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CAPTCHA IS OFFICIALLY OUTDATED A new open-source library called Cap is changing how websites stop bots. No puzzles. No traffic lights. No “select all bikes” anymore. Instead, it uses a SHA-256 proof-of-work system — simple, silent, and fast. Why devs are switching: • Only ~20KB in size • Zero... show more

divyansh tiwari

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113,183 Aufrufe • vor 2 Monaten •via X (Twitter)

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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. Color tools. Markdown editor. Everything loads instantly. No login. No tracking. No AI-generated SEO garbage. You search for one simple converter and get: - Popup spam - Trackers - Fake buttons - Autoplay videos - 40 analytics scripts And because it’s fully open-source, you can self-host the whole thing yourself. One bookmark replaced 30+ random websites for me.

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. Color tools. Markdown editor. Everything loads instantly. No login. No tracking. No AI-generated SEO garbage. You search for one simple converter and get: - Popup spam - Trackers - Fake buttons - Autoplay videos - 40 analytics scripts And because it’s fully open-source, you can self-host the whole thing yourself. One bookmark replaced 30+ random websites for me.

Hasan Toor

35,197 Aufrufe • vor 2 Monaten

ELON: EVEN OPEN-SOURCE AI IS GETTING BETTER HUMAN TESTS THAN HUMANS "We're getting to the point where even open-source AI can pass human captcha tests: 'Are you a human? Identify all the traffic lights in this picture.' You're like, okay, it's going to have no problem doing that. In fact, it'll do it better than a human and faster than a human. So, at the point at which it's better passing human tests than humans, then, what tests actually make sense?" Source: Interview with Rishi Sunak, November 2023

ELON: EVEN OPEN-SOURCE AI IS GETTING BETTER HUMAN TESTS THAN HUMANS "We're getting to the point where even open-source AI can pass human captcha tests: 'Are you a human? Identify all the traffic lights in this picture.' You're like, okay, it's going to have no problem doing that. In fact, it'll do it better than a human and faster than a human. So, at the point at which it's better passing human tests than humans, then, what tests actually make sense?" Source: Interview with Rishi Sunak, November 2023

Mario Nawfal

42,071 Aufrufe • vor 1 Jahr

Someone just open sourced a tool that honestly feels unfair for developers. you join a new company. 200,000+ lines of code. zero docs. zero context. normally you’d spend weeks just figuring out “what the hell is going on here”. now? you run ONE command. and the entire codebase just… lights up. → every file, function, class, dependency automatically mapped → full system-level understanding in minutes → interactive graph instead of boring static docs → ask “where is auth handled?” and it traces the entire flow end-to-end → shows ripple effects before you even make a commit → auto-builds architecture layers: API / Service / Data / UI → works with Claude Code, Codex, Copilot, Gemini CLI, everything → fully open source and free this is not “better documentation”. this is your codebase getting a brain. onboarding isn’t slow anymore. it’s instant.

Someone just open sourced a tool that honestly feels unfair for developers. you join a new company. 200,000+ lines of code. zero docs. zero context. normally you’d spend weeks just figuring out “what the hell is going on here”. now? you run ONE command. and the entire codebase just… lights up. → every file, function, class, dependency automatically mapped → full system-level understanding in minutes → interactive graph instead of boring static docs → ask “where is auth handled?” and it traces the entire flow end-to-end → shows ripple effects before you even make a commit → auto-builds architecture layers: API / Service / Data / UI → works with Claude Code, Codex, Copilot, Gemini CLI, everything → fully open source and free this is not “better documentation”. this is your codebase getting a brain. onboarding isn’t slow anymore. it’s instant.

divyansh tiwari

39,303 Aufrufe • vor 2 Monaten

SAMURAI vs. MetaAI's SAM 2! Traditional visual object tracking struggles in crowded, fast-moving, or self-occluded scenes, as does SAM2. Meet SAMURAI: a completely open-source adaptation of the Segment Anything Model for zero-shot visual tracking! Here's why it's a game-changer: 🚫 No need for retraining or finetuning 🎯 Boosts success rate and precision 🤖 Motion-aware memory selection 💪 Zero-shot performance on diverse datasets But that's not all: 🔬 Refines mask selection 🔮 Predicts object motion effectively 📈 Gains: 7.1% AUC on LaSOT, 3.5% AO on GOT-10k 🏆 Competes with fully supervised methods without extra training Link to the GitHub repo in the next tweet! _____ Find me → Akshay 🚀 ✔️ For more insights & tutorials on AI and Machine Learning.

