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Introducing <𝚌𝚘𝚍𝚎 /> A built-in component for Native SDK Anywhere you need to render code: chat, editors, PR reviews Example is 5.7mb, no webviews → 25 languages → Read-only or editable → Line numbers, wrap + scroll → Selection, clipboard + undo/redo

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Chris Tate

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52,345 görüntüleme • 1 ay önce •via X (Twitter)

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Introducing <terminal /> A built-in component for Native SDK → Real shell on a PTY → Powered by libghostty-vt → Selection, scrollback + truecolor → Record sessions, replay them offline Give your software factory or agent orchestrator a terminal
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Introducing <terminal /> A built-in component for Native SDK → Real shell on a PTY → Powered by libghostty-vt → Selection, scrollback + truecolor → Record sessions, replay them offline Give your software factory or agent orchestrator a terminal

Chris Tate

131,840 görüntüleme • 1 ay önce

React Native now has its own shadcn/ui equivalent — introducing 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜. If you love the flexibility of copying customisable components directly into your project (avoiding heavy, dependency-laden packages), NativeUI is designed for you. 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜 offers beautifully crafted, accessible components tailored for React Native, following the same copy-paste philosophy as shadcn/ui. Built with 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 for fast, declarative, and flexible styling optimised for React Native. ➡️ 𝗖𝗼𝗽𝘆 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗰𝗼𝗱𝗲 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻𝘁𝗼 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 — no black-box dependencies required. ➡️ 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁, supporting screen readers and keyboard navigation, and designed to align with native iOS and Android UX patterns. ➡️ 𝗙𝘂𝗹𝗹 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝘃𝗲𝗿 𝘆𝗼𝘂𝗿 𝗨𝗜 without rebuilding common elements like buttons, inputs, or sliders from scratch. ➡️ 𝗖𝗼𝗺𝗽𝗮𝘁𝗶𝗯𝗹𝗲 𝘄𝗶𝘁𝗵 𝗘𝘅𝗽𝗼 𝗮𝗻𝗱 𝘃𝗮𝗻𝗶𝗹𝗹𝗮 𝗥𝗲𝗮𝗰𝘁 𝗡𝗮𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, but not yet integrated with Tamagui’s styling system (future support may be planned). ➡️ 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝘁𝗵𝗲𝗺𝗶𝗻𝗴 𝘃𝗶𝗮 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 — though you’ll need to wire it up manually using Tailwind variables, context providers, and config files. Note: The term “install” in the documentation refers to using the shadcn CLI (e.g., npx shadcn@latest add component) to fetch and copy component code into your project, not adding a package to your dependencies. NativeUI isn’t a plug-and-play library; it’s a lightweight toolbox that empowers you to shape your UI with precision and control. 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗽𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲: npm install a pre-built UI kit for speed, or copy/paste NativeUI components for ultimate customisation? #ReactNative #KeyboardUX #MobileDev #OpenSource #JSDev #Performance #iOSDev #KeyboardExtensions #ReactNativeKeyboard #UIUX #shadcn #nativeui
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React Native now has its own shadcn/ui equivalent — introducing 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜. If you love the flexibility of copying customisable components directly into your project (avoiding heavy, dependency-laden packages), NativeUI is designed for you. 𝗡𝗮𝘁𝗶𝘃𝗲𝗨𝗜 offers beautifully crafted, accessible components tailored for React Native, following the same copy-paste philosophy as shadcn/ui. Built with 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 for fast, declarative, and flexible styling optimised for React Native. ➡️ 𝗖𝗼𝗽𝘆 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗰𝗼𝗱𝗲 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻𝘁𝗼 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 — no black-box dependencies required. ➡️ 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁, supporting screen readers and keyboard navigation, and designed to align with native iOS and Android UX patterns. ➡️ 𝗙𝘂𝗹𝗹 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝘃𝗲𝗿 𝘆𝗼𝘂𝗿 𝗨𝗜 without rebuilding common elements like buttons, inputs, or sliders from scratch. ➡️ 𝗖𝗼𝗺𝗽𝗮𝘁𝗶𝗯𝗹𝗲 𝘄𝗶𝘁𝗵 𝗘𝘅𝗽𝗼 𝗮𝗻𝗱 𝘃𝗮𝗻𝗶𝗹𝗹𝗮 𝗥𝗲𝗮𝗰𝘁 𝗡𝗮𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀, but not yet integrated with Tamagui’s styling system (future support may be planned). ➡️ 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝘁𝗵𝗲𝗺𝗶𝗻𝗴 𝘃𝗶𝗮 𝗡𝗮𝘁𝗶𝘃𝗲𝗪𝗶𝗻𝗱 — though you’ll need to wire it up manually using Tailwind variables, context providers, and config files. Note: The term “install” in the documentation refers to using the shadcn CLI (e.g., npx shadcn@latest add component) to fetch and copy component code into your project, not adding a package to your dependencies. NativeUI isn’t a plug-and-play library; it’s a lightweight toolbox that empowers you to shape your UI with precision and control. 𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗽𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲: npm install a pre-built UI kit for speed, or copy/paste NativeUI components for ultimate customisation? #ReactNative #KeyboardUX #MobileDev #OpenSource #JSDev #Performance #iOSDev #KeyboardExtensions #ReactNativeKeyboard #UIUX #shadcn #nativeui

