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Bypass Framer's 1GB Bandwidth Limit 🤔 Deploy on Vercel or maybe even Cloudflare. Use Framer free forever with mix of Reverse Proxy + Caching :> Github Repo below ;)

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Arjun Aditya

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22,271 views • 4 months ago •via X (Twitter)

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I’m reverse-engineering the Google AI mouse pointer and making it Open Source! It sees your screen. You can freeform paint anything and ask AI to modify it. It understands the app or window you mean. It can click, type, edit, navigate, and even write + execute code. A tiny cursor becoming a real computer-use agent. Made with the help of Cua which is an AMAZING reference for computer use Sorry Google DeepMind but I had to do this heyclicky Started a revolution and we will be the ones to win Also credits to Omkar Satpute for the help Feel free to modify it , update it and make it your own, we gotta beat them at this. Link to the repo in the comments:

Milind S

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How to use 50+ API keys (models) for FREE on OpenClaw API??? - go to - login or register your account - click on "more models" - click on "use case" and select what you need it for - choose the model and open it - click on "view code" → "Generate API key" many models don't allow direct deploy, so use the "view code" button to generate API access basically Nvidia NIM gives you the ability to test almost any model from their list for FREE some of them are not worse than GPT 5.2 or Claude Opus 4.6, some might even perform better depending on the task how to understand if a model is efficient and compare it with others??? - go to - type the model name in search - click on "benchmarks" - you’ll see performance tests and rankings this way you can easily compare free models with paid ones of course there are RPM limits, on many models it’s around ~40 requests per minute each model is different, after generating the API key, RPM limits are shown in the top-right corner nothing stops you from using them, many work perfectly fine, super solid option for first tests and for learning OpenClaw or any other system where you need an AI API model

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58,846 views • 5 months ago

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Built something with the goal of not leaving anyone behind. Now need some testers for this soft-launch. Check it out at Build whatever you can think of. Don't have any ideas? Just talk with your new team. Designed to get you away from the screens, not glued to them. Works great on mobile. Full real world capabilities. The free tier is perfect for people looking to get into AI and learn what its all about. The 'Empir3 Bridge' is for more advanced users and can power the whole empir3 team with your AI subscriptions... Grok, Claude, OpenAI, Google, or your local LLM. Everything runs local making it TOS friendly. Make them all work together in that same session. Fully opensource on GitHub You can use it with or without empir3. Full MCP, just point your AI at that repo and it will set it up. MIT license. New features weekly. *limited space and time until providers give me more channels.

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22,447 views • 4 days ago

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ALRIGHT! Introducing SEEDTHREE! A Three.js webgpu procedural plant generator. I'm releasing this fully open source. I built this with Claude Fable and GPT-5.5 Codex + gptimage2 over just the past few days. The best part is: given the code, your agents can add new plants to it all you want! They just need an understanding of the code and an image generator capable of creating material maps. I highly encourage you to do PRs for any extra plants you add! I'll be adding more myself in future versions. The system is fully robust: it provides complete LOD and billboards/imposters generation for the plants and you can adjust the detail levels all you want for different platform targets like mobile and/or use lower LODs as your main for extreme optimization. As this is the first release alpha version, some bugs may have been missed, I encourage you to report any bugs. The repo includes all the assets for the scenery world too, even the ambient sounds of birds and wind! This is targeted at high end laptop and PC use, it requires baking generated geometry, so you won't be using this on a phone (I mean, feel free to try, but I doubt it'll run well, lol). This is fundamentally an asset generation tool for 3d dev. Have fun! Github repo link and live demo (you don't even have to download anything for basic non-editing/contributing use!) below ⤵️

Utah teapot 🫖

29,458 views • 1 month ago

introducing OpenBench v1, an open framework for measuring AI performance and efficiency for your codebase and use case. Companies are realizing that you can't simply tokenmaxx, and are frantically looking for better ways to use and measure AI use and efficiency. One approach is evidenced by the rise in model routers: OpenRouter, Cloudflare, Databricks, Vercel, and now Ramp to name a few. But they'll also need the ability to evaluate how agents are performing in their actual use cases and codebase. A good example is DoorDash's recent evals on their codebase and prs. This will become even more important as companies explore different model + harness combinations. OpenBench will be built to this direction, focusing on the cross between correctness vs efficiency in both token use and latency. Starting off, OpenBench makes it easy for anyone to add to the task set, and run a variety of harness + models. The framework also makes it easy to add any custom harness variant, for example I've been testing codex variants with ablations against the stock codex harness. OpenBench will also have tooling to help with discovering/adding reliable tasks from your repo. Today I have integrated harnesses: codex, claude, cursor, devin, grok build, pi, and eval'ed them for different models like gpt 5.6-sol, GLM 5.2, Kimi K3, Grok 4.5, etc. measuring correctness %, token use (in/out/cache), and latency.
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Matthew Lam

