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Current progress on my AI Tattoo app 🤓 The Tattoo Playground is powered by: - CSS animations in RN (hat tip Kacper Kapuściak) - Expo Module for a native SwiftUI input view (via Kavsoft) - Custom haptic engine with expo-modules by vo—va 🇺🇦 (also used in Fuse 🤩) -...

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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 👇)
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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 👇)

Beto

91,496 просмотров • 6 месяцев назад

Every week another AI image app hits the top charts. The window is open, but not forever. That's why I built Platano. Today it's production ready. What's inside: - Expo SDK 56 - Agent skills for setup, rebranding, adding languages, and more - Fully native iOS and Android - Material You on Android, Liquid Glass with iOS 18 fallbacks - Onboarding flow battle-tested in my AI Tattoo app (live in the App Store) - RevenueCat payments - AI editor with Nano Banana 2 - Clean architecture your agent can extend - Interactive CLI to spin it up I use this as the base for every real app I'm building now, not just AI image apps. Ship something maintainable. Validate fast. (link below)
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Every week another AI image app hits the top charts. The window is open, but not forever. That's why I built Platano. Today it's production ready. What's inside: - Expo SDK 56 - Agent skills for setup, rebranding, adding languages, and more - Fully native iOS and Android - Material You on Android, Liquid Glass with iOS 18 fallbacks - Onboarding flow battle-tested in my AI Tattoo app (live in the App Store) - RevenueCat payments - AI editor with Nano Banana 2 - Clean architecture your agent can extend - Interactive CLI to spin it up I use this as the base for every real app I'm building now, not just AI image apps. Ship something maintainable. Validate fast. (link below)

Beto

11,477 просмотров • 2 месяцев назад

🚨 NEW VIDEO ALERT! Join me as we dive into integrating Computer Vision with Roboflow into your Expo iOS app using React-Native Skia! While you guys were tinkering with your LLMs you all forgot about how amazing Computer Vision is hehe 🤭 In this tutorial video, we’ll create a Swing Plane Tracer inspired by Shot Tracer, a technology utilized by LIV Golf! As always, I have so many people to thank for making this video possible. First, a huge shoutout to William Candillon for bringing video functionality to Skia! It’s still fresh, but the possibilities are incredibly exciting! 🔥 Next, thanks Tomasz Sapeta 𝝠! This is my second time leveraging Expo Modules to bridge Expo with Native iOS Code, making the transition between the two platforms truly seamless. ❤️ And of course, I can’t forget Roboflow. While many object tracking demos exist, they often feel out of reach for the average person. The models you provide for hobbyists are simply AMAZING! As someone who grew up playing competitive golf, I may not have the time to play seriously anymore, but my passion for the game remains strong. I wish I could share this tool with my 14-year-old self—he would be blown away! Thank you for helping me bring my dream project to life! 🙂
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🚨 NEW VIDEO ALERT! Join me as we dive into integrating Computer Vision with Roboflow into your Expo iOS app using React-Native Skia! While you guys were tinkering with your LLMs you all forgot about how amazing Computer Vision is hehe 🤭 In this tutorial video, we’ll create a Swing Plane Tracer inspired by Shot Tracer, a technology utilized by LIV Golf! As always, I have so many people to thank for making this video possible. First, a huge shoutout to William Candillon for bringing video functionality to Skia! It’s still fresh, but the possibilities are incredibly exciting! 🔥 Next, thanks Tomasz Sapeta 𝝠! This is my second time leveraging Expo Modules to bridge Expo with Native iOS Code, making the transition between the two platforms truly seamless. ❤️ And of course, I can’t forget Roboflow. While many object tracking demos exist, they often feel out of reach for the average person. The models you provide for hobbyists are simply AMAZING! As someone who grew up playing competitive golf, I may not have the time to play seriously anymore, but my passion for the game remains strong. I wish I could share this tool with my 14-year-old self—he would be blown away! Thank you for helping me bring my dream project to life! 🙂

Daniel Friyia 🇨🇦

13,930 просмотров • 1 год назад

Today we're launching Accomplish FREE - powered by our new hybrid model router. Since we launched Accomplish a few weeks ago, we've been blown away by what users are building with it. Hundreds of thousands of you downloaded the app and took it for a spin, but one thing kept coming up: not everyone wants to bring their own API key just to get started. So today we're fixing that with free, built-in models - made possible by a massive shift that's happened in just the past few weeks: the rise of fully hosted open-weight models - locally on your Windows machine with NVIDIA NVIDIA GeForce, on your Mac with Apple MLX, or on Accomplish cloud, for FREE. Our new hybrid routing algorithm dynamically routes between cloud models and models running locally on your machine - optimizing for local execution by automatically detecting your hardware capabilities and each sub-task's complexity: coding, visuals, simple classifications - every LLM call is routed to the best model. We also brought some of our favorite enterprise features to the free tier: scheduled task dispatch, Google Workspace integration (Google Drive Docs, Sheets, Slides) via the new Google Workspace CLI, native Slack MCP connectivity, and more. Accomplish FREE is available for macOS, Windows, and Linux. Download, send a task - and boom, it just works with ZERO configuration! Download link in bio / first comment >>
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Today we're launching Accomplish FREE - powered by our new hybrid model router. Since we launched Accomplish a few weeks ago, we've been blown away by what users are building with it. Hundreds of thousands of you downloaded the app and took it for a spin, but one thing kept coming up: not everyone wants to bring their own API key just to get started. So today we're fixing that with free, built-in models - made possible by a massive shift that's happened in just the past few weeks: the rise of fully hosted open-weight models - locally on your Windows machine with NVIDIA NVIDIA GeForce, on your Mac with Apple MLX, or on Accomplish cloud, for FREE. Our new hybrid routing algorithm dynamically routes between cloud models and models running locally on your machine - optimizing for local execution by automatically detecting your hardware capabilities and each sub-task's complexity: coding, visuals, simple classifications - every LLM call is routed to the best model. We also brought some of our favorite enterprise features to the free tier: scheduled task dispatch, Google Workspace integration (Google Drive Docs, Sheets, Slides) via the new Google Workspace CLI, native Slack MCP connectivity, and more. Accomplish FREE is available for macOS, Windows, and Linux. Download, send a task - and boom, it just works with ZERO configuration! Download link in bio / first comment >>

Or Hiltch

103,543 просмотров • 4 месяцев назад

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.
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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 просмотров • 2 месяцев назад

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)
1:02

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 просмотров • 1 год назад

My New Market Dashboard Inspired by Jeff Jeff Sun, CFTe and Clement Ang , I originally used Excel to design my own market dashboard for tracking sectors, industries, and country ETFs. However, with recent advancements in AI-assisted coding, I’ve found that an HTML-based version is not only more aesthetic but also far more powerful. It allows for advanced features like calculating ATR multiple extensions from the 50D SMA and Volatility Adjusted Relative Strength. I’ve also integrated TradingView charts and a section for tracking macro events directly on the page. I’m now sharing this dashboard with FinTwit community. If you aren’t interested in the code, you can simply view the static page after 16:30 ET, as it is automatically updated via GitHub. If you’d like to customize it, the source code is open-source and available for you to modify. Thanks for your support! Features: • Indices, sectors, industries, country ETFs • ATR%, ATR multiple extension from 50SMA, 1M-VARS (volatility-adjusted relative strength) • Macro events calendar • TradingView charts • Auto-refresh Mon–Fri 16:30 ET via GitHub Actions • Data: Yahoo Finance + investpy Live site (no setup): Source code:
0:41

