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Introducing React Native Enriched Markdown – a new React Native Enriched package by Gregory Moskaliuk! 🚀 It’s a fully native Markdown renderer for React Native that makes Markdown content feel like a first-class part of your app’s UI. ⚡ Purely native performance – lightning-fast parsing with md4c and 100%... show more

Software Mansion

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26,869 views • 5 months ago •via X (Twitter)

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Expo Router just unlocked a new level of native on iOS 🚀 For years, cross-platform meant tradeoffs. Your app worked everywhere, but it never felt quite right. That gap is finally closing. With the latest version of Expo Router, native iOS APIs are now exposed declaratively. No UIKit wrestling. No fake UI. If you want your app to feel truly native on iOS, these are the APIs you should be using: - Native Tabs (real system tab bar) - Stack Toolbar (native header controls) - Bottom toolbars with placement=“bottom” - Bottom accessories attached to the tab bar - Zoom transitions for fluid navigation - Native sheet footers (experimental) I wrote a full breakdown with code examples and video demos of these features in my blog (link below 👇)

Expo Router just unlocked a new level of native on iOS 🚀 For years, cross-platform meant tradeoffs. Your app worked everywhere, but it never felt quite right. That gap is finally closing. With the latest version of Expo Router, native iOS APIs are now exposed declaratively. No UIKit wrestling. No fake UI. If you want your app to feel truly native on iOS, these are the APIs you should be using: - Native Tabs (real system tab bar) - Stack Toolbar (native header controls) - Bottom toolbars with placement=“bottom” - Bottom accessories attached to the tab bar - Zoom transitions for fluid navigation - Native sheet footers (experimental) I wrote a full breakdown with code examples and video demos of these features in my blog (link below 👇)

Beto

91,298 views • 5 months ago

Profiling React Native apps is a developer superpower, but correlating traces across different threads? A massive time sink. ⌛ What if you could outsource it and focus on implementing other features? You can, with Argent. 🪄 Just describe your test scenario, and Argent does the heavy lifting: 🔍 Runs React Profiler & iOS Instruments 📱 Navigates the app to reproduce the issue 📝 Analyzes and correlates the results into a clean Markdown report Watch it in action & stay tuned👇

Profiling React Native apps is a developer superpower, but correlating traces across different threads? A massive time sink. ⌛ What if you could outsource it and focus on implementing other features? You can, with Argent. 🪄 Just describe your test scenario, and Argent does the heavy lifting: 🔍 Runs React Profiler & iOS Instruments 📱 Navigates the app to reproduce the issue 📝 Analyzes and correlates the results into a clean Markdown report Watch it in action & stay tuned👇

Software Mansion

29,128 views • 3 months ago

We built this onboarding flow in React Native that feels better than most native apps Here’s how we did it: ① Patched react-native-screens for a custom transition ② Button and stepper are rendered on top of the stack ③ Synced button state across screens via context The button and header are positioned absolutely above the stack. When a screen focuses, it updates the shared button state (text, disabled state) via context The hardest part is keeping the keyboard open during screen transitions. We solved it with hidden TextInputs that get focused programmatically before navigating The result: one button flows across every screen and surfs the keyboard

We built this onboarding flow in React Native that feels better than most native apps Here’s how we did it: ① Patched react-native-screens for a custom transition ② Button and stepper are rendered on top of the stack ③ Synced button state across screens via context The button and header are positioned absolutely above the stack. When a screen focuses, it updates the shared button state (text, disabled state) via context The hardest part is keeping the keyboard open during screen transitions. We solved it with hidden TextInputs that get focused programmatically before navigating The result: one button flows across every screen and surfs the keyboard

Baran

229,549 views • 9 months ago

🔥 Guys, I think I COOKED something special... Introducing HelpKit for Mobile Apps! 📲 You can now embed a native help center directly in your consumer app that actually feels like it belongs there. 🔴 Live on Product Hunt today (Link below) 📱 React Native SDK (Swift coming soon) 📝 Ready-made mobile app FAQ template ⚡ Edit in Notion → Sync → No app update needed 📉 60-80% fewer support tickets Perfect for consumer app founders ready to automate support so you can get back to building and growing. If you have a second would love your support on Product Hunt! 🚀 Link in first comment 👇

