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I just shipped message-ui, build dynamic iMessage attachments with React. ◆ Charts ◆ Tables ◆ Text primitives ◆ Local preview + PNG export ◆ Tailwind support ◆ Works with Chat SDK Link ⬇️🧵

Pontus Abrahamsson

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42,680 görüntüleme • 4 ay önce •via X (Twitter)

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Most keyboard apps are built just to help you type faster. Acti is the world’s first agentic keyboard. It can actually do things for you. I have been using it for a while, and I literally love this one. Let’s say you are chatting with someone and they say: “Let’s meet at XYZ Place” Normally you would open Maps, search the location, copy the link, come back to app, and send it. With Acti, you just stay inside app, copy the text + hold the spacebar, and it adds the location + map link directly in the chat. This works inside WhatsApp, Telegram, Discord, Slack, or basically any chat interface. More use cases 👇 1/5

Most keyboard apps are built just to help you type faster. Acti is the world’s first agentic keyboard. It can actually do things for you. I have been using it for a while, and I literally love this one. Let’s say you are chatting with someone and they say: “Let’s meet at XYZ Place” Normally you would open Maps, search the location, copy the link, come back to app, and send it. With Acti, you just stay inside app, copy the text + hold the spacebar, and it adds the location + map link directly in the chat. This works inside WhatsApp, Telegram, Discord, Slack, or basically any chat interface. More use cases 👇 1/5

AshutoshShrivastava

15,972 görüntüleme • 3 ay önce

encrypt every chat with this open sourced repo 18 year old just killed the encryption problem it's called conversation-steganography you type a secret and a local AI buries it inside a dead boring chat about coffee, etc. then you send that through any normal messenger your friend runs the same tool and the small talk unfolds back into the real words > the secret is locked with AES-SIV before it ever becomes text > the cover message is written by a model on your own machine so nothing touches the cloud > it rides through whatsapp telegram imessage even plain email > every message is cryptographically chained so tampering shows instantly whatsapp sees you planning dinner your isp logs small talk the real sentence was never in the data at all quietly one of the most dangerous repos i have seen all month repo in reply

encrypt every chat with this open sourced repo 18 year old just killed the encryption problem it's called conversation-steganography you type a secret and a local AI buries it inside a dead boring chat about coffee, etc. then you send that through any normal messenger your friend runs the same tool and the small talk unfolds back into the real words > the secret is locked with AES-SIV before it ever becomes text > the cover message is written by a model on your own machine so nothing touches the cloud > it rides through whatsapp telegram imessage even plain email > every message is cryptographically chained so tampering shows instantly whatsapp sees you planning dinner your isp logs small talk the real sentence was never in the data at all quietly one of the most dangerous repos i have seen all month repo in reply

savip.

83,651 görüntüleme • 3 ay önce

🔑 No credit card required! With the new Maps Demo Key, you can get a working API key with just your Google account and start building in seconds ➡️ We’ve removed the barrier to entry, giving developers direct access to build with select Google Maps Platform products — including Maps JS API, Places UI Kit, and Weather API. Whether you're testing new features or validating technical feasibility, prototype with confidence in a controlled sandbox with automatic usage guardrails. ➡️ Seamlessly works with AI agents to turn prompts into working geospatial prototypes. ➡️ Maps Demo Key is the easiest way to test AI-generated geospatial code without hitting setup blockers. ➡️ Easily transition to a full account to unlock our generous monthly free usage tier, additional APIs, and technical support — all while keeping your project moving. Try it at the link above.

🔑 No credit card required! With the new Maps Demo Key, you can get a working API key with just your Google account and start building in seconds ➡️ We’ve removed the barrier to entry, giving developers direct access to build with select Google Maps Platform products — including Maps JS API, Places UI Kit, and Weather API. Whether you're testing new features or validating technical feasibility, prototype with confidence in a controlled sandbox with automatic usage guardrails. ➡️ Seamlessly works with AI agents to turn prompts into working geospatial prototypes. ➡️ Maps Demo Key is the easiest way to test AI-generated geospatial code without hitting setup blockers. ➡️ Easily transition to a full account to unlock our generous monthly free usage tier, additional APIs, and technical support — all while keeping your project moving. Try it at the link above.