121,718 views • 22 days ago

The number one question I get in the Claude Code / Cowork Community: "how do I share my Cowork skills with my team?" Here's the problem. You build a great skill. You zip it up. You drop it in Slack. Your teammate downloads it, uploads it, and maybe it works. Maybe they upload it wrong. Maybe you update the skill next week and nobody gets the new version. You're now maintaining skills through chat messages and hoping for the best. That doesn't scale. I just put out a video breaking down the three methods I've tested for sharing skills and plugins across a team. From dead simple to fully synced. Method 1: Shared drive (Google Drive, SharePoint, etc). You put your skill files in a shared folder. Teammates download and upload them into Cowork. It works, but updates are manual and there's no version control. Method 2: Built-in sharing on Team and Enterprise plans. You can share any skill directly with a colleague or publish it to your org directory. When you update the skill, everyone gets the update automatically. This is the easiest path if you're on a paid plan. The catch: there's no approval workflow for org-wide sharing, so set a clear owner. Method 3: GitHub repo. This is what I use. Your entire Cowork workspace -- skills, plugins, claude.md, folder structure, project files -- lives in a private repo. Teammates clone it. When you push an update, they pull it. Everyone stays in sync. You get version history, access control, and a single source of truth. The GitHub method sounds technical, but it's really just two steps: clone the repo, point Cowork at the folder. I walk through the whole thing in the video, including how to use .gitignore to keep personal files (like your morning briefing) out of the shared repo. This works for Cowork, Claude Code, and Open Codex. The infrastructure is the same. Full video linked below. If you've found a different approach that works for your team, I want to hear about it. Comment or reply and let's figure out the best practices together.
0:51

The number one question I get in the Claude Code / Cowork Community: "how do I share my Cowork skills with my team?" Here's the problem. You build a great skill. You zip it up. You drop it in Slack. Your teammate downloads it, uploads it, and maybe it works. Maybe they upload it wrong. Maybe you update the skill next week and nobody gets the new version. You're now maintaining skills through chat messages and hoping for the best. That doesn't scale. I just put out a video breaking down the three methods I've tested for sharing skills and plugins across a team. From dead simple to fully synced. Method 1: Shared drive (Google Drive, SharePoint, etc). You put your skill files in a shared folder. Teammates download and upload them into Cowork. It works, but updates are manual and there's no version control. Method 2: Built-in sharing on Team and Enterprise plans. You can share any skill directly with a colleague or publish it to your org directory. When you update the skill, everyone gets the update automatically. This is the easiest path if you're on a paid plan. The catch: there's no approval workflow for org-wide sharing, so set a clear owner. Method 3: GitHub repo. This is what I use. Your entire Cowork workspace -- skills, plugins, claude.md, folder structure, project files -- lives in a private repo. Teammates clone it. When you push an update, they pull it. Everyone stays in sync. You get version history, access control, and a single source of truth. The GitHub method sounds technical, but it's really just two steps: clone the repo, point Cowork at the folder. I walk through the whole thing in the video, including how to use .gitignore to keep personal files (like your morning briefing) out of the shared repo. This works for Cowork, Claude Code, and Open Codex. The infrastructure is the same. Full video linked below. If you've found a different approach that works for your team, I want to hear about it. Comment or reply and let's figure out the best practices together.