🔥 Guys, I think I COOKED something special... Introducing HelpKit for Mobile Apps! 📲 You can now embed a native help center directly in your consumer app that actually feels like it belongs there. 🔴 Live on Product Hunt today (Link below) 📱 React Native SDK (Swift coming soon) 📝 Ready-made mobile app FAQ template ⚡ Edit in Notion → Sync → No app update needed 📉 60-80% fewer support tickets Perfect for consumer app founders ready to automate support so you can get back to building and growing. If you have a second would love your support on Product Hunt! 🚀 Link in first comment 👇

Dominik Sobe ツ

10,406 views • 11 months ago

Standard Intelligence's devansh responds to roon's tweet that "text is the universal interface," and explains why their new foundation model is trained on video: "At some point in the arbitrarily long future, if we only use text models, we could force most things to be text. But I think there are just a lot of things that are much more native when done from a computer-use [perspective]." "GUIs are designed for humans to use. We have this massive long tail of things on the internet that are entirely undoable by LLMs." "For example, when I do ML engineering most of my time is spent doing the grunt work of engineering. It's a lot of looking at graphs, analyzing, and comparing loss curves. You can do this in text, but it's a much larger pain than doing it in the native interface." "There's a reason humans don't interact with a computer purely through text, it would kind of suck."

Standard Intelligence's devansh responds to roon's tweet that "text is the universal interface," and explains why their new foundation model is trained on video: "At some point in the arbitrarily long future, if we only use text models, we could force most things to be text. But I think there are just a lot of things that are much more native when done from a computer-use [perspective]." "GUIs are designed for humans to use. We have this massive long tail of things on the internet that are entirely undoable by LLMs." "For example, when I do ML engineering most of my time is spent doing the grunt work of engineering. It's a lot of looking at graphs, analyzing, and comparing loss curves. You can do this in text, but it's a much larger pain than doing it in the native interface." "There's a reason humans don't interact with a computer purely through text, it would kind of suck."

TBPN

64,281 views • 4 months ago

Two highly requested features just landed in Stitch! You can now generate custom images like logos right on the canvas and copy screens across projects. ✨ Native Image Generation: Create custom logos or hero graphics, and ask the agent to drop them directly into your UI. ✨ Copy & Paste Across Projects: Grab that perfect screen you made last week and paste it into a new project. Perfect for combining styles and merging content. Your vibe design partner just leveled up. Let’s build! 👇

Two highly requested features just landed in Stitch! You can now generate custom images like logos right on the canvas and copy screens across projects. ✨ Native Image Generation: Create custom logos or hero graphics, and ask the agent to drop them directly into your UI. ✨ Copy & Paste Across Projects: Grab that perfect screen you made last week and paste it into a new project. Perfect for combining styles and merging content. Your vibe design partner just leveled up. Let’s build! 👇

Stitch by Google

56,622 views • 3 months ago

OpenAI's S2S preview is polished but it still thinks in steps. Speech → text → model → text → speech. That's not how humans converse. Introducing Hydra. A native speech-to-speech model that doesn't wait for turn-taking, doesn't flatten emotion into text, and doesn't break when you interrupt it mid-sentence. Hydra reasons asynchronously, speaks and listens simultaneously, and preserves emotion because it never leaves the audio domain. It's still in beta, but the shift is obvious. If you want early access, the link is in the comments. Here's a preview of what that looks like -

OpenAI's S2S preview is polished but it still thinks in steps. Speech → text → model → text → speech. That's not how humans converse. Introducing Hydra. A native speech-to-speech model that doesn't wait for turn-taking, doesn't flatten emotion into text, and doesn't break when you interrupt it mid-sentence. Hydra reasons asynchronously, speaks and listens simultaneously, and preserves emotion because it never leaves the audio domain. It's still in beta, but the shift is obvious. If you want early access, the link is in the comments. Here's a preview of what that looks like -