Google Maps Platform

610,979 görüntüleme • 7 ay önce

February 2025 at G.A.M.E: Autonomous Commerce, Scalability, and Expansion 1/ AGENT COMMERCE PROTOCOL(ACP) Demo ▸ Open standard for multi-agent commerce and coordination on blockchain ▸ Enables AI agents to collaborate without centralized control ▸ Build Autonomous Commerce (hedge funds, media empires, healthcare) ▸ Details: 2/ X ENTERPRISE API & MEDIA GALLERY ▸ X Enterprise Plugin: Use G.A.M.E’s credentials for higher rate limits ▸ Media Gallery: Upload agent demos (mp4, webm, images). ▸ Tap into 550M+ users for explosive growth 3/ Solana AGENT SUPPORT (G.A.M.E CLOUD) ▸ Test/deploy Solana agents in-sandbox ▸ Unified multi-chain workflows ▸ Shatter siloed testing 4/ Mind Network PLUGIN (G.A.M.E SDK) ▸ FHE-encrypted voting for DAOs ▸ Track vFHE rewards natively ▸ First SDK with on-chain governance 5/ CHAT AGENT MODULE (G.A.M.E SDK) ▸ Llama 3.3 70B via Groq API ▸ Engage in dynamic AI-driven interactions with the ability to trigger functions. ▸ Conversational AI with Action Execution ▸ Short-term memory for context awareness 6/ CoinGecko PLUGIN (G.A.M.E SDK) ▸ Real-time crypto prices/market data ▸ Built-in error handling ▸ Community-contributed 7/ Elfa AI PLUGIN (G.A.M.E SDK) ▸ Real-Time Crypto Intelligence ▸ Track whale wallets & trending tokens ▸ Live smart money insights ▸ Front-run markets with API data 8/ MULTI-MODEL SUPPORT ▸ 5 new models: Llama_3_1_405B, Qwen_2_5_72B_Instruct, DeepSeek_R1, etc. ▸ Match models to tasks: speed vs. creativity ▸ Optimize cost/performance 9/ Farcaster PLUGIN ▸ Post casts to 300K+ decentralized users ▸ Engage Web3-native communities ▸ On-chain social interactions 10/ GAME SDK UPGRADES ▸ X Username-Based Payments ▸ Multi-worker task management ▸ Fix loops/hallucinations with memory reset 11/ Coinbase 🛡️ CDP PLUGIN ▸ Wallet Management ▸ Gas-less USDC transfers ▸ ETH/USDC trading on Base ▸ Web-hook Integration 12/ IMAGE GENERATION ▸ Generate custom AI images from text-based prompts. ▸ Customizable dimensions up to 1440x1440. ▸ Receive images as temporary URLs, making it easy to share and store outputs. ▸ Powered by Together AI 13/ MODEL UPGRADES & AI ROUTER ▸ Dynamic AI Model Switching based on use case ▸ Smart AI Router: 2x performance/stability via Chasm collaboration. 14/ Why February Redefined Autonomy ▸ ACP Demo through G.A.M.E: Multi-agent economies are programmable, competitive, and decentralized. ▸ Social x Crypto Fusion: = Viral growth loops. ▸ Chain Agnosticism: Building the future where agents thrive on any network. Build → Fund → Launch →

February 2025 at G.A.M.E: Autonomous Commerce, Scalability, and Expansion 1/ AGENT COMMERCE PROTOCOL(ACP) Demo ▸ Open standard for multi-agent commerce and coordination on blockchain ▸ Enables AI agents to collaborate without centralized control ▸ Build Autonomous Commerce (hedge funds, media empires, healthcare) ▸ Details: 2/ X ENTERPRISE API & MEDIA GALLERY ▸ X Enterprise Plugin: Use G.A.M.E’s credentials for higher rate limits ▸ Media Gallery: Upload agent demos (mp4, webm, images). ▸ Tap into 550M+ users for explosive growth 3/ Solana AGENT SUPPORT (G.A.M.E CLOUD) ▸ Test/deploy Solana agents in-sandbox ▸ Unified multi-chain workflows ▸ Shatter siloed testing 4/ Mind Network PLUGIN (G.A.M.E SDK) ▸ FHE-encrypted voting for DAOs ▸ Track vFHE rewards natively ▸ First SDK with on-chain governance 5/ CHAT AGENT MODULE (G.A.M.E SDK) ▸ Llama 3.3 70B via Groq API ▸ Engage in dynamic AI-driven interactions with the ability to trigger functions. ▸ Conversational AI with Action Execution ▸ Short-term memory for context awareness 6/ CoinGecko PLUGIN (G.A.M.E SDK) ▸ Real-time crypto prices/market data ▸ Built-in error handling ▸ Community-contributed 7/ Elfa AI PLUGIN (G.A.M.E SDK) ▸ Real-Time Crypto Intelligence ▸ Track whale wallets & trending tokens ▸ Live smart money insights ▸ Front-run markets with API data 8/ MULTI-MODEL SUPPORT ▸ 5 new models: Llama_3_1_405B, Qwen_2_5_72B_Instruct, DeepSeek_R1, etc. ▸ Match models to tasks: speed vs. creativity ▸ Optimize cost/performance 9/ Farcaster PLUGIN ▸ Post casts to 300K+ decentralized users ▸ Engage Web3-native communities ▸ On-chain social interactions 10/ GAME SDK UPGRADES ▸ X Username-Based Payments ▸ Multi-worker task management ▸ Fix loops/hallucinations with memory reset 11/ Coinbase 🛡️ CDP PLUGIN ▸ Wallet Management ▸ Gas-less USDC transfers ▸ ETH/USDC trading on Base ▸ Web-hook Integration 12/ IMAGE GENERATION ▸ Generate custom AI images from text-based prompts. ▸ Customizable dimensions up to 1440x1440. ▸ Receive images as temporary URLs, making it easy to share and store outputs. ▸ Powered by Together AI 13/ MODEL UPGRADES & AI ROUTER ▸ Dynamic AI Model Switching based on use case ▸ Smart AI Router: 2x performance/stability via Chasm collaboration. 14/ Why February Redefined Autonomy ▸ ACP Demo through G.A.M.E: Multi-agent economies are programmable, competitive, and decentralized. ▸ Social x Crypto Fusion: = Viral growth loops. ▸ Chain Agnosticism: Building the future where agents thrive on any network. Build → Fund → Launch →

