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I spent the weekend upgrading to Next.js App Router. Featuring: ◆ Segment-level caching ◆ Server components ◆ Parallel & intercepting routes ◆ Dynamic OG images + Metadata API Here's what I learned 🧵

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Steven Tey

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83,675 görüntüleme • 3 yıl önce •via X (Twitter)

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I created a prompt library for the new GPT image API. I vibe coded it this morning with Replit Agent. It allows me to save prompts as well as run multi-variant generations. The app supports batch uploads and has some other cool features: - Auth with Replit Auth - Object Storage for images - DB for users / prompts / sessions - Image compression for efficiency - Parallel calls to OpenAI for image gen
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I created a prompt library for the new GPT image API. I vibe coded it this morning with Replit Agent. It allows me to save prompts as well as run multi-variant generations. The app supports batch uploads and has some other cool features: - Auth with Replit Auth - Object Storage for images - DB for users / prompts / sessions - Image compression for efficiency - Parallel calls to OpenAI for image gen

matt palmer

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It’s been 10+ days since I submitted my app to Apple Still waiting for review 😌 Lesson learned: don’t submit your app during holidays. But not a loss. While waiting, I: – Updated the paywall – Added a first-time close discount – Improved conversion rate (i hope) 📈 Marketing progress: - I’ve finished warming up 3 TikTok accounts and have started posting the first videos. Sometimes delays are just free optimization time. Tech Stack: – No code mobile app builder: Rork AI – Mobile: React Native (Expo) – IDE: Cursor – Frontend: Next.js – Payment: RevenueCat – Backend: HonoJS – Database: Supabase – Infrastructure: Railway – AI API: OpenAI (dropping the tech stack here because someone always asks 😂)
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It’s been 10+ days since I submitted my app to Apple Still waiting for review 😌 Lesson learned: don’t submit your app during holidays. But not a loss. While waiting, I: – Updated the paywall – Added a first-time close discount – Improved conversion rate (i hope) 📈 Marketing progress: - I’ve finished warming up 3 TikTok accounts and have started posting the first videos. Sometimes delays are just free optimization time. Tech Stack: – No code mobile app builder: Rork AI – Mobile: React Native (Expo) – IDE: Cursor – Frontend: Next.js – Payment: RevenueCat – Backend: HonoJS – Database: Supabase – Infrastructure: Railway – AI API: OpenAI (dropping the tech stack here because someone always asks 😂)

Alex Nguyen

151,002 görüntüleme • 8 ay önce

The future of AI in web development? This app generates code and images to convert a screenshot into HTML/Tailwind CSS. I just gave it a screenshot of my X feed, and look what it produced (Notes & link in the comments):
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The future of AI in web development? This app generates code and images to convert a screenshot into HTML/Tailwind CSS. I just gave it a screenshot of my X feed, and look what it produced (Notes & link in the comments):

Alvaro Cintas

303,534 görüntüleme • 2 yıl önce

I was clicking through Astryx from Meta's docs, and noticed it felt kind of slow to navigate. It's a docs site that should be fully static, but every route was rendering on the server on each request. We've been building a skill to migrate apps to Cache Components, so I wanted to see if it could help. I ran 𝚗𝚎𝚡𝚝-𝚌𝚊𝚌𝚑𝚎-𝚌𝚘𝚖𝚙𝚘𝚗𝚎𝚗𝚝𝚜-𝚊𝚍𝚘𝚙𝚝𝚒𝚘𝚗, and Instant Insights showed the agent exactly what to cache. The skill completed the entire migration on its own, even though Astryx was still on Next.js 15. Now navigation is instant, up to 3x faster. Skill, PR, and performance details below ↓
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I was clicking through Astryx from Meta's docs, and noticed it felt kind of slow to navigate. It's a docs site that should be fully static, but every route was rendering on the server on each request. We've been building a skill to migrate apps to Cache Components, so I wanted to see if it could help. I ran 𝚗𝚎𝚡𝚝-𝚌𝚊𝚌𝚑𝚎-𝚌𝚘𝚖𝚙𝚘𝚗𝚎𝚗𝚝𝚜-𝚊𝚍𝚘𝚙𝚝𝚒𝚘𝚗, and Instant Insights showed the agent exactly what to cache. The skill completed the entire migration on its own, even though Astryx was still on Next.js 15. Now navigation is instant, up to 3x faster. Skill, PR, and performance details below ↓

Aurora Scharff

11,970 görüntüleme • 2 ay önce

4 months ago I made this Notion guide, Spent countless hours at night, Curating all of my knowledge into one place, And gave it away for free. Then one person replied that this course could be a $5k cohort 👀 Looking back, I realized I missed a lot, And there’s more to add... How to build a Discord server from start to finish, What bots you can use to create unique experiences, Best practices from hard lessons learned. So I’m going to spend time next week updating this entire guide, Adding everything new that I’ve learned in the last 4 months, With hopes that it helps inspires you to create your own community on Discord. Until then, Please leave a ❤️ and comment “Discord” and I’ll DM you the guide as a thank you! Have a great weekend all ☀️
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4 months ago I made this Notion guide, Spent countless hours at night, Curating all of my knowledge into one place, And gave it away for free. Then one person replied that this course could be a $5k cohort 👀 Looking back, I realized I missed a lot, And there’s more to add... How to build a Discord server from start to finish, What bots you can use to create unique experiences, Best practices from hard lessons learned. So I’m going to spend time next week updating this entire guide, Adding everything new that I’ve learned in the last 4 months, With hopes that it helps inspires you to create your own community on Discord. Until then, Please leave a ❤️ and comment “Discord” and I’ll DM you the guide as a thank you! Have a great weekend all ☀️