My New Market Dashboard Inspired by Jeff Jeff Sun, CFTe and Clement Ang , I originally used Excel to design my own market dashboard for tracking sectors, industries, and country ETFs. However, with recent advancements in AI-assisted coding, I’ve found that an HTML-based version is not only more aesthetic but also far more powerful. It allows for advanced features like calculating ATR multiple extensions from the 50D SMA and Volatility Adjusted Relative Strength. I’ve also integrated TradingView charts and a section for tracking macro events directly on the page. I’m now sharing this dashboard with FinTwit community. If you aren’t interested in the code, you can simply view the static page after 16:30 ET, as it is automatically updated via GitHub. If you’d like to customize it, the source code is open-source and available for you to modify. Thanks for your support! Features: • Indices, sectors, industries, country ETFs • ATR%, ATR multiple extension from 50SMA, 1M-VARS (volatility-adjusted relative strength) • Macro events calendar • TradingView charts • Auto-refresh Mon–Fri 16:30 ET via GitHub Actions • Data: Yahoo Finance + investpy Live site (no setup): Source code:

Will Hu

152,108 просмотров • 5 месяцев назад

QVAC SDK 0.13.0 is live, and this version brings a lot of exciting updates! Local AI now plugs into your coding agent, ships as a desktop app in one command, and runs even more models. Highlights: NEW INTEGRATIONS - OpenCode and coding agents: the new @qvac/ai-sdk-provider makes QVAC a local provider. Less setup, same-model requests queue cleanly, and managed mode starts and supervises qvac serve for you. - Broader OpenAI-compatible API, validated across supported flows so covered capabilities stay consistent and testable. - Turn your QVAC project into a real desktop app for Mac, Windows, or Linux with a single command. The new Electron plugin handles the packaging and keeps the app small by including only what it needs. NEW MODELS - New pi0.5 model support - run a vision-language "robot brain" on a single ordinary graphics card, at full accuracy. - Image-to-video, fully local, via the Wan2.1 model in the Diffusion addon. - New BCI add-on: brain-computer interface transcription, fully local. Decode recorded neural signals into text on-device via the Whisper.cpp-based BCI model. IMPROVEMENTS - Whisper GPU transcription on Android, auto-picking the best backend (OpenCL on Adreno 700+, Vulkan elsewhere), unified on one ggml engine. - Parakeet steadier on mobile, with real end-of-utterance detection for streaming. - Supertonic TTS now runs full GPU across Metal, Vulkan, and OpenCL, with native streaming.
1:39

QVAC SDK 0.13.0 is live, and this version brings a lot of exciting updates! Local AI now plugs into your coding agent, ships as a desktop app in one command, and runs even more models. Highlights: NEW INTEGRATIONS - OpenCode and coding agents: the new @qvac/ai-sdk-provider makes QVAC a local provider. Less setup, same-model requests queue cleanly, and managed mode starts and supervises qvac serve for you. - Broader OpenAI-compatible API, validated across supported flows so covered capabilities stay consistent and testable. - Turn your QVAC project into a real desktop app for Mac, Windows, or Linux with a single command. The new Electron plugin handles the packaging and keeps the app small by including only what it needs. NEW MODELS - New pi0.5 model support - run a vision-language "robot brain" on a single ordinary graphics card, at full accuracy. - Image-to-video, fully local, via the Wan2.1 model in the Diffusion addon. - New BCI add-on: brain-computer interface transcription, fully local. Decode recorded neural signals into text on-device via the Whisper.cpp-based BCI model. IMPROVEMENTS - Whisper GPU transcription on Android, auto-picking the best backend (OpenCL on Adreno 700+, Vulkan elsewhere), unified on one ggml engine. - Parakeet steadier on mobile, with real end-of-utterance detection for streaming. - Supertonic TTS now runs full GPU across Metal, Vulkan, and OpenCL, with native streaming.

QVAC

20,931,912 просмотров • 1 месяц назад

Introducing Workshop: cloud + on-device agentic AI. And to celebrate, we're giving away $250k in Google Gemini AI credits. (details below). The future of AI work is neither cloud-based nor local. It's both. In Workshop Cloud, you can use agents powered by frontier models like Claude and/or open source models like Z.ai's GLM-5 to build internal tools, dashboards, and AI web apps. Or, breeze through tasks like managing your Google and Meta Ads. In Workshop Desktop, you can do all the same right on your computer, plus make desktop apps, mobile apps, and 3D creations. Our favorite part? You can power the full agent experience with local models like Qwen 3.5 family on your computer. Fully offline. 2026 is the year in which local models for agentic tasks will become viable for mainstream use. But the setup for tools like OpenClaw is like setting up Linux from scratch on your computer. Workshop Desktop is one-click to install on Windows, Mac, and Linux. It recommends which open source model you should use for your hardware and lets you download and run it right in the app. And its agent harness allows you to chat, create websites, build personal utilities, and analyze data. 100% offline. Or multitask with AI models in the cloud while running other agent threads locally. Start in Workshop Cloud when you want flexibility and speed. Download your project and continue in Workshop Desktop when you want local files, privacy, and/or better performance on large code bases. Publish from either. The agent tooling space is maturing and discerning users have come to expect a lot from their tools. We've packed Workshop with features to help you 10x your productivity. - Native support for skills - Autocompaction for seamless context management - Built-in AI for your apps - Dozens of connectors, like Google Drive, Big Query, and Supabase - dbt integration to ground your dashboards in your semantic layer - Native Github integration - Private app deployment - ... and more (+ we're shipping super fast) To access the free credit offer, RT this post and reply with "Workshop". Make sure you are following us so we can DM you the instructions to redeem. - First 100 to RT + comment get $500 in credits. - Everyone else gets up to $250 And thanks to our partners Modal, Google Gemini, and Z.ai!
3:50

