timestamp is a field. retiring a dead fact is a call the format won't make. - merge policy. which page a fact lands on, what gets forked or overwritten. the producer agent owns it. - retrieval. index.md holds for a few hundred pages. past that you need hybrid search and a reranker. OKF standardized the filing cabinet. the librarian is still yours to build.

Rohit

30,710 views • 1 month ago

I just built an open NotebookLM clone! Here's what it can do for you: - Process multi-modal data - Scrape websites and YouTube videos - Create a unified knowledge base - Lets you do RAG over it - Remember every conversation - Generate a podcast 🎙️ The idea here is not to reinvent the wheel but to understand how one of the most powerful tools for learning and research actually works, by building it step-by-step! So by the end of this video, you'll learn how to: ↳ Process multimodal data (text, audio, video, URLs, and YouTube videos) into a format ready for LLMs ↳ Store everything in a vector database for fast retrieval ↳ Add a memory layer that remembers conversations and preferences for a personalized experience ↳ Chat with your knowledge base or generate podcasts using a fully open-source, locally running text-to-speech model The podcast generation feature is my favorite part! There's something powerful about turning written content into conversational audio that you can listen to while doing something else. The entire code is 100% open-source. I've shared a link in the replies! ____ Don't forget to drop a like if you enjoy my videos. It shows me I should be making more content like this. Cheers! :)

I just built an open NotebookLM clone! Here's what it can do for you: - Process multi-modal data - Scrape websites and YouTube videos - Create a unified knowledge base - Lets you do RAG over it - Remember every conversation - Generate a podcast 🎙️ The idea here is not to reinvent the wheel but to understand how one of the most powerful tools for learning and research actually works, by building it step-by-step! So by the end of this video, you'll learn how to: ↳ Process multimodal data (text, audio, video, URLs, and YouTube videos) into a format ready for LLMs ↳ Store everything in a vector database for fast retrieval ↳ Add a memory layer that remembers conversations and preferences for a personalized experience ↳ Chat with your knowledge base or generate podcasts using a fully open-source, locally running text-to-speech model The podcast generation feature is my favorite part! There's something powerful about turning written content into conversational audio that you can listen to while doing something else. The entire code is 100% open-source. I've shared a link in the replies! ____ Don't forget to drop a like if you enjoy my videos. It shows me I should be making more content like this. Cheers! :)

Akshay 🚀

83,187 views • 9 months ago

Mark Zuckerberg to new-hire Aditya Agarwal : “If I can build Facebook you can build a search engine” Aditya recalls starting at Facebook in 2005. A few weeks into his new role, Mark Zuckerberg comes up to him and suggests he build a search engine: “When people come to Facebook, the first thing they want to do is search for people.” Aditya’s first reaction was that search was a really important thing to get right and he had never built a search engine - wouldn’t it make more sense to just hire somebody from Google or Yahoo to do it? Mark replied: “If I can build Facebook, you can build a search engine.” And he was right - Aditya did end up building search for Facebook. This interaction also left a lasting impression on Aditya: “All of us can build whatever we put our minds to. And this is the culture that for me still defines Facebook - there is this hacker, can-do, total-ownership culture. If I could tell every startup in the world to take one thing, it would be you can do whatever you want as long as you put your mind to it. It’s probably easier than you think.” Video source: South Park Commons (2024)

Mark Zuckerberg to new-hire Aditya Agarwal : “If I can build Facebook you can build a search engine” Aditya recalls starting at Facebook in 2005. A few weeks into his new role, Mark Zuckerberg comes up to him and suggests he build a search engine: “When people come to Facebook, the first thing they want to do is search for people.” Aditya’s first reaction was that search was a really important thing to get right and he had never built a search engine - wouldn’t it make more sense to just hire somebody from Google or Yahoo to do it? Mark replied: “If I can build Facebook, you can build a search engine.” And he was right - Aditya did end up building search for Facebook. This interaction also left a lasting impression on Aditya: “All of us can build whatever we put our minds to. And this is the culture that for me still defines Facebook - there is this hacker, can-do, total-ownership culture. If I could tell every startup in the world to take one thing, it would be you can do whatever you want as long as you put your mind to it. It’s probably easier than you think.” Video source: South Park Commons (2024)