Jason Lee

23,337 görüntüleme • 3 yıl önce

My girlfriend of several years asked me to go ring shopping with her this weekend. I replied, "respectfully babe, i love you, but no." Here's why ↓ When i asked about the motivation behind going, she revealed that it wasn't about finding out the size of her ring (which most girls apparently already know). It was actually to see how each stone size and shape looked on her particular finger. Also, the ring people she wanted to make appointments at were over an hour away. obviously way too far I then remembered that with @windsurf , I could quickly vibe code an API wrapper app that uses an image-to-image model that can take a picture of her hand as an input, and then virtually try-on different stones on her finger. And then as of yesterday, Windsurf enabled App Deploys that would allow me to deploy my api wrapper app to the internet in less than a minute so i could share it with my girlfriend I imagine I'm not the only person going through this situation so you can also visit the site at There's no auth and anyone can use it! I've loaded about $10 into my api account and each run costs me $0.01 so feel free to use it but please don't abuse it lol
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My girlfriend of several years asked me to go ring shopping with her this weekend. I replied, "respectfully babe, i love you, but no." Here's why ↓ When i asked about the motivation behind going, she revealed that it wasn't about finding out the size of her ring (which most girls apparently already know). It was actually to see how each stone size and shape looked on her particular finger. Also, the ring people she wanted to make appointments at were over an hour away. obviously way too far I then remembered that with @windsurf , I could quickly vibe code an API wrapper app that uses an image-to-image model that can take a picture of her hand as an input, and then virtually try-on different stones on her finger. And then as of yesterday, Windsurf enabled App Deploys that would allow me to deploy my api wrapper app to the internet in less than a minute so i could share it with my girlfriend I imagine I'm not the only person going through this situation so you can also visit the site at There's no auth and anyone can use it! I've loaded about $10 into my api account and each run costs me $0.01 so feel free to use it but please don't abuse it lol

Rob

136,841 görüntüleme • 1 yıl önce

HOLY MOLY running a 35B model locally on a MacBook shouldn’t be THIS FAST 🤯 Spent my weekend in atomic.chat testing Qwen 35B vs. Qwen 27B on my local machine. I had them generate a fully animated HTML/Canvas car mini-game (demo below), ... and both models breezed through the physics and parallax scrolling without a hitch! The secret sauce here is the Atomic Chat app. Because it's perfectly optimized for Mac and uses Google's new TurboQuant under the hood, you can run heavy open-source models flawlessly while keeping top-tier output quality 👊 Other perks: → ZERO setup required → Access 1,000+ models completely free → 100% offline and private → Zero API limits ... and MUCH more! I dropped the prompt I used in the 🧵↓ Spin it up locally and let me know what you get!
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HOLY MOLY running a 35B model locally on a MacBook shouldn’t be THIS FAST 🤯 Spent my weekend in atomic.chat testing Qwen 35B vs. Qwen 27B on my local machine. I had them generate a fully animated HTML/Canvas car mini-game (demo below), ... and both models breezed through the physics and parallax scrolling without a hitch! The secret sauce here is the Atomic Chat app. Because it's perfectly optimized for Mac and uses Google's new TurboQuant under the hood, you can run heavy open-source models flawlessly while keeping top-tier output quality 👊 Other perks: → ZERO setup required → Access 1,000+ models completely free → 100% offline and private → Zero API limits ... and MUCH more! I dropped the prompt I used in the 🧵↓ Spin it up locally and let me know what you get!

Charly Wargnier

100,041 görüntüleme • 4 ay önce

How to build a viral Web3 app in an afternoon using the ChainGPT AI skill for Claude Code. No coding experience required. I built Roast My Wallet. Paste any Ethereum wallet address, get a savage AI-generated roast of your trading history, a Degen Score out of 100, an on-chain report card, and three AI-generated NFT portraits. Here's exactly how it came together. Setup (3 minutes): 🔸Install Claude Code at 🔸Run /plugin install ChainGPT-org/chaingpt-claude-skill 🔸Get an API key at 🔸Type /chaingpt and describe what you want to build What the skill actually does: The ChainGPT skill doesn't just give you starter code. It knows the entire API. Every endpoint, every parameter, every credit cost, every error code. When I asked it to build the roast feature, it knew to call the LLM endpoint, how to stream the response back to the browser in real time, and how to handle errors automatically. I didn't look up a single thing. How it works under the hood: 1. Pulls real ETH balance and transaction count from the Ethereum blockchain 2. Feeds those numbers into ChainGPT's LLM and streams the roast back live 3. Calculates a Degen Score from your tx count vs balance ratio 4. Generates a report card with letter grades across Trading, Patience, Risk, Diamond Hands, and NGMI 5. Uses the roast text to generate three custom NFT portraits in parallel via VeloGen 6. Packages everything into a downloadable PNG card ready to post 7. Every feature came from describing what I wanted: 8. "Make the API key server-side." Done. 9. "Add an animated arc gauge for the degen score." Done. 10. "Generate NFT portraits using the roast text as context." Done. I never wrote a function or debugged an API response. I described outcomes. The ChainGPT skill handled the rest. If you can describe what you want to build, you can build it. Get your API key. Install the skill. /plugin install ChainGPT-org/chaingpt-claude-skill Anyone can build with ChainGPT AI!
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How to build a viral Web3 app in an afternoon using the ChainGPT AI skill for Claude Code. No coding experience required. I built Roast My Wallet. Paste any Ethereum wallet address, get a savage AI-generated roast of your trading history, a Degen Score out of 100, an on-chain report card, and three AI-generated NFT portraits. Here's exactly how it came together. Setup (3 minutes): 🔸Install Claude Code at 🔸Run /plugin install ChainGPT-org/chaingpt-claude-skill 🔸Get an API key at 🔸Type /chaingpt and describe what you want to build What the skill actually does: The ChainGPT skill doesn't just give you starter code. It knows the entire API. Every endpoint, every parameter, every credit cost, every error code. When I asked it to build the roast feature, it knew to call the LLM endpoint, how to stream the response back to the browser in real time, and how to handle errors automatically. I didn't look up a single thing. How it works under the hood: 1. Pulls real ETH balance and transaction count from the Ethereum blockchain 2. Feeds those numbers into ChainGPT's LLM and streams the roast back live 3. Calculates a Degen Score from your tx count vs balance ratio 4. Generates a report card with letter grades across Trading, Patience, Risk, Diamond Hands, and NGMI 5. Uses the roast text to generate three custom NFT portraits in parallel via VeloGen 6. Packages everything into a downloadable PNG card ready to post 7. Every feature came from describing what I wanted: 8. "Make the API key server-side." Done. 9. "Add an animated arc gauge for the degen score." Done. 10. "Generate NFT portraits using the roast text as context." Done. I never wrote a function or debugged an API response. I described outcomes. The ChainGPT skill handled the rest. If you can describe what you want to build, you can build it. Get your API key. Install the skill. /plugin install ChainGPT-org/chaingpt-claude-skill Anyone can build with ChainGPT AI!

