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We built a full-system iOS fuzzer using QEMU+AFL, dup2() I/O channels, hypercalls, syscall enumeration & __syscall tricks on undocumented architecture. Bridged gap between fuzzing theory & closed-source systems. Instructions and code Course and book The course has an exam and a certification. #iOSFuzzing #SecurityResearch #Fuzzing

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fuzzsociety

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19,472 просмотров • 9 месяцев назад •via X (Twitter)

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GoldenEye 007 N64 running natively on iOS and iPadOS for the first time. Not an emulator. The GoldenEye decompilation community spent years reconstructing the original game. GoldenEye64Recomp, N64Recomp, N64ModernRuntime, and RT64 provided the static recompilation, runtime, and rendering foundations. I built GoldenPad on top: the iPhone and iPad app, touch interface, native audio and saves, controller support, and the mobile lifecycle work needed to make it run on real devices. It runs great on iPhone. GoldenEye runs better through GoldenPad than it has in any N64 emulator I have tried on iOS. Native ARM64 code, high-resolution Metal rendering, and modern dual-stick controls make a huge difference. No JIT, no N64 code being interpreted at runtime, and no streaming from a Mac. This is the original 1997 N64 game, not the XBLA version, and it is not based on GoldenEye Metal. I am actively working on full local split-screen multiplayer for iPad. MacOS support soon. I built the iOS and iPadOS integration with 5.6 Sol. Open sourcing it all below. Happy Friday!

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Andrew Ng

142,256 просмотров • 1 год назад

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I recreated Meta’s new AI Glasses on my Quest, and the complete source code is available! 👓 Hey everyone! I’ve received a lot of feedback and messages about my latest project, where I used the Quest to recreate the features shown during Meta’s keynote. To answer your questions I decided to create a cool video to explain how I built it but also, share the full source code on Patreon 👇 Watch the making-of: Download the source code: This project includes: ✨ A UI navigable with microgestures 🤏 A pinch & twist hand-tracking input system 🎧 An audio player with volume control via pinch & twist 📸 A camera that zooms with pinch & twist 🖼️ A carousel to browse captured pictures 🧠 And a contextual AI that reacts to what you’re looking at Hope you guys will like it and that it will help you on your own project!! 😊

Quentin Valembois

15,977 просмотров • 1 год назад

Built out a yolo /remote-control in the Codex cli using /goal. - /remote-control starts a tiny server on laptop - generates fresh token and qr code - phone connects through webapp - full sync between phone and laptop codex - touch grass A glimpse into what OpenAI will cook up, we're just waiting on that Codex iOS now 👀 Also a demo of how powerful /goal is, especially prototyping, most of this was done overnight by Codex while I was sleeping lol
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Built out a yolo /remote-control in the Codex cli using /goal. - /remote-control starts a tiny server on laptop - generates fresh token and qr code - phone connects through webapp - full sync between phone and laptop codex - touch grass A glimpse into what OpenAI will cook up, we're just waiting on that Codex iOS now 👀 Also a demo of how powerful /goal is, especially prototyping, most of this was done overnight by Codex while I was sleeping lol

