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I built an open-source analytics platform that replaces your $50/mo subscription. Self-hosted. SQLite. Deploy on a $4 VPS in 5 minutes. 📊 Real-time visitor tracking 💳 Stripe conversion tracking (no webhooks) 🤝 Built-in affiliate system 🌍 Cloudflare geo data 🗄️ Zero config database No monthly fees. No data sharing.... show more

Mohd Danish

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279,337 views • 4 months ago •via X (Twitter)

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self.dll

47,511 views • 2 months ago

9 repos that mass replace a $150,000/year NBA analytics department. all free. all open source. -> replaces Second Spectrum and SportVU YOLO tracks every player and ball from any broadcast. assigns teams by jersey color. court keypoint detection builds a tactical top-down map. speed, distance, passes - all from a TV feed. no sensors. -> replaces paid NBA prediction services ($100/mo) XGBoost + Neural Net. moneyline and totals. Kelly Criterion sizing. 69% accuracy. pulls odds from FanDuel/DraftKings automatically. the most starred NBA betting repo on GitHub. -> replaces an entire quant sports desk 5-model ensemble: XGBoost + PyTorch MLP + Ridge + Lasso + baseline. Optuna-tuned hyperparameters. SQLite database with box scores, play-by-play, betting lines, injury reports. production-grade. -> replaces manual daily prediction workflows XGBoost/LightGBM with GitHub Actions automation. scrapes new data, retrains models, outputs daily win probabilities. set it and forget it. -> replaces ELO subscription services custom ELO + Ridge + XGBoost + Neural Networks ensemble. full data scraping pipeline. comprehensive visualizations. FiveThirtyEight-style ratings from scratch. -> replaces Four Factors analytics dashboards ELO rating system + Four Factors + PCA dimensionality reduction. detailed comparison of 10+ models. honest 65.3% accuracy - because that's what real NBA prediction looks like. -> replaces computer vision analytics platforms ($500/mo) YOLO player/ball tracking. automatic team assignment. court keypoints. pass and interception detection. speed and distance. full tactical view. modular architecture. -> replaces shot tracking hardware YOLOv8 detects ball and hoop in real-time. linear regression predicts trajectory. registers makes and misses automatically. works on any video feed. -> replaces paid sports data subscriptions ($300/mo) official Python client for NBA. com API. box scores, play-by-play, shot charts, player tracking. 40+ years of data. zero cost. the foundation every NBA ML project is built on. like + bookmark you'll need this when you build your first NBA prediction bot

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zostaff

102,941 views • 2 months ago

Meet Cabinet: Your KB + AI Team on Disk🗄️ for the longest time I’ve been frustrated that LLMs are stateless and scattered. every chat starts from zero. no memory, no team, nothing that persists. So I built Cabinet: your AI team on disk 📁 ├─ 📝 .kb/ - CSVs, notes, webapps, everything) ├─ ⏰ .jobs/ - tasks & routines └─ 🧠 .agents/ dump your data, bring your own AI, and it researches, drafts, and ships while you sleep. no db. no lock-in. just files you own.

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Hila Shmuel

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Fabriq

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Code With Antonio

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Mario Nawfal

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Crypto Picsou

87,609 views • 5 months ago

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zostaff

222,146 views • 2 months ago

I built a self-hosted Sentry clone that runs entirely on Cloudflare Workers, and I think it showcases one of the most underrated features in the Cloudflare ecosystem: Service Bindings. Let me explain why this matters. When you have multiple Cloudflare Workers (an API, a webhook handler, a cron job), they all need common things: error tracking, authentication, rate limiting, metrics. The typical solution? External HTTP calls to third-party services. That means: - 50-200ms latency per call - Egress fees - Your data leaving your infrastructure - Another vendor to manage Service bindings let Workers call each other directly inside Cloudflare's network. No HTTP. No internet. Just internal RPC with <5ms latency. With Workers Sentinel, any Worker in my account can just point Sentry-SDK into the Service binding, and have all errors flow into one centralized dashboard, stored in Durable Objects with SQLite. No external calls. No added latency. Service bindings aren't just for error tracking. You can centralize: 🔐 Authentication — One Worker that validates tokens for all your services 📊 Metrics — Centralized collection without external observability costs 🚦 Rate Limiting — Shared counters that actually work across Workers 🚩 Feature Flags — Instant propagation, no deployment needed Think of it as building your own internal microservices mesh, but at the edge, with zero network overhead. Workers Sentinel uses two Durable Objects: - AuthState (singleton) — users, sessions, projects - ProjectState (per-project) — issues, events, stats Events are fingerprinted and grouped intelligently. The dashboard is a Vue.js app served from the same Worker. I could say i built this to learn Durable Objects or that I needed error tracking for side projects, but honestly I just need a way to show my wife why I'm sending $200/month to some guy named Claudio who apparently helps me write code. The whole thing is open source. Deploy it to your Cloudflare account, point your Sentry SDKs at it, and you're done. But more importantly: take a closer look at service bindings. They're the glue that turns a collection of Workers into an actual platform. Most Cloudflare customers I talk to aren't using them, and they're missing out. To the Sentry team: I love your work. Genuinely. Sentry is battle-tested, has incredible features, and is what you should use for anything that matters. This project is a toy. A learning exercise. A weekend hack that got slightly out of hand. Please do not trust your production errors to this dummy clone. If your startup goes down at 3 AM because Workers Sentinel missed an edge case, that's on you. I warned you. Use the real thing. But if you want to learn about Durable Objects, service bindings, and how error tracking works under the hood? Clone away. Your Workers shouldn't be islands. Connect them.

