Canva AI

Canva AI AI

Canva AI AI

Canva AI AI

Canva AI AI

Canva AI AI

Canva AI AI

Canva AI AI

Canva AI AI

Canva AI AI

Canva AI AI

i built a jarvis-style claude code setup that flagged my worst week a month early. pulled out the student core and open sourced it

i've had a jarvis-style claude code setup running my life for a while. last month it flagged a brutal week a month out, before i'd noticed it myself. the video is that moment, visualized: my semester the way nūs sees it, every class, deadline, note and skill as one linked graph, with the crunch week lighting up. it's a stylized render of the vault, not a screen recording of the terminal. so i pulled out the student core and open sourced it as nūs (greek nous, the mind). the whole product is a folder: one CLAUDE.md with the chief-of-staff personality and hard rules (never invent a deadline, refuse to write your assignments, nothing leaves your machine), six markdown slash commands, and your syllabus PDFs. no server, no account, no telemetry: claude code is the entire engine. what the six commands do: /setup reads your syllabus PDFs page by page, writes one file per class (grading breakdown + every deadline), builds a combined calendar, and flags "crunch weeks": any 7-day window with 3+ deliverables or 2+ exams. /brief morning brief: what's due today, the week ahead, the one thing worth starting early even though it isn't due yet, and one focus line. under 15 lines. /due what's due tonight, or /due friday, or /due this week. tight table, bolds any exam inside 7 days. /quest three small binary quests a day: one on your nearest deadline, one that front-loads the next crunch, one rep from your active skill track (no track yet? it pulls from your goals file and nudges you to /level one). tracks a streak. /level name a skill, it builds a Lv1 - Lv10 path with daily 20-45 min reps that auto-feed into /quest. miyagi mode: early levels feel almost too easy on purpose. /capture paste anything (a thought, lecture notes, a to-do), it cleans it, tags it, and files it where the AI will find it later, so tomorrow's brief is smarter. the part that surprised me: there's no code to hide. the product IS the prompts. here's the rules block from CLAUDE.md: Never invent a deadline. Every date you state must trace to a file in school/. If a syllabus is ambiguous, flag it: "verify in Canvas/LMS, syllabus unclear." Academic integrity. Help the user plan, schedule, understand concepts, and quiz themselves. Do NOT write assignment submissions for them. If their school bans AI on coursework, planning and studying is the line. Respect it. Never delete user files in school/raw/ or goals/. Only add or update generated files. Nothing leaves this machine. No emails, posts, or API calls with the user's data unless they explicitly ask. honest scope notes: it needs claude code + a claude subscription (~$20/mo). the product is free and open, the engine isn't. no way around that right now. the syllabus is the source of truth, not your LMS. if a professor moves a due date in canvas and doesn't update the syllabus, nūs is blind to it until you /capture the change. it literally reminds you to verify against canvas rather than trusting itself. it's a terminal tool. no app, no push notifications, no background sync. it runs when you invoke it. the "morning brief at login" thing is something you wire up yourself. since it's all markdown you can read the whole thing in one sitting. if you think /quest's rules are wrong, open the file and tell me why, i genuinely want this sub to tear the prompt design apart. repo: https://github.com/pdaime/nus submitted by /u/Daf150 [link] [comments]

Telos. Build shared AI workspaces for creation, simulation, verification, MCP tools, and replayable receipts.