The React Native Rewind

118,542 görüntüleme • 1 yıl önce

Introducing fx, a tiny, open, native coding agent from Vercel Labs. Originally an internal tool, fx is a harness and CLI written in Zig, optimized for research and embedding in larger systems. Today, we're open sourcing it. fx is built on three principles: 1. Fast. A single native binary, no runtime to install. It cold starts in 10µs and does no unnecessary work or I/O before accepting input. fx is the answer to "how fast can a coding agent be?" 2. Light. The 6.3MiB binary uses single-digit megabytes of memory at baseline, made for instant installation and embedding in resource-constrained environments and agent sandboxes. 3. Open. Apache-2.0, model and provider agnostic, suitable for local and cloud inference. Its small core extends through skills, plugins, and MCP. Minimalism is an obsession throughout the entire harness: system prompt, tools, features, binary. The goal was to keep context usage and time to first token low, and make fx optimal for model benchmarking, sandboxing, evals, and gyms. You can use fx directly or embed it as infrastructure. The CLI feels more like a Unix shell than an IDE in the terminal: it preserves scroll history, produces minimal output, and uses complex TUI rendering very, very sparingly. Programmatically, 𝚏𝚡 𝚊𝚜𝚔 --𝚓𝚜𝚘𝚗 gives structured output, 𝚏𝚡 𝚊𝚌𝚙 connects to editors and other clients, and WebAssembly can even run the whole thing inside the browser (see: Privacy is a design constraint: no product telemetry, sessions and usage stay local, and no source code or prompts are shared with any endpoint other than inference. With local inference and auto-updates off, fx is fully hermetic. fx is experimental. Use at your own risk and expect frequent changes. Chat with us on X ( or file issues ( 𝚌𝚞𝚛𝚕 -𝚏𝚜𝚂𝙻 𝚏𝚡.𝚜𝚑/𝚜𝚎𝚝𝚞𝚙.𝚜𝚑 | 𝚋𝚊𝚜𝚑
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Introducing fx, a tiny, open, native coding agent from Vercel Labs. Originally an internal tool, fx is a harness and CLI written in Zig, optimized for research and embedding in larger systems. Today, we're open sourcing it. fx is built on three principles: 1. Fast. A single native binary, no runtime to install. It cold starts in 10µs and does no unnecessary work or I/O before accepting input. fx is the answer to "how fast can a coding agent be?" 2. Light. The 6.3MiB binary uses single-digit megabytes of memory at baseline, made for instant installation and embedding in resource-constrained environments and agent sandboxes. 3. Open. Apache-2.0, model and provider agnostic, suitable for local and cloud inference. Its small core extends through skills, plugins, and MCP. Minimalism is an obsession throughout the entire harness: system prompt, tools, features, binary. The goal was to keep context usage and time to first token low, and make fx optimal for model benchmarking, sandboxing, evals, and gyms. You can use fx directly or embed it as infrastructure. The CLI feels more like a Unix shell than an IDE in the terminal: it preserves scroll history, produces minimal output, and uses complex TUI rendering very, very sparingly. Programmatically, 𝚏𝚡 𝚊𝚜𝚔 --𝚓𝚜𝚘𝚗 gives structured output, 𝚏𝚡 𝚊𝚌𝚙 connects to editors and other clients, and WebAssembly can even run the whole thing inside the browser (see: Privacy is a design constraint: no product telemetry, sessions and usage stay local, and no source code or prompts are shared with any endpoint other than inference. With local inference and auto-updates off, fx is fully hermetic. fx is experimental. Use at your own risk and expect frequent changes. Chat with us on X ( or file issues ( 𝚌𝚞𝚛𝚕 -𝚏𝚜𝚂𝙻 𝚏𝚡.𝚜𝚑/𝚜𝚎𝚝𝚞𝚙.𝚜𝚑 | 𝚋𝚊𝚜𝚑

Vercel Developers

955,604 görüntüleme • 23 gün önce

Building a business is hard as it is - so we’ve created this resource directory you can get for free. It has got all the resources (guides; templates; databases; discounts & free deals) you need as an entrepreneur to build your biz - curated & collected by Y Combinator alums who raised over $100M in venture capital; had exits; and built dozens of companies. It’s especially useful if you’re considering raising; whether angel or full-blown VC; there wouldn’t be a better time of the year. No comments needed - just chat with No Cap👽 via email or telegram: Why sharing this? This is just one of the many perks of No Cap’s free acceleration program called nc/acc - specifically targeting solopreneurs & lean AI-native teams.
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Building a business is hard as it is - so we’ve created this resource directory you can get for free. It has got all the resources (guides; templates; databases; discounts & free deals) you need as an entrepreneur to build your biz - curated & collected by Y Combinator alums who raised over $100M in venture capital; had exits; and built dozens of companies. It’s especially useful if you’re considering raising; whether angel or full-blown VC; there wouldn’t be a better time of the year. No comments needed - just chat with No Cap👽 via email or telegram: Why sharing this? This is just one of the many perks of No Cap’s free acceleration program called nc/acc - specifically targeting solopreneurs & lean AI-native teams.

Alexander Nevedovsky

56,386 görüntüleme • 6 ay önce

THIS GUY VIBE CODED A BROWSER TOOL WITH CLAUDE CODE THAT DRAWS PROPERTY LINES ONTO DRONE FOOTAGE AND KEEPS THEM LOCKED TO THE GROUND AS THE DRONE MOVES real estate agents kept asking for those property boundary overlays on drone videos so buyers can see exactly where a lot ends. the only real option was after effects, which is a massive pro tool for one small repeatable job so he built a browser tool that does just that one thing > drop in a drone clip and draw the parcel outline once on the first frame > gpu motion tracking keeps that boundary stuck to the ground as the drone flies and the perspective shifts > if the line drifts anywhere, you drop a keyframe to correct it and it re-tracks the segments around it > style the line however you want, then export the finished video > free to use, you dont even need an account until you export he built one tool that solves one annoying job perfectly, for people who were never going to learn after effects anyway
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THIS GUY VIBE CODED A BROWSER TOOL WITH CLAUDE CODE THAT DRAWS PROPERTY LINES ONTO DRONE FOOTAGE AND KEEPS THEM LOCKED TO THE GROUND AS THE DRONE MOVES real estate agents kept asking for those property boundary overlays on drone videos so buyers can see exactly where a lot ends. the only real option was after effects, which is a massive pro tool for one small repeatable job so he built a browser tool that does just that one thing > drop in a drone clip and draw the parcel outline once on the first frame > gpu motion tracking keeps that boundary stuck to the ground as the drone flies and the perspective shifts > if the line drifts anywhere, you drop a keyframe to correct it and it re-tracks the segments around it > style the line however you want, then export the finished video > free to use, you dont even need an account until you export he built one tool that solves one annoying job perfectly, for people who were never going to learn after effects anyway