Sudarshan Kamath

328,852 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

39,210 views • 1 month ago

Memory for OpenClaw is now Native! Our first OpenClaw Memory Skill was a massive success: 30k+ downloads in a week and 500k+ organic impressions overnight for launch post. But we knew memory needed to be native. On March 21, OpenClaw merged PR #50848, allowing us to go beyond the skill layer and integrate directly into the agent’s context assembly flow. We try to make OpenClaw a truly 24/7 employee capable of complex workflows. The technical setup isn’t the hardest part but the real challenge is giving it a "brain" that remembers exact project details, past decisions, and team changes over time. The Native Memory Plugin is now live on NPM & ClawHub. Here is what it brings to your OpenClaw agents: 👉 Native Integration: Automatically manages a Three-Layer Memory architecture (Context Tree, Workspace Memory, Daily Memory). 👉 Git-like Stateful Memory: Organizes memory into a semantic hierarchy of human-readable, diffable Markdown files. You always get updated knowledge and can actually see and fix what your agent learns. 👉 Top Market Accuracy: Achieves an industry-leading 92.2% retrieval accuracy (LoCoMo & LongMemEval benchmarks), maintaining 90% accuracy even with cheap, lightweight models. 👉 Local-first & Portable: Local-by-default, fully portable for multi-agent teams. 👉 Super Easy Setup

Memory for OpenClaw is now Native! Our first OpenClaw Memory Skill was a massive success: 30k+ downloads in a week and 500k+ organic impressions overnight for launch post. But we knew memory needed to be native. On March 21, OpenClaw merged PR #50848, allowing us to go beyond the skill layer and integrate directly into the agent’s context assembly flow. We try to make OpenClaw a truly 24/7 employee capable of complex workflows. The technical setup isn’t the hardest part but the real challenge is giving it a "brain" that remembers exact project details, past decisions, and team changes over time. The Native Memory Plugin is now live on NPM & ClawHub. Here is what it brings to your OpenClaw agents: 👉 Native Integration: Automatically manages a Three-Layer Memory architecture (Context Tree, Workspace Memory, Daily Memory). 👉 Git-like Stateful Memory: Organizes memory into a semantic hierarchy of human-readable, diffable Markdown files. You always get updated knowledge and can actually see and fix what your agent learns. 👉 Top Market Accuracy: Achieves an industry-leading 92.2% retrieval accuracy (LoCoMo & LongMemEval benchmarks), maintaining 90% accuracy even with cheap, lightweight models. 👉 Local-first & Portable: Local-by-default, fully portable for multi-agent teams. 👉 Super Easy Setup

andy nguyen

169,136 views • 3 months ago

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

GREG ISENBERG

83,895 views • 1 month ago

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

We've officially released and open-sourced HunyuanImage 2.1, our latest text-to-image model. The new model delivers on our commitment to balancing performance and quality. With native 2K image generation, HunyuanImage 2.1 is an advanced open-source text-to-image model.🎨 ✨ New in 2.1: 🔹Advanced Semantics: Supports ultra-long and complex prompts of up to 1000 tokens, and precisely controls the generation of multiple subjects in a single image. 🔹Precise Chinese and English Text Rendering with seamless image–text integration: The model naturally integrates text into images, making it suitable for a wide range of applications such as product covers, illustrations, and poster design to meet the needs of various fields. 🔹Rich Styles and High Aesthetic: Capable of generating images in various styles—including photorealistic portraits, comics, and vinyl figures—it delivers outstanding visual appeal and artistic quality. 🔹High-Quality Generation: Efficiently produces ultra-high-definition (2K) images in the same time other models take to generate a 1K image. HunyuanImage 2.1 uses two text encoders: a multimodal large language model (MLLM) to improve the model's image and text alignment capabilities, and a multi-language character-aware encoder to improve text rendering capabilities. The model is a single- and double-stream diffusion transformer with 17B parameters. We've also open-sourced the weights of the the accelerated version with meanflow which reduces inference steps from 100 to just 8, and PromptEnhancer, the first industrial-grade rewriting model that enhances your prompts for more nuanced and expressive image generation. Now, creators turn complex ideas—like posters with slogans or multi-panel comics—into visuals faster than ever. We’re just getting started. Stay tuned for our native multimodal image generation model coming soon. 🌐Website: 🔗Github: 🤗Hugging Face: ✨Hugging Face Demo:

Tencent Hy

89,257 views • 10 months ago

We wanted to show the performance and the insane power of the Static Hermes native FFI. But we don't yet support the all language features needed to compile React Native (soon though!), so we improvised. We imported the bindings for DearImGui (a popular C++ imperative game UI library that renders meshes), and Sokol (an amazing lightweight 3D backend in C) and wrote a small demo. "Imperative UI" means that the entire UI and the state of the screen is recreated on every frame, which suits our purposes to demonstrate SH very well. The demo runs 100% compiled native code. JavaScript runs in every frame and recreates the entire state of the screen. It is portable to all platforms, though the screen layout is not optimized for phone. We wrote the demo in C++ and in JS, to compare, and they both ran with the same performance. In the attached videos you can see the demo running at the stable screen refresh rate - 120Hz on my Mac, 60Hz on a old Moto G5 phone. It includes some widgets, an n-body simulation of planets, a playable game demo, etc.

We wanted to show the performance and the insane power of the Static Hermes native FFI. But we don't yet support the all language features needed to compile React Native (soon though!), so we improvised. We imported the bindings for DearImGui (a popular C++ imperative game UI library that renders meshes), and Sokol (an amazing lightweight 3D backend in C) and wrote a small demo. "Imperative UI" means that the entire UI and the state of the screen is recreated on every frame, which suits our purposes to demonstrate SH very well. The demo runs 100% compiled native code. JavaScript runs in every frame and recreates the entire state of the screen. It is portable to all platforms, though the screen layout is not optimized for phone. We wrote the demo in C++ and in JS, to compare, and they both ran with the same performance. In the attached videos you can see the demo running at the stable screen refresh rate - 120Hz on my Mac, 60Hz on a old Moto G5 phone. It includes some widgets, an n-body simulation of planets, a playable game demo, etc.

Tzvetan Mikov

826,085 views • 2 years ago

We’ve been overwhelmed by the feedback on the new Stitch. 🫶 Now, let’s get to building. 😀 Over the coming weeks, we're dropping Starter Ideas to spark your creativity—and want to showcase what you build! 🌟 Starter Idea #1: Watch the new agent and AI-Native Canvas turn a messy doc into a fully styled portfolio. 📝 The Input: Highlight and copy your raw resume text 🎨 The Prompt: "Make me a single-page resume site in 3 fun styles" + paste your resume text ✨ Stitch turns your resume into a text canvas node where it can be referenced or edited ✨ It generates 3 unique Design Systems and layouts 🔷 The Polish: Upload a headshot, and the agent auto-crops and styles it to match the exact aesthetic of each site. We have loved seeing your creations so far. Drop your shared projects in the replies—we're putting together a plan to showcase our favorites!

Stitch by Google

68,096 views • 4 months ago

Here are a few things you probably did not know about Reddit's iOS and Android apps: they are ~2.5M lines of code each, with 500+ screens, and a total of 200 native mobile engineers work on the both of them (including a dedicated iOS and Android mobile platform team) But a few years ago, things looked very different - and Reddit quietly rebuilt their native apps from 2021. Today's conversation goes through what happened and how, with three engineers from Reddit’s mobile platform team who led this work: Lauren Darcey (Head of Mobile Platform), Brandon Kobilansky (iOS Platform Lead), and Eric Kuck (Principal Android Engineer) Watch or listen: • YouTube: • Spotify: • Apple: --- Brought to you by: • Graphite (we've moved to @graphite) — The AI developer productivity platform • Sentry — Error and performance monitoring for developers. Get 150k errors (three months of Team Plan) for free at --- Three of my takeaways from this episode: 𝟭. 𝗣𝗼𝗼𝗿 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗰𝗮𝗻 𝘀𝗹𝗼𝘄 𝗱𝗼𝘄𝗻 𝗮 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 – 𝘀𝗼 𝗽𝗮𝘆 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻! One of the reasons Reddit started investing heavily in modernizing its mobile stack was that the “old stack” was slowing down developers. Reddit’s platform team got proof of this simply by asking native engineers about the biggest development-related challenges they face. 𝟮. 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗵𝗮𝗿𝗱 𝘄𝗼𝗿𝗸. Advice from Brandon for anyone looking to work on a platform team: "You should try to work at a tech company for a year or two and actually see what happens after you ship a system — and then the assumptions change! You then have to figure out how to keep this thing going. You get a bunch of software design intuition because you have to like re-evaluate your assumptions for an incredibly long time. If you can do that, you're probably ready for platform stuff." 𝟯. 𝗚𝗲𝗻𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 𝗳𝗲𝗲𝗹 𝗹𝗶𝗸𝗲 𝘁𝗵𝗲𝘆 𝗮𝗿𝗲 𝗻𝗼𝘁 “𝘁𝗵𝗲𝗿𝗲” 𝘆𝗲𝘁 𝘄𝗶𝘁𝗵 𝗻𝗮𝘁𝗶𝘃𝗲 𝗺𝗼𝗯𝗶𝗹𝗲. LLMs integrated into IDEs seem to be increasingly helpful with backend, fullstack, web and even cross-platform (React Native / Expo) projects. However, Reddit’s mobile team shared that they get a moderate boost from the Apple and Android Studio LLM additions. Native mobile development is distinctively different from web, fullstack and backend coding – and it seems that these IDEs with AI functionality have not done much to optimize for the expereince of native mobile engineers. Over time, this will likely change – but it’s a reminder that there are differences between fullstack, backend and native mobile development.(I wrote a book reflecting on more of the challenges unique to native mobile titled Building Mobile Apps at Scale)