https://preview.redd.it/d92rgcm8y1ch1.png?width=1280&format=png&auto=webp&s=16c449f00929a2bbb0d52e68c4bba8260859b3e8 Telos is a zero-dependency local workbench for creating, simulating, and replaying AI work. It ships a five-server MCP surface plus CLI fallbacks: doctors for CI, presentation, accessibility, performance, and compatibility, a creative engine with deterministic kernels and ten measurement meters, model-foundry and learning-forge lanes, and research proof packets spanning causal, embodied, and quantum demos. It ties gather, index, forum, and crucible into one operator map you can run with a single node demo/run.mjs. Every run writes a receipt you can re-check. My favorite part of the whole project was building the transpiler, and watching the previously distant parts come closer and closer to a convergence. I have always been more of a media/color/rendering nerd, so being able to apply it here in an applicable, utility based approach was a lot of fun. Deterministic kernels (ordered dither, pixel sort, harmonograph, clustered light), a WebGPU/WebGL/canvas/static renderer selection contract, and ten runnable meters across histogram, dither, splat, cluster, audio, flicker, curvature, interaction, uncertainty, and frame-budget signals. The visual surface lives at demo/index.html https://github.com/HarperZ9/telos submitted by /u/MeAndClaudeMakeHeat [link] [comments]

I made an "evolving scene" presentation creation skill

There are plenty of HTML slide deck generators out there. But I believe this one is doing something different. It uses what I'm calling an "evolving-scene model": one shared diagrammatic canvas where a stable set of entities morphs across named steps. Boxes appear, move, connect, collapse, and re-label as the talk develops. The continuity between steps is the key differentiator. And I came up with that model because that's basically how my brain works. I tend to understand things by building a map of concepts, then visualizing how they connect and layer on top of each other like building blocks. A "normal" slide deck typically treats each slide as a distinct departure from the previous one. Yes, PowerPoint has animations, but "evolving scenes" are challenging to make at best. But once I had this method working, I knew I was going to use it again. So I turned the creation process into a skill: /and-scene:presentation. It interviews you about the topic, visual style, content, and what should happen visually. Then it scaffolds the browser app if needed, codes up the presentation, and verifies the visual styling for you. The README has a couple of videos of example presentations too, which is probably the fastest way to get a feel for what it can do. Disclosure: I’m the author. It’s free and MIT licensed, with no paid tier. Built by Claude Code, for users of Claude Code and similar agents. Get it here: https://github.com/Codagent-AI/and-scene submitted by /u/paulcaplan [link] [comments]

Fable Started, Opus Finished: IronClaim, throwback late 90s style wargame

I threw Fable a goal for a game I enjoyed decades ago, MAX, and then backed away. It built a workable prototype within a few hours and it was playable by the time Fable was shut off. I've since QA'd and worked through new features with Opus, like the first 2 acts of a campaign (still not fully played through) as a way to burn a few tokens I don't spend on work each week. It's playable at: https://iron-claim.com > I want you to build a turn-based tactical strategy game called IRONCLAIM in this project — a spiritual successor to the 1996 game M.A.X. (Mechanized Assault & Exploration), with its own original setting, names, art, and lore (do NOT reuse anything from M.A.X.). Build it with Next.js & react and it has to support online play: a host creates a room and others join, like modern multiplayer web games. That next.js/react constraint is purely for deployment — use whatever fits inside it (canvas/Phaser/Pixi, whatever you judge best). I don't want to think about that side. > It's a hex-grid game where players raise an economy from raw resources, design and upgrade mech units, and fight over a contested mining world. FOUR mechanics are sacred and must be fully implemented and FUN — do not simplify them away: > 1. Unit design & upgrades — chassis with stats you upgrade globally plus build-time loadout choices; combined arms must matter (a balanced force beats a pure tank ball). > 2. The transport gambit — transports carry units through gaps in the line, and unloaded units KEEP their full turn so you can dump a swarm deep in enemy territory. Keep it devastating; balance it by making transports soft and detectable, not by nerfing the dump. > 3. Signature/Detection fog of war — vision and detection are separate layers; units have a signature, detectors (radar/AWAC/sonar) reveal them, cover lowers signature and raises defense. Recon and counter-recon are the core mind-game. > 4. Ammo & supply — limited ammo per unit, resupply via supply units/depots, logistics is a real constraint on the swarm. > Build hotseat + AI opponent FIRST (a complete game with no netcode), then add online rooms. Use 64-bit-safe counters and write a test that simulates a 1,000-turn game with no overflow or desync — there must be NO turn cap, ever. You have full autonomy. Don't stop for my input until there's a playable build I can review end to end. Have fun, be creative — you're an expert strategy-game designer and developer. submitted by /u/EdgarDruin [link] [comments]

no more hitting rate limits in claude

found this FREE chrome extension that lets me know how much message/tokens i have left before hitting the rate limit! submitted by /u/john2219 [link] [comments]