SAMURAI vs. MetaAI's SAM 2! Traditional visual object tracking struggles in crowded, fast-moving, or self-occluded scenes, as does SAM2. Meet SAMURAI: a completely open-source adaptation of the Segment Anything Model for zero-shot visual tracking! Here's why it's a game-changer: 🚫 No need for retraining or finetuning 🎯 Boosts success rate and precision 🤖 Motion-aware memory selection 💪 Zero-shot performance on diverse datasets But that's not all: 🔬 Refines mask selection 🔮 Predicts object motion effectively 📈 Gains: 7.1% AUC on LaSOT, 3.5% AO on GOT-10k 🏆 Competes with fully supervised methods without extra training Link to the GitHub repo in the next tweet! _____ Find me → Akshay 🚀 ✔️ For more insights & tutorials on AI and Machine Learning.

Akshay 🚀

363,328 Aufrufe • vor 1 Jahr

We are excited to announce the release of a new OpenArm teleoperation device: KER (Kinematic Equivalent Replica). This is a lightweight leader device that shares the exact same joint structure as OpenArm 2.0. KER uses only encoders for each joint, no motors, allowing for smooth and easy operation with zero resistance. Every joint maps 1:1 to OpenArm 2.0, so no coordinate transforms are needed. This release marks the completion of the OpenArm 2.0 ecosystem. KER, OpenArm, and the Cell are all open source and are ready to be used in state-of-the-art reproducible research! 🦾 #OpenArm #OpenSource #Robotics

We are excited to announce the release of a new OpenArm teleoperation device: KER (Kinematic Equivalent Replica). This is a lightweight leader device that shares the exact same joint structure as OpenArm 2.0. KER uses only encoders for each joint, no motors, allowing for smooth and easy operation with zero resistance. Every joint maps 1:1 to OpenArm 2.0, so no coordinate transforms are needed. This release marks the completion of the OpenArm 2.0 ecosystem. KER, OpenArm, and the Cell are all open source and are ready to be used in state-of-the-art reproducible research! 🦾 #OpenArm #OpenSource #Robotics

Enactic

111,520 Aufrufe • vor 26 Tagen

Open source is dead. That’s not a statement we ever thought we’d make. Cal.com was built on open source. It shaped our product, our community, and our growth. But the world has changed faster than our principles could keep up. AI has fundamentally altered the security landscape. What once required time, expertise, and intent can now be automated at scale. Code is no longer just read. It is scanned, mapped, and exploited. Near zero cost. In that world, transparency becomes exposure. Especially at scale. After a lot of deliberation, we’ve made the decision to close the core Cal.com codebase. This is not a rejection of what open source gave us. It’s a response to what risks AI is making possible. We’re still supporting builders, releasing the core code under a new MIT-licensed open source project called cal. diy for hobbyists and tinkerers, but our priority now is simple: Protecting our customers and community at all costs. This may not be the most popular call. But we believe many companies will come to the same conclusion. My full explanation below ↓

Open source is dead. That’s not a statement we ever thought we’d make. Cal.com was built on open source. It shaped our product, our community, and our growth. But the world has changed faster than our principles could keep up. AI has fundamentally altered the security landscape. What once required time, expertise, and intent can now be automated at scale. Code is no longer just read. It is scanned, mapped, and exploited. Near zero cost. In that world, transparency becomes exposure. Especially at scale. After a lot of deliberation, we’ve made the decision to close the core Cal.com codebase. This is not a rejection of what open source gave us. It’s a response to what risks AI is making possible. We’re still supporting builders, releasing the core code under a new MIT-licensed open source project called cal. diy for hobbyists and tinkerers, but our priority now is simple: Protecting our customers and community at all costs. This may not be the most popular call. But we believe many companies will come to the same conclusion. My full explanation below ↓