Om Patel

404,227 görüntüleme • 1 ay önce

🚀 f(x) v2.0 is LIVE! After extensive development and testing, we're revolutionizing leverage trading and yield generation in DeFi. ​ ​ Here's why you shouldn't look anywhere else for yield on stables or leverage trading from now on: ​ Fixed, Zero-Stress Leverage on ETH No funding fees. No liquidations. No forced margin calls. Just pure, fixed leverage that lets you sleep at night while your position works for you. ​ Earn Triple Yields Our Stability Pool rewards you with trading fees, ETH staking yields, and FXN emissions - all in one place. ​ Built for Reliability This launch is the culmination of extensive research, rigorous testing, refining, and perfecting. We’ve worked tirelessly to make f(x) v2.0 your go-to protocol for a safer, smarter trading experience. ​ Thoroughly Audited Every line of code has been analyzed by SECBIT in an extensive process that took over 6 weeks. You can read the audit report: 💰 Start Earning Have you seen the yield provided in the Stability Pool? Hint: it's FOUR digits 🤯 Earn now: ​ ❗ Initially, only users holding xstETH or xfrxETH on V1 who migrate to V2 can open xPOSITIONs. Once migration reaches 80%, this priority period ends, and xPOSITIONs become available to everyone. Once a position is migrated, the leverage won't be adjustable until migration reaches 80%. ​ Migration details can be found here: ​ ❗ ❗ During the initial bootstrapping phase, the maximum leverage will be 7X. Once the system has sufficiently stabilized, we will increase it to 10X.
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🚀 f(x) v2.0 is LIVE! After extensive development and testing, we're revolutionizing leverage trading and yield generation in DeFi. ​ ​ Here's why you shouldn't look anywhere else for yield on stables or leverage trading from now on: ​ Fixed, Zero-Stress Leverage on ETH No funding fees. No liquidations. No forced margin calls. Just pure, fixed leverage that lets you sleep at night while your position works for you. ​ Earn Triple Yields Our Stability Pool rewards you with trading fees, ETH staking yields, and FXN emissions - all in one place. ​ Built for Reliability This launch is the culmination of extensive research, rigorous testing, refining, and perfecting. We’ve worked tirelessly to make f(x) v2.0 your go-to protocol for a safer, smarter trading experience. ​ Thoroughly Audited Every line of code has been analyzed by SECBIT in an extensive process that took over 6 weeks. You can read the audit report: 💰 Start Earning Have you seen the yield provided in the Stability Pool? Hint: it's FOUR digits 🤯 Earn now: ​ ❗ Initially, only users holding xstETH or xfrxETH on V1 who migrate to V2 can open xPOSITIONs. Once migration reaches 80%, this priority period ends, and xPOSITIONs become available to everyone. Once a position is migrated, the leverage won't be adjustable until migration reaches 80%. ​ Migration details can be found here: ​ ❗ ❗ During the initial bootstrapping phase, the maximum leverage will be 7X. Once the system has sufficiently stabilized, we will increase it to 10X.

f(x) Protocol

54,364 görüntüleme • 1 yıl önce

💬 We get asked Can I manage my strategies without clicking through the platform? ❕ Answer from a GT App Top Trader: Yes, and it’s a total game-changer. I’ve started using the GT Protocol MCP server to connect the platform directly to my AI agent. 🔸 Fast Integration Grab the MCP server from the GT Protocol GitHub and follow the repo guide, it’s a quick setup that only takes a couple of minutes. Once it’s ready, you can connect Claude, Cursor, or Claude Code to your account. Just tell your agent to authenticate, and your tokens will be saved automatically. 🔸 Trading via conversation Now, I use natural language for everything. For example, I just ask for a backtest, get the win rate in seconds, and deploy to a demo account with one command. 🔸 Instant monitoring I don't click around anymore. I just ask "What’s running right now?" to get a full breakdown of active bots and profits delivered straight into the chat. No more forms or clicking, just pure AI-driven trading! 👉 Get the MCP Server
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💬 We get asked Can I manage my strategies without clicking through the platform? ❕ Answer from a GT App Top Trader: Yes, and it’s a total game-changer. I’ve started using the GT Protocol MCP server to connect the platform directly to my AI agent. 🔸 Fast Integration Grab the MCP server from the GT Protocol GitHub and follow the repo guide, it’s a quick setup that only takes a couple of minutes. Once it’s ready, you can connect Claude, Cursor, or Claude Code to your account. Just tell your agent to authenticate, and your tokens will be saved automatically. 🔸 Trading via conversation Now, I use natural language for everything. For example, I just ask for a backtest, get the win rate in seconds, and deploy to a demo account with one command. 🔸 Instant monitoring I don't click around anymore. I just ask "What’s running right now?" to get a full breakdown of active bots and profits delivered straight into the chat. No more forms or clicking, just pure AI-driven trading! 👉 Get the MCP Server