I Got Fed up with ChatGPT’s New Flatter Dark Mode, so I Restored the Original Charcoal Version

The newer ChatGPT dark mode felt a little too flat/light to me, so I put together a small userscript that restores a deeper charcoal-style palette. It mainly fixes: darker main canvas darker composer/input bar better sidebar separation readable lifted message bubbles bottom dock/footer strip cleanup light mode stays unaffected I also tried to avoid the usual “dark theme hack” problem where broad CSS overrides turn everything into blocky rectangles. The script mostly uses ChatGPT’s own theme variables, then does a targeted composer/bottom-dock cleanup. Install: Greasy Fork Source / screenshots / notes: GitHub Privacy note: it does not collect data, make network requests, store conversations, use analytics, or modify ChatGPT functionality. It only applies local visual styling on chatgpt.com and chat.openai.com. Not affiliated with OpenAI — just a visual fix for people who preferred the old darker feel. submitted by /u/TonyHMeow [link] [comments]

I created a tank based game called “Scorched Steel” using Claude Code

Hey r/ClaudeAI! I recently built a small web game called **Scorched Steel**, inspired by the classic games I used to play in my childhood. It is completely free to try right in your browser. What the game is: Scorched Steel is a turn-based artillery game where you have to calculate angles and power to defeat the enemy tanks. How I used Claude Code to build it: I used Claude Code extensively to bring this idea to life. Since I was just starting out, Claude acted as my co-pilot and helped me with: Core Logic: Claude generated the math for the projectile physics and collision detection. UI & Graphics: It helped me structure the HTML/CSS and set up the game loop on the canvas. Debugging: Whenever a mechanic broke, I fed the error logs back into Claude to help me refactor the code and fix the bugs quickly. The process of going from an idea to a deployed game was incredibly fast thanks to Claude Code. The game is completely free to play. I’d love for you to test it out and give me any feedback on the gameplay, or let me know if you have any questions about the prompts I used! You can play it here: https://scorched-steel.vercel.app/ Thanks! submitted by /u/arpan171 [link] [comments]

Launching the Agentic AI World Cup — Design a multi-agent swarm visually to win up to $100

Hey everyone, Two months ago, We launched AgentSwarms to help developers learn and build POC using Agentic AI. Since then, over 3,800 learners have joined the platform. Now, it’s time to see what you can actually design when the gloves come off. This week, We're officially launching the Agentic AI World Cup. The twist? No complex boilerplate environment setup required. This competition is entirely focused on architectural design using the platform's visual canvas builder. 🏆 The Challenge Use the visual canvas builder to orchestrate a multi-agent swarm that solves a legitimate, real-world workflow problem. We want to see how creatively and robustly you can map out state transitions, routing logic, and multi-agent collaboration visually. 🎁 The Prizes 🥇 Winner — $100 Amazon Gift Card + Featured Spotlight on AgentSwarms 🥈 1st Runner-up — $50 Amazon Gift Card + Featured Spotlight on AgentSwarms 🥉 2nd Runner-up — $25 Amazon Gift Card + Featured Spotlight on AgentSwarms 📋 How to Enter Build & Publish: Open up the visual canvas builder on AgentSwarms. Design your multi-agent architecture and publish it to the Community with a detailed text write-up explaining your logic. Record & Submit: Record a quick video walkthrough of your visual swarm executing its workflow. Email a Google Drive link of the recording to hello@agentswarms.fyi. ⚖️ What the Judges Care About We are evaluating raw architectural design and execution logic: Problem Severity: Does this swarm solve a real, practical problem? Graph Logic: How clean and efficient is your visual routing and orchestration? Resilience: How well does your design handle edge cases or unexpected node outputs? Documentation: Is your community write-up detailed enough that someone else looking at your canvas can immediately understand the workflow? ⏱️ Deadlines Submission Deadline: July 10, 2026 Winners Announced: July 25, 2026 If you’ve been wanting to whiteboard a complex multi-agent system and actually see it run, this is the perfect sandbox to do it. If you have any questions and need any support drop us an email. submitted by /u/Outside-Risk-8912 [link] [comments]