Introducing Workshop: cloud + on-device agentic AI. And to celebrate, we're giving away $250k in Google Gemini AI credits. (details below). The future of AI work is neither cloud-based nor local. It's both. In Workshop Cloud, you can use agents powered by frontier models like Claude and/or open source models like Z.ai's GLM-5 to build internal tools, dashboards, and AI web apps. Or, breeze through tasks like managing your Google and Meta Ads. In Workshop Desktop, you can do all the same right on your computer, plus make desktop apps, mobile apps, and 3D creations. Our favorite part? You can power the full agent experience with local models like Qwen 3.5 family on your computer. Fully offline. 2026 is the year in which local models for agentic tasks will become viable for mainstream use. But the setup for tools like OpenClaw is like setting up Linux from scratch on your computer. Workshop Desktop is one-click to install on Windows, Mac, and Linux. It recommends which open source model you should use for your hardware and lets you download and run it right in the app. And its agent harness allows you to chat, create websites, build personal utilities, and analyze data. 100% offline. Or multitask with AI models in the cloud while running other agent threads locally. Start in Workshop Cloud when you want flexibility and speed. Download your project and continue in Workshop Desktop when you want local files, privacy, and/or better performance on large code bases. Publish from either. The agent tooling space is maturing and discerning users have come to expect a lot from their tools. We've packed Workshop with features to help you 10x your productivity. - Native support for skills - Autocompaction for seamless context management - Built-in AI for your apps - Dozens of connectors, like Google Drive, Big Query, and Supabase - dbt integration to ground your dashboards in your semantic layer - Native Github integration - Private app deployment - ... and more (+ we're shipping super fast) To access the free credit offer, RT this post and reply with "Workshop". Make sure you are following us so we can DM you the instructions to redeem. - First 100 to RT + comment get $500 in credits. - Everyone else gets up to $250 And thanks to our partners Modal, Google Gemini, and Z.ai!

Workshop AI

28,745 просмотров • 4 месяцев назад

Where are we in the consumer AI cycle? I went on Bloomberg Technology to discuss what we a16z are seeing from studying the data since 2023. So far, a rising tide has lifted all boats. But, we're starting to see ChatGPT, Claude, and Gemini diverge in approach to winning the consumer. My main takeaways 👇 1. ChatGPT is still dominant - but the race for "default AI" is on. On web, ChatGPT is 2.7x larger than #2 Gemini. On mobile, 2.5x larger. But, ~20% of weekly ChatGPT users are now also using Gemini in the same week - and Claude is growing quickly with developer and prosumer use cases. 2. AI is going global - but not where you'd expect. We built a per-capita adoption index by country - Singapore ranks #1, followed by Hong Kong, the UAE, South Korea. The U.S. is down at #20 - with both a smaller % of white collar workers for whom AI is valuable, and more cultural pushback. 3. The creative tool category has completely transformed. In our first edition, 7 of 9 creative tools were image generators. Today only 3 remain - video, music, and voice have taken their slots. OpenAI and Google focusing on native image gen means you need to be specialized to standalone. 4. Agents are here. Manus and Genspark both made the web ranks - and if our data was for February (not January), OpenClaw would have debuted at #30! But, it has still not escaped containment to the mainstream consumer - expect more packaging around agents for specific use cases. 5. AI is leaving the browser. Between Cursor, Claude Code + Cowork, Granola, Wispr, and others, users are increasingly interacting with AI via desktop apps. We will have to evolve our methodology as AI becomes more ambient - and can't be measured by a website hit or app download. 6. AI-enhanced (not AI-native) players are waking up. For the first time, we included six legacy products that are now meaningfully AI enabled. Only 3 made the top 15 on web - including Notion, which reports 50% of ARR from AI. The others? Canva, Freepik, Grammarly, Capcut, and Picsart. Thanks to Caroline Hyde and Ed Ludlow for having me on - can't wait to see what the report looks like in another 6 months!
3:42

Where are we in the consumer AI cycle? I went on Bloomberg Technology to discuss what we a16z are seeing from studying the data since 2023. So far, a rising tide has lifted all boats. But, we're starting to see ChatGPT, Claude, and Gemini diverge in approach to winning the consumer. My main takeaways 👇 1. ChatGPT is still dominant - but the race for "default AI" is on. On web, ChatGPT is 2.7x larger than #2 Gemini. On mobile, 2.5x larger. But, ~20% of weekly ChatGPT users are now also using Gemini in the same week - and Claude is growing quickly with developer and prosumer use cases. 2. AI is going global - but not where you'd expect. We built a per-capita adoption index by country - Singapore ranks #1, followed by Hong Kong, the UAE, South Korea. The U.S. is down at #20 - with both a smaller % of white collar workers for whom AI is valuable, and more cultural pushback. 3. The creative tool category has completely transformed. In our first edition, 7 of 9 creative tools were image generators. Today only 3 remain - video, music, and voice have taken their slots. OpenAI and Google focusing on native image gen means you need to be specialized to standalone. 4. Agents are here. Manus and Genspark both made the web ranks - and if our data was for February (not January), OpenClaw would have debuted at #30! But, it has still not escaped containment to the mainstream consumer - expect more packaging around agents for specific use cases. 5. AI is leaving the browser. Between Cursor, Claude Code + Cowork, Granola, Wispr, and others, users are increasingly interacting with AI via desktop apps. We will have to evolve our methodology as AI becomes more ambient - and can't be measured by a website hit or app download. 6. AI-enhanced (not AI-native) players are waking up. For the first time, we included six legacy products that are now meaningfully AI enabled. Only 3 made the top 15 on web - including Notion, which reports 50% of ARR from AI. The others? Canva, Freepik, Grammarly, Capcut, and Picsart. Thanks to Caroline Hyde and Ed Ludlow for having me on - can't wait to see what the report looks like in another 6 months!

Olivia Moore

10,851 просмотров • 5 месяцев назад

QVAC SDK 0.12.0 is now live, bringing longer context, increased memory optimisation, new modalities, and broader ecosystem support directly to your device. Key Features and Updates: - TurboQuant KV-Cache Quantization: Fit much longer context in the same memory. TurboQuant, an algorithm from Google Research, compresses the KV cache by up to 5x, near-lossless. - Text-to-Video: Generate video from a text prompt, fully local, with the new wan2.1 model in the Diffusion addon - Apple Metal Performance for Flux2-klein: Diffusion on Apple Silicon now matches MLX performance, the native benchmark for Apple GPUs - Robot Control (new VLA addon): A GGML-based Vision-Language-Action addon brings fast, efficient robot control to edge devices - Coding Assistant / Harness Support: QVAC now works with OpenCode and OpenClaw as a local provider. A new @qvac/ai-sdk-provider package automates model registry and provider integration - Cross-Platform Voice: Text-to-speech and Parakeet transcription moved from ONNX to the GGML engine for better CPU and GPU support on macOS, iOS, Windows, Linux, and Android. Parakeet also adds long-term streaming diarization (tracking who spoke when on live audio) - Faster Lightweight Visual Classification: A new GGML-based Classification addon delivers millisecond-level classification, useful where a vision-language model (VLM) would be unnecessarily slow - Under the Hood: Fabric synced to llama.cpp v8828 (from v8189), plus GPU acceleration added to image-upscale models for faster results Full release notes:
1:29