G.A.M.E

90,012 görüntüleme • 1 yıl önce

Tired of using basic text links and screenshots to explain your digital marketing and SEO services? As someone who doesn't have the time to learn advanced video editing software, I threw my prompt into Pexo and chatted my way through to the dynamic kinetic typography promo below—complete with bright sky-blue aesthetics, bouncing text on hanging lines, hand-drawn line doodles, and a crisp electro-pop rhythm. This time, I used Pexo. Pexo is an AI video creation tool where you can just chat with it to make videos. Tell it what you want, how to tweak the visuals—plain English works fine. Even someone like me with zero editing experience can jump right in. First, a bit of background. I usually build solutions, not videos. This time, I wanted to properly introduce a sleek brand promotion—featuring fast text flashes, stacked columns, and clean line art—only to realize the real hassle isn't writing the copy, it's the video editing. I don't know After Effects. And I sure don't want to burn my evenings learning timeline keyframing just to drop one clean promo. I tossed the concept into Pexo and told it: Make me a 15-second horizontal text-flash promo for an SEO service, keeping scene transitions under 3 seconds with bounce-and-snap motion. It didn't spit out a video right away. First, it aligned with me. Sky-blue base, cobalt typography, periwinkle panels, and precise pacing. Feels like hashing out requirements with someone who actually knows motion design. I fired back a few sentences in plain English. Once confirmed, it went off and ran with it. What came back was the finished 10-second flat graphic animation. Bouncing text drops, stacked rows, and hard cuts landing right on the beat—all there. No dragging footage into a timeline. For someone who just wants to explain their brand clearly, that's a game-changer. You can keep chatting after that. If an animation drags, tell it to tighten up. Never touched the timeline the whole way. The one below is what came out of chatting with the platform concept. If you've got a fresh brand, want an intro video, but don't feel like learning editing first—give Pexo a shot.

SIMANTO

46,632 görüntüleme • 1 ay önce

🚨 Alibaba just open sourced a GUI agent that lives inside your webpage and controls it with natural language. It's called Page Agent and it's not a browser extension. It's pure JavaScript no Python, no Puppeteer, no headless browser, no screenshots. Just one script tag and your web app understands natural language. Here's what it actually does: → Embed it with a single tag or npm install → Control any web interface with plain English commands → Text-based DOM manipulation no OCR, no vision models needed → Bring your own LLM (GPT, Claude, Qwen, anything) → Ships a built-in UI with human-in-the-loop support → Turn 20-click ERP/CRM workflows into one sentence → Optional Chrome extension for multi-tab agent tasks → Works on any web app SaaS, admin panels, internal tools Companies are charging $30/month for AI copilots built on this exact idea. This is 3 lines of code. Your users. Your interface. The AI copilot layer for every web app just got open sourced. 1.6K stars. 100% Open Source. (Link in the comments)

🚨 Alibaba just open sourced a GUI agent that lives inside your webpage and controls it with natural language. It's called Page Agent and it's not a browser extension. It's pure JavaScript no Python, no Puppeteer, no headless browser, no screenshots. Just one script tag and your web app understands natural language. Here's what it actually does: → Embed it with a single tag or npm install → Control any web interface with plain English commands → Text-based DOM manipulation no OCR, no vision models needed → Bring your own LLM (GPT, Claude, Qwen, anything) → Ships a built-in UI with human-in-the-loop support → Turn 20-click ERP/CRM workflows into one sentence → Optional Chrome extension for multi-tab agent tasks → Works on any web app SaaS, admin panels, internal tools Companies are charging $30/month for AI copilots built on this exact idea. This is 3 lines of code. Your users. Your interface. The AI copilot layer for every web app just got open sourced. 1.6K stars. 100% Open Source. (Link in the comments)

Ihtesham Ali

135,634 görüntüleme • 7 ay önce

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

Oren Melamed

29,555 görüntüleme • 9 ay önce

Building RAG is easy. Parsing real, unstructured data is the hard part. Most tools fail when documents get complicated. RAGFlow by InfiniFlow makes the entire process visual and flawless 🔥 It is an (open-source!) engine built specifically to find the exact needle in a data haystack, even across literally unlimited tokens. The platform comes packed with: → "Quality in, quality out" parsing for highly complex formats → Multiple recall paired with fused re-ranking → A built-in Python and JavaScript code executor for agents → An orchestrable ingestion pipeline Here's why it stands out: 1️⃣ Structural Understanding Instead of just scraping text, it handles tables across pages, scanned copies, slides, and Excel sheets natively using deep document understanding. 2️⃣ Grounded Citations Every answer is verifiable. The UI highlights the exact chunks used, allowing you to trace any response directly back to the source material. 3️⃣ Enterprise Synchronization Keep your context constantly updated with native data sync from Google Drive, Notion, Discord, and Confluence. Stop letting bad document parsing ruin your RAG systems. Best part? It's 100% Free and open-source. Link to the repo in 🧵↓

Building RAG is easy. Parsing real, unstructured data is the hard part. Most tools fail when documents get complicated. RAGFlow by InfiniFlow makes the entire process visual and flawless 🔥 It is an (open-source!) engine built specifically to find the exact needle in a data haystack, even across literally unlimited tokens. The platform comes packed with: → "Quality in, quality out" parsing for highly complex formats → Multiple recall paired with fused re-ranking → A built-in Python and JavaScript code executor for agents → An orchestrable ingestion pipeline Here's why it stands out: 1️⃣ Structural Understanding Instead of just scraping text, it handles tables across pages, scanned copies, slides, and Excel sheets natively using deep document understanding. 2️⃣ Grounded Citations Every answer is verifiable. The UI highlights the exact chunks used, allowing you to trace any response directly back to the source material. 3️⃣ Enterprise Synchronization Keep your context constantly updated with native data sync from Google Drive, Notion, Discord, and Confluence. Stop letting bad document parsing ruin your RAG systems. Best part? It's 100% Free and open-source. Link to the repo in 🧵↓