Matthew Lam

106,802 просмотров • 4 месяцев назад

New short course: Prompt Engineering with Llama 2, built in collaboration with Meta AI at Meta, and taught by Amit Sangani! Meta's Llama 2 has been game-changing for AI. Building with open source lets you control your own data, scrutinize errors, update (or not) the models as you please, and work alongside the global community advancing open models. Llama isn't a single model, it's a collection of models. In this course, you'll: - Learn the differences between different Llama 2 flavors, and when to use each. - Prompt the Llama chat models -- you'll also see how Llama's instruction tags work -- so they can help you with day-to-day tasks, like writing or summarization. - Use advanced prompting, like few-shot prompting for classification, and chain-of-thought prompting for solving logic problems. - Use specialized models in the Llama collection for specific tasks, like Code Llama to help you write, analyze, and improve code, and Llama Guard, which checks prompts and model responses for harmful content. The course also touches on how to run Llama 2 locally on your own computer. I hope you’ll take this course and try out these powerful, open models!
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New short course: Prompt Engineering with Llama 2, built in collaboration with Meta AI at Meta, and taught by Amit Sangani! Meta's Llama 2 has been game-changing for AI. Building with open source lets you control your own data, scrutinize errors, update (or not) the models as you please, and work alongside the global community advancing open models. Llama isn't a single model, it's a collection of models. In this course, you'll: - Learn the differences between different Llama 2 flavors, and when to use each. - Prompt the Llama chat models -- you'll also see how Llama's instruction tags work -- so they can help you with day-to-day tasks, like writing or summarization. - Use advanced prompting, like few-shot prompting for classification, and chain-of-thought prompting for solving logic problems. - Use specialized models in the Llama collection for specific tasks, like Code Llama to help you write, analyze, and improve code, and Llama Guard, which checks prompts and model responses for harmful content. The course also touches on how to run Llama 2 locally on your own computer. I hope you’ll take this course and try out these powerful, open models!

Andrew Ng

162,875 просмотров • 2 лет назад

I've been building a music player with Next.js for fun. Here's a quick demo of how it works (it's open source!) • Demo: • Code: If you want to learn more about how it's built, here's more details ↓ I'm using Postgres (with Drizzle) to store information about the songs and playlists. Audio and image files are stored in Vercel Blob (object storage), and the URLs are then referenced in the database. For the UI, I'm using shadcn/ui (so Tailwind CSS and Radix). This made it easy to copy/paste in some nice components, like the dropdown menus. I built the entire first version of the UI in v0 and then iterated from there, feeding it my Drizzle schema as a source in the project and having it scaffold some of the boilerplate for me: I added support for keyboard navigation (using arrow keys) or vim motions (j/k to go up/down, and h/l to go between playlists and tracks). Also, space to toggle the now playing song, and / to focus the search input. The search function has a nice utility to highlight the currently searched text on the page in yellow. Then, I was exploring how to pass metadata from my application to macOS or iOS. Turns out there's an API for that – MediaSession. Web apps can share metadata about what media is playing (title, artist, album artwork) and sync play/pause/seek with system media controls. Works across modern browsers — even integrates with iOS dynamic island and shows up on lock screens: I set up my app like a PWA – it has a manifest.json file, so it can be installed to my iOS home screen or added to my dock on macOS. On iOS, it then uses the full screen height `100dvh` (dynamic viewport) and has padding on the bottom for the safe area with the `env()` CSS function. Finally, I was able to use the Vercel AI SDK in a script to clean up the metadata on audio files I downloaded from YouTube. Bonus: I even was able to dogfood the React Compiler, which helped me fix a performance bug! That's all! It's fun to make personal software:
2:03

I've been building a music player with Next.js for fun. Here's a quick demo of how it works (it's open source!) • Demo: • Code: If you want to learn more about how it's built, here's more details ↓ I'm using Postgres (with Drizzle) to store information about the songs and playlists. Audio and image files are stored in Vercel Blob (object storage), and the URLs are then referenced in the database. For the UI, I'm using shadcn/ui (so Tailwind CSS and Radix). This made it easy to copy/paste in some nice components, like the dropdown menus. I built the entire first version of the UI in v0 and then iterated from there, feeding it my Drizzle schema as a source in the project and having it scaffold some of the boilerplate for me: I added support for keyboard navigation (using arrow keys) or vim motions (j/k to go up/down, and h/l to go between playlists and tracks). Also, space to toggle the now playing song, and / to focus the search input. The search function has a nice utility to highlight the currently searched text on the page in yellow. Then, I was exploring how to pass metadata from my application to macOS or iOS. Turns out there's an API for that – MediaSession. Web apps can share metadata about what media is playing (title, artist, album artwork) and sync play/pause/seek with system media controls. Works across modern browsers — even integrates with iOS dynamic island and shows up on lock screens: I set up my app like a PWA – it has a manifest.json file, so it can be installed to my iOS home screen or added to my dock on macOS. On iOS, it then uses the full screen height `100dvh` (dynamic viewport) and has padding on the bottom for the safe area with the `env()` CSS function. Finally, I was able to use the Vercel AI SDK in a script to clean up the metadata on audio files I downloaded from YouTube. Bonus: I even was able to dogfood the React Compiler, which helped me fix a performance bug! That's all! It's fun to make personal software:

Lee Robinson

118,631 просмотров • 2 лет назад

Introducing Perplexi-Tube, the Perplexity for YouTube that I built in 20 minutes... A Mobile app that uses Deepseek and Gemini I used Cursor and the React Native template built that makes building mobile apps incredibly easy. Tomorrow I'm releasing a full tutorial on how to create a mobile app with react native. You will need - XCode and Simulator (free) - Cursor - A Clerk Account (for auth, free) - A Deepseek API Key - A Gemini API Key (Optional, we used it because deepseek has been down today) Video will be released tomorrow, and I will open source this code as well.
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Introducing Perplexi-Tube, the Perplexity for YouTube that I built in 20 minutes... A Mobile app that uses Deepseek and Gemini I used Cursor and the React Native template built that makes building mobile apps incredibly easy. Tomorrow I'm releasing a full tutorial on how to create a mobile app with react native. You will need - XCode and Simulator (free) - Cursor - A Clerk Account (for auth, free) - A Deepseek API Key - A Gemini API Key (Optional, we used it because deepseek has been down today) Video will be released tomorrow, and I will open source this code as well.

Riley Brown

118,021 просмотров • 1 год назад

New short course on advanced retrieval for RAG (retrieval augmented generation)! RAG fetches relevant documents to give context to an LLM. In Advanced Retrieval for AI with Chroma, taught by Chroma founder anton 🇺🇸, you’ll learn: (i) Query expansion using an LLM to rewrite and improve a query, by either generating either additional relevant queries or a hypothetical answer to the query. (ii) Reranking using a cross-encoder - a model trained to measure similarity between two inputs presented simultaneously. Reranking reorders retrieved documents based on the cross-encoder similarity measure. (iii) Constructing and training an Embedding Adaptor, which is a model that adapts the embedding values to be more relevant to your use case. Each of these techniques can help you build much better RAG systems. Please sign up for the course here:
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New short course on advanced retrieval for RAG (retrieval augmented generation)! RAG fetches relevant documents to give context to an LLM. In Advanced Retrieval for AI with Chroma, taught by Chroma founder anton 🇺🇸, you’ll learn: (i) Query expansion using an LLM to rewrite and improve a query, by either generating either additional relevant queries or a hypothetical answer to the query. (ii) Reranking using a cross-encoder - a model trained to measure similarity between two inputs presented simultaneously. Reranking reorders retrieved documents based on the cross-encoder similarity measure. (iii) Constructing and training an Embedding Adaptor, which is a model that adapts the embedding values to be more relevant to your use case. Each of these techniques can help you build much better RAG systems. Please sign up for the course here:

Andrew Ng

191,219 просмотров • 2 лет назад

An end-to-end Machine Learning system, starting with a dataset and going all the way to monitoring it in production. We start next Monday! We'll build everything using open-source tools, and I'll show you how to deploy your system on different cloud platforms. I've been running this live course for 1.5+ years now, and about 1,500 students have taken it. It's the best Machine Learning Engineering class I'm aware of by 10 miles. More information is in the attached video. Link to join below. See you next week!
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An end-to-end Machine Learning system, starting with a dataset and going all the way to monitoring it in production. We start next Monday! We'll build everything using open-source tools, and I'll show you how to deploy your system on different cloud platforms. I've been running this live course for 1.5+ years now, and about 1,500 students have taken it. It's the best Machine Learning Engineering class I'm aware of by 10 miles. More information is in the attached video. Link to join below. See you next week!

Santiago

36,625 просмотров • 1 год назад