Bailey Pumfleet

1,561,028 Aufrufe • vor 3 Monaten

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 Aufrufe • vor 8 Monaten

🚨Architects are going to hate this. Someone just open sourced a full 3D building editor that runs entirely in your browser. No AutoCAD. No Revit. No $5,000/year licenses. It's called Pascal Editor. Built with React Three Fiber and WebGPU -- meaning it renders directly on your GPU at near-native speed. Here's what's inside this thing: → A full building/level/wall/zone hierarchy you can edit in real time → An ECS-style architecture where every object updates through GPU-powered systems → Zustand state management with full undo/redo built in → Next.js frontend so it deploys as a web app, not a desktop install → Dirty node tracking -- only re-renders what changed, not the whole scene Here's the wildest part: You can stack, explode, or solo individual building levels. Select a zone, drag a wall, reshape a slab -- all in 3D, all in the browser. Architecture firms pay $50K+ per seat for BIM software that does this workflow. This is free. 100% Open Source.

🚨Architects are going to hate this. Someone just open sourced a full 3D building editor that runs entirely in your browser. No AutoCAD. No Revit. No $5,000/year licenses. It's called Pascal Editor. Built with React Three Fiber and WebGPU -- meaning it renders directly on your GPU at near-native speed. Here's what's inside this thing: → A full building/level/wall/zone hierarchy you can edit in real time → An ECS-style architecture where every object updates through GPU-powered systems → Zustand state management with full undo/redo built in → Next.js frontend so it deploys as a web app, not a desktop install → Dirty node tracking -- only re-renders what changed, not the whole scene Here's the wildest part: You can stack, explode, or solo individual building levels. Select a zone, drag a wall, reshape a slab -- all in 3D, all in the browser. Architecture firms pay $50K+ per seat for BIM software that does this workflow. This is free. 100% Open Source.

Guri Singh

2,967,409 Aufrufe • vor 4 Monaten

Introducing Buccaneer. Ethereum's first and only private currency. Watch how it breaks a trail and funds a brand new wallet with ETH, without paying gas fees in ETH, all in under 5 minutes. Most importantly of all, this is happening completely on-chain. No L2s, no roll-ups. Just an ERC20 token and some contracts. This project has achieved what every other privacy project could only dream of, funded with just $750k years ago from a loyal, tight knit community to accomplish this. Nobody even knows this exists yet. Ethereum is no longer an open, transparent network. This protocol cannot be taken down or stopped, it lives entirely within Ethereum alongside a DApp capable of running on IPFS. This is the ultimate censorship resistance. It is impossible to stop the flow of money in and out of this. The possibilities and implications of this technology are only just beginning to be explored. The protocol is live and fully operational. Check it out: Welcome to privacy.

Buccaneer VSTARK

16,143 Aufrufe • vor 1 Jahr

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 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 2 Jahren

Brembo’s Sensify Braking System is now in series production on vehicles from a major undisclosed global automaker. Sensify is a software controlled, fluid-free brake-by-wire system developed by Brembo. It uses no brake fluid. Instead, it uses electric actuators at each wheel, AI-driven software, and a pedal simulator to deliver customizable and precise braking without hydraulic fluid or a traditional master cylinder. Each wheel has its own motor that squeezes the pads onto the rotors (with redundant windings for safety). Advantages of Sensify: • Independent electric braking at each wheel with AI for shorter stops and maximum grip. • Seamless integration with ADAS and autonomous driving systems. • Eliminates brake fluid, removing maintenance, leaks, and environmental issues. • Zero brake drag via full pad retraction, improving EV range and reduced wear. • Fully customizable pedal feel and response through software for any driving mode. • Faster than hydraulic ABS with better stability in low-grip scenarios. I wonder which car it’s going on. Brembo said annual production volumes with the manufacturer are expected to reach hundreds of thousands.

Brembo’s Sensify Braking System is now in series production on vehicles from a major undisclosed global automaker. Sensify is a software controlled, fluid-free brake-by-wire system developed by Brembo. It uses no brake fluid. Instead, it uses electric actuators at each wheel, AI-driven software, and a pedal simulator to deliver customizable and precise braking without hydraulic fluid or a traditional master cylinder. Each wheel has its own motor that squeezes the pads onto the rotors (with redundant windings for safety). Advantages of Sensify: • Independent electric braking at each wheel with AI for shorter stops and maximum grip. • Seamless integration with ADAS and autonomous driving systems. • Eliminates brake fluid, removing maintenance, leaks, and environmental issues. • Zero brake drag via full pad retraction, improving EV range and reduced wear. • Fully customizable pedal feel and response through software for any driving mode. • Faster than hydraulic ABS with better stability in low-grip scenarios. I wonder which car it’s going on. Brembo said annual production volumes with the manufacturer are expected to reach hundreds of thousands.