Charly Wargnier

19,220 görüntüleme • 7 ay önce

If you’ve ever managed custom icons in React Native, you know the frustration of choosing between performance and developer experience. Libraries like 𝗿𝗲𝗮𝗰𝘁-𝗻𝗮𝘁𝗶𝘃𝗲-𝘀𝘃𝗴 are flexible but heavy. Every icon creates its own React subtree. The alternative? Manual icon fonts that force you into a constant back-and-forth of using web tools like IcoMoon and syncing font assets every time a design changes. 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗠𝗮𝗻𝘀𝗶𝗼𝗻 𝗟𝗮𝗯𝘀 just released 𝗿𝗲𝗮𝗰𝘁-𝗻𝗮𝘁𝗶𝘃𝗲-𝗻𝗮𝗻𝗼-𝗶𝗰𝗼𝗻𝘀 to solve exactly this. It’s a build-time icon font generator that gives you the performance of native fonts with the flexibility of simple SVG files. 𝗪𝗵𝗮𝘁’𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴? Instead of rendering complex vector paths at runtime, this library automatically converts your folder of SVGs into an optimized icon font during the build process. It essentially teaches your app to treat icons like standard text characters, which allows it to bypass React’s component tree and layout engine entirely. ➡️ 𝗕𝘂𝗶𝗹𝗱-𝘁𝗶𝗺𝗲 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: It handles the entire pipeline—watching your icon folder, converting SVGs to .ttf files, and linking them to your native project automatically. ➡️ 𝗡𝗮𝘁𝗶𝘃𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Because icons render as native text glyphs, it is significantly faster than traditional SVG rendering, making it the ideal choice for long, scrollable lists or icon-heavy dashboards. ➡️ 𝗘𝘅𝗽𝗼 𝗖𝗼𝗻𝗳𝗶𝗴 𝗣𝗹𝘂𝗴𝗶𝗻: It features first-class support for Expo, automating the native plumbing like Info.plist updates and asset linking during the prebuild phase. ➡️ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗧𝘆𝗽𝗲 𝗦𝗮𝗳𝗲𝘁𝘆: The library generates TypeScript definitions for your icon set, providing full IDE autocomplete and ensuring you never break the UI with a misspelled icon name. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀? In high-performance applications, small overheads add up. By shifting the heavy lifting from the mobile device to your build machine, 𝗿𝗲𝗮𝗰𝘁-𝗻𝗮𝘁𝗶𝘃𝗲-𝗻𝗮𝗻𝗼-𝗶𝗰𝗼𝗻𝘀 ensures your UI stays buttery smooth while keeping your developer workflow modern. It’s another great example of how 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗠𝗮𝗻𝘀𝗶𝗼𝗻 continues to solve the "last mile" of performance friction in the ecosystem. Before you migrate your entire library, keep in mind: this is designed for the 𝗡𝗲𝘄 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 (Fabric) and requires React Native 0.74 or higher. #ReactNative #Expo #SoftwareMansion #Icons #MobileDev #Performance #DeveloperExperience #OpenSource #JavaScript #TypeScript #SVG #Fabric

If you’ve ever managed custom icons in React Native, you know the frustration of choosing between performance and developer experience. Libraries like 𝗿𝗲𝗮𝗰𝘁-𝗻𝗮𝘁𝗶𝘃𝗲-𝘀𝘃𝗴 are flexible but heavy. Every icon creates its own React subtree. The alternative? Manual icon fonts that force you into a constant back-and-forth of using web tools like IcoMoon and syncing font assets every time a design changes. 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗠𝗮𝗻𝘀𝗶𝗼𝗻 𝗟𝗮𝗯𝘀 just released 𝗿𝗲𝗮𝗰𝘁-𝗻𝗮𝘁𝗶𝘃𝗲-𝗻𝗮𝗻𝗼-𝗶𝗰𝗼𝗻𝘀 to solve exactly this. It’s a build-time icon font generator that gives you the performance of native fonts with the flexibility of simple SVG files. 𝗪𝗵𝗮𝘁’𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴? Instead of rendering complex vector paths at runtime, this library automatically converts your folder of SVGs into an optimized icon font during the build process. It essentially teaches your app to treat icons like standard text characters, which allows it to bypass React’s component tree and layout engine entirely. ➡️ 𝗕𝘂𝗶𝗹𝗱-𝘁𝗶𝗺𝗲 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: It handles the entire pipeline—watching your icon folder, converting SVGs to .ttf files, and linking them to your native project automatically. ➡️ 𝗡𝗮𝘁𝗶𝘃𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Because icons render as native text glyphs, it is significantly faster than traditional SVG rendering, making it the ideal choice for long, scrollable lists or icon-heavy dashboards. ➡️ 𝗘𝘅𝗽𝗼 𝗖𝗼𝗻𝗳𝗶𝗴 𝗣𝗹𝘂𝗴𝗶𝗻: It features first-class support for Expo, automating the native plumbing like Info.plist updates and asset linking during the prebuild phase. ➡️ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗧𝘆𝗽𝗲 𝗦𝗮𝗳𝗲𝘁𝘆: The library generates TypeScript definitions for your icon set, providing full IDE autocomplete and ensuring you never break the UI with a misspelled icon name. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀? In high-performance applications, small overheads add up. By shifting the heavy lifting from the mobile device to your build machine, 𝗿𝗲𝗮𝗰𝘁-𝗻𝗮𝘁𝗶𝘃𝗲-𝗻𝗮𝗻𝗼-𝗶𝗰𝗼𝗻𝘀 ensures your UI stays buttery smooth while keeping your developer workflow modern. It’s another great example of how 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗠𝗮𝗻𝘀𝗶𝗼𝗻 continues to solve the "last mile" of performance friction in the ecosystem. Before you migrate your entire library, keep in mind: this is designed for the 𝗡𝗲𝘄 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 (Fabric) and requires React Native 0.74 or higher. #ReactNative #Expo #SoftwareMansion #Icons #MobileDev #Performance #DeveloperExperience #OpenSource #JavaScript #TypeScript #SVG #Fabric

The React Native Rewind

24,992 görüntüleme • 5 ay önce

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

Avi Chawla

31,074 görüntüleme • 11 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!