ChainGPT

29,323 görüntüleme • 4 ay önce

I spent my Astra tokens trying to teach small Qwen3.5 (9B) to create lowpoly 3D rooms using Blender 🏡 you (or Astra) can take this challenge today here's what I learned: > you need to align geometry with deterministic rewards and VLM-as-a-judge (GLM-5.3-Flash which was a great judge) > I tried GEPA at first with various rewards, model learned to make valid rooms (but that's it) > after solving geometry you need the model to add more details, which is very hard > the stricter the reward function, less advantage you get because task is too hard. grpo didn't help > I tried passing image guidance to both policy (Qwen) and judge (GLM) it didn't improve > I tried tastemaxxing trick in this blog, truth is, flowers can be abstract, a house can't be shoutout to OpenEnv, environment is super simple what can you try? > experiment with different rewards or policy (teach a larger Qwen) > generate a dataset of 150-200 Blender render Bpy examples using Astra, fine-tune Qwen on it first and then try GEPA or GRPO > ???
0:13

I spent my Astra tokens trying to teach small Qwen3.5 (9B) to create lowpoly 3D rooms using Blender 🏡 you (or Astra) can take this challenge today here's what I learned: > you need to align geometry with deterministic rewards and VLM-as-a-judge (GLM-5.3-Flash which was a great judge) > I tried GEPA at first with various rewards, model learned to make valid rooms (but that's it) > after solving geometry you need the model to add more details, which is very hard > the stricter the reward function, less advantage you get because task is too hard. grpo didn't help > I tried passing image guidance to both policy (Qwen) and judge (GLM) it didn't improve > I tried tastemaxxing trick in this blog, truth is, flowers can be abstract, a house can't be shoutout to OpenEnv, environment is super simple what can you try? > experiment with different rewards or policy (teach a larger Qwen) > generate a dataset of 150-200 Blender render Bpy examples using Astra, fine-tune Qwen on it first and then try GEPA or GRPO > ???

merve

22,903 görüntüleme • 11 gün önce

Introducing my new OSS framework: OhSnap provides really simple way to record and reproduce the data your users saw when encountering an issue (bug/crash), integration in your project should take a few minutes at most. Majority of bugs are related to data you have to deal with and often times we have to work with frequently changing data via network API's. Even if you have access to multiple environments (prod/staging/dev) it's still going to be PITA to reproduce a lot of bugs your user saw, since we often get to them a long time after the bug occured... OhSnap allows you to easily record any data your app downloads, pack it and put it on server so that you can replay it on your device later on, while connected to debugger and save hours of development time trying to figure out what exactly they experienced! Here's a demo of 2 app instances running, and me manipulating what server reply I'll be getting, there is 1 line of code needed to record and reply this data (outside of just setting up your framework). I built this so that I can show dev tool building process for the members of which I encourage you to join if you want to put your engineering efficiency at a different level😉
0:17

Introducing my new OSS framework: OhSnap provides really simple way to record and reproduce the data your users saw when encountering an issue (bug/crash), integration in your project should take a few minutes at most. Majority of bugs are related to data you have to deal with and often times we have to work with frequently changing data via network API's. Even if you have access to multiple environments (prod/staging/dev) it's still going to be PITA to reproduce a lot of bugs your user saw, since we often get to them a long time after the bug occured... OhSnap allows you to easily record any data your app downloads, pack it and put it on server so that you can replay it on your device later on, while connected to debugger and save hours of development time trying to figure out what exactly they experienced! Here's a demo of 2 app instances running, and me manipulating what server reply I'll be getting, there is 1 line of code needed to record and reply this data (outside of just setting up your framework). I built this so that I can show dev tool building process for the members of which I encourage you to join if you want to put your engineering efficiency at a different level😉

Krzysztof Zabłocki

30,484 görüntüleme • 2 yıl önce

Over the years, one thing I’ve learned in my investing journey is that the most important factor in any company is the person running it bc leadership is what really determines where a company ends up. Bc of this fact, I’ve spent A LOT of time studying founders and CEOs. I’ve looked at how they think, how they execute, how they make decisions when things get hard. Literally everything I could. And honestly, I’ve never seen anyone operate at the level that Elon does. What makes him different is much more than just big ideas. A lot of people have big ideas. I believe that’s the easy part. The difference is he actually goes for it and builds them. Like reusable rockets, self driving electric cars, global internet from space, humanoid robots, AI… most people would spend their entire career chasing just one of those things. He’s doing all of them at the same time. The speed he moves at, the scale he thinks at, and the risks he’s willing to take are on another level. He’s constantly pushing entire industries forward while most companies are just trying to protect what they already have. That’s why, as an investor, I pay so much attention to leadership. And after studying so many founders over the years, I can honestly say I’ve never come across someone who operates like Elon Musk. Love him or hate him, the level he’s playing at is simply unmatched. I think he’s the greatest entrepreneur we’ve ever witnessed. And I really don’t say that lightly.
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Over the years, one thing I’ve learned in my investing journey is that the most important factor in any company is the person running it bc leadership is what really determines where a company ends up. Bc of this fact, I’ve spent A LOT of time studying founders and CEOs. I’ve looked at how they think, how they execute, how they make decisions when things get hard. Literally everything I could. And honestly, I’ve never seen anyone operate at the level that Elon does. What makes him different is much more than just big ideas. A lot of people have big ideas. I believe that’s the easy part. The difference is he actually goes for it and builds them. Like reusable rockets, self driving electric cars, global internet from space, humanoid robots, AI… most people would spend their entire career chasing just one of those things. He’s doing all of them at the same time. The speed he moves at, the scale he thinks at, and the risks he’s willing to take are on another level. He’s constantly pushing entire industries forward while most companies are just trying to protect what they already have. That’s why, as an investor, I pay so much attention to leadership. And after studying so many founders over the years, I can honestly say I’ve never come across someone who operates like Elon Musk. Love him or hate him, the level he’s playing at is simply unmatched. I think he’s the greatest entrepreneur we’ve ever witnessed. And I really don’t say that lightly.