Nic Cruz Patane

95,168 Aufrufe • vor 2 Monaten

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.

CyrilXBT

37,120 Aufrufe • vor 2 Monaten

Open source software is GREAT. But "open source" AI is NOT like software - it's VERY different. Rob Miles cuts through the bullshit: ROB: Oh, hey, Meta. I heard Llama's weights leaked. That's rough, man. Information security's hard. How you holding up? META: Oh, we're great. Yeah, we're fine. We... actually, that was deliberate. We meant to do that. ROB MILES: Oh, really? META: Yeah... well, the second time anyway. It's called open source. Look it up. ROB MILES: Oh. Well, I love free and open source software, but do those principles really apply to network weights? How does that work? META: Open source is good for users because it lets them read the source code and see what the program is really doing and how it works. ROB MILES: Wait, have you found a way to tell how a model works by looking at its weights? META: No. But, it lets developers all over the world spot bugs in the code and submit patches. ROB: Wait, people are fixing bugs in Llama's weights? META: Well, no. People can fine tune it themselves, though. ROB: ?? Other companies offer fine tuning through APIs. ... So, hang on, if you can't actually read the code and know what it's doing, then network weights are effectively a compiled binary. So, in what sense is this open source? Why not call it like public weights? Why call it open source at all? META: I love open source. ROB: Well, I know a lot of your employees do, but you don't love anything. You're a giant corporation. What's in it for you? META: I love, love open source.

Open source software is GREAT. But "open source" AI is NOT like software - it's VERY different. Rob Miles cuts through the bullshit: ROB: Oh, hey, Meta. I heard Llama's weights leaked. That's rough, man. Information security's hard. How you holding up? META: Oh, we're great. Yeah, we're fine. We... actually, that was deliberate. We meant to do that. ROB MILES: Oh, really? META: Yeah... well, the second time anyway. It's called open source. Look it up. ROB MILES: Oh. Well, I love free and open source software, but do those principles really apply to network weights? How does that work? META: Open source is good for users because it lets them read the source code and see what the program is really doing and how it works. ROB MILES: Wait, have you found a way to tell how a model works by looking at its weights? META: No. But, it lets developers all over the world spot bugs in the code and submit patches. ROB: Wait, people are fixing bugs in Llama's weights? META: Well, no. People can fine tune it themselves, though. ROB: ?? Other companies offer fine tuning through APIs. ... So, hang on, if you can't actually read the code and know what it's doing, then network weights are effectively a compiled binary. So, in what sense is this open source? Why not call it like public weights? Why call it open source at all? META: I love open source. ROB: Well, I know a lot of your employees do, but you don't love anything. You're a giant corporation. What's in it for you? META: I love, love open source.

AI Notkilleveryoneism Memes ⏸️

107,362 Aufrufe • vor 2 Jahren

Researchers built a new RAG approach that: - does not need a vector DB. - does not embed data. - involves no chunking. - performs no similarity search. And it hit 98.7% accuracy on a financial benchmark (SOTA). Here's the core problem with RAG that this new approach solves: Traditional RAG chunks documents, embeds them into vectors, and retrieves based on semantic similarity. But similarity ≠ relevance. When you ask "What were the debt trends in 2023?", a vector search returns chunks that look similar. But the actual answer might be buried in some Appendix, referenced on some page, in a section that shares zero semantic overlap with your query. Traditional RAG would likely never find it. PageIndex (open-source) solves this. Instead of chunking and embedding, PageIndex builds a hierarchical tree structure from your documents, like an intelligent table of contents. Then it uses reasoning to traverse that tree. For instance, the model doesn't ask: "What text looks similar to this query?" Instead, it asks: "Based on this document's structure, where would a human expert look for this answer?" That's a fundamentally different approach with: - No arbitrary chunking that breaks context. - No vector DB infrastructure to maintain. - Traceable retrieval to see exactly why it chose a specific section. - The ability to see in-document references ("see Table 5.3") the way a human would. But here's the deeper issue that it solves. Vector search treats every query as independent. But documents have structure and logic, like sections that reference other sections and context that builds across pages. PageIndex respects that structure instead of flattening it into embeddings. Do note that this approach may not make sense in every use case since traditional vector search is still fast, simple, and works well for many applications. But for professional documents that require domain expertise and multi-step reasoning, this tree-based, reasoning-first approach shines. For instance, PageIndex achieved 98.7% accuracy on FinanceBench, significantly outperforming traditional vector-based RAG systems on complex financial document analysis. Everything is fully open-source, so you can see the full implementation in GitHub and try it yourself. I have shared the GitHub repo in the replies!