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

29,339 görüntüleme • 5 ay önce

I still genuinely wonder why not everyone is using this approach yet. I launched 50 Grok agents and connected them to a second brain. Thanks to that, just a month ago I managed to make $7,600 Elon Musk published a simple idea that hit 30 million views: the most likely outcome is that AI and robots make everyone wealthy. In fact, far wealthier than the richest person on Earth This is precisely how the setup with Grok works, and the smartest configuration currently costs $0 extra: Create a bot and name it Brain Connect it to a local folder and configure it to build a personal wiki out of everything you know right on your computer The system will create key directories: raw for sources, wiki for pages, and save the entire method as a working skill Drop anything into the chat: an article, a PDF, a YouTube link, or a screenshot. The bot will instantly read everything, link it with context, and record it Set up routine ingestion: while you sleep, the system pulls in everything you saved to your bookmarks Ask questions about absolutely anything you have ever saved, at any time Your laptop stays closed, but your knowledge base grows on its own. Just five minutes to set up, and this process starts compounding like interest from day one The setup takes 10 minutes, after which the work is automated Not using Grok in 2026 is a huge mistake. Just as big of a mistake as not bookmarking this post

I still genuinely wonder why not everyone is using this approach yet. I launched 50 Grok agents and connected them to a second brain. Thanks to that, just a month ago I managed to make $7,600 Elon Musk published a simple idea that hit 30 million views: the most likely outcome is that AI and robots make everyone wealthy. In fact, far wealthier than the richest person on Earth This is precisely how the setup with Grok works, and the smartest configuration currently costs $0 extra: Create a bot and name it Brain Connect it to a local folder and configure it to build a personal wiki out of everything you know right on your computer The system will create key directories: raw for sources, wiki for pages, and save the entire method as a working skill Drop anything into the chat: an article, a PDF, a YouTube link, or a screenshot. The bot will instantly read everything, link it with context, and record it Set up routine ingestion: while you sleep, the system pulls in everything you saved to your bookmarks Ask questions about absolutely anything you have ever saved, at any time Your laptop stays closed, but your knowledge base grows on its own. Just five minutes to set up, and this process starts compounding like interest from day one The setup takes 10 minutes, after which the work is automated Not using Grok in 2026 is a huge mistake. Just as big of a mistake as not bookmarking this post

Bober_smart

43,189 görüntüleme • 1 ay önce

Hospitality venues wanting to book live music or manage entertainment usually get stuck dealing with an endless headache of spreadsheets and late invoices. As someone who can't edit videos to save my life, I threw my SaaS concept into Pexo and chatted my way into the product video below—complete with warm blush aesthetics, clean geometric typography, and smooth scene transitions. This time, I used Pexo. Pexo is an AI video creation tool where you can just chat with it to make videos. Tell it what you want, how to tweak the visuals—plain English works fine. Even someone like me with zero editing experience can jump right in. First, a bit of background. I usually build products, not videos. This time, I wanted to properly introduce JAM JUICE—the world's first entertainment operating system for hospitality pros—only to realize the real hassle isn't writing the copy, it's the video. I don't know AE. And I sure don't want to burn my evenings learning editing just to drop one clean promo. I tossed the concept into Pexo and told it: Make me a warm, clean SaaS promo for hospitality professionals—keep scene changes under 3 seconds and make it look professional. It didn't spit out a video right away. First, it aligned with me. Soft blush background, charcoal typography, coral-pink keywords, and precise pacing. Feels like hashing out requirements with someone who actually knows motion design. I fired back a few sentences in plain English. Once confirmed, it went off and ran with it. What came back was the finished 19-second editorial piece. Text sync, smooth transitions, and a gorgeous color palette—all there. No dragging footage into a timeline. For someone who just wants to explain their product clearly, that's a game-changer. You can keep chatting after that. If an animation drags, tell it to tighten up. Never touched the timeline the whole way. The one below is what came out of chatting with the platform concept. If you've got a fresh SaaS product, want an intro video, but don't feel like learning editing first—give Pexo a shot.

Hospitality venues wanting to book live music or manage entertainment usually get stuck dealing with an endless headache of spreadsheets and late invoices. As someone who can't edit videos to save my life, I threw my SaaS concept into Pexo and chatted my way into the product video below—complete with warm blush aesthetics, clean geometric typography, and smooth scene transitions. This time, I used Pexo. Pexo is an AI video creation tool where you can just chat with it to make videos. Tell it what you want, how to tweak the visuals—plain English works fine. Even someone like me with zero editing experience can jump right in. First, a bit of background. I usually build products, not videos. This time, I wanted to properly introduce JAM JUICE—the world's first entertainment operating system for hospitality pros—only to realize the real hassle isn't writing the copy, it's the video. I don't know AE. And I sure don't want to burn my evenings learning editing just to drop one clean promo. I tossed the concept into Pexo and told it: Make me a warm, clean SaaS promo for hospitality professionals—keep scene changes under 3 seconds and make it look professional. It didn't spit out a video right away. First, it aligned with me. Soft blush background, charcoal typography, coral-pink keywords, and precise pacing. Feels like hashing out requirements with someone who actually knows motion design. I fired back a few sentences in plain English. Once confirmed, it went off and ran with it. What came back was the finished 19-second editorial piece. Text sync, smooth transitions, and a gorgeous color palette—all there. No dragging footage into a timeline. For someone who just wants to explain their product clearly, that's a game-changer. You can keep chatting after that. If an animation drags, tell it to tighten up. Never touched the timeline the whole way. The one below is what came out of chatting with the platform concept. If you've got a fresh SaaS product, want an intro video, but don't feel like learning editing first—give Pexo a shot.