JJ Englert

16,176 views • 4 months ago

🚨 THIS 100% FREE GITHUB REPO JUST MADE EVERY AI NOTE-TAKING APP POINTLESS. Drop any source in article, PDF, YouTube transcript, or Journal entry. Claude reads it, links it to everything in your vault, and files it into a connected knowledge graph on your own machine. Most tools save your notes. This one connects them. Every source gets linked to everything you already know. Karpathy described the idea - 15 skills. Four vault methods. Works with Claude Code, Codex, and Gemini. Most people have years of knowledge buried in folders they open twice a year. This turns all of it into a second brain that works while you sleep. MY ARTICLE COVERS THE EXACT METHOD I BUILT TO DO THE SAME THING. Guide below!
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🚨 THIS 100% FREE GITHUB REPO JUST MADE EVERY AI NOTE-TAKING APP POINTLESS. Drop any source in article, PDF, YouTube transcript, or Journal entry. Claude reads it, links it to everything in your vault, and files it into a connected knowledge graph on your own machine. Most tools save your notes. This one connects them. Every source gets linked to everything you already know. Karpathy described the idea - 15 skills. Four vault methods. Works with Claude Code, Codex, and Gemini. Most people have years of knowledge buried in folders they open twice a year. This turns all of it into a second brain that works while you sleep. MY ARTICLE COVERS THE EXACT METHOD I BUILT TO DO THE SAME THING. Guide below!

Kanika

38,795 views • 8 days ago

I built a mobile app to check Paddle revenue (because they don't have one): 👉 - Use your Paddle API key (read-only and scoped) - Live data with beautiful and useful graphs built with native Swift UI. - Multi-account supported, unified revenue metrics. - Data stay on device, no server (api requests are sent directly from your phone) - Home widgets - I made it free to download on App Store (once it's approved) - Buy the source code for $19 and customize it however you want (save 5hrs of prompting if you try to do it yourself). Some interesting facts about this side project: - I vibe coded with 100% claude code remotely on my Mac Mini (with my AI assistant setup) in less than 24 hours. - I have read 0 line of code in this project and never opened Xcode myself. - My AI assistant designed the app with GPT Image 2, built the app with Swift UI, test it on simulator (via screenshots), send the test build to TestFlight for me to test, and invited me to the app store connect account so I can test on my phone, then the AI submitted the app to App Store and currently waiting for approval. - For the website, I ask it to come up with a domain name, I bought it via manually and give it access via Cloudflare API, the AI design and create a static website with GitHub, test it with lighthouse CLI, deploy via GitHub pages, config the domain DNS, deploy the website. - Then I sign up an account with Polar payment, create an API key and ask the AI to setup a store, add payment, link with the account, and add the payment to the website. The entire process happened in the last 24 hours with me only talking to the AI via Telegram. This is such a fun side project not only to create an app that I wish exists, but also to push the limit of what I can use AI for, and so far I'm very impressed. I'll create so much more apps! It feels like I have unlocked a super power.
0:33

I built a mobile app to check Paddle revenue (because they don't have one): 👉 - Use your Paddle API key (read-only and scoped) - Live data with beautiful and useful graphs built with native Swift UI. - Multi-account supported, unified revenue metrics. - Data stay on device, no server (api requests are sent directly from your phone) - Home widgets - I made it free to download on App Store (once it's approved) - Buy the source code for $19 and customize it however you want (save 5hrs of prompting if you try to do it yourself). Some interesting facts about this side project: - I vibe coded with 100% claude code remotely on my Mac Mini (with my AI assistant setup) in less than 24 hours. - I have read 0 line of code in this project and never opened Xcode myself. - My AI assistant designed the app with GPT Image 2, built the app with Swift UI, test it on simulator (via screenshots), send the test build to TestFlight for me to test, and invited me to the app store connect account so I can test on my phone, then the AI submitted the app to App Store and currently waiting for approval. - For the website, I ask it to come up with a domain name, I bought it via manually and give it access via Cloudflare API, the AI design and create a static website with GitHub, test it with lighthouse CLI, deploy via GitHub pages, config the domain DNS, deploy the website. - Then I sign up an account with Polar payment, create an API key and ask the AI to setup a store, add payment, link with the account, and add the payment to the website. The entire process happened in the last 24 hours with me only talking to the AI via Telegram. This is such a fun side project not only to create an app that I wish exists, but also to push the limit of what I can use AI for, and so far I'm very impressed. I'll create so much more apps! It feels like I have unlocked a super power.

