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🔥 Hono developers, we’ve built something just for you. A native UX for exploring and testing Hono APIs to instantly generate OpenAPI docs, auto-discover routes, & test endpoints? Yes! Yusuke Wada Craig Dennis Ricky Harshil sunil pai Wes Bos Cloudflare Developers

Fiberplane

1,441 subscribers

11,912 views • 1 year ago •via X (Twitter)

Science & Technology

Anya Rossi• Live Now

Private livecam show

8 Comments

Arindam Majumder 𝕏1 year ago

@honojs @openapi @yusukebe @craigsdennis @rickyrobinett @harshil1712 @threepointone @wesbos This is cool I'll try this out

SecurityPal1 year ago

Streamline your vendor assessment and mitigate third-party risks with SecurityPal's Vendor Assess. See how CAx enhances security for top companies like @OpenAI @Figma @MongoDB & @Airtable. Check our latest blog🔗: #VendorManagement #RiskManagement

Maciej Pulikowski 🔥🚀1 year ago

@honojs @openapi @yusukebe @craigsdennis @rickyrobinett @harshil1712 @threepointone @wesbos Looks really good! 👍🏻

Nico Baier1 year ago

@honojs @openapi @yusukebe @craigsdennis @rickyrobinett @harshil1712 @threepointone @wesbos Where can I find it?

Fiberplane1 year ago

@honojs @openapi @yusukebe @craigsdennis @rickyrobinett @harshil1712 @threepointone @wesbos

Dennis 🇰🇪1 year ago

@honojs @openapi @yusukebe @craigsdennis @rickyrobinett @harshil1712 @threepointone @wesbos This looks exactly like swagger/scalar

Anurag Bhagsain1 year ago

@honojs @openapi @yusukebe @craigsdennis @rickyrobinett @harshil1712 @threepointone @wesbos @kevinrwhitley itty router? 👀

KadimElifba1 year ago

@honojs @openapi @yusukebe @craigsdennis @rickyrobinett @harshil1712 @threepointone @wesbos This looks nice but I want to know how do you separate production and development environments? I have checked your docs but could not find an answer.

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Katana Inu

12,756 views • 6 months ago

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Ishraq Khan

125,140 views • 7 months ago

This n8n system auto-generates Shopify PDPs at scale 🤯 And it's about to be HUGE for e-commerce & Shopify owners. OpenAI just launched Instant Checkout → people can now discover AND buy products directly in ChatGPT. This AI system is perfect for e-comm operators & agencies who need catalog-scale optimization fast. But instead of manually writing PDPs one at a time and hoping ChatGPT finds you... Just use this AI workflow and auto-generate complete, AI-ready product pages in seconds. Each PDP is structured for both human buyers and AI shopping agents to parse instantly. Here's how it works: → Drop product into Airtable (name, price, specs, image, availability) → One click in n8n triggers full generation → AI creates SEO-friendly title automatically → Writes 5 benefit-driven bullets in seconds → Generates conversational FAQs → Builds comparison blocks (who it's for, alternatives) → Outputs Schema JSON-LD aligned to OpenAI's feed spec Built 100% in n8n + Airtable. Catalog SEO at scale. Want the complete Airtable + n8n workflow? Comment "PDP" + like and I'll send it over (must be following so I can DM)

This n8n system auto-generates Shopify PDPs at scale 🤯 And it's about to be HUGE for e-commerce & Shopify owners. OpenAI just launched Instant Checkout → people can now discover AND buy products directly in ChatGPT. This AI system is perfect for e-comm operators & agencies who need catalog-scale optimization fast. But instead of manually writing PDPs one at a time and hoping ChatGPT finds you... Just use this AI workflow and auto-generate complete, AI-ready product pages in seconds. Each PDP is structured for both human buyers and AI shopping agents to parse instantly. Here's how it works: → Drop product into Airtable (name, price, specs, image, availability) → One click in n8n triggers full generation → AI creates SEO-friendly title automatically → Writes 5 benefit-driven bullets in seconds → Generates conversational FAQs → Builds comparison blocks (who it's for, alternatives) → Outputs Schema JSON-LD aligned to OpenAI's feed spec Built 100% in n8n + Airtable. Catalog SEO at scale. Want the complete Airtable + n8n workflow? Comment "PDP" + like and I'll send it over (must be following so I can DM)