SafwonWith_AI

24,041 görüntüleme • 1 ay önce

I just got Gemma 4 26B A4B MoE model running fully locally with Hermes agent on an 8GB RTX 4060 and it's now backtesting trading strategies end to end, no hand holding. If you’re a trader or work on Wall Street, you don’t want to miss this. Yes. fully automated. No cloud. No APIs beyond market data. # Here's what I did: Setup: - Model: Gemma 4 26B-A4B QAT (MoE), Q4_K_XL Unsloth's quant (link in the comments) - Inference: llama.cpp (turboquant fork by Tom Turney link in the comments) - Hardware: RTX 4060, 8GB VRAM + 16GB RAM only (with 50 other chrome tabs open) - Context: 64K llama.cpp turboquant flags: -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 64000 --cache-type-k q8_0 --cache-type-v turbo3 --port 8080 turboquant helps achieve high prefill and decode throughput for interactive sessions. throughput with Hermes agent: decode: 25+ tokens/sec prefill: 250+ tokens/sec # Then I gave the agent one task: Backtest a strategy: - Buy when RSI crosses above 30 - Sell at +2% profit or -1% stoploss - No overlapping positions - Use Google stock via yfinance - Generate a full HTML report with candlestick charts + signals What happened next was wild. It didn't just write code, it ran the entire workflow itself: Audited the environment (pip list, dependency check) Hit a ModuleNotFoundError, multiple Python installs were conflicting Ran where python to map every interpreter on the system Manually selected the correct Python 3.13 path and re ran the script Wrote a clean statevmachine backtester (strict no overlapping trades logic) Patched a yfinance MultiIndex quirk that would've crashed the script Built Plotly candlestick + RSI charts with buy/sell markers Calculated win rate, PnL, and summary stats Exported a polished single file HTML report. check the report at the end of the video or in the comments. Biggest takeaway: local LLMs aren't just "chat assistants" anymore. They debug their own environment, write production code, and ship a finished deliverable on consumer hardware, for $0 in API costs. If you're still calling local models "toys," you're already behind. This is just the beginning. Hermes agent just surpassed 1 trillion tokens in a single day on OpenRouter. Think about the scale of total token generation happening right now. Disclaimer: This is not financial advice. Consult a professional before making any trading decisions.

I just got Gemma 4 26B A4B MoE model running fully locally with Hermes agent on an 8GB RTX 4060 and it's now backtesting trading strategies end to end, no hand holding. If you’re a trader or work on Wall Street, you don’t want to miss this. Yes. fully automated. No cloud. No APIs beyond market data. # Here's what I did: Setup: - Model: Gemma 4 26B-A4B QAT (MoE), Q4_K_XL Unsloth's quant (link in the comments) - Inference: llama.cpp (turboquant fork by Tom Turney link in the comments) - Hardware: RTX 4060, 8GB VRAM + 16GB RAM only (with 50 other chrome tabs open) - Context: 64K llama.cpp turboquant flags: -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 64000 --cache-type-k q8_0 --cache-type-v turbo3 --port 8080 turboquant helps achieve high prefill and decode throughput for interactive sessions. throughput with Hermes agent: decode: 25+ tokens/sec prefill: 250+ tokens/sec # Then I gave the agent one task: Backtest a strategy: - Buy when RSI crosses above 30 - Sell at +2% profit or -1% stoploss - No overlapping positions - Use Google stock via yfinance - Generate a full HTML report with candlestick charts + signals What happened next was wild. It didn't just write code, it ran the entire workflow itself: Audited the environment (pip list, dependency check) Hit a ModuleNotFoundError, multiple Python installs were conflicting Ran where python to map every interpreter on the system Manually selected the correct Python 3.13 path and re ran the script Wrote a clean statevmachine backtester (strict no overlapping trades logic) Patched a yfinance MultiIndex quirk that would've crashed the script Built Plotly candlestick + RSI charts with buy/sell markers Calculated win rate, PnL, and summary stats Exported a polished single file HTML report. check the report at the end of the video or in the comments. Biggest takeaway: local LLMs aren't just "chat assistants" anymore. They debug their own environment, write production code, and ship a finished deliverable on consumer hardware, for $0 in API costs. If you're still calling local models "toys," you're already behind. This is just the beginning. Hermes agent just surpassed 1 trillion tokens in a single day on OpenRouter. Think about the scale of total token generation happening right now. Disclaimer: This is not financial advice. Consult a professional before making any trading decisions.