I built a self-hosted Sentry clone that runs entirely on Cloudflare Workers, and I think it showcases one of the most underrated features in the Cloudflare ecosystem: Service Bindings. Let me explain why this matters. When you have multiple Cloudflare Workers (an API, a webhook handler, a cron job), they all need common things: error tracking, authentication, rate limiting, metrics. The typical solution? External HTTP calls to third-party services. That means: - 50-200ms latency per call - Egress fees - Your data leaving your infrastructure - Another vendor to manage Service bindings let Workers call each other directly inside Cloudflare's network. No HTTP. No internet. Just internal RPC with <5ms latency. With Workers Sentinel, any Worker in my account can just point Sentry-SDK into the Service binding, and have all errors flow into one centralized dashboard, stored in Durable Objects with SQLite. No external calls. No added latency. Service bindings aren't just for error tracking. You can centralize: 🔐 Authentication — One Worker that validates tokens for all your services 📊 Metrics — Centralized collection without external observability costs 🚦 Rate Limiting — Shared counters that actually work across Workers 🚩 Feature Flags — Instant propagation, no deployment needed Think of it as building your own internal microservices mesh, but at the edge, with zero network overhead. Workers Sentinel uses two Durable Objects: - AuthState (singleton) — users, sessions, projects - ProjectState (per-project) — issues, events, stats Events are fingerprinted and grouped intelligently. The dashboard is a Vue.js app served from the same Worker. I could say i built this to learn Durable Objects or that I needed error tracking for side projects, but honestly I just need a way to show my wife why I'm sending $200/month to some guy named Claudio who apparently helps me write code. The whole thing is open source. Deploy it to your Cloudflare account, point your Sentry SDKs at it, and you're done. But more importantly: take a closer look at service bindings. They're the glue that turns a collection of Workers into an actual platform. Most Cloudflare customers I talk to aren't using them, and they're missing out. To the Sentry team: I love your work. Genuinely. Sentry is battle-tested, has incredible features, and is what you should use for anything that matters. This project is a toy. A learning exercise. A weekend hack that got slightly out of hand. Please do not trust your production errors to this dummy clone. If your startup goes down at 3 AM because Workers Sentinel missed an edge case, that's on you. I warned you. Use the real thing. But if you want to learn about Durable Objects, service bindings, and how error tracking works under the hood? Clone away. Your Workers shouldn't be islands. Connect them.

Gabriel Massadas

28,656 views • 6 months ago

OpenClaw is the fastest growing open-source project in history, but it can be a pain to set up. We (Aethir ) built Aethir Claw so you can deploy your AI agent on our decentralized VPS infrastructure in minutes. No DevOps degree required. Hit play. I'll walk you through the full onboarding, start to finish in under 5 mins! This is just the beginning...

OpenClaw is the fastest growing open-source project in history, but it can be a pain to set up. We (Aethir ) built Aethir Claw so you can deploy your AI agent on our decentralized VPS infrastructure in minutes. No DevOps degree required. Hit play. I'll walk you through the full onboarding, start to finish in under 5 mins! This is just the beginning...