QVAC SDK 0.12.0 is now live, bringing longer context, increased memory optimisation, new modalities, and broader ecosystem support directly to your device. Key Features and Updates: - TurboQuant KV-Cache Quantization: Fit much longer context in the same memory. TurboQuant, an algorithm from Google Research, compresses the KV cache by up to 5x, near-lossless. - Text-to-Video: Generate video from a text prompt, fully local, with the new wan2.1 model in the Diffusion addon - Apple Metal Performance for Flux2-klein: Diffusion on Apple Silicon now matches MLX performance, the native benchmark for Apple GPUs - Robot Control (new VLA addon): A GGML-based Vision-Language-Action addon brings fast, efficient robot control to edge devices - Coding Assistant / Harness Support: QVAC now works with OpenCode and OpenClaw as a local provider. A new @qvac/ai-sdk-provider package automates model registry and provider integration - Cross-Platform Voice: Text-to-speech and Parakeet transcription moved from ONNX to the GGML engine for better CPU and GPU support on macOS, iOS, Windows, Linux, and Android. Parakeet also adds long-term streaming diarization (tracking who spoke when on live audio) - Faster Lightweight Visual Classification: A new GGML-based Classification addon delivers millisecond-level classification, useful where a vision-language model (VLM) would be unnecessarily slow - Under the Hood: Fabric synced to llama.cpp v8828 (from v8189), plus GPU acceleration added to image-upscale models for faster results Full release notes:

QVAC

9,932,369 просмотров • 2 месяцев 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

118,242 просмотров • 1 год назад

GeoLibre v1.2.0 is here! 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. This release packs in 35+ pull requests of new capabilities. A few highlights: - Run SQL right in the browser. The SQL Workspace pairs DuckDB Spatial with a new in-browser PostGIS engine (PGlite), so you can query layers, local files, and remote URLs without a server. - A smarter attribute table. Add fields, run a field calculator, and explore your data with a built-in Charts panel (histogram, scatter, bar, line, and box plots). - More ways to add data. OpenStreetMap PBF extracts, Cloud-Optimized NetCDF/HDF via kerchunk, georeferenced video overlays, authenticated 3D Tiles, and a Layer builder for custom overlays. - Better visualization. Heatmap rendering, point clustering, and H3 hexagonal grids for spatial binning. - New analysis and routing. A Directions plugin, plus Spatial Join, Select by Value, and Select by Location vector tools. - Print and share. A print layout composer that exports your map to PNG or PDF. - Work faster. A command palette (Ctrl/Cmd + K), global keyboard shortcuts, and undo/redo for layer and style operations. - Built for everyone. New internationalization framework, an accessibility pass with automated axe checks, an installable offline-capable PWA web build, React error boundaries, and Playwright end-to-end tests. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #DuckDB #GeoLibre
1:25

GeoLibre v1.2.0 is here! 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. This release packs in 35+ pull requests of new capabilities. A few highlights: - Run SQL right in the browser. The SQL Workspace pairs DuckDB Spatial with a new in-browser PostGIS engine (PGlite), so you can query layers, local files, and remote URLs without a server. - A smarter attribute table. Add fields, run a field calculator, and explore your data with a built-in Charts panel (histogram, scatter, bar, line, and box plots). - More ways to add data. OpenStreetMap PBF extracts, Cloud-Optimized NetCDF/HDF via kerchunk, georeferenced video overlays, authenticated 3D Tiles, and a Layer builder for custom overlays. - Better visualization. Heatmap rendering, point clustering, and H3 hexagonal grids for spatial binning. - New analysis and routing. A Directions plugin, plus Spatial Join, Select by Value, and Select by Location vector tools. - Print and share. A print layout composer that exports your map to PNG or PDF. - Work faster. A command palette (Ctrl/Cmd + K), global keyboard shortcuts, and undo/redo for layer and style operations. - Built for everyone. New internationalization framework, an accessibility pass with automated axe checks, an installable offline-capable PWA web build, React error boundaries, and Playwright end-to-end tests. Try the live demo: Star it on GitHub: Docs and roadmap: Release notes: #GIS #OpenSource #Geospatial #MapLibre #WebGIS #DuckDB #GeoLibre

Qiusheng Wu

39,959 просмотров • 2 месяцев назад

QVAC SDK 0.15.0 is live. This release adds multiple prompts batching, brings a native AMD GPU backend to the stack, moves more vision encoders onto mobile GPUs, and adds a second local coding-agent integration. Main highlights: - Prompt batching for the LLM addon. Batch multiple prompts into one job and process them concurrently, with each answer returned the moment its generation finishes. - Native AMD GPU backend. A first-class HIP/ROCm backend in @qvac/vla-ggml, auto-selected over Vulkan with clean fallback when ROCm is absent. - A second local coding agent. OpenClaw joins OpenCode for local, cloud-free agent workflows. AGENTS - OpenCode plugin update (@qvac/opencode-plugin). Aligned with the current SDK, CLI, and AI SDK provider packages. A fresh install runs OpenCode against managed local QVAC models out of the box, from the default qvac/qwen3.5-9b, with no manual qvac serve setup. - OpenClaw plugin (@qvac/openclaw-plugin). A second coding-agent integration alongside OpenCode. A fresh setup installs the plugin, creates a local qvac provider through onboarding, and runs a QVAC model through OpenClaw🦞's local service path. LANGUAGE MODELS - Prompt batching (LLM addon). Batch multiple prompts in one job and run them concurrently, each answer returns the moment its generation finishes, no waiting on the others. - Reasoning-context trimming on hybrid + recurrent models (@qvac/llm-llamacpp). remove_thinking_from_context now works beyond pure-attention models. Same JS API, no throw. VOICE AND SPEECH - Transcription (transcription-parakeet 0.9.0). More robust CPU fallback on GPU failure and a faster Vulkan backend on Pixel 9. - Text-to-speech features (tts-ggml 0.4.0). Adds LavaSR for noise removal and adjustable output frequency up to 48 kHz, plus Japanese via Chatterbox. - Text-to-speech fixes (tts-ggml 0.4.1). CPU fallback on GPU failure, a q8_0 KV crash fix on Metal with Chatterbox. VISION - Qwen3.5 vision encoder on GPU (Android). Image encoder moves onto the phone GPU, with a smarter tile-grid preprocessor and default image-token caps, for flagship Android: Vulkan on Mali (Pixel 9 Pro) and OpenCL on Adreno 830 (Galaxy S25). - Gemma-4 vision encoder on GPU (Android). Vision encoder runs on the phone GPU instead of CPU, same flagship Android targets. PLATFORM AND PERFORMANCE - AMD GPU backend (@qvac/vla-ggml). Native HIP/ROCm backend, auto-selected over Vulkan with clean fallback when ROCm is absent (Linux x64 only). Comes with ~23% faster than Vulkan, ~14% faster than PyTorch-ROCm, parity preserved. Unified code style. A cleaner, more consistent, easier-to-contribute codebase. Let's build. npm install @qvac/sdk
1:25