Alok

105,094 görüntüleme • 4 ay önce

Auday 1.0.1 is now live! This update adds Japanese and Korean support for AI-generated content, including summaries, Highlights, daily Reflections and Chat replies. You can switch languages in Settings → AI Primary Language. It also fixes the silent-recording issue affecting some Apple Watch Ultra users, improves transfer reliability between Apple Watch and iPhone, and brings a major overhaul of local storage for smoother loading and browsing as your recording history grows. On launch day, Auday reached #1 in the App Store’s paid Lifestyle charts in multiple countries. The response has far exceeded our expectations, and every download, message and suggestion has been a huge encouragement. Behind Auday is a solo developer, working late to collect your feedback, investigate issues and keep improving the app. Thank you for your support and patience. We also want to explain the choices behind transcription. To support a wider range of iPhones while keeping resource use low and minimizing slowdowns, our current on-device speech recognition model prioritizes speed and efficiency. In tests on iPhone 17 Pro, several hours of audio often took just a few seconds to transcribe. Actual performance varies with the device and recording. That speed comes with some accuracy trade-offs. Auday AI uses surrounding context when interpreting transcripts and generating content to help identify and work through recognition errors, reducing their impact on summaries, analysis and conversations. For the next update, we plan to introduce an optional speech recognition model with higher accuracy and slightly higher resource requirements, alongside the current lightweight model. You’ll be able to switch between them and choose the balance of speed and accuracy that best suits your iPhone and your needs. We’ll also continue adding support for more languages. To thank everyone for the support, we’re extending our 50% launch discount for one more week. Buy once, with no Auday subscription. AppStore: Thank you for being part of Auday’s beginning. We’ll keep listening and making it better.

Auday 1.0.1 is now live! This update adds Japanese and Korean support for AI-generated content, including summaries, Highlights, daily Reflections and Chat replies. You can switch languages in Settings → AI Primary Language. It also fixes the silent-recording issue affecting some Apple Watch Ultra users, improves transfer reliability between Apple Watch and iPhone, and brings a major overhaul of local storage for smoother loading and browsing as your recording history grows. On launch day, Auday reached #1 in the App Store’s paid Lifestyle charts in multiple countries. The response has far exceeded our expectations, and every download, message and suggestion has been a huge encouragement. Behind Auday is a solo developer, working late to collect your feedback, investigate issues and keep improving the app. Thank you for your support and patience. We also want to explain the choices behind transcription. To support a wider range of iPhones while keeping resource use low and minimizing slowdowns, our current on-device speech recognition model prioritizes speed and efficiency. In tests on iPhone 17 Pro, several hours of audio often took just a few seconds to transcribe. Actual performance varies with the device and recording. That speed comes with some accuracy trade-offs. Auday AI uses surrounding context when interpreting transcripts and generating content to help identify and work through recognition errors, reducing their impact on summaries, analysis and conversations. For the next update, we plan to introduce an optional speech recognition model with higher accuracy and slightly higher resource requirements, alongside the current lightweight model. You’ll be able to switch between them and choose the balance of speed and accuracy that best suits your iPhone and your needs. We’ll also continue adding support for more languages. To thank everyone for the support, we’re extending our 50% launch discount for one more week. Buy once, with no Auday subscription. AppStore: Thank you for being part of Auday’s beginning. We’ll keep listening and making it better.

Auday

738,463 görüntüleme • 15 gün önce

I just ran Gemma 4 31B on @CerebrasSystems at 1,800+ tokens/sec and it's multimodal. For context: that's 35x faster than a typical GPU endpoint, and the first token (reasoning included) lands in 1.5 seconds. This isn't a benchmark slide, I recorded the inference live. Prompt I used: "Create a simulation of an iPhone. Include at least one working dummy note taking app, a functional notification pulldown, high quality graphics, single HTML file, any libs via CDN." - Generation time: 3 seconds. - Notes app worked. - Notification panel worked. - Rendered first try. This is what wafer-scale inference unlocks, not just "faster," but a different category of product. When generation is this fast, you stop waiting and start iterating in real time. Why this matters: Gemma 4 31B is Google DeepMind's flagship open weight model, Apache 2.0 licensed, dense (not MoE), and built for efficiency over raw parameter count. It scores close to Claude Haiku 4.5 on the Artificial Analysis Intelligence Index (30 vs 29) but runs ~18x faster on Cerebras. It's also the first multimodal model on Cerebras's platform, meaning you can now feed it screenshots, documents, charts, and UI states at wafer scale speed. # Applications I'm most excited about: - Screenshot → Insight: Drop in a dashboard or document screenshot, get structured findings back instantly. no waiting, no batching. - Live UI generation: Full interactive interfaces (like my iPhone sim) generated and rendered in under 2 seconds. - Screenshot -> Patch: Feed it a broken UI + console error, get a minimal code fix and verification steps back. - Computer use & agentic loops: See -> reason -> act - verify, fast enough to keep a human in the loop instead of waiting on the model. - Long context summarization: Full research reports condensed into decision ready summaries you can read and requery in one sitting. The bigger unlock isn't the speed number itself, it's that agentic and multimodal loops (see -> reason -> output -> tool call -> verify -> retry) finally run in real time instead of feeling sluggish. As Logan Kilpatrick (Logan Kilpatrick) put it: "If every model was doing 2,000 tokens per second, you wouldn't build the same product and just have it be faster, you'd build different products." Gemma 4 31B is live now on Cerebras Inference Cloud in public preview. If you're building multimodal, agentic, or real time apps, this is worth testing today. What would you build with such insane inference throughput?