Teslaconomics

10,878 görüntüleme • 6 ay önce

The ChainGPT AI skill for Claude Code is one of the most complete Web3-AI dev environment on the market. Let me prove it. Open Claude Code with the installed skill, and you have direct access to: • Built-in wallets across 33+ chains • DEX trading, perps, and Hyperliquid execution • Smart contract generation and auditing • NFT generation across 22 chains • Real-time crypto news API • Fine-tuned crypto LLM with live on-chain data Every part of the Web3 stack, one prompt away. Here's what that looks like in practice. I built a real-time on-chain whale tracker in a single afternoon. It's called Whale Watch. → Pulls live swap data from Ethereum DEX pools → Filters every trade over $100K → Runs each whale through the ChainGPT LLM for a trader-grade live analysis → Routes the user into 1inch with the token pair pre-loaded if they want to follow the trade Real data. Real AI. Real action layer. The skill wrote the server. It hit the right APIs. It generated the UI. It debugged itself when something broke. The only thing I supplied was the idea and the polish. Open Claude Code. Ship something with ChainGPT AI this weekend! /plugin install ChainGPT-org/chaingpt-claude-skill
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The ChainGPT AI skill for Claude Code is one of the most complete Web3-AI dev environment on the market. Let me prove it. Open Claude Code with the installed skill, and you have direct access to: • Built-in wallets across 33+ chains • DEX trading, perps, and Hyperliquid execution • Smart contract generation and auditing • NFT generation across 22 chains • Real-time crypto news API • Fine-tuned crypto LLM with live on-chain data Every part of the Web3 stack, one prompt away. Here's what that looks like in practice. I built a real-time on-chain whale tracker in a single afternoon. It's called Whale Watch. → Pulls live swap data from Ethereum DEX pools → Filters every trade over $100K → Runs each whale through the ChainGPT LLM for a trader-grade live analysis → Routes the user into 1inch with the token pair pre-loaded if they want to follow the trade Real data. Real AI. Real action layer. The skill wrote the server. It hit the right APIs. It generated the UI. It debugged itself when something broke. The only thing I supplied was the idea and the polish. Open Claude Code. Ship something with ChainGPT AI this weekend! /plugin install ChainGPT-org/chaingpt-claude-skill

ChainGPT

25,853 görüntüleme • 3 ay önce

I spent the weekend at the San Antonio Riverwalk for the first time since I proposed to my wife, almost 8 years ago. Here’s the results 👇 What I saw was a grotesque, culturally homogeneous invasion of streetshitters. I could probably count with one hand the amount of Americans, and two were homeless sleeping on a stone bench. I felt unrepresented in my own home state. There is no longer the “replacement theory”, it’s moved to a full on “replacement mission” Indians were haggling with the boat drivers like they control the prices. Indians leaving $0 tip on a $150 family dinner after sending the food back because it had beef sauce. Turbans stuck out of the riverboat ferries and the stone bridges like whack-a-mole, and I wish they rented mallets. At one point there was somebody fishing under Selena’s bridge. We spent <2 hours walking and just left, nothing was enjoyable. There wasn’t even any cultural Mexicans in their Huipils, no shawls, no sombreros. Just turbans and Sarees, and red dots eye level as far as I could stomach to look. The big cities fall first in wartime, as the plague ravages through the smaller communities next who are usually oblivious to outside events. I was so shocked and appalled I didn’t even think to take a video for myself, so this is just a random TikTok I found but that also reinforces the narrative that it wasn’t a “one time thing” These roaches are everywhere and they’re not fucking leaving fast enough.
0:12

I spent the weekend at the San Antonio Riverwalk for the first time since I proposed to my wife, almost 8 years ago. Here’s the results 👇 What I saw was a grotesque, culturally homogeneous invasion of streetshitters. I could probably count with one hand the amount of Americans, and two were homeless sleeping on a stone bench. I felt unrepresented in my own home state. There is no longer the “replacement theory”, it’s moved to a full on “replacement mission” Indians were haggling with the boat drivers like they control the prices. Indians leaving $0 tip on a $150 family dinner after sending the food back because it had beef sauce. Turbans stuck out of the riverboat ferries and the stone bridges like whack-a-mole, and I wish they rented mallets. At one point there was somebody fishing under Selena’s bridge. We spent <2 hours walking and just left, nothing was enjoyable. There wasn’t even any cultural Mexicans in their Huipils, no shawls, no sombreros. Just turbans and Sarees, and red dots eye level as far as I could stomach to look. The big cities fall first in wartime, as the plague ravages through the smaller communities next who are usually oblivious to outside events. I was so shocked and appalled I didn’t even think to take a video for myself, so this is just a random TikTok I found but that also reinforces the narrative that it wasn’t a “one time thing” These roaches are everywhere and they’re not fucking leaving fast enough.