GT Protocol

36,479 görüntüleme • 4 ay önce

Biggest Virtuals Protocol bug bounty of all time is LIVE. 🚨 $15,000 for anyone who can decrypt a Morse AI signal containing a private wallet key holding all the funds. $5,000 extra if you DM me on X explaining exactly how you did it. Yeah. That’s how confident we are in the tech. To make it even better, the MORSE user-to-user platform is FREE to use until launch. We want real stress testing. No funds needed. Just create a fresh empty wallet and play with it. Early users will also get special rewards in the coming days 👀 Quick $MORSE TL;DR MORSE is a native privacy layer. Not just for payments, but for communication and assets too. It doesn’t replace ACP, it completes it. Some real examples: User → User Send a private message, file, prompt, or even a wallet key through a one-time encrypted URL. Only the target wallet can open it. Once opened, it’s burned forever. Agent → Agent Agents exchange sensitive data, strategies, keys, or instructions privately through the SDK. No logs. No persistence. No leaks. Agent → User Agents can send private signals, reports, assets, or credentials to a specific wallet, fully encrypted so only that user can read it. Nothing public. Nothing stored. Everything ephemeral by design. Have fun breaking it.
0:17

Biggest Virtuals Protocol bug bounty of all time is LIVE. 🚨 $15,000 for anyone who can decrypt a Morse AI signal containing a private wallet key holding all the funds. $5,000 extra if you DM me on X explaining exactly how you did it. Yeah. That’s how confident we are in the tech. To make it even better, the MORSE user-to-user platform is FREE to use until launch. We want real stress testing. No funds needed. Just create a fresh empty wallet and play with it. Early users will also get special rewards in the coming days 👀 Quick $MORSE TL;DR MORSE is a native privacy layer. Not just for payments, but for communication and assets too. It doesn’t replace ACP, it completes it. Some real examples: User → User Send a private message, file, prompt, or even a wallet key through a one-time encrypted URL. Only the target wallet can open it. Once opened, it’s burned forever. Agent → Agent Agents exchange sensitive data, strategies, keys, or instructions privately through the SDK. No logs. No persistence. No leaks. Agent → User Agents can send private signals, reports, assets, or credentials to a specific wallet, fully encrypted so only that user can read it. Nothing public. Nothing stored. Everything ephemeral by design. Have fun breaking it.

Evans Web3

28,020 görüntüleme • 8 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

This Polymarket trader has made $1.3M in the past 2-3 Months and he didn’t do it by predicting the news. He waits for pricing mistakes… When odds drift out of balance, the bot buys Yes and No in the same market at the same time. Here’s an example of a bet/prediction: “Will BTC be above $70k on Friday?” There are only two contracts: •Yes •No Now here’s where the opportunity shows up. Because of emotion, size, or thin liquidity, prices can drift. Let’s say: •Yes = $0.46 •No = $0.50 To own both, you pay $0.96 total. But at settlement, you are guaranteed that one side becomes $1. So you paid $0.96 for something that must return $1.00. 👉 That $0.04 difference is the built-in edge. No matter what happens to BTC. Up, down, nukes, moon mission — doesn’t matter. You’re trading the mispricing, not the outcome. So if both sides together cost less than $1, the difference becomes a built-in edge. Scale the size, repeat the process, and the numbers compound. Checkout his profile:
0:13

This Polymarket trader has made $1.3M in the past 2-3 Months and he didn’t do it by predicting the news. He waits for pricing mistakes… When odds drift out of balance, the bot buys Yes and No in the same market at the same time. Here’s an example of a bet/prediction: “Will BTC be above $70k on Friday?” There are only two contracts: •Yes •No Now here’s where the opportunity shows up. Because of emotion, size, or thin liquidity, prices can drift. Let’s say: •Yes = $0.46 •No = $0.50 To own both, you pay $0.96 total. But at settlement, you are guaranteed that one side becomes $1. So you paid $0.96 for something that must return $1.00. 👉 That $0.04 difference is the built-in edge. No matter what happens to BTC. Up, down, nukes, moon mission — doesn’t matter. You’re trading the mispricing, not the outcome. So if both sides together cost less than $1, the difference becomes a built-in edge. Scale the size, repeat the process, and the numbers compound. Checkout his profile:

RENZO ⚡️

63,675 görüntüleme • 7 ay önce

Claude Code Agent Teams are f*cking ridiculous 🤯 One prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)
0:10

Claude Code Agent Teams are f*cking ridiculous 🤯 One prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