Mike Futia

44,868 views • 9 months ago

#1 skill for developers in 2026: Automate everything you can using AI. I bet my lunch your team is dealing with all of these: • Stale documentation • Outdated dependencies • Poor test coverage • Deprecated APIs Every company I work with has these same problems. You can solve all of these right now. Automatically. Using AI. Here are 3 examples. Watch the attached video: I'm using Ona Automations to tackle this. These are background agents that run in the cloud, in a fully configured dev environment with your toolchain, your dependencies, and your services. You can run an unlimited number of these agents in parallel and across all your repositories. Claude Code and Codex only run locally, so they are hard to scale, and you can't run them when your computer is closed. Ona runs in the cloud. Here are the three examples: 1. Test coverage Run a nightly automation to identify any untested code paths, generate candidate tests, verify they pass, and open draft PRs. You wake up every morning to PRs that improve your test coverage. 2. Dependency upgrades Configure a weekly automation that bumps a dependency version, runs your full test suite, and reports any regressions. If everything is clean, it opens a PR. If something breaks, it opens a report so you can decide what to do. 3. Documentation auditing Set up a weekly automation that checks recent commits against your README file and setup guides, identifies broken examples and outdated instructions, and opens a PR with fixes.

#1 skill for developers in 2026: Automate everything you can using AI. I bet my lunch your team is dealing with all of these: • Stale documentation • Outdated dependencies • Poor test coverage • Deprecated APIs Every company I work with has these same problems. You can solve all of these right now. Automatically. Using AI. Here are 3 examples. Watch the attached video: I'm using Ona Automations to tackle this. These are background agents that run in the cloud, in a fully configured dev environment with your toolchain, your dependencies, and your services. You can run an unlimited number of these agents in parallel and across all your repositories. Claude Code and Codex only run locally, so they are hard to scale, and you can't run them when your computer is closed. Ona runs in the cloud. Here are the three examples: 1. Test coverage Run a nightly automation to identify any untested code paths, generate candidate tests, verify they pass, and open draft PRs. You wake up every morning to PRs that improve your test coverage. 2. Dependency upgrades Configure a weekly automation that bumps a dependency version, runs your full test suite, and reports any regressions. If everything is clean, it opens a PR. If something breaks, it opens a report so you can decide what to do. 3. Documentation auditing Set up a weekly automation that checks recent commits against your README file and setup guides, identifies broken examples and outdated instructions, and opens a PR with fixes.

Santiago

25,691 views • 4 months ago

🌆 Digital Evidence, Real Estate, and the Next Wave of Real-World Adoption Dave Berg, CPO at Constellation, breaks down how they are building real onchain infrastructure that solves real problems. Not hypothetical use cases. Not hype cycles. Actual products people can use right now. 1. Digital Evidence. Authenticity for the internet. Constellation is anchoring digital fingerprints of files, images, documents, and data streams directly onto the network. Why does this matter? Because in a world filled with AI content, fake screenshots, edited PDFs, and manipulated media, proving the origin of information is becoming one of the most valuable capabilities we have. Developers and non developers can use simple APIs, or even vibe code with AI tools like Claude, to anchor and verify data instantly. Everyday users can anchor real-world data right now onto Constellation network with zero blockchain knowledge, using devices they already use every single day. 2. Proof of Management for real assets. This leads into what might be one of the most practical DLT products released in years. Real Estate Ledger. A digital guidebook for any property: • Permits • Warranties • Proof of maintenance • Vendor history • Manuals • Insurance • Improvements • Receipts • Photos Everything tied to the property, all cryptographically timestamped. If you have ever tried to sell a house, maintain one, or prove something to an insurer, you instantly understand how useful this is. Imagine handing a buyer a clean, verified report of every repair, every vendor, every upgrade, and every warranty. Imagine builders uploading materials and documentation during construction so the next owner knows exactly what is behind the walls. Imagine insurance claims based on truth instead of paperwork chaos. This is not a pitch deck about tokenizing real estate one day. This is infrastructure that exists right now. 3. Constellation is solving real adoption problems for Web3. No need to rebuild your business to onboard. No need to run your own nodes unless you want to. No need to become a blockchain expert. Just clean APIs, onchain trust, and applications anyone can understand. Authenticity and truth are scarce assets, Constellation (DAG) is building rails that protect them. Podcast powered by Constellation²