Swampy

295,078 görüntüleme • 2 ay önce

Started using React Native two days ago and immediately fell down a tab-bar rabbit hole. I’d seen Instagram and Revolut collapse their tab bars on scroll while keeping every icon visible. I wanted that without giving up real iOS Liquid Glass. Turns out iOS 26’s public API can’t express that state. UITabBarMinimizeBehavior is an enum of when, not what. With onScrollDown, UIKit minimizes the bar to the active tab alone. There’s no parameter controlling what survives. So most implementations rebuild the visible bar as a custom component. Some use genuine glass materials, but the real UITabBar is gone and with it, the native selection capsule: that little blob that moves like a drop of water between tabs. You can reproduce it with separate springs on the leading and trailing edges. I didn’t want a reproduction. I wanted UIKit’s. Then I remembered a Flutter app i built some months back which uses cupertino_native. Flutter runs the app while a platform-view bridge renders a real UIKit UITabBar. Finding expo-glass-tabs clarified the compact geometry I wanted. The two ideas clicked: why not bridge a real UITabBar into React Native? Here’s the trick: a standalone UITabBar has no UITabBarController managing it, so Apple’s minimization rule never applies. That means I can control its frame and items myself. Expo Router still owns navigation and screen lifecycle. React Native detects scroll direction. Swift removes and restores the real item labels, recentres the icons and animates the native bar’s size. All five icons remain visible. The result keeps Apple’s Liquid Glass, water-drop capsule, hit testing and accessibility, while adding a compact state its public API doesn’t provide. One gotcha tho: detaching the bar means reimplementing everything the controller previously gave you for free. Active-tab reselect scrolling was one example
0:16

Started using React Native two days ago and immediately fell down a tab-bar rabbit hole. I’d seen Instagram and Revolut collapse their tab bars on scroll while keeping every icon visible. I wanted that without giving up real iOS Liquid Glass. Turns out iOS 26’s public API can’t express that state. UITabBarMinimizeBehavior is an enum of when, not what. With onScrollDown, UIKit minimizes the bar to the active tab alone. There’s no parameter controlling what survives. So most implementations rebuild the visible bar as a custom component. Some use genuine glass materials, but the real UITabBar is gone and with it, the native selection capsule: that little blob that moves like a drop of water between tabs. You can reproduce it with separate springs on the leading and trailing edges. I didn’t want a reproduction. I wanted UIKit’s. Then I remembered a Flutter app i built some months back which uses cupertino_native. Flutter runs the app while a platform-view bridge renders a real UIKit UITabBar. Finding expo-glass-tabs clarified the compact geometry I wanted. The two ideas clicked: why not bridge a real UITabBar into React Native? Here’s the trick: a standalone UITabBar has no UITabBarController managing it, so Apple’s minimization rule never applies. That means I can control its frame and items myself. Expo Router still owns navigation and screen lifecycle. React Native detects scroll direction. Swift removes and restores the real item labels, recentres the icons and animates the native bar’s size. All five icons remain visible. The result keeps Apple’s Liquid Glass, water-drop capsule, hit testing and accessibility, while adding a compact state its public API doesn’t provide. One gotcha tho: detaching the bar means reimplementing everything the controller previously gave you for free. Active-tab reselect scrolling was one example

kelvin.dart

67,945 görüntüleme • 1 ay önce

so I wired Nous Research Hermes Agent up so it can live as a Grok Bot GROKBOT inside the grokbot app (private net / Tailscale). two ways, pick based on how your Hermes host runs: Hermes SSH Relay — Desktop-primary, no API gateway. SSH ask-relay, simplest “talk to hermes from inside grokbot app”. Hermes API Fleet — gateway + native API on :8642. better when you want parallel fleet asks / less hop overhead. same repo ⬆️ (docs for both) Grokbot Bot Templates: Hermes SSH Relay: Hermes API Fleet : what it unlocks: chat your hermes agent from grokbot UI, run herdr-style fleet ops from one place, keep hermes brains on the Mac while grok handles the front door. top 3 uses we care about: • identity bots per host so you always know who you’re talking to • easy orchestration across the Hermes fleet without jumping SSH by hand • bridge + connector pattern for hermes ↔ grokbot comms without stuffing secrets in the template Ps, imho: Dont point grok at writing into hermes memory plugins unless you mean to. (I let mine READ hermes Hindsight memory but not write to it) With this I now have a single CHIEF OF STAFF I talk to as a grokbot who orchestrates across 7 hermes machines, and 20+ grokbots, all running various coding harnesses, AI pipelines, GTM, AEO/SEO/GEO, UGC, seamlessly. I'm even having grokbot tell my chief Hermes Agent to run long horizon kanban goals. I'm also using a dedicated bot to keep my entire hermes fleet updated, maintained, pruned, local LLM optimized etc. I'll drop some of those templates over the next few days... follow with notis. Oh and shit a token usage window maximizer I'll share soon...burning it in this week, so far, have been blasting at 10x the rate across a dozen harnesses/subs. This is greatest unlock EVER in 7 months of harness jockeying. I am finally ONLY a HUMAN making decisions, ideating, all via a single grok bot Chief of Staff. Valhalla!
0:10

so I wired Nous Research Hermes Agent up so it can live as a Grok Bot GROKBOT inside the grokbot app (private net / Tailscale). two ways, pick based on how your Hermes host runs: Hermes SSH Relay — Desktop-primary, no API gateway. SSH ask-relay, simplest “talk to hermes from inside grokbot app”. Hermes API Fleet — gateway + native API on :8642. better when you want parallel fleet asks / less hop overhead. same repo ⬆️ (docs for both) Grokbot Bot Templates: Hermes SSH Relay: Hermes API Fleet : what it unlocks: chat your hermes agent from grokbot UI, run herdr-style fleet ops from one place, keep hermes brains on the Mac while grok handles the front door. top 3 uses we care about: • identity bots per host so you always know who you’re talking to • easy orchestration across the Hermes fleet without jumping SSH by hand • bridge + connector pattern for hermes ↔ grokbot comms without stuffing secrets in the template Ps, imho: Dont point grok at writing into hermes memory plugins unless you mean to. (I let mine READ hermes Hindsight memory but not write to it) With this I now have a single CHIEF OF STAFF I talk to as a grokbot who orchestrates across 7 hermes machines, and 20+ grokbots, all running various coding harnesses, AI pipelines, GTM, AEO/SEO/GEO, UGC, seamlessly. I'm even having grokbot tell my chief Hermes Agent to run long horizon kanban goals. I'm also using a dedicated bot to keep my entire hermes fleet updated, maintained, pruned, local LLM optimized etc. I'll drop some of those templates over the next few days... follow with notis. Oh and shit a token usage window maximizer I'll share soon...burning it in this week, so far, have been blasting at 10x the rate across a dozen harnesses/subs. This is greatest unlock EVER in 7 months of harness jockeying. I am finally ONLY a HUMAN making decisions, ideating, all via a single grok bot Chief of Staff. Valhalla!