QVAC SDK 0.15.0 is live. This release adds multiple prompts batching, brings a native AMD GPU backend to the stack, moves more vision encoders onto mobile GPUs, and adds a second local coding-agent integration. Main highlights: - Prompt batching for the LLM addon. Batch multiple prompts into one job and process them concurrently, with each answer returned the moment its generation finishes. - Native AMD GPU backend. A first-class HIP/ROCm backend in @qvac/vla-ggml, auto-selected over Vulkan with clean fallback when ROCm is absent. - A second local coding agent. OpenClaw joins OpenCode for local, cloud-free agent workflows. AGENTS - OpenCode plugin update (@qvac/opencode-plugin). Aligned with the current SDK, CLI, and AI SDK provider packages. A fresh install runs OpenCode against managed local QVAC models out of the box, from the default qvac/qwen3.5-9b, with no manual qvac serve setup. - OpenClaw plugin (@qvac/openclaw-plugin). A second coding-agent integration alongside OpenCode. A fresh setup installs the plugin, creates a local qvac provider through onboarding, and runs a QVAC model through OpenClaw🦞's local service path. LANGUAGE MODELS - Prompt batching (LLM addon). Batch multiple prompts in one job and run them concurrently, each answer returns the moment its generation finishes, no waiting on the others. - Reasoning-context trimming on hybrid + recurrent models (@qvac/llm-llamacpp). remove_thinking_from_context now works beyond pure-attention models. Same JS API, no throw. VOICE AND SPEECH - Transcription (transcription-parakeet 0.9.0). More robust CPU fallback on GPU failure and a faster Vulkan backend on Pixel 9. - Text-to-speech features (tts-ggml 0.4.0). Adds LavaSR for noise removal and adjustable output frequency up to 48 kHz, plus Japanese via Chatterbox. - Text-to-speech fixes (tts-ggml 0.4.1). CPU fallback on GPU failure, a q8_0 KV crash fix on Metal with Chatterbox. VISION - Qwen3.5 vision encoder on GPU (Android). Image encoder moves onto the phone GPU, with a smarter tile-grid preprocessor and default image-token caps, for flagship Android: Vulkan on Mali (Pixel 9 Pro) and OpenCL on Adreno 830 (Galaxy S25). - Gemma-4 vision encoder on GPU (Android). Vision encoder runs on the phone GPU instead of CPU, same flagship Android targets. PLATFORM AND PERFORMANCE - AMD GPU backend (@qvac/vla-ggml). Native HIP/ROCm backend, auto-selected over Vulkan with clean fallback when ROCm is absent (Linux x64 only). Comes with ~23% faster than Vulkan, ~14% faster than PyTorch-ROCm, parity preserved. Unified code style. A cleaner, more consistent, easier-to-contribute codebase. Let's build. npm install @qvac/sdk

QVAC

29,259,075 просмотров • 1 месяц назад

How I created these landing pages with Gemini 3 from start to finish First, I start with the hero section. It includes the nav bar, eyebrow, headline, subheadline, cta, social proof and visual. I spend 50% of the time here because it sets the colors, typography, spacing, which AI uses for the rest of the site consistently. “Create the hero section for my {app} called {name} in the style of {reference_site}”. Pro tip: use a screenshot and you’ll get way better results. Let’s get into the details. For icons, I prompt: “Use Iconify {icon set name}”. Most people use Lucide, but there are hundreds of open-source sets on Iconify like Solar, HeroIcons, Iconoir, Phosphor, etc. Just need to mention in the prompt. Same for custom fonts. For the animation, I prompt: “Animate fade in, slide in, blur in, element by element. Use 'both' instead of 'forwards'. Don't use opacity 0.”. This creates a subtle intro animation the first time users land on your page. Gemini 3 is an excellent animator. For example, I created the beam animation with this prompt: “Add noodles that connect and beam animate into the right circle. Add subtle details to the right beam animation circle with sonar and decorations.” For background animation, I use Unicorn Studio. Remix one of their templates and watch people click on your cover like crazy. What can I say, people love lasers. In the hero or right below it, social proof is super important. You can put ratings or logos, or both. For logos, you can prompt: “Animate the logos with marquee animation looping infinitely using duplicated items and alpha mask.”. Now the CTA. AI always creates basic buttons, which is fine 99% of the time. But Gemini 3 now sets a high bar for baseline design, so you will need to stand out. That’s why I put the extra human touch on animations and lickable buttons. I suggest browsing UIVerse and Codepen for buttons and reference the code in your prompt: “Change main button {code} and secondary button {code}. Add a 1px border beam animation around the pill-shaped main button on hover.”. Once you’re happy with the hero, you’ll want to craft new sections based on your business. Features, action plan, pricing, testimonials, FAQ, CTA and footer are the popular ones. Insert a screenshot and prompt “Adapt a new section, change texts, names and numbers”. Gemini 3 is very smart. It reads your existing styles and site concept and will tastefully mold new designs to fit perfectly into your current site. Finally, repeat the same prompts for icons, buttons and animations. Use midjourney images and remix using Nano Banana Pro. Ask ChatGPT to come up with better headlines, features, ctas, etc. Don’t skip the human part, this is where you’re irreplaceable. Start prompting top-tier landing pages and watch your numbers grow.
0:37

How I created these landing pages with Gemini 3 from start to finish First, I start with the hero section. It includes the nav bar, eyebrow, headline, subheadline, cta, social proof and visual. I spend 50% of the time here because it sets the colors, typography, spacing, which AI uses for the rest of the site consistently. “Create the hero section for my {app} called {name} in the style of {reference_site}”. Pro tip: use a screenshot and you’ll get way better results. Let’s get into the details. For icons, I prompt: “Use Iconify {icon set name}”. Most people use Lucide, but there are hundreds of open-source sets on Iconify like Solar, HeroIcons, Iconoir, Phosphor, etc. Just need to mention in the prompt. Same for custom fonts. For the animation, I prompt: “Animate fade in, slide in, blur in, element by element. Use 'both' instead of 'forwards'. Don't use opacity 0.”. This creates a subtle intro animation the first time users land on your page. Gemini 3 is an excellent animator. For example, I created the beam animation with this prompt: “Add noodles that connect and beam animate into the right circle. Add subtle details to the right beam animation circle with sonar and decorations.” For background animation, I use Unicorn Studio. Remix one of their templates and watch people click on your cover like crazy. What can I say, people love lasers. In the hero or right below it, social proof is super important. You can put ratings or logos, or both. For logos, you can prompt: “Animate the logos with marquee animation looping infinitely using duplicated items and alpha mask.”. Now the CTA. AI always creates basic buttons, which is fine 99% of the time. But Gemini 3 now sets a high bar for baseline design, so you will need to stand out. That’s why I put the extra human touch on animations and lickable buttons. I suggest browsing UIVerse and Codepen for buttons and reference the code in your prompt: “Change main button {code} and secondary button {code}. Add a 1px border beam animation around the pill-shaped main button on hover.”. Once you’re happy with the hero, you’ll want to craft new sections based on your business. Features, action plan, pricing, testimonials, FAQ, CTA and footer are the popular ones. Insert a screenshot and prompt “Adapt a new section, change texts, names and numbers”. Gemini 3 is very smart. It reads your existing styles and site concept and will tastefully mold new designs to fit perfectly into your current site. Finally, repeat the same prompts for icons, buttons and animations. Use midjourney images and remix using Nano Banana Pro. Ask ChatGPT to come up with better headlines, features, ctas, etc. Don’t skip the human part, this is where you’re irreplaceable. Start prompting top-tier landing pages and watch your numbers grow.