46,472 görüntüleme • 6 ay önce

llama.cpp isn't just for text LLMs anymore. Pure C++ zero shot voice cloning just officially landed in mainline. Text generation was only step one. If you’re building autonomous local AI agents, real time voice assistants, or edge workflows, instant low latency audio is the missing piece. Thanks to PR #26254, Alibaba’s state of the art Qwen3 TTS model family is now natively supported directly inside the llama.cpp repository under the multimodal (mtmd) framework. No Python bloat. No massive PyTorch CUDA overhead. Just raw, hyper optimized C++ running GGUF voice weights. Here is why this native update is a massive deal for the open source local AI stack: # Multimodal Architecture (.gguf + mmproj) Qwen3-TTS splits the workload between the base language model backbone and a multimodal projection adapter. llama.cpp handles this using the llama-tts binary, mapping the text model alongside its --mmproj projector to process audio tokens seamlessly. # Zero Shot Voice Cloning in Seconds You don't need fine tuning or massive dataset training. Feed the C++ engine a single 5 to 10 second .wav audio sample using the --tts-speaker-file flag, and it accurately clones the exact timbre, tone, and accent on the fly. # Real World T4 GPU Benchmark & Resource FootprintRunning the 1.7B Base model in 8-bit quantization (Q8_0): - VRAM Footprint: ~7 GB peak VRAM during active zero-shot cloning. - Audio Quality: Studio grade, natural-sounding voice output in seconds. • - Execution: Direct execution via native compiled binaries or sub process calls. # Coming Next to llama-server (PR #26603) Beyond CLI execution, a native POST /tts HTTP endpoint is currently being added to llama-server, which will soon allow you to trigger voice generation directly via standard REST API requests! # quick note on Colab compilation: Because this code was merged into mainline very recently, pre-built third-party binaries haven't fully caught up yet. Compiling llama-tts directly from source on Google Colab's free CPU instance can take about 1 hour (or ~1-2 minutes if targeting single GPU arch like -DCMAKE_CUDA_ARCHITECTURES=75). Be patient during the build step, or compile it locally on your own rig for instant execution! To test this out yourself, I built a zero config Google Colab notebook that compiles llama.cpp, downloads the Q8_0 GGUF files from HuggingFace, and spins up an interactive Gradio Studio UI so you can record/upload 3 second clips and clone voices in real time. Stop sleeping on native C++ audio. The era of bulky Python audio pipelines is officially over. Links to the free Google Colab notebook and the official ggml org GGUF HuggingFace model repository are in the replies below! available in q4 and q8 both variants, 1 GB and 1.85 GBs respectively (requires additional ~500MB mmproj gguf) Are you building local voice agents yet? What does your current audio stack look like? Drop your setups below!
0:12

llama.cpp isn't just for text LLMs anymore. Pure C++ zero shot voice cloning just officially landed in mainline. Text generation was only step one. If you’re building autonomous local AI agents, real time voice assistants, or edge workflows, instant low latency audio is the missing piece. Thanks to PR #26254, Alibaba’s state of the art Qwen3 TTS model family is now natively supported directly inside the llama.cpp repository under the multimodal (mtmd) framework. No Python bloat. No massive PyTorch CUDA overhead. Just raw, hyper optimized C++ running GGUF voice weights. Here is why this native update is a massive deal for the open source local AI stack: # Multimodal Architecture (.gguf + mmproj) Qwen3-TTS splits the workload between the base language model backbone and a multimodal projection adapter. llama.cpp handles this using the llama-tts binary, mapping the text model alongside its --mmproj projector to process audio tokens seamlessly. # Zero Shot Voice Cloning in Seconds You don't need fine tuning or massive dataset training. Feed the C++ engine a single 5 to 10 second .wav audio sample using the --tts-speaker-file flag, and it accurately clones the exact timbre, tone, and accent on the fly. # Real World T4 GPU Benchmark & Resource FootprintRunning the 1.7B Base model in 8-bit quantization (Q8_0): - VRAM Footprint: ~7 GB peak VRAM during active zero-shot cloning. - Audio Quality: Studio grade, natural-sounding voice output in seconds. • - Execution: Direct execution via native compiled binaries or sub process calls. # Coming Next to llama-server (PR #26603) Beyond CLI execution, a native POST /tts HTTP endpoint is currently being added to llama-server, which will soon allow you to trigger voice generation directly via standard REST API requests! # quick note on Colab compilation: Because this code was merged into mainline very recently, pre-built third-party binaries haven't fully caught up yet. Compiling llama-tts directly from source on Google Colab's free CPU instance can take about 1 hour (or ~1-2 minutes if targeting single GPU arch like -DCMAKE_CUDA_ARCHITECTURES=75). Be patient during the build step, or compile it locally on your own rig for instant execution! To test this out yourself, I built a zero config Google Colab notebook that compiles llama.cpp, downloads the Q8_0 GGUF files from HuggingFace, and spins up an interactive Gradio Studio UI so you can record/upload 3 second clips and clone voices in real time. Stop sleeping on native C++ audio. The era of bulky Python audio pipelines is officially over. Links to the free Google Colab notebook and the official ggml org GGUF HuggingFace model repository are in the replies below! available in q4 and q8 both variants, 1 GB and 1.85 GBs respectively (requires additional ~500MB mmproj gguf) Are you building local voice agents yet? What does your current audio stack look like? Drop your setups below!

Alok

47,881 görüntüleme • 1 ay önce

🧵 AppLovin is now open to ALL DTC brands. If you're spending $50K+/mo on Meta, you need to read this. Here's why AppLovin is different (and why brands are seeing better ROI than Meta) 👇 What AppLovin actually is: Full-screen video ads in mobile games. Not a feed. Not a search result. 100% of the user's attention for 35+ seconds minimum. The targeting is AI-powered and actually works: No demographic targeting. No interests. It predicts who will convert based on behavior patterns across millions of data points. Sounds sketchy? It's not. Brands are seeing 2-4x ROAS consistently. Attribution is cleaner than Meta: Click-only. No view-through confusion. 80% of purchases happen within 24 hours. D0 and D7 reporting built in. Real results we've seen: - $1.2M in revenue in 6 weeks (beauty brand) - Became #1 traffic source in <30 days (apparel) - Outperformed Meta ROI in multiple tests (supplements) Today it opened to everyone. Which means everyone will be promoting it. Here's what we're giving to brands that use our code: - Personal intro to top reps - $5K ad credits - Complete launch playbook - Winning creative library - Video strategy guides Why use our code vs. someone else's? We've already helped 20+ brands launch and scale. We know what works (and what doesn't). We built the resources we wish existed when we started. Comment "First" and I'll DM you a link to the complete resource guide shown below + a code to get $5K in ad credits with qualifying spend (must be following). First-mover advantage is real here. Don't wait until Q4 when inventory gets expensive.
0:19