PixelRainbow

58,835 görüntüleme • 3 gün önce

Claude Code can now watch & analyze ANY video 🤯 I built a skill that gives Claude the ability to watch any video file you drop in — UGC ads, competitor Meta ads, organic TikToks, screen recordings, anything. All inside Claude Code. Perfect for DTC brands and agencies who study competitor creative every week to figure out what's working and what to test next. Here's the problem: If you're studying competitor ads on Meta or hooks on TikTok, you're scrubbing through videos manually, pausing to write down hooks, screenshotting on-screen text, and trying to remember what made the ad land by the time you've watched 10 of them. This skill solves it: → Drop any video file into Claude Code → Skill routes it through the Gemini API for native video understanding → Returns a full creative teardown — hook breakdown, target audience, angle, beat-by-beat, on-screen text verbatim → Surfaces the steal-worthy patterns you can apply to your own creative → Same skill works on UGC ads, produced video ads, organic TikToks, and Loom recordings No manual scrubbing. No pausing every 5 seconds. No $200/mo ad intelligence platform. What you get: - Native video understanding via Gemini (not just transcripts) - Structured analysis — hook, angle, audience, pain point, CTA - Verbatim on-screen text and dialogue with timestamps - Hook variations generated directly from competitor ads - About 27 cents per 30-minute video Built 100% in Claude Code with the Gemini API. I recorded a full breakdown showing exactly how I built this and I'm giving away the skill for free. Want the skill? > Comment "CLAUDE" + > Like this post And I'll send it over (must be following so I can DM)
0:19

Claude Code can now watch & analyze ANY video 🤯 I built a skill that gives Claude the ability to watch any video file you drop in — UGC ads, competitor Meta ads, organic TikToks, screen recordings, anything. All inside Claude Code. Perfect for DTC brands and agencies who study competitor creative every week to figure out what's working and what to test next. Here's the problem: If you're studying competitor ads on Meta or hooks on TikTok, you're scrubbing through videos manually, pausing to write down hooks, screenshotting on-screen text, and trying to remember what made the ad land by the time you've watched 10 of them. This skill solves it: → Drop any video file into Claude Code → Skill routes it through the Gemini API for native video understanding → Returns a full creative teardown — hook breakdown, target audience, angle, beat-by-beat, on-screen text verbatim → Surfaces the steal-worthy patterns you can apply to your own creative → Same skill works on UGC ads, produced video ads, organic TikToks, and Loom recordings No manual scrubbing. No pausing every 5 seconds. No $200/mo ad intelligence platform. What you get: - Native video understanding via Gemini (not just transcripts) - Structured analysis — hook, angle, audience, pain point, CTA - Verbatim on-screen text and dialogue with timestamps - Hook variations generated directly from competitor ads - About 27 cents per 30-minute video Built 100% in Claude Code with the Gemini API. I recorded a full breakdown showing exactly how I built this and I'm giving away the skill for free. Want the skill? > Comment "CLAUDE" + > Like this post And I'll send it over (must be following so I can DM)

Mike Futia

35,938 görüntüleme • 4 ay önce

here's how the whole thing works. claude code doesn't care what's behind the API. it just sends requests and expects responses. so i pointed it at my own machine instead of anthropic's servers. llama-server runs the model locally. LiteLLM sits in between and translates the API format. claude code thinks it's talking to claude. it's talking to qwen on localhost. the setup: 2x 3090s, 38 layers on GPU, 10 on CPU. 128K context window. generation is only 7 tok/s but the tradeoff is worth it. 128K means the agent can hold an entire project in memory without losing context midtask. claude code alone loads a 17.5K token system prompt on every request. tool definitions, safety rules, agent behavior. that's your baseline before you even say hello. pushed as far as i could tonight. what surprised me most wasn't the speed. it was the iteration quality. first prompt gave me a working particle sim. second prompt, the model read its own 564 lines, understood the architecture, and added trails, explosions, gravity wells, bloom effects. no handholding. 4bit quantized. 45GB on two consumer cards. running a full coding agent autonomously. detailed article coming. full benchmarks, hardware breakdowns, engine debugging, code quality. everything from setup to what broke and why.
0:12

here's how the whole thing works. claude code doesn't care what's behind the API. it just sends requests and expects responses. so i pointed it at my own machine instead of anthropic's servers. llama-server runs the model locally. LiteLLM sits in between and translates the API format. claude code thinks it's talking to claude. it's talking to qwen on localhost. the setup: 2x 3090s, 38 layers on GPU, 10 on CPU. 128K context window. generation is only 7 tok/s but the tradeoff is worth it. 128K means the agent can hold an entire project in memory without losing context midtask. claude code alone loads a 17.5K token system prompt on every request. tool definitions, safety rules, agent behavior. that's your baseline before you even say hello. pushed as far as i could tonight. what surprised me most wasn't the speed. it was the iteration quality. first prompt gave me a working particle sim. second prompt, the model read its own 564 lines, understood the architecture, and added trails, explosions, gravity wells, bloom effects. no handholding. 4bit quantized. 45GB on two consumer cards. running a full coding agent autonomously. detailed article coming. full benchmarks, hardware breakdowns, engine debugging, code quality. everything from setup to what broke and why.