Meng To

287,300 просмотров • 8 месяцев назад

🐆 Rapid-MLX v0.12 is here. We’ve officially evolved from a simple chat app into a full-fledged, on-device AI studio for Apple Silicon! 🖥️✨ We didn't just push the MLX inference engine to its limits and expand support for a massive lineup of local open-source models—we are alpha-launching the highly anticipated Desktop Version. (A huge shoutout to the IoTeX community for grinding through the closed beta with us. Your feedback was incredible and helped shape this beast.) Here are the game-changing features you can run on your Mac right now, 100% free and 100% offline 👇 🚀 Blazing Fast Local LLMs Run anything from 4B up to Qwen3.5-122B completely offline. No guessing games—we recommend models matched perfectly to your Mac's actual RAM. Rich chat includes syntax highlighting, markdown tables, and honest tok/s metrics. 🎨 Local Image Generation A brand new Images tab to render directly on your machine. Pick a model (FLUX.2-klein, Z-Image-Turbo), prompt, and refine. Everything lands in a visual filmstrip. 👁️ Vision & Live Web Tools Attach an image and chat about it with local vision. Need real-time data? Our built-in web tools (weather, search, page-fetch) run mid-answer with strict, transparent privacy controls. 🤖 Plug-and-Play Coding Agents Wire up Claude Code, Codex, Cline, or Continue in seconds. One copy-paste from the Launch tab spins up a local OpenAI/Anthropic-compatible endpoint. 🔒 Private by Design Everything runs on-device. Signed, notarized, and entirely local. Your data stays yours. Turn your Mac into an AI powerhouse today. ⚡️
0:37

🐆 Rapid-MLX v0.12 is here. We’ve officially evolved from a simple chat app into a full-fledged, on-device AI studio for Apple Silicon! 🖥️✨ We didn't just push the MLX inference engine to its limits and expand support for a massive lineup of local open-source models—we are alpha-launching the highly anticipated Desktop Version. (A huge shoutout to the IoTeX community for grinding through the closed beta with us. Your feedback was incredible and helped shape this beast.) Here are the game-changing features you can run on your Mac right now, 100% free and 100% offline 👇 🚀 Blazing Fast Local LLMs Run anything from 4B up to Qwen3.5-122B completely offline. No guessing games—we recommend models matched perfectly to your Mac's actual RAM. Rich chat includes syntax highlighting, markdown tables, and honest tok/s metrics. 🎨 Local Image Generation A brand new Images tab to render directly on your machine. Pick a model (FLUX.2-klein, Z-Image-Turbo), prompt, and refine. Everything lands in a visual filmstrip. 👁️ Vision & Live Web Tools Attach an image and chat about it with local vision. Need real-time data? Our built-in web tools (weather, search, page-fetch) run mid-answer with strict, transparent privacy controls. 🤖 Plug-and-Play Coding Agents Wire up Claude Code, Codex, Cline, or Continue in seconds. One copy-paste from the Launch tab spins up a local OpenAI/Anthropic-compatible endpoint. 🔒 Private by Design Everything runs on-device. Signed, notarized, and entirely local. Your data stays yours. Turn your Mac into an AI powerhouse today. ⚡️

raullen

33,690 просмотров • 2 дней назад

What's next for OpenTUI? Here's a technical write-up. Over the last few months OpenTUI gained a lot of stability improvements, new unnecessary but fun features like live audio streaming, and useful features like rendering to the scrollback buffer mixed with a live TUI, called footer mode. Overall the feature set enables building large and complex applications. React and Solid make it super simple and convenient. There is still so much to do though. Three big milestones we have set out to achieve are: - Moving most of the behavioural logic currently living in TypeScript down to the native Zig core - Node compatibility - Optimizing the hell out of primitives like text rendering The render tree mechanisms are currently only usable from TypeScript. Think of the DOM, but controllable like a scene graph. Elements in the render tree are called renderables. They can expose a render method to draw themselves. All renderables are derived from a BaseRenderable. Renderables and the render tree will become native primitives. Building blocks usable from any language bindings. Reducing the TypeScript bindings to a very thin layer, with all the behavioural logic living in the native binary. Moving this down is not just a matter of porting TypeScript classes to Zig. TypeScript currently owns the tree, dirty-state propagation, layout reads, culling, and render ordering. If it still has to walk every node and call into native code for each step, we keep most of the complexity and add FFI overhead. Whole passes and their state need to move together. We took a big step towards this recently by building yoga-layout into the native binary. It exposes part of the official yoga-layout TypeScript package via FFI. Only the API surface that is actually used by OpenTUI. Covered by the test suite of the original yoga-layout package. This already gave a median speedup of ~2.5x, and up to 30x for narrow scenarios. The yoga-layout integration is useful beyond the speedup. Built-in text and editor measurement can now happen entirely in native code during layout instead of calling back into JavaScript. I ran an experiment last month taking this even further, having GPT 5.6 port yoga-layout from C++ to Zig, which gave extremely good results. It would be a burden to maintain right now though, so that's off the table for now. I might come back to it. Simon Klee is working relentlessly on Node compatibility and already has a full Node version of OpenCode running. Node got FFI support in v26.4.0, thanks to help from the Node community, namely Matteo Collina and Paolo Insogna. Behaviour and interfaces seem similar between Node and Bun, but there are some major differences. To get the best performance out of the Node FFI implementation, its usage has to follow some rules. Node has three ways to call native functions: the generic C++/libffi path, the SharedBuffer path, and the V8 Fast API. The generic path converts every argument in Node's C++ layer and then calls the function through libffi. It is flexible, but also the slowest option for frequently called functions. The SharedBuffer path is a middle ground. JavaScript writes scalar values and BigInt pointers into a small per-function buffer, reducing some conversion work. The actual native call still goes through libffi though. Typed arrays used as pointers cannot be packed into this buffer and fall back to the generic path. The path we really want is the V8 Fast API. Node generates a small machine-code trampoline for the exact function signature, allowing optimized JavaScript to call the native function without going through the generic converter or libffi. This only applies to JavaScript-to-native calls. Callbacks from native code into JavaScript still use libffi closures. Getting onto this path is quite strict. A signature can have at most eight arguments and everything must fit into CPU registers. x86-64 Unix systems have room for six GP (general-purpose) and eight FP (floating-point) arguments. AArch64 has room for seven GP and eight FP arguments. Anything that spills onto the stack falls back to a slower path. These are Node fast-path restrictions, not general FFI restrictions. Bun also does not support passing structs by value through its current FFI API. OpenTUI uses bun-ffi-structs to pack ABI-aligned struct data into an ArrayBuffer and passes a pointer instead. Despite the name, the package also works with Node. Pointers need some care too. Typed arrays and ArrayBuffers normally have to be resolved into BigInt addresses first. Eligible functions with exactly one pointer argument get another Fast API entrypoint that can extract the address directly from the buffer. An eligible signature is still not enough. V8 has to optimize a direct call with a fixed number of consistently typed arguments. Wrappers that collect arguments and forward them using spread or Reflect.apply can hide that call shape and keep the function on a slower path. The practical rules are: keep hot signatures within register limits, use direct fixed-arity calls with stable argument types, reuse owned buffers safely, and batch small operations. Then measure the real call site, because eligibility only makes a function fast-capable. We have to design the ABI around these constraints where it makes sense and gives the expected performance improvement. The third big area is text rendering. Today a Text renderable accepts a string, StyledText, or a tree of TextNodes. Before rendering, the TextNode tree is walked and flattened into styled chunks. Those chunks are packed in TypeScript, sent through FFI, copied into a native TextBuffer, and stored in a rope. Styles are represented separately as highlights. A TextBufferView then wraps the rope into visual lines, which are drawn into the visible buffer. This works, but updates are much more expensive than they should be. setStyledText effectively throws away and rebuilds the rope, copies and reparses all text and recreates the style highlights. Changing one TextNode also walks and flattens the complete tree before going through this path again. Text and style segments should instead live directly in the rope and support incremental replacement. Memory ownership is split between retained JavaScript buffers, the native memory registry, rope arenas, wrapping caches, styled-text storage, and highlights. Different operations preserve or reset different parts of that state. This is hard to reason about and can retain memory for much longer than expected. Text storage needs clearer ownership, with fewer lifetimes split across JavaScript and native code. The public API reflects the same split. The t template literal is convenient, but creates another intermediate chunk representation that is mutable, not cached, and not merged. Text also maintains both StyledText content and a special TextNode tree, which do not compose properly. TextNode is only a style scope, not a normal layout primitive, so Text renderables cannot naturally compose inside each other. I think this should become one Text primitive backed directly by rope segments. The template literal API might disappear or become a very thin helper around those native segments. Editing has another temporary layer in TypeScript. Extmarks currently monkey-patch editing operations, scan and adjust all marks after changes, maintain their own undo state, and recreate native highlights. They should become native marks anchored directly in the rope. A proper mark tree, similar to Neovim's marktree, could update marks together with edits, undo, and redo, and provide the foundation for highlights and concealment. Text wrapping has also become too complex. Supporting CJK, emoji, combining characters, ZWJ sequences, tabs, and different terminal width rules currently mixes byte offsets, grapheme indexes, and display-cell columns across several custom algorithms. Dirty views rewrap the complete document. Measurement and drawing can repeat some of the same work. The wrapping implementation needs an overhaul, but the exact shape is still open. The goal is to make Unicode handling easier to maintain, avoid repeated full-document work, and clearly separate byte offsets, graphemes, and terminal display cells. None of this will happen as one big rewrite. We will replace pieces when we understand the problem well enough and when the result is clearly simpler, faster, or more useful. To achieve all of this we might break public interfaces. Thanks to OpenCode and a lot of good models, migration to a new version with breaking changes mostly is not an issue anymore. What do you want to see next for OpenTUI?
0:57