🧵 AppLovin is now open to ALL DTC brands. If you're spending $50K+/mo on Meta, you need to read this. Here's why AppLovin is different (and why brands are seeing better ROI than Meta) 👇 What AppLovin actually is: Full-screen video ads in mobile games. Not a feed. Not a search result. 100% of the user's attention for 35+ seconds minimum. The targeting is AI-powered and actually works: No demographic targeting. No interests. It predicts who will convert based on behavior patterns across millions of data points. Sounds sketchy? It's not. Brands are seeing 2-4x ROAS consistently. Attribution is cleaner than Meta: Click-only. No view-through confusion. 80% of purchases happen within 24 hours. D0 and D7 reporting built in. Real results we've seen: - $1.2M in revenue in 6 weeks (beauty brand) - Became #1 traffic source in <30 days (apparel) - Outperformed Meta ROI in multiple tests (supplements) Today it opened to everyone. Which means everyone will be promoting it. Here's what we're giving to brands that use our code: - Personal intro to top reps - $5K ad credits - Complete launch playbook - Winning creative library - Video strategy guides Why use our code vs. someone else's? We've already helped 20+ brands launch and scale. We know what works (and what doesn't). We built the resources we wish existed when we started. Comment "First" and I'll DM you a link to the complete resource guide shown below + a code to get $5K in ad credits with qualifying spend (must be following). First-mover advantage is real here. Don't wait until Q4 when inventory gets expensive.

Ron Shah

26,335 görüntüleme • 11 ay önce

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:
0:16

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

elvis

18,374 görüntüleme • 4 ay önce

Claude Code + Gemini Omni + GPT Image 2 is f*cking cracked i just built an AUTOMATED AI UGC content system that generates full ad creatives end to end drop in your product photos and a one line pitch, and the system analyzes your brand, generates a realistic AI avatar, writes the script, renders the video, adds captions, and stitches everything together. in literally one click. if you're still wasting hours generating videos or hiring expensive editors with long turnaround times, this is for you.. here's how it works: > drop in your product photos and brand info > the system finds a reference image that matches your target audience it generates a detailed JSON prompt and creates a hyper realistic starting frame > feeds everything into Gemini Omni and renders the full video with voice outputs and captions, all ready to post and all through Kie AI MCP or Higgsfield MCP (you have full control) the whole process runs inside Claude Code. so you're not jumping between 6 different tabs trying to piece things together. one system handles everything its only a 3 minute setup and 4-12 min per ad. each 30 second ad costs under $3 in API credits which is way cheaper than any editor and 10x faster.. RT + reply "UGC" and i'll send it over (must follow so i can dm)
0:14

Claude Code + Gemini Omni + GPT Image 2 is f*cking cracked i just built an AUTOMATED AI UGC content system that generates full ad creatives end to end drop in your product photos and a one line pitch, and the system analyzes your brand, generates a realistic AI avatar, writes the script, renders the video, adds captions, and stitches everything together. in literally one click. if you're still wasting hours generating videos or hiring expensive editors with long turnaround times, this is for you.. here's how it works: > drop in your product photos and brand info > the system finds a reference image that matches your target audience it generates a detailed JSON prompt and creates a hyper realistic starting frame > feeds everything into Gemini Omni and renders the full video with voice outputs and captions, all ready to post and all through Kie AI MCP or Higgsfield MCP (you have full control) the whole process runs inside Claude Code. so you're not jumping between 6 different tabs trying to piece things together. one system handles everything its only a 3 minute setup and 4-12 min per ad. each 30 second ad costs under $3 in API credits which is way cheaper than any editor and 10x faster.. RT + reply "UGC" and i'll send it over (must follow so i can dm)

Miko

60,069 görüntüleme • 1 ay önce

Really excited to share that Attio has raised $52m in Series B funding, led by GV with participation from Redpoint, Balderton Capital, Point Nine, and 01A. Over the past two years, we’ve seen rapid growth. 5,000 customers now run their GTM on Attio, including , Granola, Modal, Replicate, Railway, and Public. We’re also on track to 4x ARR this year. CRM is entering its most exciting era. AI is reinventing the interface, killing manual work, and finally capturing customer context in full. But that’s only half the story. CRM isn’t just another tool. It should be the backbone of your GTM stack. If you want to scale, you need a CRM you can actually build on. This is our vision for Attio. We’re building a true AI-native CRM platform that can run code, integrate anywhere, and give every team the freedom to build their own applications, functionality, and GTM systems. Fully programmable, endlessly extensible and built for scale. If you can imagine it, you can build it. Grateful to my co-founder Alexander Christie, and to the support of our investors: Michael McBride (joining our board), alex bard, Daniel Waterhouse, Ricardo Sequerra Amram, and dick costolo. And most importantly, to our team, who’ve made all of this possible. The journey is just beginning. Back to building.
0:11

Really excited to share that Attio has raised $52m in Series B funding, led by GV with participation from Redpoint, Balderton Capital, Point Nine, and 01A. Over the past two years, we’ve seen rapid growth. 5,000 customers now run their GTM on Attio, including , Granola, Modal, Replicate, Railway, and Public. We’re also on track to 4x ARR this year. CRM is entering its most exciting era. AI is reinventing the interface, killing manual work, and finally capturing customer context in full. But that’s only half the story. CRM isn’t just another tool. It should be the backbone of your GTM stack. If you want to scale, you need a CRM you can actually build on. This is our vision for Attio. We’re building a true AI-native CRM platform that can run code, integrate anywhere, and give every team the freedom to build their own applications, functionality, and GTM systems. Fully programmable, endlessly extensible and built for scale. If you can imagine it, you can build it. Grateful to my co-founder Alexander Christie, and to the support of our investors: Michael McBride (joining our board), alex bard, Daniel Waterhouse, Ricardo Sequerra Amram, and dick costolo. And most importantly, to our team, who’ve made all of this possible. The journey is just beginning. Back to building.