Here are a few things you probably did not know about Reddit's iOS and Android apps: they are ~2.5M lines of code each, with 500+ screens, and a total of 200 native mobile engineers work on the both of them (including a dedicated iOS and Android mobile platform team) But a few years ago, things looked very different - and Reddit quietly rebuilt their native apps from 2021. Today's conversation goes through what happened and how, with three engineers from Reddit’s mobile platform team who led this work: Lauren Darcey (Head of Mobile Platform), Brandon Kobilansky (iOS Platform Lead), and Eric Kuck (Principal Android Engineer) Watch or listen: • YouTube: • Spotify: • Apple: --- Brought to you by: • Graphite (we've moved to @graphite) — The AI developer productivity platform • Sentry — Error and performance monitoring for developers. Get 150k errors (three months of Team Plan) for free at --- Three of my takeaways from this episode: 𝟭. 𝗣𝗼𝗼𝗿 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗰𝗮𝗻 𝘀𝗹𝗼𝘄 𝗱𝗼𝘄𝗻 𝗮 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 – 𝘀𝗼 𝗽𝗮𝘆 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻! One of the reasons Reddit started investing heavily in modernizing its mobile stack was that the “old stack” was slowing down developers. Reddit’s platform team got proof of this simply by asking native engineers about the biggest development-related challenges they face. 𝟮. 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗵𝗮𝗿𝗱 𝘄𝗼𝗿𝗸. Advice from Brandon for anyone looking to work on a platform team: "You should try to work at a tech company for a year or two and actually see what happens after you ship a system — and then the assumptions change! You then have to figure out how to keep this thing going. You get a bunch of software design intuition because you have to like re-evaluate your assumptions for an incredibly long time. If you can do that, you're probably ready for platform stuff." 𝟯. 𝗚𝗲𝗻𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 𝗳𝗲𝗲𝗹 𝗹𝗶𝗸𝗲 𝘁𝗵𝗲𝘆 𝗮𝗿𝗲 𝗻𝗼𝘁 “𝘁𝗵𝗲𝗿𝗲” 𝘆𝗲𝘁 𝘄𝗶𝘁𝗵 𝗻𝗮𝘁𝗶𝘃𝗲 𝗺𝗼𝗯𝗶𝗹𝗲. LLMs integrated into IDEs seem to be increasingly helpful with backend, fullstack, web and even cross-platform (React Native / Expo) projects. However, Reddit’s mobile team shared that they get a moderate boost from the Apple and Android Studio LLM additions. Native mobile development is distinctively different from web, fullstack and backend coding – and it seems that these IDEs with AI functionality have not done much to optimize for the expereince of native mobile engineers. Over time, this will likely change – but it’s a reminder that there are differences between fullstack, backend and native mobile development.(I wrote a book reflecting on more of the challenges unique to native mobile titled Building Mobile Apps at Scale)