What's next for OpenTUI? Here's a technical write-up. Over the last few months OpenTUI gained a lot of stability improvements, new unnecessary but fun features like live audio streaming, and useful features like rendering to the scrollback buffer mixed with a live TUI, called footer mode. Overall the feature set enables building large and complex applications. React and Solid make it super simple and convenient. There is still so much to do though. Three big milestones we have set out to achieve are: - Moving most of the behavioural logic currently living in TypeScript down to the native Zig core - Node compatibility - Optimizing the hell out of primitives like text rendering The render tree mechanisms are currently only usable from TypeScript. Think of the DOM, but controllable like a scene graph. Elements in the render tree are called renderables. They can expose a render method to draw themselves. All renderables are derived from a BaseRenderable. Renderables and the render tree will become native primitives. Building blocks usable from any language bindings. Reducing the TypeScript bindings to a very thin layer, with all the behavioural logic living in the native binary. Moving this down is not just a matter of porting TypeScript classes to Zig. TypeScript currently owns the tree, dirty-state propagation, layout reads, culling, and render ordering. If it still has to walk every node and call into native code for each step, we keep most of the complexity and add FFI overhead. Whole passes and their state need to move together. We took a big step towards this recently by building yoga-layout into the native binary. It exposes part of the official yoga-layout TypeScript package via FFI. Only the API surface that is actually used by OpenTUI. Covered by the test suite of the original yoga-layout package. This already gave a median speedup of ~2.5x, and up to 30x for narrow scenarios. The yoga-layout integration is useful beyond the speedup. Built-in text and editor measurement can now happen entirely in native code during layout instead of calling back into JavaScript. I ran an experiment last month taking this even further, having GPT 5.6 port yoga-layout from C++ to Zig, which gave extremely good results. It would be a burden to maintain right now though, so that's off the table for now. I might come back to it. Simon Klee is working relentlessly on Node compatibility and already has a full Node version of OpenCode running. Node got FFI support in v26.4.0, thanks to help from the Node community, namely Matteo Collina and Paolo Insogna. Behaviour and interfaces seem similar between Node and Bun, but there are some major differences. To get the best performance out of the Node FFI implementation, its usage has to follow some rules. Node has three ways to call native functions: the generic C++/libffi path, the SharedBuffer path, and the V8 Fast API. The generic path converts every argument in Node's C++ layer and then calls the function through libffi. It is flexible, but also the slowest option for frequently called functions. The SharedBuffer path is a middle ground. JavaScript writes scalar values and BigInt pointers into a small per-function buffer, reducing some conversion work. The actual native call still goes through libffi though. Typed arrays used as pointers cannot be packed into this buffer and fall back to the generic path. The path we really want is the V8 Fast API. Node generates a small machine-code trampoline for the exact function signature, allowing optimized JavaScript to call the native function without going through the generic converter or libffi. This only applies to JavaScript-to-native calls. Callbacks from native code into JavaScript still use libffi closures. Getting onto this path is quite strict. A signature can have at most eight arguments and everything must fit into CPU registers. x86-64 Unix systems have room for six GP (general-purpose) and eight FP (floating-point) arguments. AArch64 has room for seven GP and eight FP arguments. Anything that spills onto the stack falls back to a slower path. These are Node fast-path restrictions, not general FFI restrictions. Bun also does not support passing structs by value through its current FFI API. OpenTUI uses bun-ffi-structs to pack ABI-aligned struct data into an ArrayBuffer and passes a pointer instead. Despite the name, the package also works with Node. Pointers need some care too. Typed arrays and ArrayBuffers normally have to be resolved into BigInt addresses first. Eligible functions with exactly one pointer argument get another Fast API entrypoint that can extract the address directly from the buffer. An eligible signature is still not enough. V8 has to optimize a direct call with a fixed number of consistently typed arguments. Wrappers that collect arguments and forward them using spread or Reflect.apply can hide that call shape and keep the function on a slower path. The practical rules are: keep hot signatures within register limits, use direct fixed-arity calls with stable argument types, reuse owned buffers safely, and batch small operations. Then measure the real call site, because eligibility only makes a function fast-capable. We have to design the ABI around these constraints where it makes sense and gives the expected performance improvement. The third big area is text rendering. Today a Text renderable accepts a string, StyledText, or a tree of TextNodes. Before rendering, the TextNode tree is walked and flattened into styled chunks. Those chunks are packed in TypeScript, sent through FFI, copied into a native TextBuffer, and stored in a rope. Styles are represented separately as highlights. A TextBufferView then wraps the rope into visual lines, which are drawn into the visible buffer. This works, but updates are much more expensive than they should be. setStyledText effectively throws away and rebuilds the rope, copies and reparses all text and recreates the style highlights. Changing one TextNode also walks and flattens the complete tree before going through this path again. Text and style segments should instead live directly in the rope and support incremental replacement. Memory ownership is split between retained JavaScript buffers, the native memory registry, rope arenas, wrapping caches, styled-text storage, and highlights. Different operations preserve or reset different parts of that state. This is hard to reason about and can retain memory for much longer than expected. Text storage needs clearer ownership, with fewer lifetimes split across JavaScript and native code. The public API reflects the same split. The t template literal is convenient, but creates another intermediate chunk representation that is mutable, not cached, and not merged. Text also maintains both StyledText content and a special TextNode tree, which do not compose properly. TextNode is only a style scope, not a normal layout primitive, so Text renderables cannot naturally compose inside each other. I think this should become one Text primitive backed directly by rope segments. The template literal API might disappear or become a very thin helper around those native segments. Editing has another temporary layer in TypeScript. Extmarks currently monkey-patch editing operations, scan and adjust all marks after changes, maintain their own undo state, and recreate native highlights. They should become native marks anchored directly in the rope. A proper mark tree, similar to Neovim's marktree, could update marks together with edits, undo, and redo, and provide the foundation for highlights and concealment. Text wrapping has also become too complex. Supporting CJK, emoji, combining characters, ZWJ sequences, tabs, and different terminal width rules currently mixes byte offsets, grapheme indexes, and display-cell columns across several custom algorithms. Dirty views rewrap the complete document. Measurement and drawing can repeat some of the same work. The wrapping implementation needs an overhaul, but the exact shape is still open. The goal is to make Unicode handling easier to maintain, avoid repeated full-document work, and clearly separate byte offsets, graphemes, and terminal display cells. None of this will happen as one big rewrite. We will replace pieces when we understand the problem well enough and when the result is clearly simpler, faster, or more useful. To achieve all of this we might break public interfaces. Thanks to OpenCode and a lot of good models, migration to a new version with breaking changes mostly is not an issue anymore. What do you want to see next for OpenTUI?