I just ran Gemma 4 31B on @CerebrasSystems at 1,800+ tokens/sec and it's multimodal. For context: that's 35x faster than a typical GPU endpoint, and the first token (reasoning included) lands in 1.5 seconds. This isn't a benchmark slide, I recorded the inference live. Prompt I used: "Create a simulation of an iPhone. Include at least one working dummy note taking app, a functional notification pulldown, high quality graphics, single HTML file, any libs via CDN." - Generation time: 3 seconds. - Notes app worked. - Notification panel worked. - Rendered first try. This is what wafer-scale inference unlocks, not just "faster," but a different category of product. When generation is this fast, you stop waiting and start iterating in real time. Why this matters: Gemma 4 31B is Google DeepMind's flagship open weight model, Apache 2.0 licensed, dense (not MoE), and built for efficiency over raw parameter count. It scores close to Claude Haiku 4.5 on the Artificial Analysis Intelligence Index (30 vs 29) but runs ~18x faster on Cerebras. It's also the first multimodal model on Cerebras's platform, meaning you can now feed it screenshots, documents, charts, and UI states at wafer scale speed. # Applications I'm most excited about: - Screenshot → Insight: Drop in a dashboard or document screenshot, get structured findings back instantly. no waiting, no batching. - Live UI generation: Full interactive interfaces (like my iPhone sim) generated and rendered in under 2 seconds. - Screenshot -> Patch: Feed it a broken UI + console error, get a minimal code fix and verification steps back. - Computer use & agentic loops: See -> reason -> act - verify, fast enough to keep a human in the loop instead of waiting on the model. - Long context summarization: Full research reports condensed into decision ready summaries you can read and requery in one sitting. The bigger unlock isn't the speed number itself, it's that agentic and multimodal loops (see -> reason -> output -> tool call -> verify -> retry) finally run in real time instead of feeling sluggish. As Logan Kilpatrick (Logan Kilpatrick) put it: "If every model was doing 2,000 tokens per second, you wouldn't build the same product and just have it be faster, you'd build different products." Gemma 4 31B is live now on Cerebras Inference Cloud in public preview. If you're building multimodal, agentic, or real time apps, this is worth testing today. What would you build with such insane inference throughput?

Alok

12,962 görüntüleme • 3 ay önce

[ OUR STRENGTH LIES IN OUR UNITY ] THREAD🧵 In my previous post, I shared a little about myself. Today, I want to talk about us - #Jungkook's fans, our fanbases and how much more we could do for him if we supported each other more. There are dozens of fanbases around the world dedicated solely to Jungkook, along with many accounts that share updates about him every day. News, translations, charts, statistics, analysis, voting campaigns, streaming projects, charity initiatives, and large-scale fan projects - an enormous amount of work goes into all of this. I've been doing this myself for several years, so I know very well how much time, effort and stress even a single post can take. Organizing major projects can take months. That's why I genuinely wish we'd pay more attention to and appreciate the people who dedicate their time to supporting Jungkook. We often talk about the importance of streaming, buying albums, voting and supporting fan projects. And all of that really matters. It plays a huge role in supporting and promoting Jungkook. But there's another simple way to support him that I think we often underestimate, one that doesn't require much time, effort, or money: engaging with posts about him. — Come across an interesting post about his music, vocals, or performances? Give it a like and repost it. — See news about a new record or achievement? Help spread the word. — Is there an important voting or streaming campaign going on? Share the information so more fans can get involved. — Even if it's just a beautiful photo or video of Jungkook from an official source, why not show the post some support? — Most importantly, don't overlook official content about Jungkook from media outlets, music publications, magazines and brands he works with. Read their articles and social media posts, leave comments and share them. It's important for the interest in Jungkook to be visible not just within our fandom, but beyond it as well. All of this takes just a few seconds, but it helps posts gain more attention and reach new audiences. Imagine if even a portion of Jungkook's fans started doing this regularly. There are so many of us, after all. And the more people discover his music, talent and achievements, the better. You don't need money, thousands of followers, or your own fanbase to support him this way. Everyone can contribute.

[ OUR STRENGTH LIES IN OUR UNITY ] THREAD🧵 In my previous post, I shared a little about myself. Today, I want to talk about us - #Jungkook's fans, our fanbases and how much more we could do for him if we supported each other more. There are dozens of fanbases around the world dedicated solely to Jungkook, along with many accounts that share updates about him every day. News, translations, charts, statistics, analysis, voting campaigns, streaming projects, charity initiatives, and large-scale fan projects - an enormous amount of work goes into all of this. I've been doing this myself for several years, so I know very well how much time, effort and stress even a single post can take. Organizing major projects can take months. That's why I genuinely wish we'd pay more attention to and appreciate the people who dedicate their time to supporting Jungkook. We often talk about the importance of streaming, buying albums, voting and supporting fan projects. And all of that really matters. It plays a huge role in supporting and promoting Jungkook. But there's another simple way to support him that I think we often underestimate, one that doesn't require much time, effort, or money: engaging with posts about him. — Come across an interesting post about his music, vocals, or performances? Give it a like and repost it. — See news about a new record or achievement? Help spread the word. — Is there an important voting or streaming campaign going on? Share the information so more fans can get involved. — Even if it's just a beautiful photo or video of Jungkook from an official source, why not show the post some support? — Most importantly, don't overlook official content about Jungkook from media outlets, music publications, magazines and brands he works with. Read their articles and social media posts, leave comments and share them. It's important for the interest in Jungkook to be visible not just within our fandom, but beyond it as well. All of this takes just a few seconds, but it helps posts gain more attention and reach new audiences. Imagine if even a portion of Jungkook's fans started doing this regularly. There are so many of us, after all. And the more people discover his music, talent and achievements, the better. You don't need money, thousands of followers, or your own fanbase to support him this way. Everyone can contribute.

fan | JUNGKOOK INDEX

13,669 görüntüleme • 10 gün önce