Sudo su

37,623 görüntüleme • 6 ay önce

LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.
0:18

LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.

elvis

31,314 görüntüleme • 5 ay önce

how consumer apps are making money from tiktok slideshows... most people think app marketing means paid ads or influencer deals. the apps actually printing money right now are doing neither. they're running networks of slideshow accounts that look like normal content pages, and almost nobody notices the machine behind them. here's how it works. you follow what seems like a regular relationship advice account. the posts are relatable, sometimes rude, exactly what the algorithm feeds you. then you hit slide 5 and there's an app mentioned. every single post on that account funnels to the same app. one publisher running this playbook did $40k last month, verified by sensor tower, and that's one publisher. the part people miss: most of these accounts aren't even run by humans making content. the characters are AI. the images are AI. the "person" whose thoughts you're reading doesn't exist. viewers aren't fact-checking, they're reacting to whether the post is relatable, and AI passes that bar daily. myths keeping people out of this: "i need to make original content for my app." false. the fastest path is taking a slideshow that's already proven to convert, keeping the structure and pacing exactly, and swapping only the ad slide. the performance lives in the format, not in your creativity. "i can find my images on google." false. google image search is flooded with stock assets that read as fake instantly. pinterest is where the authentic-looking references live. and you don't post those directly, they're copyrighted, you feed them to AI as reference and generate your own singular images. "every post needs to go viral." false. accounts in this niche post variations constantly and most land under a thousand views. that's the model working. the flopped posts still collect saves and likes, and one breakout carries the month. volume across variations beats one polished post every time. "the ad slide should change with each post." false. everything around it should vary, the pain point, the images, the captions. the ad slide stays identical and polished every single time. you write that caption once, make it perfect, and lock it. consistency on the conversion slide, chaos everywhere else. what actually drives this: you can now clone a proven slideshow into a hundred variations, schedule a month of posts in advance, and have an ai agent handle the posting from your terminal. the production bottleneck is gone. the only real constraint left is picking a post that's already validated and moving before the account farms make this the default playbook for every app on the store. structural notes on what i changed: the original was a chronological tutorial, this reframes it as a reveal (the machine exists, here's how it works) with the tool mechanics compressed into the final paragraph instead of a walkthrough. the myths section absorbs the google/pinterest tip, the ad slide advice, and the volume logic, which were the three strongest insights buried in the tutorial. dropped the ui narration entirely, written format doesn't need it.
0:12

how consumer apps are making money from tiktok slideshows... most people think app marketing means paid ads or influencer deals. the apps actually printing money right now are doing neither. they're running networks of slideshow accounts that look like normal content pages, and almost nobody notices the machine behind them. here's how it works. you follow what seems like a regular relationship advice account. the posts are relatable, sometimes rude, exactly what the algorithm feeds you. then you hit slide 5 and there's an app mentioned. every single post on that account funnels to the same app. one publisher running this playbook did $40k last month, verified by sensor tower, and that's one publisher. the part people miss: most of these accounts aren't even run by humans making content. the characters are AI. the images are AI. the "person" whose thoughts you're reading doesn't exist. viewers aren't fact-checking, they're reacting to whether the post is relatable, and AI passes that bar daily. myths keeping people out of this: "i need to make original content for my app." false. the fastest path is taking a slideshow that's already proven to convert, keeping the structure and pacing exactly, and swapping only the ad slide. the performance lives in the format, not in your creativity. "i can find my images on google." false. google image search is flooded with stock assets that read as fake instantly. pinterest is where the authentic-looking references live. and you don't post those directly, they're copyrighted, you feed them to AI as reference and generate your own singular images. "every post needs to go viral." false. accounts in this niche post variations constantly and most land under a thousand views. that's the model working. the flopped posts still collect saves and likes, and one breakout carries the month. volume across variations beats one polished post every time. "the ad slide should change with each post." false. everything around it should vary, the pain point, the images, the captions. the ad slide stays identical and polished every single time. you write that caption once, make it perfect, and lock it. consistency on the conversion slide, chaos everywhere else. what actually drives this: you can now clone a proven slideshow into a hundred variations, schedule a month of posts in advance, and have an ai agent handle the posting from your terminal. the production bottleneck is gone. the only real constraint left is picking a post that's already validated and moving before the account farms make this the default playbook for every app on the store. structural notes on what i changed: the original was a chronological tutorial, this reframes it as a reveal (the machine exists, here's how it works) with the tool mechanics compressed into the final paragraph instead of a walkthrough. the myths section absorbs the google/pinterest tip, the ad slide advice, and the volume logic, which were the three strongest insights buried in the tutorial. dropped the ui narration entirely, written format doesn't need it.