Mark

43,032 views • 4 months ago

I just added payments to my app... by typing one simple prompt. Replit's new Stripe integration is a game-changer plug in payments instantly no setup hassle. Turn any idea into a revenue-generating product in minutes: subscriptions, storefronts, paid tools all handled automatically with a sandbox for safe testing. Anyone can build and monetize now. Here's a quick step-by-step to get started ↓ 1. Head to Replit and create a new workspace it's your all-in-one spot for building. 2. Fire up Agent in your workspace this is where the magic happens. Prompt something like: "Add Stripe payments for subscriptions" or "Integrate one-time payments with Stripe." Agent sets it up automatically: handles the connection, data models and flow. No keys or dashboards needed. 3. Test in the built-in sandbox run payments safely see transactions hit your database, no real money involved. 4. Ready to launch? Connect your own Stripe account to go live seamless switch from test to revenue. That's it idea to income in minutes. My referral (get $10 credit on Replit Core):

I just added payments to my app... by typing one simple prompt. Replit's new Stripe integration is a game-changer plug in payments instantly no setup hassle. Turn any idea into a revenue-generating product in minutes: subscriptions, storefronts, paid tools all handled automatically with a sandbox for safe testing. Anyone can build and monetize now. Here's a quick step-by-step to get started ↓ 1. Head to Replit and create a new workspace it's your all-in-one spot for building. 2. Fire up Agent in your workspace this is where the magic happens. Prompt something like: "Add Stripe payments for subscriptions" or "Integrate one-time payments with Stripe." Agent sets it up automatically: handles the connection, data models and flow. No keys or dashboards needed. 3. Test in the built-in sandbox run payments safely see transactions hit your database, no real money involved. 4. Ready to launch? Connect your own Stripe account to go live seamless switch from test to revenue. That's it idea to income in minutes. My referral (get $10 credit on Replit Core):

Meer | AI Tools & News

17,593 views • 7 months ago

7 repos that mass replace a $50,000/year sports analytics department. all free. all open source. -> replaces Hawkeye-level court analysis YOLO tracks players and ball from any broadcast. ResNet50 extracts court keypoints. homography converts pixels to real meters. speed, position, aggression - all from a TV feed. -> replaces paid sports data subscriptions ($500/mo) every ATP match since 1968. rankings, results, stats. 1.5K stars. the holy grail dataset that every tennis ML project is built on. -> replaces point-level data feeds ($200/mo) point-by-point data for every Grand Slam since 2011. the kind of granularity you need for live Bayesian models. -> replaces shot-by-shot scouting reports 5,000+ matches charted shot by shot. direction, depth, error type. crowdsourced and free. -> replaces pre-match and in-match prediction services ELO + serve/return stats → win probability. updates during the match. exactly what a live Bayesian engine needs. -> replaces ball trajectory prediction tools CV analysis + CatBoost bounce prediction + separate court detector neural net. most advanced open-source tennis CV pipeline. -> replaces traditional bookmaker APIs Polymarket CLOB API. real-time share prices, orderbook depth, bid/ask spreads. no margin, no bookmaker - just the crowd. trade positions mid-match, not just pre-match. total before: $50K/year sports analytics stack total now: $0 like + bookmark you'll need this when you build your first tennis bot

7 repos that mass replace a $50,000/year sports analytics department. all free. all open source. -> replaces Hawkeye-level court analysis YOLO tracks players and ball from any broadcast. ResNet50 extracts court keypoints. homography converts pixels to real meters. speed, position, aggression - all from a TV feed. -> replaces paid sports data subscriptions ($500/mo) every ATP match since 1968. rankings, results, stats. 1.5K stars. the holy grail dataset that every tennis ML project is built on. -> replaces point-level data feeds ($200/mo) point-by-point data for every Grand Slam since 2011. the kind of granularity you need for live Bayesian models. -> replaces shot-by-shot scouting reports 5,000+ matches charted shot by shot. direction, depth, error type. crowdsourced and free. -> replaces pre-match and in-match prediction services ELO + serve/return stats → win probability. updates during the match. exactly what a live Bayesian engine needs. -> replaces ball trajectory prediction tools CV analysis + CatBoost bounce prediction + separate court detector neural net. most advanced open-source tennis CV pipeline. -> replaces traditional bookmaker APIs Polymarket CLOB API. real-time share prices, orderbook depth, bid/ask spreads. no margin, no bookmaker - just the crowd. trade positions mid-match, not just pre-match. total before: $50K/year sports analytics stack total now: $0 like + bookmark you'll need this when you build your first tennis bot

zostaff

35,722 views • 3 months ago

APPLE JUST SHOWED HOW TO RUN 10 AI AGENTS LOCALLY ON MAC - NO CLOUD, NO API KEYS, ZERO COSTS 00:10 Apple engineer says: "your data stays on your device, AI available anywhere at any time, zero usage costs" agent reads your code, checks GitHub, finds what needs attention and writes a report - all on your Mac, nothing goes to the internet 10 agents work simultaneously - one writes code, another tests, third fixes bugs - parallel with no queue built a full iPad app from scratch in 2 minutes, fixed its own mistakes and compiled without issues takes 5 minutes to set up - and you never pay again for a bot that runs 24/7