Launching the Agentic AI World Cup — Design a multi-agent swarm visually to win up to $100

Hey everyone, Two months ago, We launched AgentSwarms to help developers learn and build POC using Agentic AI. Since then, over 3,800 learners have joined the platform. Now, it’s time to see what you can actually design when the gloves come off. This week, We're officially launching the Agentic AI World Cup. The twist? No complex boilerplate environment setup required. This competition is entirely focused on architectural design using the platform's visual canvas builder. 🏆 The Challenge Use the visual canvas builder to orchestrate a multi-agent swarm that solves a legitimate, real-world workflow problem. We want to see how creatively and robustly you can map out state transitions, routing logic, and multi-agent collaboration visually. 🎁 The Prizes 🥇 Winner — $100 Amazon Gift Card + Featured Spotlight on AgentSwarms 🥈 1st Runner-up — $50 Amazon Gift Card + Featured Spotlight on AgentSwarms 🥉 2nd Runner-up — $25 Amazon Gift Card + Featured Spotlight on AgentSwarms 📋 How to Enter Build & Publish: Open up the visual canvas builder on AgentSwarms. Design your multi-agent architecture and publish it to the Community with a detailed text write-up explaining your logic. Record & Submit: Record a quick video walkthrough of your visual swarm executing its workflow. Email a Google Drive link of the recording to hello@agentswarms.fyi. ⚖️ What the Judges Care About We are evaluating raw architectural design and execution logic: Problem Severity: Does this swarm solve a real, practical problem? Graph Logic: How clean and efficient is your visual routing and orchestration? Resilience: How well does your design handle edge cases or unexpected node outputs? Documentation: Is your community write-up detailed enough that someone else looking at your canvas can immediately understand the workflow? ⏱️ Deadlines Submission Deadline: July 10, 2026 Winners Announced: July 25, 2026 If you’ve been wanting to whiteboard a complex multi-agent system and actually see it run, this is the perfect sandbox to do it. If you have any questions and need any support drop us an email. submitted by /u/Outside-Risk-8912 [link] [comments]

You asked for DeepLearning.ai-style notebooks for AgentSwarms—so we built 67 of them (TypeScript/LangChain/LangGraph/LlamaIndex/AgentsSDK/VercelAI).

Hey everyone, A few months ago, We shared the visual canvas we built for AgentSwarms. The response was incredible, but the most common piece of feedback was: "The visual canvas is great for architecture, but I need to see the actual code to really understand how to deploy this." You wanted deep-dive, code-first labs—the kind you see on DeepLearning.ai—but for multi-agent systems, faster and with more flexibility. We’ve spent the last few weeks heads-down engineering a completely new Interactive Notebooks section. As of today, we have 67 TypeScript-based notebooks live on the site (with more dropping soon). What’s in the library: We’ve covered everything from basic LangChain fundamentals to complex enterprise-level multi-agent workflows. Everything runs entirely in your browser using TypeScript—no Docker, no Python venv, no local dependencies. A personal favorite: I’m particularly excited about the "Failure Mode & Error Handling" notebook. We’ve all seen agents that work perfectly in a demo but crash in production the moment a tool times out or an LLM returns garbage. This notebook walks through: How to build deterministic validation gates between nodes. How to force an orchestrator to "catch" a worker failure and dynamically re-route or re-prompt. How to handle state recovery when a multi-agent loop gets stuck in a hallucination cycle. Why we built this: I’m tired of seeing AI "tutorials" that are just static blog posts. To master Agentic AI, you need to be able to tweak a system prompt, break the code, watch the error trace, and fix the routing logic in real-time. The entire library of 67 labs is 100% free to use. If you’re currently wrestling with how to make your agents production-grade, I’d love for you to check them out and let me know if there’s a specific "failure mode" or architecture pattern you’d like us to add to the next batch of notebooks. Try it out here: agentswarms.fyi submitted by /u/Outside-Risk-8912 [link] [comments]