kmdr

29,430 просмотров • 23 дней назад

Everyone is sleeping on Meta's SAM 3 release. But it's actually a big deal. Here's why: Companies spend millions paying humans to label images and videos frame by frame. A single autonomous driving dataset? Months of work, hundreds of annotators, millions in cost. Without labeled data, you can't train custom models. Without custom models, you're stuck with generic solutions. This is why most companies never move past pilots. SAM 3 breaks this cycle. First let's look at the evolution: SAM 1 segmented objects when you clicked on them. Revolutionary, but one object at a time. SAM 2 added video tracking with memory. Game-changing, but you still manually prompted every object. SAM 3 changes everything with text prompts. Type "yellow school bus" and it finds ALL of them in your image or video. Not just one. Every instance across thousands of frames. Now here's where people get confused: "Can't I just use GPT-5 or Gemini for this?" No, and here's why that's a terrible approach. Large multimodal LLMs are great for reasoning, but they're slow and expensive for production visual tasks. You're paying API costs per image, waiting seconds for responses, getting inconsistent results. SAM 3 runs in 30 milliseconds on a single GPU for 100+ objects. That's 100x faster, and you own the infrastructure. More importantly, SAM 3 gives you precise pixel-level masks, not descriptions. Try asking an LLM to segment every defective part on a manufacturing line in real-time. It won't work. SAM 3 does this effortlessly. The real breakthrough is their data engine. Meta built an AI-human hybrid system that's 5x faster for complex annotations. They trained SAM 3 on 4 million unique visual concepts - 50x more than existing benchmarks like LVIS. SAM 3 is trained on 4 million unique visual concepts, it handles everything: - Text-based concept search - Interactive refinement with clicks - Video tracking across frames - Zero-shot detection of new concepts The model is open source. Weights, code, and benchmarks are on GitHub. If you're building computer vision applications, this is the foundation model to evaluate. The annotation time savings alone will pay for integration costs within weeks. Find the relevant links in the next tweet!
0:27

Everyone is sleeping on Meta's SAM 3 release. But it's actually a big deal. Here's why: Companies spend millions paying humans to label images and videos frame by frame. A single autonomous driving dataset? Months of work, hundreds of annotators, millions in cost. Without labeled data, you can't train custom models. Without custom models, you're stuck with generic solutions. This is why most companies never move past pilots. SAM 3 breaks this cycle. First let's look at the evolution: SAM 1 segmented objects when you clicked on them. Revolutionary, but one object at a time. SAM 2 added video tracking with memory. Game-changing, but you still manually prompted every object. SAM 3 changes everything with text prompts. Type "yellow school bus" and it finds ALL of them in your image or video. Not just one. Every instance across thousands of frames. Now here's where people get confused: "Can't I just use GPT-5 or Gemini for this?" No, and here's why that's a terrible approach. Large multimodal LLMs are great for reasoning, but they're slow and expensive for production visual tasks. You're paying API costs per image, waiting seconds for responses, getting inconsistent results. SAM 3 runs in 30 milliseconds on a single GPU for 100+ objects. That's 100x faster, and you own the infrastructure. More importantly, SAM 3 gives you precise pixel-level masks, not descriptions. Try asking an LLM to segment every defective part on a manufacturing line in real-time. It won't work. SAM 3 does this effortlessly. The real breakthrough is their data engine. Meta built an AI-human hybrid system that's 5x faster for complex annotations. They trained SAM 3 on 4 million unique visual concepts - 50x more than existing benchmarks like LVIS. SAM 3 is trained on 4 million unique visual concepts, it handles everything: - Text-based concept search - Interactive refinement with clicks - Video tracking across frames - Zero-shot detection of new concepts The model is open source. Weights, code, and benchmarks are on GitHub. If you're building computer vision applications, this is the foundation model to evaluate. The annotation time savings alone will pay for integration costs within weeks. Find the relevant links in the next tweet!

Akshay 🚀

46,421 просмотров • 8 месяцев назад

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.
1:24

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.

Kingnet AI

45,966 просмотров • 1 год назад