Tony Dinh

43,922 views • 2 months ago

CLAUDE + THIS GITHUB IS FUCK*NG DANGEROUS. Threw in $10. This bot turned it into $33,000. I didn’t do anything just dropped this GitHub repo into Claude. This open source project is completely free (link below). Spent a couple of hours in Claude Code cleaning it up, understanding how it works, and making a few tweaks. Ran it. Thought it was just another scam, but decided to try anyway since it’s free. After two months of tweaking, I got it working. My bot made over $30,000. Here’s proof [ Now I just leave the logs open on my second monitor and watch it execute trade after trade. The key is in the timing. The bot waits until the last two minutes of every 5-minute BTC round. By that point, Bitcoin has already made most of its move, and the direction is usually pretty clearly defined. At that moment, it enters with the trend, buying contracts priced from $0.80 to $0.99. A few minutes later, if the move continues, the position gets liquidated at $1. The logic is strangely simple: → Wait until about 2 minutes are left before the round closes → Check that BTC has already moved $70–100 during this interval → Always operate with the move, never against it → Hedge only a small portion of the position if the market is too unbalanced (e.g., 95/5) It’s like buying a lottery ticket when the result is already practically announced. The code is public, and anyone can use it on their PC GitHub below 👇🏼
0:21

CLAUDE + THIS GITHUB IS FUCK*NG DANGEROUS. Threw in $10. This bot turned it into $33,000. I didn’t do anything just dropped this GitHub repo into Claude. This open source project is completely free (link below). Spent a couple of hours in Claude Code cleaning it up, understanding how it works, and making a few tweaks. Ran it. Thought it was just another scam, but decided to try anyway since it’s free. After two months of tweaking, I got it working. My bot made over $30,000. Here’s proof [ Now I just leave the logs open on my second monitor and watch it execute trade after trade. The key is in the timing. The bot waits until the last two minutes of every 5-minute BTC round. By that point, Bitcoin has already made most of its move, and the direction is usually pretty clearly defined. At that moment, it enters with the trend, buying contracts priced from $0.80 to $0.99. A few minutes later, if the move continues, the position gets liquidated at $1. The logic is strangely simple: → Wait until about 2 minutes are left before the round closes → Check that BTC has already moved $70–100 during this interval → Always operate with the move, never against it → Hedge only a small portion of the position if the market is too unbalanced (e.g., 95/5) It’s like buying a lottery ticket when the result is already practically announced. The code is public, and anyone can use it on their PC GitHub below 👇🏼

AdiiX

68,630 views • 24 days ago

Just received the first SMT PCB of the Raspberry Pi RTL-SDR/LoRa/GPS/RTC Hat from JLCPCB, and everything works like a charm. I even implemented the GPS PPS indicator as Jeff Geerling suggested. I can think of some fun use cases with it, like using an RTL-SDR to listen in on emergency broadcasts, converting the audio to text, and broadcasting it over LoRa to a public Meshtastic or Meshcore channel. If you have better ideas, drop a comment and let me know! Maybe a perfect hat for the #DragonOs when using with Raspberry Pi. Dustin (BusySignal-KE2EFX) cemaxecuter If you want to be notified when we go live or join our beta testing program, just fill out the form below.
2:09

Just received the first SMT PCB of the Raspberry Pi RTL-SDR/LoRa/GPS/RTC Hat from JLCPCB, and everything works like a charm. I even implemented the GPS PPS indicator as Jeff Geerling suggested. I can think of some fun use cases with it, like using an RTL-SDR to listen in on emergency broadcasts, converting the audio to text, and broadcasting it over LoRa to a public Meshtastic or Meshcore channel. If you have better ideas, drop a comment and let me know! Maybe a perfect hat for the #DragonOs when using with Raspberry Pi. Dustin (BusySignal-KE2EFX) cemaxecuter If you want to be notified when we go live or join our beta testing program, just fill out the form below.