🌆 Digital Evidence, Real Estate, and the Next Wave of Real-World Adoption Dave Berg, CPO at Constellation, breaks down how they are building real onchain infrastructure that solves real problems. Not hypothetical use cases. Not hype cycles. Actual products people can use right now. 1. Digital Evidence. Authenticity for the internet. Constellation is anchoring digital fingerprints of files, images, documents, and data streams directly onto the network. Why does this matter? Because in a world filled with AI content, fake screenshots, edited PDFs, and manipulated media, proving the origin of information is becoming one of the most valuable capabilities we have. Developers and non developers can use simple APIs, or even vibe code with AI tools like Claude, to anchor and verify data instantly. Everyday users can anchor real-world data right now onto Constellation network with zero blockchain knowledge, using devices they already use every single day. 2. Proof of Management for real assets. This leads into what might be one of the most practical DLT products released in years. Real Estate Ledger. A digital guidebook for any property: • Permits • Warranties • Proof of maintenance • Vendor history • Manuals • Insurance • Improvements • Receipts • Photos Everything tied to the property, all cryptographically timestamped. If you have ever tried to sell a house, maintain one, or prove something to an insurer, you instantly understand how useful this is. Imagine handing a buyer a clean, verified report of every repair, every vendor, every upgrade, and every warranty. Imagine builders uploading materials and documentation during construction so the next owner knows exactly what is behind the walls. Imagine insurance claims based on truth instead of paperwork chaos. This is not a pitch deck about tokenizing real estate one day. This is infrastructure that exists right now. 3. Constellation is solving real adoption problems for Web3. No need to rebuild your business to onboard. No need to run your own nodes unless you want to. No need to become a blockchain expert. Just clean APIs, onchain trust, and applications anyone can understand. Authenticity and truth are scarce assets, Constellation (DAG) is building rails that protect them. Podcast powered by Constellation²

Generation Infinity

170,375 views • 7 months ago

NOBODY wants to send their data to Google or OpenAI. Yet here we are, shipping proprietary code, customer information, and sensitive business logic to closed-source APIs we don't control. While everyone's chasing the latest closed-source releases, open-source models are quietly becoming the practical choice for many production systems. Here's what everyone is missing: Open-source models are catching up fast, and they bring something the big labs can't: privacy, speed, and control. I built a playground to test this myself. Used CometML's Opik to evaluate models on real code generation tasks - testing correctness, readability, and best practices against actual GitHub repos. Here's what surprised me: OSS models like MiniMax-M2, Kimi k2 performed on par with the likes of Gemini 3 and Claude Sonnet 4.5 on most tasks. But practically MiniMax-M2 turns out to be a winner as it's twice as fast and 12x cheaper when you compare it to models like Sonnet 4.5. Well, this isn't just about saving money. When your model is smaller and faster, you can deploy it in places closed-source APIs can't reach: ↳ Real-time applications that need sub-second responses ↳ Edge devices where latency kills user experience ↳ On-premise systems where data never leaves your infrastructure MiniMax-M2 runs with only 10B activated parameters. That efficiency means lower latency, higher throughput, and the ability to handle interactive agents without breaking the bank. The intelligence-to-cost ratio here changes what's possible. You're not choosing between quality and affordability anymore. You're not sacrificing privacy for performance. The gap is closing, and in many cases, it's already closed. If you're building anything that needs to be fast, private, or deployed at scale, it's worth taking a look at what's now available. MiniMax-M2 is 100% open-source, free for developers right now. I have shared the link to their GitHub repo in the next tweet. You will also find the code for the playground and evaluations I've done.