APPLE JUST SHOWED HOW TO RUN 10 AI AGENTS LOCALLY ON MAC - NO CLOUD, NO API KEYS, ZERO COSTS 00:10 Apple engineer says: "your data stays on your device, AI available anywhere at any time, zero usage costs" agent reads your code, checks GitHub, finds what needs attention and writes a report - all on your Mac, nothing goes to the internet 10 agents work simultaneously - one writes code, another tests, third fixes bugs - parallel with no queue built a full iPad app from scratch in 2 minutes, fixed its own mistakes and compiled without issues takes 5 minutes to set up - and you never pay again for a bot that runs 24/7

Noisy

524,181 views • 1 month ago

𝗚𝗲𝗺𝗺𝗮 𝟰 + 𝗢𝗹𝗹𝗮𝗺𝗮 + 𝗢𝗽𝗲𝗻𝗖𝗹𝗮𝘄 𝗶𝘀 𝗮 𝗳𝘂𝗹𝗹𝘆 𝗹𝗼𝗰𝗮𝗹 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝘁𝗵𝗮𝘁 𝗿𝘂𝗻𝘀 𝗳𝗿𝗲𝗲 𝗳𝗼𝗿𝗲𝘃𝗲𝗿. #3 open model in the world. Runs on your laptop. Zero API costs. Zero data leaving your machine. Setup in 3 steps: → Install Ollama and run "ollama pull gemma4" — downloads the model → Install OpenClaw and select Ollama as your provider → Connect Telegram — message your agent from anywhere, completely local One prompt built a working SEO calculator — HTML, CSS, JavaScript, no debugging. Coding benchmark jumped from 110 to 2,150 vs the previous Gemma. Apache 2.0. No restrictions. No monthly bill. No surprises. Want the full setup guide? DM me.

𝗚𝗲𝗺𝗺𝗮 𝟰 + 𝗢𝗹𝗹𝗮𝗺𝗮 + 𝗢𝗽𝗲𝗻𝗖𝗹𝗮𝘄 𝗶𝘀 𝗮 𝗳𝘂𝗹𝗹𝘆 𝗹𝗼𝗰𝗮𝗹 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝘁𝗵𝗮𝘁 𝗿𝘂𝗻𝘀 𝗳𝗿𝗲𝗲 𝗳𝗼𝗿𝗲𝘃𝗲𝗿. #3 open model in the world. Runs on your laptop. Zero API costs. Zero data leaving your machine. Setup in 3 steps: → Install Ollama and run "ollama pull gemma4" — downloads the model → Install OpenClaw and select Ollama as your provider → Connect Telegram — message your agent from anywhere, completely local One prompt built a working SEO calculator — HTML, CSS, JavaScript, no debugging. Coding benchmark jumped from 110 to 2,150 vs the previous Gemma. Apache 2.0. No restrictions. No monthly bill. No surprises. Want the full setup guide? DM me.

Julian Goldie SEO

76,908 views • 3 months ago

We deployed a fully private AI agent on NuNet in under 5 minutes 🚀 OpenClaw🦞 running Qwen through ollama , one of the hottest open source model families right now, entirely on decentralized compute. No cloud. No API keys. No data leaving the machine. This is what private AI looks like when you actually build it instead of just talking about it. Your model. Your hardware. Your rules. Full walkthrough showing exactly how it works: What should we deploy next?

We deployed a fully private AI agent on NuNet in under 5 minutes 🚀 OpenClaw🦞 running Qwen through ollama , one of the hottest open source model families right now, entirely on decentralized compute. No cloud. No API keys. No data leaving the machine. This is what private AI looks like when you actually build it instead of just talking about it. Your model. Your hardware. Your rules. Full walkthrough showing exactly how it works: What should we deploy next?