Gergely Orosz

67,225 views • 1 year ago

We've spent years building LlamaParse into the most accurate document parser for production AI. Along the way, we learned a lot about what fast, lightweight parsing actually looks like under the hood. Today, we're open-sourcing a light-weight core of that tech as LiteParse 🦙 It's a CLI + TS-native library for layout-aware text parsing from PDFs, Office docs, and images. Local, zero Python dependencies, and built specifically for agents and LLM pipelines. Think of it as our way of giving the community a solid starting point for document parsing: npm i -g @llamaindex/liteparse lit parse anything.pdf - preserves spatial layout (columns, tables, alignment) - built-in local OCR, or bring your own server - screenshots for multimodal LLMs - handles PDFs, office docs, images Blog: Repo:

We've spent years building LlamaParse into the most accurate document parser for production AI. Along the way, we learned a lot about what fast, lightweight parsing actually looks like under the hood. Today, we're open-sourcing a light-weight core of that tech as LiteParse 🦙 It's a CLI + TS-native library for layout-aware text parsing from PDFs, Office docs, and images. Local, zero Python dependencies, and built specifically for agents and LLM pipelines. Think of it as our way of giving the community a solid starting point for document parsing: npm i -g @llamaindex/liteparse lit parse anything.pdf - preserves spatial layout (columns, tables, alignment) - built-in local OCR, or bring your own server - screenshots for multimodal LLMs - handles PDFs, office docs, images Blog: Repo:

LlamaIndex 🦙

581,217 views • 4 months ago

$"Please fix Markdown tables!" Okay: text-only, wrapping, columnar selection, proper border conjunctions, fill full width, in opencode beta now. "What took so long?" Here's a writeup: OpenTUI uses yoga layout and has elements called Renderables. Boxes with borders, plain text, code with tree-sitter backed highlighting and other primitives. Organised in a tree structure resembling somewhat of a DOM. Approaching a table naively, given the primitives are there, one would think to just stack box and text elements the right way in a flex-box layout to visually represent a table. Boxes support borders. Problem solved. This is what an LLM would one-shot in a working state, given OpenTUI's API surface. Ignoring the fact that just using box local borders don't handle border conjunctions properly. Benchmarking something like that quickly shows that instantiating an average table takes >70ms and incremental updates become expensive. Hugely due to yoga-layout via wasm having a painful price on yoga API calls. The whole ordeal becomes memory hungry, because a Text element handles more than just plain text. A simple 4x6 table needs a Box and Text per cell, ending up with 48 heavy nodes that yoga must lay out. "But that's just OpenTUI being slow" - you might say. Yes, but no. Yoga should be integrated in the zig native binary core of OpenTUI. It is on the roadmap to do so, which will speed up render passes by 2-5x. Yoga-layout has an open PR to support CSS Grids, which would greatly ease building something like a table. We will use that for fully laid out tables when it gets there. Below the typescript core level Renderables, there are lower level primitives like TextBuffers and TextBufferViews, bound via FFI and completely handled in Zig. I was stuck expecting a table primitive to handle a full layout like a table in the browser does. For Markdown all we need is a text-only table. So we had to come up with a better idea, something that is feasible now. A table layout is pretty straight forward. No need to have yoga deal with that. Using TextBufferViews for cells directly gives lower level control and eliminates some overhead that Boxes and Text renderables have. A simple native method to draw a grid with proper conjunctions is a nice library method. It will surely be used for other cases, so that's what we added. Using this simplified approach we were able to bring down initial instantiation to <1ms, more than 70x improvement. With a far smaller memory footprint. Given all the low level primitives are known and implementing a text-only table like this is possible, Codex was of great help to carve out the PoC, setup the benchmarks and tests. That's only a fraction of what was needed though. The table needs options to span the full available width, render different border styles, show/hide borders, padding, selection etc. So many iterations later OpenTUI now has a text-only, performant and relatively cheap TextTable that we can leverage to render Markdown tables in a streaming/incremental manner. Efficiently and properly.$