NOBODY wants to send their data to Google or OpenAI. Yet here we are, shipping proprietary code, customer information, and sensitive business logic to closed-source APIs we don't control. While everyone's chasing the latest closed-source releases, open-source models are quietly becoming the practical choice for many production systems. Here's what everyone is missing: Open-source models are catching up fast, and they bring something the big labs can't: privacy, speed, and control. I built a playground to test this myself. Used CometML's Opik to evaluate models on real code generation tasks - testing correctness, readability, and best practices against actual GitHub repos. Here's what surprised me: OSS models like MiniMax-M2, Kimi k2 performed on par with the likes of Gemini 3 and Claude Sonnet 4.5 on most tasks. But practically MiniMax-M2 turns out to be a winner as it's twice as fast and 12x cheaper when you compare it to models like Sonnet 4.5. Well, this isn't just about saving money. When your model is smaller and faster, you can deploy it in places closed-source APIs can't reach: ↳ Real-time applications that need sub-second responses ↳ Edge devices where latency kills user experience ↳ On-premise systems where data never leaves your infrastructure MiniMax-M2 runs with only 10B activated parameters. That efficiency means lower latency, higher throughput, and the ability to handle interactive agents without breaking the bank. The intelligence-to-cost ratio here changes what's possible. You're not choosing between quality and affordability anymore. You're not sacrificing privacy for performance. The gap is closing, and in many cases, it's already closed. If you're building anything that needs to be fast, private, or deployed at scale, it's worth taking a look at what's now available. MiniMax-M2 is 100% open-source, free for developers right now. I have shared the link to their GitHub repo in the next tweet. You will also find the code for the playground and evaluations I've done.

Akshay 🚀

50,323 views • 7 months ago

Introducing Fintarget 2.0 Change the way you automate your trades by visiting What’s Fresh & Exciting? 1) A clutter‑free screen that feels like second nature. 2) Intraday Algos, On‑Demand: You can now utilize all the functionalities like journey, next week, monthly options and much more. 3) Positional Trades on Auto‑Pilot: You can now automate positional trades on fintarget Set it, forget it, wake up to executed plans (BETA) 4) Backtest Before You Blast Off: Fintarget is now connected with Stockmock.in (Backtesting, Simulator & Builder) . With your purchase of a Fintarget subscription, you'll get Stockmock credits added to your account. 5) Why formulate strategies when you can replicate: Fintarget now makes it possible for you to copy-paste the strategy link directly from Stockmock . 6) TradingView Plug-In Magic: Automate your strategies and initiate entry by triggering from TradingView signals. 7) Deep Dive Analytics: Find your edge, correct your flaws, level up. 8) Automate your trades with 11 brokers now: Trade anywhere without having to switch tabs. 9) Pre-built Strategies: Don't know where to start? We’ve got lucrative, ready-to-go strategies designed for you. 10) Custom leg control: Choose limit or market, auto-switch if unfilled, and much more. 11) Forward-Test Sandbox: Fintarget now includes Forward Testing. Run practice rounds with zero risk. 12) Live runs + backtest credits + forward test credits: Plans starting at just ₹299. 13) Refer & Earn: Refer your friends, get rewarded forever. 14) Pocket-Perfect On-the-Go: Full power, palm-sized convenience. 15) Broker Tie-ups: Now Fintarget is available for use by everyone from @thefirstock and Upstox users 16) Want to start your automation journey?: All users are given free forward testing plans for up to 2 strategies daily. A special thanks to Nitish Narang Stockmock.in (Backtesting, Simulator & Builder) and @thefirstock team for all the guidance and support.