NuNet 🌐

87,581 views • 4 months ago

I am very excited to release GeoLibre v1.0 🌍 GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. In this video, I walk through GeoLibre 1.0 step by step: how to install and run it, load vector and raster data, stream gigabyte-scale cloud-native datasets without downloading them, edit attributes and geometry, work with PMTiles, 3D tiles, LiDAR point clouds, and DuckDB, and share your projects as static links. GeoLibre is built on MapLibre GL JS, React + TypeScript, Tauri (Rust), DuckDB, and Try it: GitHub: Full video tutorial: If you have feature requests or run into bugs, leave a comment or open an issue on GitHub. And if you find GeoLibre useful, please give it a star. #GIS #OpenSource #Geospatial #MapLibre #Python #DataVisualization #GeoLibre

I am very excited to release GeoLibre v1.0 🌍 GeoLibre is a free and open-source, lightweight, cloud-native GIS platform for visualizing, exploring, and analyzing geospatial data. One application that runs everywhere: in your web browser, as a native desktop app, on your phone, and inside a Jupyter notebook. No account, no server, no cost. Everything runs locally and your data stays private. In this video, I walk through GeoLibre 1.0 step by step: how to install and run it, load vector and raster data, stream gigabyte-scale cloud-native datasets without downloading them, edit attributes and geometry, work with PMTiles, 3D tiles, LiDAR point clouds, and DuckDB, and share your projects as static links. GeoLibre is built on MapLibre GL JS, React + TypeScript, Tauri (Rust), DuckDB, and Try it: GitHub: Full video tutorial: If you have feature requests or run into bugs, leave a comment or open an issue on GitHub. And if you find GeoLibre useful, please give it a star. #GIS #OpenSource #Geospatial #MapLibre #Python #DataVisualization #GeoLibre

Qiusheng Wu

39,312 views • 1 month ago

The corporate system wants you trading time for a paycheck. The alternative is building automated leverage. You do not need a team of engineers. You just need the right open-source architecture. Here are 10 GitHub repos to automate your workflows, replace manual labor, and direct your own reality: 1. n8n Bypass expensive SaaS subscriptions. Build custom AI automation workflows that run on your own servers. 2. Ollama Stop sending your private data to massive API providers. Run heavy AI models locally on your own machine. Complete privacy. 3. Open Interpreter Let language models control your computer. Automate the repetitive corporate tasks they pay you to do manually. 4. Aider An AI pair programmer that lives in your terminal. Stop writing boilerplate code and focus strictly on the architecture. 5. Dify An open-source LLM app development platform. Build and deploy functional AI agents in minutes, not months. 6. Flowise A drag-and-drop UI to build customized LLM flows. You do not need to be a senior developer to build massive leverage. 7. Supabase Spin up a Postgres database, authentication, and instant APIs. Own your backend entirely. 8. Auto-GPT Give an AI an objective and let it execute. It browses the web, writes code, and chains thoughts together autonomously. 9. Outline An open-source knowledge base for your personal leverage. Stop losing your documentation in arbitrary corporate systems. 10. NocoDB Turn any database into a smart spreadsheet. Keep your data on your own infrastructure and stop paying for convenience. The secret to tech survival? Stop playing by their rules. Build your own systems and take your leverage with you.

The corporate system wants you trading time for a paycheck. The alternative is building automated leverage. You do not need a team of engineers. You just need the right open-source architecture. Here are 10 GitHub repos to automate your workflows, replace manual labor, and direct your own reality: 1. n8n Bypass expensive SaaS subscriptions. Build custom AI automation workflows that run on your own servers. 2. Ollama Stop sending your private data to massive API providers. Run heavy AI models locally on your own machine. Complete privacy. 3. Open Interpreter Let language models control your computer. Automate the repetitive corporate tasks they pay you to do manually. 4. Aider An AI pair programmer that lives in your terminal. Stop writing boilerplate code and focus strictly on the architecture. 5. Dify An open-source LLM app development platform. Build and deploy functional AI agents in minutes, not months. 6. Flowise A drag-and-drop UI to build customized LLM flows. You do not need to be a senior developer to build massive leverage. 7. Supabase Spin up a Postgres database, authentication, and instant APIs. Own your backend entirely. 8. Auto-GPT Give an AI an objective and let it execute. It browses the web, writes code, and chains thoughts together autonomously. 9. Outline An open-source knowledge base for your personal leverage. Stop losing your documentation in arbitrary corporate systems. 10. NocoDB Turn any database into a smart spreadsheet. Keep your data on your own infrastructure and stop paying for convenience. The secret to tech survival? Stop playing by their rules. Build your own systems and take your leverage with you.

Katyayani Shukla

16,964 views • 3 months ago