Vileer

63,794 views • 3 months ago

Security Bulletin: WebKit's handling of passkeys can cause iCloud Private Relay to leak your device's real IP address. The same behaviour affects all iOS browsers that rely on proxy connections to hide the device's IP, including Psylo, Onion Browser, and other proxy or Tor browsers on iOS. The IP address is exposed as soon as you open a website that supports passkeys, even if you never try to sign in with one. For example, simply opening facebook[.]com in Safari is enough to expose your real IP address to Facebook. We also found two more WebKit issues: • DNS prefetching bypasses the proxy tunnel and leaks DNS queries. • WebTransport ignores the established proxy connection and exposes the device's real IP address. These behaviours affect Safari and all proxy browsers on iOS, since Apple requires all iOS browsers to use WebKit. Note that VPNs are not affected by these issues, since they tunnel the device's entire network traffic at the system level. We've updated Psylo to disable passkeys and WebTransport by default in all silos, and you can re-enable them per silo. We also disabled DNS prefetching entirely. Please update to version 1.3.1. We built a website that detects these leaks. The video below shows them in action. The full blog post is linked in the replies.
0:23

Security Bulletin: WebKit's handling of passkeys can cause iCloud Private Relay to leak your device's real IP address. The same behaviour affects all iOS browsers that rely on proxy connections to hide the device's IP, including Psylo, Onion Browser, and other proxy or Tor browsers on iOS. The IP address is exposed as soon as you open a website that supports passkeys, even if you never try to sign in with one. For example, simply opening facebook[.]com in Safari is enough to expose your real IP address to Facebook. We also found two more WebKit issues: • DNS prefetching bypasses the proxy tunnel and leaks DNS queries. • WebTransport ignores the established proxy connection and exposes the device's real IP address. These behaviours affect Safari and all proxy browsers on iOS, since Apple requires all iOS browsers to use WebKit. Note that VPNs are not affected by these issues, since they tunnel the device's entire network traffic at the system level. We've updated Psylo to disable passkeys and WebTransport by default in all silos, and you can re-enable them per silo. We also disabled DNS prefetching entirely. Please update to version 1.3.1. We built a website that detects these leaks. The video below shows them in action. The full blog post is linked in the replies.

Psylo: Privacy Browser & Proxy

55,577 views • 7 days ago

Toyota had a single access key sitting in a public GitHub repo. Nobody caught it for years. By the time it was found in 2022, customer data belonging to hundreds of thousands of people had been exposed the entire time. That’s not a hypothetical. That’s one hardcoded secret, forgotten in a repo, doing quiet damage for years. It’s called Infisical, and it exists because “just put it in a .env file” is how almost every credential leak starts. → Centralizes every API key, secret, and cert across dev, staging, and prod, with full versioning and point-in-time rollback → Scans 140+ secret types across your files, directories, and entire git history, the same kind of scanning that catches leaks like Toyota’s before they sit exposed for years → Agent Vault brokers your AI agents’ access to external APIs: the agent only ever sees a placeholder, the real secret gets injected at a proxy layer it never touches, so a prompt-injected agent can’t leak what it was never given → Honey tokens plant decoy credentials next to your real ones, so the second an attacker touches a fake key, your team gets an alert instead of a breach report → Full audit trail on every credential your team and your AI tools use, plus a private PKI to issue and manage certificates without a third-party CA GitGuardian tracked over 28 million new secrets leaked on public GitHub in 2025 alone. Most companies still find out the same way Toyota did: too late, by accident, years after the fact. MIT License (core). 12,700+ GitHub stars. Self-host free, unlimited users.
0:22

Toyota had a single access key sitting in a public GitHub repo. Nobody caught it for years. By the time it was found in 2022, customer data belonging to hundreds of thousands of people had been exposed the entire time. That’s not a hypothetical. That’s one hardcoded secret, forgotten in a repo, doing quiet damage for years. It’s called Infisical, and it exists because “just put it in a .env file” is how almost every credential leak starts. → Centralizes every API key, secret, and cert across dev, staging, and prod, with full versioning and point-in-time rollback → Scans 140+ secret types across your files, directories, and entire git history, the same kind of scanning that catches leaks like Toyota’s before they sit exposed for years → Agent Vault brokers your AI agents’ access to external APIs: the agent only ever sees a placeholder, the real secret gets injected at a proxy layer it never touches, so a prompt-injected agent can’t leak what it was never given → Honey tokens plant decoy credentials next to your real ones, so the second an attacker touches a fake key, your team gets an alert instead of a breach report → Full audit trail on every credential your team and your AI tools use, plus a private PKI to issue and manage certificates without a third-party CA GitGuardian tracked over 28 million new secrets leaked on public GitHub in 2025 alone. Most companies still find out the same way Toyota did: too late, by accident, years after the fact. MIT License (core). 12,700+ GitHub stars. Self-host free, unlimited users.

Harman

29,372 views • 1 month ago