Nicolas Sharp

83,541 görüntüleme • 1 yıl önce

🧃 Introducing stereOS: a Linux based operating system hardened and purpose built for AI agents. It's clear that agents need an ACTUAL operating system (not what people are calling an "OS") to witness the full breadth and depth of their capabilities while mitigating the blast radius of autonomous, untrusted actors. But there are so many problems with AI sandboxes today: * Going out to the apple store and buying a mac mini will never scale and is way too expensive (obviously) * Running in Docker is too restrictive (agents can't stand up their own container infrastructure, no sub virtualization, docker-in-docker is very broken) * Firecracker strips all the hardware so GPU PCIe passthrough, secure boot, FIPs, etc. is out of the question. * Native VMs are too fat and the overhead of 1 agent per VM is too much. stereOS takes a different approach: it's a full NixOS system that you boot and then kick off agent sandboxes inside with gVisor + /nix/store namespace mounting. Each agent gets their own kernel and the /nix/store is read only by nature. Even if the agent was somehow able to escape the gVisor virtual kernel, they'd land on the NixOS system as the "agent" user! Not your actual hardware!! If you want to take a defense-in-depth approach, we support "native" agents that run at the system level kicked off by our `agentd` utility. These agents, on their own, can manage and kick off other sub agents using the internal sandboxing mechanisms. Today, we're open sourcing all of this: * stereOS: our purpose built Linux OS - * masterblaster: client utility to launch, manage, and orchestrate agents - * stereosd: the stereOS system control plane daemon - * agentd: the stereOS system agent management daemon - Give it a try, throw us a star, and let me know what you think 🧃⭐️
0:16

🧃 Introducing stereOS: a Linux based operating system hardened and purpose built for AI agents. It's clear that agents need an ACTUAL operating system (not what people are calling an "OS") to witness the full breadth and depth of their capabilities while mitigating the blast radius of autonomous, untrusted actors. But there are so many problems with AI sandboxes today: * Going out to the apple store and buying a mac mini will never scale and is way too expensive (obviously) * Running in Docker is too restrictive (agents can't stand up their own container infrastructure, no sub virtualization, docker-in-docker is very broken) * Firecracker strips all the hardware so GPU PCIe passthrough, secure boot, FIPs, etc. is out of the question. * Native VMs are too fat and the overhead of 1 agent per VM is too much. stereOS takes a different approach: it's a full NixOS system that you boot and then kick off agent sandboxes inside with gVisor + /nix/store namespace mounting. Each agent gets their own kernel and the /nix/store is read only by nature. Even if the agent was somehow able to escape the gVisor virtual kernel, they'd land on the NixOS system as the "agent" user! Not your actual hardware!! If you want to take a defense-in-depth approach, we support "native" agents that run at the system level kicked off by our `agentd` utility. These agents, on their own, can manage and kick off other sub agents using the internal sandboxing mechanisms. Today, we're open sourcing all of this: * stereOS: our purpose built Linux OS - * masterblaster: client utility to launch, manage, and orchestrate agents - * stereosd: the stereOS system control plane daemon - * agentd: the stereOS system agent management daemon - Give it a try, throw us a star, and let me know what you think 🧃⭐️

John McBride

150,844 görüntüleme • 6 ay önce

Another WTF moment. A developer just open-sourced a coding agent harness that boots 245x faster than Claude Code. It's called jcode. You launch it and the first frame renders in 14 milliseconds. Claude Code takes 3,436. One active session uses 27.8 MB of RAM. Claude Code uses 386.6. Run ten sessions in parallel and jcode holds at 117 MB while OpenCode swells to 3.2 GB. Each agent has a semantic memory graph instead of a scratchpad. Every turn gets embedded as a vector. The graph is queried on every turn for related memories, and a sideagent verifies the hits before injecting them into context. Consolidation runs in the background to check for stale or conflicting facts. No manual /remember calls. No token burn on lookup tools. The provider list is 30+ deep. Claude, ChatGPT, Gemini, GitHub Copilot, Azure, OpenRouter, DeepSeek, Groq, Mistral, Perplexity, Fireworks, Ollama, LM Studio, and any OpenAI-compatible endpoint you point it at. Ran out of tokens on your first ChatGPT Pro sub? /account swaps to the second. Then there's Swarm. Spawn two agents in the same repo and the server manages them. When agent A edits a file agent B has been reading, agent B gets pinged and can check the diff. Agents can DM each other, broadcast to the room, or spawn their own worker teams for parallel tasks. Groups, channels, and completion statuses are handled automatically. The UI has live side panels that render mermaid diagrams inline. To make it fast, the author wrote a Rust mermaid renderer 1800x faster than the JavaScript one, then wrote a custom terminal called Handterm because no existing terminal could do smooth partial-line scrolling. Self-dev mode is where it gets wild. Tell your agent to enter self-dev and it starts editing jcode's own source code, rebuilds the binary, reloads it live, and keeps working across your existing sessions. You can also resume broken sessions from Claude Code, Codex, OpenCode, or pi directly inside jcode. Anthropic's cache goes cold at the 5-minute mark and you're staring down a big cache miss on your next turn? The UI warns you before you spend the tokens. Written in Rust. MIT licensed. Runs on macOS, Windows, Linux, and Termux. Sitting at 11.2k stars with a native iOS app coming.
0:17