Introducing Fintarget 2.0 Change the way you automate your trades by visiting What’s Fresh & Exciting? 1) A clutter‑free screen that feels like second nature. 2) Intraday Algos, On‑Demand: You can now utilize all the functionalities like journey, next week, monthly options and much more. 3) Positional Trades on Auto‑Pilot: You can now automate positional trades on fintarget Set it, forget it, wake up to executed plans (BETA) 4) Backtest Before You Blast Off: Fintarget is now connected with Stockmock.in (Backtesting, Simulator & Builder) . With your purchase of a Fintarget subscription, you'll get Stockmock credits added to your account. 5) Why formulate strategies when you can replicate: Fintarget now makes it possible for you to copy-paste the strategy link directly from Stockmock . 6) TradingView Plug-In Magic: Automate your strategies and initiate entry by triggering from TradingView signals. 7) Deep Dive Analytics: Find your edge, correct your flaws, level up. 8) Automate your trades with 11 brokers now: Trade anywhere without having to switch tabs. 9) Pre-built Strategies: Don't know where to start? We’ve got lucrative, ready-to-go strategies designed for you. 10) Custom leg control: Choose limit or market, auto-switch if unfilled, and much more. 11) Forward-Test Sandbox: Fintarget now includes Forward Testing. Run practice rounds with zero risk. 12) Live runs + backtest credits + forward test credits: Plans starting at just ₹299. 13) Refer & Earn: Refer your friends, get rewarded forever. 14) Pocket-Perfect On-the-Go: Full power, palm-sized convenience. 15) Broker Tie-ups: Now Fintarget is available for use by everyone from @thefirstock and Upstox users 16) Want to start your automation journey?: All users are given free forward testing plans for up to 2 strategies daily. A special thanks to Nitish Narang Stockmock.in (Backtesting, Simulator & Builder) and @thefirstock team for all the guidance and support.

Saketh R

48,990 views • 1 year ago

Most AI app builders can generate a prototype. The real question is: Can they generate something you can actually launch? I recently explored MeDo, an AI-powered full-stack app generation platform that goes beyond just creating a frontend. With a single prompt, it can generate the frontend UI, backend logic, database structure, and plugin integrations. All in one workflow, without requiring coding or deployment experience. What stood out to me is the focus on building real products rather than simple demos. Whether you're creating SaaS tools, internal dashboards, e-commerce applications, games, or business automation tools. the goal is to move from idea → working product as quickly as possible. A few notable features: ✅ Full-stack generation from a single prompt ✅ Plugin ecosystem with integrations like Stripe and AI tools ✅ Auto bug-fixing capabilities ✅ Website cloning from screenshots ✅ Built-in UI enhancement options ✅ Marketplace of ready-made apps you can customize The AI app builder space is becoming increasingly competitive, and platforms are starting to differentiate themselves based on how close they get to a production-ready result rather than just generating code. For builders, founders, and creators exploring no-code AI development, it's an interesting category worth watching. Try it today and launch your next project with just a prompt:

Most AI app builders can generate a prototype. The real question is: Can they generate something you can actually launch? I recently explored MeDo, an AI-powered full-stack app generation platform that goes beyond just creating a frontend. With a single prompt, it can generate the frontend UI, backend logic, database structure, and plugin integrations. All in one workflow, without requiring coding or deployment experience. What stood out to me is the focus on building real products rather than simple demos. Whether you're creating SaaS tools, internal dashboards, e-commerce applications, games, or business automation tools. the goal is to move from idea → working product as quickly as possible. A few notable features: ✅ Full-stack generation from a single prompt ✅ Plugin ecosystem with integrations like Stripe and AI tools ✅ Auto bug-fixing capabilities ✅ Website cloning from screenshots ✅ Built-in UI enhancement options ✅ Marketplace of ready-made apps you can customize The AI app builder space is becoming increasingly competitive, and platforms are starting to differentiate themselves based on how close they get to a production-ready result rather than just generating code. For builders, founders, and creators exploring no-code AI development, it's an interesting category worth watching. Try it today and launch your next project with just a prompt:

Markandey Sharma

49,777 views • 1 month ago

n8n vs cursor, building a keyword research workflow Plus I'll give you my code no strings attached... you can use my version, my code, or try to use the logic to build it as a node-based workflow...whatever! what you need: - firecrawl API key - perplexity API key - dataforseo API key goal: - input a URL - use Firecrawl to scrape the site - use Perplexity to generate seed keywords and understand the target market, competitors - use DataforSEO to research the keywords and associated data my process: 1) used claude code to build the n8n workflow to go from 0-1, then debug (still debugging after an hour) - structure was good but too many nodes and some errors. - debugging was a pain. building it manually would have taken a long time. - I much prefer natural language w/ an agent. 2) used claude code to just build a simple app where I input a couple API keys (working in 30 minutes) - a few tweaks during the testing process, mostly just around output formatting. - working from the first go, debugging was way easier. - I can easily expand on the functionality. - I have a simple and clean UX out of the gate, no need to use google docs, airtable, etc etc conclusion: OPTION 2 IS MUCH MORE EFFICIENT AND FUN. I've attached a video demo. I'm also going to share a link for you to test it, and the GitHub repo -- it's all yours!

n8n vs cursor, building a keyword research workflow Plus I'll give you my code no strings attached... you can use my version, my code, or try to use the logic to build it as a node-based workflow...whatever! what you need: - firecrawl API key - perplexity API key - dataforseo API key goal: - input a URL - use Firecrawl to scrape the site - use Perplexity to generate seed keywords and understand the target market, competitors - use DataforSEO to research the keywords and associated data my process: 1) used claude code to build the n8n workflow to go from 0-1, then debug (still debugging after an hour) - structure was good but too many nodes and some errors. - debugging was a pain. building it manually would have taken a long time. - I much prefer natural language w/ an agent. 2) used claude code to just build a simple app where I input a couple API keys (working in 30 minutes) - a few tweaks during the testing process, mostly just around output formatting. - working from the first go, debugging was way easier. - I can easily expand on the functionality. - I have a simple and clean UX out of the gate, no need to use google docs, airtable, etc etc conclusion: OPTION 2 IS MUCH MORE EFFICIENT AND FUN. I've attached a video demo. I'm also going to share a link for you to test it, and the GitHub repo -- it's all yours!

The Boring Marketer

33,377 views • 1 year ago

🤖 AigentX 🤝 TON 🌐 We're proud to announce a public community test to support TON 💎 developers, and we've just unveiled a groundbreaking AI Support Agent designed to supercharge TON app development. Introducing our AI agent tailored for developers - a game-changer in blockchain app development support! Why It's A Must Have 🧠 Extensive Knowledge: Mastering over 5,000 pages of blockchain documentation. 🔍 Smart, Sourced Answers: Delivers the best solutions backed by reliable sources. 📱 Telegram Integration: Accessible real-time support right within the Telegram bot. 🌱 Evolving Intelligence: Constantly improves based on your interactions and feedback. ✅ High Accuracy: Over 98% precision in answers, offering straightforward, factual insights. Taking Ton App Development to New Heights If you find TON Blockchain's documentation confusing, traditional AI assistants relying on outdated knowledge (like TON on Solidity) may fall short. No worries! Meet our AI Agent. Under the hood, it boasts: 📚 KnowledgeBaseAI Agent: Instantly locates the right documentation. 🤖 InitialGeneration LLM: Crafts concise, accurate responses. 🧠 5 More Advanced Neural Networks: We have also included 5 more advanced Neural Network modules which enhances connection testing, deep thinking, formatting, and fact-checking. 🚀 Ready to revolutionize your TON app development? Start by clicking 'Start' on Ask your #TON development queries, and embark on a smoother development journey!