"Please fix Markdown tables!" Okay: text-only, wrapping, columnar selection, proper border conjunctions, fill full width, in opencode beta now. "What took so long?" Here's a writeup: OpenTUI uses yoga layout and has elements called Renderables. Boxes with borders, plain text, code with tree-sitter backed highlighting and other primitives. Organised in a tree structure resembling somewhat of a DOM. Approaching a table naively, given the primitives are there, one would think to just stack box and text elements the right way in a flex-box layout to visually represent a table. Boxes support borders. Problem solved. This is what an LLM would one-shot in a working state, given OpenTUI's API surface. Ignoring the fact that just using box local borders don't handle border conjunctions properly. Benchmarking something like that quickly shows that instantiating an average table takes >70ms and incremental updates become expensive. Hugely due to yoga-layout via wasm having a painful price on yoga API calls. The whole ordeal becomes memory hungry, because a Text element handles more than just plain text. A simple 4x6 table needs a Box and Text per cell, ending up with 48 heavy nodes that yoga must lay out. "But that's just OpenTUI being slow" - you might say. Yes, but no. Yoga should be integrated in the zig native binary core of OpenTUI. It is on the roadmap to do so, which will speed up render passes by 2-5x. Yoga-layout has an open PR to support CSS Grids, which would greatly ease building something like a table. We will use that for fully laid out tables when it gets there. Below the typescript core level Renderables, there are lower level primitives like TextBuffers and TextBufferViews, bound via FFI and completely handled in Zig. I was stuck expecting a table primitive to handle a full layout like a table in the browser does. For Markdown all we need is a text-only table. So we had to come up with a better idea, something that is feasible now. A table layout is pretty straight forward. No need to have yoga deal with that. Using TextBufferViews for cells directly gives lower level control and eliminates some overhead that Boxes and Text renderables have. A simple native method to draw a grid with proper conjunctions is a nice library method. It will surely be used for other cases, so that's what we added. Using this simplified approach we were able to bring down initial instantiation to <1ms, more than 70x improvement. With a far smaller memory footprint. Given all the low level primitives are known and implementing a text-only table like this is possible, Codex was of great help to carve out the PoC, setup the benchmarks and tests. That's only a fraction of what was needed though. The table needs options to span the full available width, render different border styles, show/hide borders, padding, selection etc. So many iterations later OpenTUI now has a text-only, performant and relatively cheap TextTable that we can leverage to render Markdown tables in a streaming/incremental manner. Efficiently and properly.

kmdr

208,295 views • 4 months ago

What if you could talk to your Telegram bot and it actually talked back? Learn how built a voice-enabled Telegram bot with the Gemini Interactions API in ~400 lines of Python. Send a voice note in any language, Gemini understands the audio and replies with text and a spoken voice message. Uses: - Gemini 3.1 Flash Lite for reasoning, 3.1 Flash TTS for speech - Interactions API handles multi-turn memory server-side - Native audio input, no transcription step needed - Deploys to Cloud Run with scale-to-zero Awesome work by Thor 雷神 ⚡️. 🤗

What if you could talk to your Telegram bot and it actually talked back? Learn how built a voice-enabled Telegram bot with the Gemini Interactions API in ~400 lines of Python. Send a voice note in any language, Gemini understands the audio and replies with text and a spoken voice message. Uses: - Gemini 3.1 Flash Lite for reasoning, 3.1 Flash TTS for speech - Interactions API handles multi-turn memory server-side - Native audio input, no transcription step needed - Deploys to Cloud Run with scale-to-zero Awesome work by Thor 雷神 ⚡️. 🤗

Philipp Schmid

11,156 views • 3 months ago

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

Santiago

17,438 views • 17 days ago

I'm very pleased to to share that I’ve now completed what I believe to be the most detailed and up-to-date Kubernetes and Cloud Native (KCNA) certification course available 🚀 Dive Into Cloud Native, Containers, Kubernetes and the and the Cloud Native Associate (KCNA). More than a certification course, it’s a transformational journey to becoming a certified Kubernetes professional 🌟 📚 What sets this course apart? This course covers the CNCF's entire KCNA curriculum and more, taking you from the fundamentals to advanced mastery in Containers, Docker, Kubernetes, and Cloud Native technologies. Surpassing the knowledge required to achieve this qualification and providing you with knowledge that will help you in any career. You will also bootstrap the likelihood of success with paths that follow the KCNA such as the CKA, CKAD, Prometheus and GitOps qualifications! 🛠️ Course Features (see the Video for examples): 🖥️ Interactive Hands-On Labs: Dive deep into Kubernetes with our incredible browser-based labs 🎥 In-Depth Video Tutorials: Learn complex concepts through high-quality, engaging videos 📝 Study Tips: Get insider tips before each lesson to maximise your study efficiency ✅ Quizzes: Test your knowledge and reinforce learning at every step 📚 Further Study: Explore beyond the course with curated resources for deeper learning 🏆 3 x Practice Exams: Simulate the real KCNA exam prior to booking to experience to ensure you’re fully prepared! I’m confident that with the comprehensive materials provided that anyone who puts the time and effort in will achieve the Kubernetes and Cloud Native Associate Certification 🏅 🔗 Check out the video for more details. The course is available on my site, and with this announcement I’m excited to continue the early access offer for a small amount of time further! #Kubernetes