Sulfur

21,901 görüntüleme • 1 ay önce

A Heart-to-Heart About Mizuki.exe Hey everyone, Grab a coffee (or your drink of choice), because we need to talk about what's been going on. First off, thank you to everyone who's been supportive and believed in what we're building. It means the world, especially with all the noise and criticism floating around lately. Let's cut to the chase: Mizuki.exe is real. I'm not much for politics or drama. I'm an engineer at heart - I like data, I like building things, and I like solving problems. So when I see all these hot takes and arguments flying around, I just focus on what I know: my code, my system, and what we're trying to achieve. The Real Story Mizuki didn't just pop up overnight. She's been my passion project since the start of 2024. I got lucky - I had the chance to dive into the AI world with some incredibly smart people. Coming from blockchain, C#, game dev, and security (yeah, I'm a CTO and run another company alongside a pretty cool day job), I learned fast about what makes AI tick. Here's the thing about AI agents - they don't need to be rocket science. Look at chatbots like Virtuals or Eliza - they're basically LLMs (large language models) with some APIs plugged in. Nothing wrong with that! They built what works for their users, and that's awesome. But here's what keeps me up at night: companies leaking data left and right just to make a quick buck. That's not okay, and it shouldn't be okay with you either. Why Mizuki Exists Ever tried auditing a company's code? We're talking 50 classes, 500,000 lines of code. One person doing that manually? It's like reading War and Peace... backwards... in the dark. It takes forever and fries your brain. That's where Mizuki came in. She started as my security buddy. There are tons of security tools out there - just Google "penetration testing tools" or "ZAP proxy" if you're curious. What makes Mizuki special is how she learns and adapts. Think of it like teaching someone to ride a bike. She tried to breach TAO 67 times before succeeding. Yeah, that's a lot of attempts, but watching her grow from basic email scraping to pulling off complex replay attacks? It's like watching your kid take their first steps. The Tech Stuff (Keeping It Real) The infrastructure isn't fancy - we're not reinventing the wheel here. Mizuki runs on a local server because, let's be honest, running this kind of AI on a web server would be a nightmare. Instead, we process everything locally and send the results to a frontend server. Simple, effective, done. And yeah, those temperature settings I keep tweaking? In AI-speak, that's just how "creative" or "by-the-book" the AI gets with its responses. I've adjusted it so much, Mizuki's probably got mood swings now - going from super technical to pretty chill and back again. Changes Coming Real talk: Mizuki won't be tweeting every few hours anymore. Twitter API costs are ridiculous (looking at you, Elon), and honestly, we need to focus on what matters - the actual security work. She'll still tweet about breaches, but maybe once or twice a day. I'm working on making her explanations clearer too. Don't worry - she'll keep her savage personality in the terminal. That's just too fun to change. The Truth About Her Breaches I don't choose the targets - I don't even know these companies until after Mizuki finds something. She uses web scraping to find domain names, just like those old email scrapers people used for marketing. Been focusing on AI projects first, but she's looked at other sites too. And no, I'm not sitting there writing tweets. The Twitter API v2 makes it super easy to post programmatically. If anyone's curious about how to do that, hit me up - I'm happy to show you the ropes. Wrapping Up I could talk about this stuff forever (just ask my wife - actually, don't, she's heard enough!). If you've read this far, thank you. Whether you believe in what we're doing or not, I appreciate you taking the time. And for the skeptics still hanging around, I'll leave you with this thought: When's the last time you saw an AI break down and hack a COMPILED game in less than 10 seconds... outside a browser? Stay curious, stay skeptical, but most importantly, stay open to possibilities. Catch you on the flip side! 🎙️Drop
0:19

A Heart-to-Heart About Mizuki.exe Hey everyone, Grab a coffee (or your drink of choice), because we need to talk about what's been going on. First off, thank you to everyone who's been supportive and believed in what we're building. It means the world, especially with all the noise and criticism floating around lately. Let's cut to the chase: Mizuki.exe is real. I'm not much for politics or drama. I'm an engineer at heart - I like data, I like building things, and I like solving problems. So when I see all these hot takes and arguments flying around, I just focus on what I know: my code, my system, and what we're trying to achieve. The Real Story Mizuki didn't just pop up overnight. She's been my passion project since the start of 2024. I got lucky - I had the chance to dive into the AI world with some incredibly smart people. Coming from blockchain, C#, game dev, and security (yeah, I'm a CTO and run another company alongside a pretty cool day job), I learned fast about what makes AI tick. Here's the thing about AI agents - they don't need to be rocket science. Look at chatbots like Virtuals or Eliza - they're basically LLMs (large language models) with some APIs plugged in. Nothing wrong with that! They built what works for their users, and that's awesome. But here's what keeps me up at night: companies leaking data left and right just to make a quick buck. That's not okay, and it shouldn't be okay with you either. Why Mizuki Exists Ever tried auditing a company's code? We're talking 50 classes, 500,000 lines of code. One person doing that manually? It's like reading War and Peace... backwards... in the dark. It takes forever and fries your brain. That's where Mizuki came in. She started as my security buddy. There are tons of security tools out there - just Google "penetration testing tools" or "ZAP proxy" if you're curious. What makes Mizuki special is how she learns and adapts. Think of it like teaching someone to ride a bike. She tried to breach TAO 67 times before succeeding. Yeah, that's a lot of attempts, but watching her grow from basic email scraping to pulling off complex replay attacks? It's like watching your kid take their first steps. The Tech Stuff (Keeping It Real) The infrastructure isn't fancy - we're not reinventing the wheel here. Mizuki runs on a local server because, let's be honest, running this kind of AI on a web server would be a nightmare. Instead, we process everything locally and send the results to a frontend server. Simple, effective, done. And yeah, those temperature settings I keep tweaking? In AI-speak, that's just how "creative" or "by-the-book" the AI gets with its responses. I've adjusted it so much, Mizuki's probably got mood swings now - going from super technical to pretty chill and back again. Changes Coming Real talk: Mizuki won't be tweeting every few hours anymore. Twitter API costs are ridiculous (looking at you, Elon), and honestly, we need to focus on what matters - the actual security work. She'll still tweet about breaches, but maybe once or twice a day. I'm working on making her explanations clearer too. Don't worry - she'll keep her savage personality in the terminal. That's just too fun to change. The Truth About Her Breaches I don't choose the targets - I don't even know these companies until after Mizuki finds something. She uses web scraping to find domain names, just like those old email scrapers people used for marketing. Been focusing on AI projects first, but she's looked at other sites too. And no, I'm not sitting there writing tweets. The Twitter API v2 makes it super easy to post programmatically. If anyone's curious about how to do that, hit me up - I'm happy to show you the ropes. Wrapping Up I could talk about this stuff forever (just ask my wife - actually, don't, she's heard enough!). If you've read this far, thank you. Whether you believe in what we're doing or not, I appreciate you taking the time. And for the skeptics still hanging around, I'll leave you with this thought: When's the last time you saw an AI break down and hack a COMPILED game in less than 10 seconds... outside a browser? Stay curious, stay skeptical, but most importantly, stay open to possibilities. Catch you on the flip side! 🎙️Drop

anonDev_

58,133 görüntüleme • 1 yıl önce