🤖 AigentX 🤝 TON 🌐 We're proud to announce a public community test to support TON 💎 developers, and we've just unveiled a groundbreaking AI Support Agent designed to supercharge TON app development. Introducing our AI agent tailored for developers - a game-changer in blockchain app development support! Why It's A Must Have 🧠 Extensive Knowledge: Mastering over 5,000 pages of blockchain documentation. 🔍 Smart, Sourced Answers: Delivers the best solutions backed by reliable sources. 📱 Telegram Integration: Accessible real-time support right within the Telegram bot. 🌱 Evolving Intelligence: Constantly improves based on your interactions and feedback. ✅ High Accuracy: Over 98% precision in answers, offering straightforward, factual insights. Taking Ton App Development to New Heights If you find TON Blockchain's documentation confusing, traditional AI assistants relying on outdated knowledge (like TON on Solidity) may fall short. No worries! Meet our AI Agent. Under the hood, it boasts: 📚 KnowledgeBaseAI Agent: Instantly locates the right documentation. 🤖 InitialGeneration LLM: Crafts concise, accurate responses. 🧠 5 More Advanced Neural Networks: We have also included 5 more advanced Neural Network modules which enhances connection testing, deep thinking, formatting, and fact-checking. 🚀 Ready to revolutionize your TON app development? Start by clicking 'Start' on Ask your #TON development queries, and embark on a smoother development journey!

AGIX | $AGX

377,406 views • 2 years ago

Every day we hunt for the tools that turn “impossible” into “anyone can do it.” This morning I found something different. Not a new tool. A whole platform that’s turning Gen Z into hardware poets and accessibility pioneers in 48 hours. What we saw inside REDHackathon just quietly rewrote my mental map of where the real AI frontier is moving. Let me show you. A Chinese Gen Z team just built a full AI product from scratch in 48 hours and took second place at REDHackathon, a competition built to push young developers to ship real functional AI software under a brutal deadline. The product is called Attune and it actually works. The traditional UI was like a static map — you're the traveler, and you have to know exactly where you're going. But Attune has built a living ecosystem. The UI can sense your needs and react accordingly. The interface comes to you. Clean UI, sharp interaction design, production level output in two days. Local heuristics instantly grab clickable elements on the page and pop up a radial menu near your cursor. AI runs async as an enhancement layer, not a dependency — so no lag. In 48 hours, they also solved: style loss on cloned DOM, input sync back to original elements, and popup repositioning. Shadow DOM inline styles, a proxy input system, relayout logic — the works. This is not a school project. This is what happens when serious builders get the right environment to compete in. rednote built REDHackathon to give exactly this kind of talent a global stage and Attune is proof the standard is high. rednote #redhackathon #rednote #technology #AI Watch the demo and see it for yourself:

Every day we hunt for the tools that turn “impossible” into “anyone can do it.” This morning I found something different. Not a new tool. A whole platform that’s turning Gen Z into hardware poets and accessibility pioneers in 48 hours. What we saw inside REDHackathon just quietly rewrote my mental map of where the real AI frontier is moving. Let me show you. A Chinese Gen Z team just built a full AI product from scratch in 48 hours and took second place at REDHackathon, a competition built to push young developers to ship real functional AI software under a brutal deadline. The product is called Attune and it actually works. The traditional UI was like a static map — you're the traveler, and you have to know exactly where you're going. But Attune has built a living ecosystem. The UI can sense your needs and react accordingly. The interface comes to you. Clean UI, sharp interaction design, production level output in two days. Local heuristics instantly grab clickable elements on the page and pop up a radial menu near your cursor. AI runs async as an enhancement layer, not a dependency — so no lag. In 48 hours, they also solved: style loss on cloned DOM, input sync back to original elements, and popup repositioning. Shadow DOM inline styles, a proxy input system, relayout logic — the works. This is not a school project. This is what happens when serious builders get the right environment to compete in. rednote built REDHackathon to give exactly this kind of talent a global stage and Attune is proof the standard is high. rednote #redhackathon #rednote #technology #AI Watch the demo and see it for yourself:

AI Frontliner

215,833 views • 3 months ago