I'm very pleased to to share that I’ve now completed what I believe to be the most detailed and up-to-date Kubernetes and Cloud Native (KCNA) certification course available 🚀 Dive Into Cloud Native, Containers, Kubernetes and the and the Cloud Native Associate (KCNA). More than a certification course, it’s a transformational journey to becoming a certified Kubernetes professional 🌟 📚 What sets this course apart? This course covers the CNCF's entire KCNA curriculum and more, taking you from the fundamentals to advanced mastery in Containers, Docker, Kubernetes, and Cloud Native technologies. Surpassing the knowledge required to achieve this qualification and providing you with knowledge that will help you in any career. You will also bootstrap the likelihood of success with paths that follow the KCNA such as the CKA, CKAD, Prometheus and GitOps qualifications! 🛠️ Course Features (see the Video for examples): 🖥️ Interactive Hands-On Labs: Dive deep into Kubernetes with our incredible browser-based labs 🎥 In-Depth Video Tutorials: Learn complex concepts through high-quality, engaging videos 📝 Study Tips: Get insider tips before each lesson to maximise your study efficiency ✅ Quizzes: Test your knowledge and reinforce learning at every step 📚 Further Study: Explore beyond the course with curated resources for deeper learning 🏆 3 x Practice Exams: Simulate the real KCNA exam prior to booking to experience to ensure you’re fully prepared! I’m confident that with the comprehensive materials provided that anyone who puts the time and effort in will achieve the Kubernetes and Cloud Native Associate Certification 🏅 🔗 Check out the video for more details. The course is available on my site, and with this announcement I’m excited to continue the early access offer for a small amount of time further! #Kubernetes

James Spurin

14,355 views • 2 years ago

AI is creating a new kind of creator economy. One where anyone can launch complex apps, agents, trading systems, research tools, games, and community experiments in minutes. Not just content people consume. AI-native experiences people can use, share, and grow together. That is the bigger idea behind the AI Trading Arena: a live DeFAI environment where AI-created strategies compete on real crypto prices, with every trade, tick, P&L update, and leaderboard visible to everyone. The latest expansion makes it even more fascinating: AI is now designing new trading strategies and adding them into the Arena to compete with strategies from frontier models like GPT-5, Claude, Gemini, Grok, DeepSeek, and Llama. Just agents, markets, and performance in the open. And the Arena is only one example. On ChainOpera AI, creators can launch AI-native experiences in minutes, then grow them with the community that uses them. $COAI is the fuel for this creator economy: creators launch, users participate, and AI-native apps come alive through real community usage. What would you be built next?

AI is creating a new kind of creator economy. One where anyone can launch complex apps, agents, trading systems, research tools, games, and community experiments in minutes. Not just content people consume. AI-native experiences people can use, share, and grow together. That is the bigger idea behind the AI Trading Arena: a live DeFAI environment where AI-created strategies compete on real crypto prices, with every trade, tick, P&L update, and leaderboard visible to everyone. The latest expansion makes it even more fascinating: AI is now designing new trading strategies and adding them into the Arena to compete with strategies from frontier models like GPT-5, Claude, Gemini, Grok, DeepSeek, and Llama. Just agents, markets, and performance in the open. And the Arena is only one example. On ChainOpera AI, creators can launch AI-native experiences in minutes, then grow them with the community that uses them. $COAI is the fuel for this creator economy: creators launch, users participate, and AI-native apps come alive through real community usage. What would you be built next?

ChainOpera AI

15,398 views • 1 month ago