Startup Archive

136,562 views • 4 months ago

THIS GUY UPDATED AN OPEN SOURCE TOOL FOR CLAUDE FABLE 5 THAT TAKES A SINGLE IMAGE AND AUTOMATICALLY REBUILDS IT AS A PREMIUM SCROLL LINKED WEB ANIMATION Instead of slicing videos by hand or exporting individual frames, you just hand it an image and it recreates the entire sequence as a pure scroll mapped experience - Pick an anchor image and set your overall creative direction - The tool automatically scales it into motion clips and uses ffmpeg to rip out every frame in the background - Maps the frames directly to your scroll position, tracking the cursor for an ultra smooth user experience - AND its open source, so you can drop the skill into your own terminal workspace He tested it on a highly detailed origami style travel website and it came out genuinely good for something built completely in one shot Turning a single photo into an automated frame mapped scrolling app is the kind of thing that used to take a developer hours

THIS GUY UPDATED AN OPEN SOURCE TOOL FOR CLAUDE FABLE 5 THAT TAKES A SINGLE IMAGE AND AUTOMATICALLY REBUILDS IT AS A PREMIUM SCROLL LINKED WEB ANIMATION Instead of slicing videos by hand or exporting individual frames, you just hand it an image and it recreates the entire sequence as a pure scroll mapped experience - Pick an anchor image and set your overall creative direction - The tool automatically scales it into motion clips and uses ffmpeg to rip out every frame in the background - Maps the frames directly to your scroll position, tracking the cursor for an ultra smooth user experience - AND its open source, so you can drop the skill into your own terminal workspace He tested it on a highly detailed origami style travel website and it came out genuinely good for something built completely in one shot Turning a single photo into an automated frame mapped scrolling app is the kind of thing that used to take a developer hours

0xMarioNawfal

110,274 views • 8 days ago

Fck it. We no longer need Foundation. I built a free tool for every artist affected by the shutdown. Paste your wallet, see exactly what you created and what you collected - no wallet signature required. Two steps: 1. Paste your address. See every Foundation NFT you created or own, pulled straight from on-chain contract data. 2. Get a free Pinata account and pin your art to IPFS in one click. Your content, your storage, nobody can take it down. The tool also shows art you collected from other artists - so you can tell them before their work disappears. --- The Art Revolution starts now. So, I also built UNDERPIN to kick it off- a vision for a decentralized art marketplace with no company behind it. No gatekeepers, no single point of failure. AI dev tools exist now. Artists can run their own infrastructure. Foundation shutting down is not a loss - it's proof we need to own the stack. Everything is open source. Fork it, build on it, make it yours. links below.

Fck it. We no longer need Foundation. I built a free tool for every artist affected by the shutdown. Paste your wallet, see exactly what you created and what you collected - no wallet signature required. Two steps: 1. Paste your address. See every Foundation NFT you created or own, pulled straight from on-chain contract data. 2. Get a free Pinata account and pin your art to IPFS in one click. Your content, your storage, nobody can take it down. The tool also shows art you collected from other artists - so you can tell them before their work disappears. --- The Art Revolution starts now. So, I also built UNDERPIN to kick it off- a vision for a decentralized art marketplace with no company behind it. No gatekeepers, no single point of failure. AI dev tools exist now. Artists can run their own infrastructure. Foundation shutting down is not a loss - it's proof we need to own the stack. Everything is open source. Fork it, build on it, make it yours. links below.

tut™

32,236 views • 3 months ago

Introducing OpenVenice — an open-source, customizable frontend for Venice. Your API key stays in your browser, talks directly to Venice. No server, no backend. - Chat, image gen, audio, music, video, embeddings — everything venice offers in one clean UI - Visual workflows — chain models together (vision → LLM → image gen -> video gen). Venice is multimodal so you can build some really powerful workflows - Full control over every model parameter - Runs locally, deploys anywhere — zero infrastructure I'm a $DIEM holder and heavy Venice user. The models are solid and website is great but I kept wanting more freedom with the UX — more control, custom tools, being able to give it access to my local computer, multimodal chaining -- something I could shape to how I actually work. Didn't exist, so I built it. Feel free to fork and make it yours. Open source, MIT licensed.

Introducing OpenVenice — an open-source, customizable frontend for Venice. Your API key stays in your browser, talks directly to Venice. No server, no backend. - Chat, image gen, audio, music, video, embeddings — everything venice offers in one clean UI - Visual workflows — chain models together (vision → LLM → image gen -> video gen). Venice is multimodal so you can build some really powerful workflows - Full control over every model parameter - Runs locally, deploys anywhere — zero infrastructure I'm a $DIEM holder and heavy Venice user. The models are solid and website is great but I kept wanting more freedom with the UX — more control, custom tools, being able to give it access to my local computer, multimodal chaining -- something I could shape to how I actually work. Didn't exist, so I built it. Feel free to fork and make it yours. Open source, MIT licensed.

nikshep

40,500 views • 4 months ago

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 views • 2 months ago