Researchers built a new RAG approach that: - does not need a vector DB. - does not embed data. - involves no chunking. - performs no similarity search. And it hit 98.7% accuracy on a financial benchmark (SOTA). Here's the core problem with RAG that this new approach solves: Traditional RAG chunks documents, embeds them into vectors, and retrieves based on semantic similarity. But similarity ≠ relevance. When you ask "What were the debt trends in 2023?", a vector search returns chunks that look similar. But the actual answer might be buried in some Appendix, referenced on some page, in a section that shares zero semantic overlap with your query. Traditional RAG would likely never find it. PageIndex (open-source) solves this. Instead of chunking and embedding, PageIndex builds a hierarchical tree structure from your documents, like an intelligent table of contents. Then it uses reasoning to traverse that tree. For instance, the model doesn't ask: "What text looks similar to this query?" Instead, it asks: "Based on this document's structure, where would a human expert look for this answer?" That's a fundamentally different approach with: - No arbitrary chunking that breaks context. - No vector DB infrastructure to maintain. - Traceable retrieval to see exactly why it chose a specific section. - The ability to see in-document references ("see Table 5.3") the way a human would. But here's the deeper issue that it solves. Vector search treats every query as independent. But documents have structure and logic, like sections that reference other sections and context that builds across pages. PageIndex respects that structure instead of flattening it into embeddings. Do note that this approach may not make sense in every use case since traditional vector search is still fast, simple, and works well for many applications. But for professional documents that require domain expertise and multi-step reasoning, this tree-based, reasoning-first approach shines. For instance, PageIndex achieved 98.7% accuracy on FinanceBench, significantly outperforming traditional vector-based RAG systems on complex financial document analysis. Everything is fully open-source, so you can see the full implementation in GitHub and try it yourself. I have shared the GitHub repo in the replies!

Avi Chawla

972,347 Aufrufe • vor 6 Monaten

each mac is fully controlled by a single AGENT. it can operate browsers with insane efficiency and interact directly with native mac apps. under each agent there are multiple SOTA SUBAGENTS including claude code. we can literally tell dozens of agents to post content on their own accounts simultaneously. not building a social media bot farm obviously, just a random example. the whole system is extremely human friendly. zero setup needed. no config. just works out of the box. this is designed for daily use on your personal machine. the mac mini cluster is just for testing, training, and exploring fun use cases. no download link yet. the current flowithOS beta online is a completely different thing. this new version is way more powerful with a much better experience. might open internal testing further next week.

each mac is fully controlled by a single AGENT. it can operate browsers with insane efficiency and interact directly with native mac apps. under each agent there are multiple SOTA SUBAGENTS including claude code. we can literally tell dozens of agents to post content on their own accounts simultaneously. not building a social media bot farm obviously, just a random example. the whole system is extremely human friendly. zero setup needed. no config. just works out of the box. this is designed for daily use on your personal machine. the mac mini cluster is just for testing, training, and exploring fun use cases. no download link yet. the current flowithOS beta online is a completely different thing. this new version is way more powerful with a much better experience. might open internal testing further next week.