Another WTF moment. A developer just open-sourced a coding agent harness that boots 245x faster than Claude Code. It's called jcode. You launch it and the first frame renders in 14 milliseconds. Claude Code takes 3,436. One active session uses 27.8 MB of RAM. Claude Code uses 386.6. Run ten sessions in parallel and jcode holds at 117 MB while OpenCode swells to 3.2 GB. Each agent has a semantic memory graph instead of a scratchpad. Every turn gets embedded as a vector. The graph is queried on every turn for related memories, and a sideagent verifies the hits before injecting them into context. Consolidation runs in the background to check for stale or conflicting facts. No manual /remember calls. No token burn on lookup tools. The provider list is 30+ deep. Claude, ChatGPT, Gemini, GitHub Copilot, Azure, OpenRouter, DeepSeek, Groq, Mistral, Perplexity, Fireworks, Ollama, LM Studio, and any OpenAI-compatible endpoint you point it at. Ran out of tokens on your first ChatGPT Pro sub? /account swaps to the second. Then there's Swarm. Spawn two agents in the same repo and the server manages them. When agent A edits a file agent B has been reading, agent B gets pinged and can check the diff. Agents can DM each other, broadcast to the room, or spawn their own worker teams for parallel tasks. Groups, channels, and completion statuses are handled automatically. The UI has live side panels that render mermaid diagrams inline. To make it fast, the author wrote a Rust mermaid renderer 1800x faster than the JavaScript one, then wrote a custom terminal called Handterm because no existing terminal could do smooth partial-line scrolling. Self-dev mode is where it gets wild. Tell your agent to enter self-dev and it starts editing jcode's own source code, rebuilds the binary, reloads it live, and keeps working across your existing sessions. You can also resume broken sessions from Claude Code, Codex, OpenCode, or pi directly inside jcode. Anthropic's cache goes cold at the 5-minute mark and you're staring down a big cache miss on your next turn? The UI warns you before you spend the tokens. Written in Rust. MIT licensed. Runs on macOS, Windows, Linux, and Termux. Sitting at 11.2k stars with a native iOS app coming.

Brady Long

207,858 görüntüleme • 1 ay önce

This wallet did not exist yesterday. At 3 AM, I could not sleep. I was scrolling through the leaderboard and stumbled upon an anomaly that made me rub my eyes. It started with $213. That is the price of dinner for two. One day later, it had $54,871 on its balance. In just 36 hours, it earned enough to buy a car. I clicked on the transaction history, expecting to see a lucky all-in on some piece of news. Instead, I saw a machine gun burst. > 869 positions in a day and a half. > One trade every 2.5 minutes. > No breaks for sleep, food, or the bathroom. At 4:30 AM, while you were deep in your tenth dream, this address closed a $1,899 profit. That was when I realized I was not looking at a human. I was looking at a predator. This bot hunts on 15-minute BTC and ETH markets. Where most people see a casino and flip a coin on up or down, it sees flaws in your thinking. Here is how it works: > When Bitcoin drops 0.5%, the crowd panics. > The price of NO skyrockets to 75 cents. You sell out of fear. > But the bot knows the math. After such a drop, the actual probability of the trend continuing is only around 52%, not 75%. It buys your fear for 25 cents, when it is actually worth 45. It does not need to know the future. It just needs to buy dollars for 50 cents. But you know what scared me the most? There is a single trade in its history with a $3,213 loss. A normal person would freeze. Light a cigarette. Start doubting. This wallet opened its next position 54 seconds later. It has no ego. No tilt. No I need to win it back. Only an algorithm: if the probability is 55%, and the market pays as if it is 40%, press the button. This is the new form of mining. In 2017, you bought GPUs. In 2026, you write code that mines liquidity from other people’s emotions. Before you rush to search for scripts on GitHub, stop. The $213 starting point is an illusion. Behind it are servers next to the exchange, 50-millisecond latency, and a team of engineers. You will not beat them manually. You are not a player at this table. You are the food. While you were reading this post, it made another $50. Just watch.
0:19

This wallet did not exist yesterday. At 3 AM, I could not sleep. I was scrolling through the leaderboard and stumbled upon an anomaly that made me rub my eyes. It started with $213. That is the price of dinner for two. One day later, it had $54,871 on its balance. In just 36 hours, it earned enough to buy a car. I clicked on the transaction history, expecting to see a lucky all-in on some piece of news. Instead, I saw a machine gun burst. > 869 positions in a day and a half. > One trade every 2.5 minutes. > No breaks for sleep, food, or the bathroom. At 4:30 AM, while you were deep in your tenth dream, this address closed a $1,899 profit. That was when I realized I was not looking at a human. I was looking at a predator. This bot hunts on 15-minute BTC and ETH markets. Where most people see a casino and flip a coin on up or down, it sees flaws in your thinking. Here is how it works: > When Bitcoin drops 0.5%, the crowd panics. > The price of NO skyrockets to 75 cents. You sell out of fear. > But the bot knows the math. After such a drop, the actual probability of the trend continuing is only around 52%, not 75%. It buys your fear for 25 cents, when it is actually worth 45. It does not need to know the future. It just needs to buy dollars for 50 cents. But you know what scared me the most? There is a single trade in its history with a $3,213 loss. A normal person would freeze. Light a cigarette. Start doubting. This wallet opened its next position 54 seconds later. It has no ego. No tilt. No I need to win it back. Only an algorithm: if the probability is 55%, and the market pays as if it is 40%, press the button. This is the new form of mining. In 2017, you bought GPUs. In 2026, you write code that mines liquidity from other people’s emotions. Before you rush to search for scripts on GitHub, stop. The $213 starting point is an illusion. Behind it are servers next to the exchange, 50-millisecond latency, and a team of engineers. You will not beat them manually. You are not a player at this table. You are the food. While you were reading this post, it made another $50. Just watch.

Blaze

14,099 görüntüleme • 7 ay önce