You asked for DeepLearning.ai-style notebooks for AgentSwarms—so we built 67 of them (TypeScript/LangChain/LangGraph/LlamaIndex/OpenAI-AgentsSDK/VercelAI).

Hey everyone, A few months ago, We shared the visual canvas we built for AgentSwarms. The response was incredible, but the most common piece of feedback was: "The visual canvas is great for architecture, but I need to see the actual code to really understand how to deploy this." You wanted deep-dive, code-first labs—the kind you see on DeepLearning.ai—but for multi-agent systems, faster and with more flexibility. We’ve spent the last few weeks heads-down engineering a completely new Interactive Notebooks section. As of today, we have 67 TypeScript-based notebooks live on the site (with more dropping soon). What’s in the library: We’ve covered everything from basic LangChain fundamentals to complex enterprise-level multi-agent workflows. Everything runs entirely in your browser using TypeScript—no Docker, no Python venv, no local dependencies. A personal favorite: I’m particularly excited about the "Failure Mode & Error Handling" notebook. We’ve all seen agents that work perfectly in a demo but crash in production the moment a tool times out or an LLM returns garbage. This notebook walks through: How to build deterministic validation gates between nodes. How to force an orchestrator to "catch" a worker failure and dynamically re-route or re-prompt. How to handle state recovery when a multi-agent loop gets stuck in a hallucination cycle. Why we built this: I’m tired of seeing AI "tutorials" that are just static blog posts. To master Agentic AI, you need to be able to tweak a system prompt, break the code, watch the error trace, and fix the routing logic in real-time. The entire library of 67 labs is 100% free to use. If you’re currently wrestling with how to make your agents production-grade, I’d love for you to check them out and let me know if there’s a specific "failure mode" or architecture pattern you’d like us to add to the next batch of notebooks. Try it out here: agentswarms.fyi submitted by /u/Outside-Risk-8912 [link] [comments]

Blast Arena: a Bomberman-style browser game built entirely with Claude Code

🎮 Play (free, nothing to install): https://bomberman-coral.vercel.app What it is: an 8-level arcade game where each level introduces a new mechanic — classic crate-bombing, enemies that pathfind toward you, ice you slide on, electric floors, a minion-spawning boss, teleport portals, enemies that plant their own bombs, and conveyor belts. Between levels you spend gold in an upgrade shop with an unlock tree (max one line to open advanced ones). There's a public per-level leaderboard with clear times, full touch controls on mobile, and procedurally drawn graphics/synthesized audio — zero asset files. How Claude helped: honestly, it built all of it. I steered with plain-language prompts ("make it look premium", "levels should get harder", "make it work on mobile", "my scores disappeared — fix it") and Claude Code wrote the vanilla JS/canvas engine, the particle and audio systems, the enemy AI, and the Vercel serverless leaderboard. The interesting parts were the bugs: it diagnosed a leaderboard race condition (concurrent score submissions silently overwriting each other through a CDN-cached read-modify-write) and redesigned the storage so every score is its own write-conflict-free record. It also wrote its own headless test harness that simulates full playthroughs, since there was no test framework in a plain HTML project. Transparency / security notes: No account, no login, no personal data requested. You pick a nickname to play — that nickname and your level clear times are publicly visible on the leaderboard, so don't use your real name. Game progress (gold/upgrades) is stored only in your browser's localStorage. The site uses Vercel Web Analytics (anonymous, cookie-free page counts). Nicknames are profanity-filtered server-side. Everything runs client-side except the leaderboard API (a small Vercel function). No downloads, no permissions, no credentials touched. Feedback very welcome — especially on difficulty balance in levels 5–8, which were tuned by an AI that can't actually feel panic. 😄 submitted by /u/igoroliveiragg [link] [comments]