Derek Nee

27,594 Aufrufe • vor 5 Monaten

Anthropic's most viral feature is now open-source! Until now, Anthropic's Generative UI capabilities only existed inside its own products. CopilotKit🪁 just shipped Open Generative UI, an open-source implementation of Claude Artifacts that works in any app. The agent generates HTML/SVG at runtime, and CopilotKit streams it token-by-token into a sandboxed iframe inside the app's chat. So the user can watch the UI assemble itself in real time, not after the full response is ready. The sandbox is fully isolated with no access to the parent app, the DOM, or user data. So if the agent hallucinates broken markup or unexpected JavaScript, nothing leaks outside the iframe. Under the hood, the agent does not select from pre-built components. Instead, it generates arbitrary visuals from scratch every time. The output is unconstrained by default, but you can shape it by defining prompt-based skills that teach the agent specific visual formats or guidelines. For instance, a skill prompt can guide the agent toward producing a Chart.js dashboard with proper axis labels and responsive sizing, or an interactive 3D model with rotation controls. The video below shows this in action, and the output quality you see actually comes from the skills layer. Open Generative UI runs on AG-UI, so it works out of the box with LangGraph, CrewAI, Mastra, Google ADK, AWS Strands, and more. It also ships with a standalone MCP server that plugs into Claude Code, Cursor, or any MCP-compatible client. And the entire stack is built on top of CopilotKit, the open-source frontend framework for agents and generative UI. 30k+ GitHub stars, with SDKs for React, Next.js, Angular, and Vue. I have shared the GitHub repo and a live playground in the replies!

Anthropic's most viral feature is now open-source! Until now, Anthropic's Generative UI capabilities only existed inside its own products. CopilotKit🪁 just shipped Open Generative UI, an open-source implementation of Claude Artifacts that works in any app. The agent generates HTML/SVG at runtime, and CopilotKit streams it token-by-token into a sandboxed iframe inside the app's chat. So the user can watch the UI assemble itself in real time, not after the full response is ready. The sandbox is fully isolated with no access to the parent app, the DOM, or user data. So if the agent hallucinates broken markup or unexpected JavaScript, nothing leaks outside the iframe. Under the hood, the agent does not select from pre-built components. Instead, it generates arbitrary visuals from scratch every time. The output is unconstrained by default, but you can shape it by defining prompt-based skills that teach the agent specific visual formats or guidelines. For instance, a skill prompt can guide the agent toward producing a Chart.js dashboard with proper axis labels and responsive sizing, or an interactive 3D model with rotation controls. The video below shows this in action, and the output quality you see actually comes from the skills layer. Open Generative UI runs on AG-UI, so it works out of the box with LangGraph, CrewAI, Mastra, Google ADK, AWS Strands, and more. It also ships with a standalone MCP server that plugs into Claude Code, Cursor, or any MCP-compatible client. And the entire stack is built on top of CopilotKit, the open-source frontend framework for agents and generative UI. 30k+ GitHub stars, with SDKs for React, Next.js, Angular, and Vue. I have shared the GitHub repo and a live playground in the replies!

Akshay 🚀

86,515 Aufrufe • vor 2 Monaten

lol the only way to grow on this platform now is to be included in the starter packs for new users to select. These are the accounts x selected under the meme account starter pack. There are literally only like 5 meme accounts in this starter pack. How tf is Martin Shkreli a meme account? I have noticed a few accounts that have exploded out of no where with zero viral posts. Literally have gained hundreds of thousands of followers in just a few months. All that growth came from here. While every other large account is stagnant and not growing. wtf is this app anymore

lol the only way to grow on this platform now is to be included in the starter packs for new users to select. These are the accounts x selected under the meme account starter pack. There are literally only like 5 meme accounts in this starter pack. How tf is Martin Shkreli a meme account? I have noticed a few accounts that have exploded out of no where with zero viral posts. Literally have gained hundreds of thousands of followers in just a few months. All that growth came from here. While every other large account is stagnant and not growing. wtf is this app anymore

MERICA MEMED

24,022 Aufrufe • vor 28 Tagen

🚨BREAKING: Border Patrol agents are caught on camera unlawfully stopping and threatening a U.S. citizen at a gas station, with zero legal basis. In the video, a Border Patrol agent approaches a man who is simply pumping gas into an unmarked vehicle and immediately demands, “Let me see your ID.” The man correctly responds, “I don’t have to show it to you.” Instead of stating any lawful reason for the stop, the agent escalates and asks, “How do I know you’re a U.S. citizen?” That question alone exposes the violation. The man is wearing normal clothing, committing no crime, not crossing a border, not driving, and not suspected of anything. There is no traffic stop, no reasonable suspicion, no probable cause, and no legal authority to demand proof of citizenship in this context. So, what the agent is really saying is this: If you don’t look white enough, you must prove you belong. That is racial profiling. It is unconstitutional. And it is illegal. The agent then issues a threat: “This can go two ways. I can take you in, or you show me your ID.” That is not a choice, it’s coercion. Faced with the threat of unlawful detention, the man is forced to hand over his identification, surrendering his rights under duress. This is exactly how constitutional rights are stripped away, not by law, but by intimidation. If Border Patrol can demand “proof of citizenship” from Americans at a gas station with no cause, then no one’s rights are safe.

🚨BREAKING: Border Patrol agents are caught on camera unlawfully stopping and threatening a U.S. citizen at a gas station, with zero legal basis. In the video, a Border Patrol agent approaches a man who is simply pumping gas into an unmarked vehicle and immediately demands, “Let me see your ID.” The man correctly responds, “I don’t have to show it to you.” Instead of stating any lawful reason for the stop, the agent escalates and asks, “How do I know you’re a U.S. citizen?” That question alone exposes the violation. The man is wearing normal clothing, committing no crime, not crossing a border, not driving, and not suspected of anything. There is no traffic stop, no reasonable suspicion, no probable cause, and no legal authority to demand proof of citizenship in this context. So, what the agent is really saying is this: If you don’t look white enough, you must prove you belong. That is racial profiling. It is unconstitutional. And it is illegal. The agent then issues a threat: “This can go two ways. I can take you in, or you show me your ID.” That is not a choice, it’s coercion. Faced with the threat of unlawful detention, the man is forced to hand over his identification, surrendering his rights under duress. This is exactly how constitutional rights are stripped away, not by law, but by intimidation. If Border Patrol can demand “proof of citizenship” from Americans at a gas station with no cause, then no one’s rights are safe.

Jesus Freakin Congress

1,403,350 Aufrufe • vor 6 Monaten

Introducing 🌸 Kanna – a web ui for Claude Code + Codex running right in your browser (and my first open source project!) It's sort of like if Claude Code & Codex had a baby with a much better ui/ux 💅 Here's what makes Kanna special: 🔀 One-click switch between Claude Code / Codex 🌐 Runs in your browser on localhost - no app switching 🧩 Embedded split terminals, persisted between chats with a beautiful horizontal scrolling UX 📁 Full project file browser built in, embedded editor coming soon too. Coming soon: 🔒 End-to-end encryption + remote access via reverse proxy 🌿 Git integration, diffs, PRs 🧱 Plugins I wanted to replace switching between cursor, the github app, browser & (most recently) the codex app. The browser is hardest to recreate yet also the most flexible so it makes sense to build in the browser itself. Install in 5 seconds: bun install -g kanna-code then just type: kanna Kanna is purely a ui/ux layer. It uses your existing CLIs, is 100% compliant & no data ever leaves your machine. If running claude or codex in your terminal works, kanna works too. Kanna natively supports every tool call, model, reasoning effort, fast mode, plan mode, compaction, user questions, web searching, skills, agents, mcps, everything. There are a few libraries that do this but none that I found as comprehensive. All feedback welcome!

Introducing 🌸 Kanna – a web ui for Claude Code + Codex running right in your browser (and my first open source project!) It's sort of like if Claude Code & Codex had a baby with a much better ui/ux 💅 Here's what makes Kanna special: 🔀 One-click switch between Claude Code / Codex 🌐 Runs in your browser on localhost - no app switching 🧩 Embedded split terminals, persisted between chats with a beautiful horizontal scrolling UX 📁 Full project file browser built in, embedded editor coming soon too. Coming soon: 🔒 End-to-end encryption + remote access via reverse proxy 🌿 Git integration, diffs, PRs 🧱 Plugins I wanted to replace switching between cursor, the github app, browser & (most recently) the codex app. The browser is hardest to recreate yet also the most flexible so it makes sense to build in the browser itself. Install in 5 seconds: bun install -g kanna-code then just type: kanna Kanna is purely a ui/ux layer. It uses your existing CLIs, is 100% compliant & no data ever leaves your machine. If running claude or codex in your terminal works, kanna works too. Kanna natively supports every tool call, model, reasoning effort, fast mode, plan mode, compaction, user questions, web searching, skills, agents, mcps, everything. There are a few libraries that do this but none that I found as comprehensive. All feedback welcome!

Superdaddy

43,285 Aufrufe • vor 4 Monaten