PROJECT HELP

project -> A Next.js whiteboard app where users draw on a canvas (tldraw), type a prompt, and click Enhance The canvas is exported as a base64 PNG, sent to an AI vision model to generate a detailed image prompt, which is then passed to Pollinations.ai to generate a refined image shown in a preview overlay. NEED ->We need a free vision API that accepts a base64 image + text prompt and returns a text response. OpenRouter keeps routing to wrong models. Looking for a reliable free vision model (Gemini, LLaVA, or any) that works without a credit card. or if any replacement of pollination ai? submitted by /u/travishead_137 [link] [comments]

GPT 5.5 vs Fable/Mythos 5 Tamagotchi Showdown

Well, how do I start this, I think we first need some important context. Chai: https://preview.redd.it/egngyea5cf6h1.png?width=1080&format=png&auto=webp&s=9ade63fbc584b7fab28dba4914bc3fcb877f557f Hasbullah / Hasbi: https://preview.redd.it/dufpxbb6cf6h1.png?width=1080&format=png&auto=webp&s=5113f03cc948b2584cd6f2f22e80b74b7f31fd8e Together, Chasbinder was born. Ok maybe this wasn't important... At least you now know AI didn't write this... I think. However, it's important to note, that my Openclaw Agent running through Codex GPT 5.5 xHigh helped enable this test. The same prompt was given to 6 different models on their highest reasoning/think setting via OpenRouter with only one shot. The test was simple, I just wanted my agent Chasbi to have its own cool interactive homepage and I thought of a Tamagotchi game that could be actually playable. You can see the prompt below and breakdown of cost. So here are the results, why don't you try to guess who made what before you reveal the results and see if you got it right? (GPT 5.5, Opus 4.8, Fable/Mythos 5. Gemini 3.5 Flash, Deepseek V4 Pro, Qwen 3.7 Max). https://chasbi.uk/t1 = Gemini 3.5 Flash <- Click to Reveal https://chasbi.uk/t2 = Qwen 3.7 Max <- Click to Reveal https://chasbi.uk/t3 = Claude Opus 4.8 <- Click to Reveal https://chasbi.uk/t4 = Claude Fable/Mythos 5 <- Click to Reveal https://chasbi.uk/t5 = ChatGPT 5.5 <- Click to Reveal https://chasbi.uk/t6 = Deepseek V4 Pro <- Click to Reveal Did you get it right? Well they were all through OpenRouter API with their highest available reasoning setting, everything else was at default and heres the breakdown of how the tokens were tokenised by each provider and the cost for each. https://preview.redd.it/6ecw4xufcf6h1.png?width=1080&format=png&auto=webp&s=983dfcf5a59b87946b5ec712d78c8c003007f9e1 https://preview.redd.it/960chj8gcf6h1.png?width=1080&format=png&auto=webp&s=e7954b7be0b6866be3f154a774281a809e0b3948 So they were all done around the same time at 8AM BST except for Fable/Mythos 5 which I did the day before at 06:50PM BST if that matters, as we're like 5-6 hours ahead of the US it could make all the difference in the world in terms of performance. I am on the Codex Max plan and I stuck it out, because GPT 5.5 xHigh has been amazing for me, except since last week whether it's OpenAI reallocating resources for their launch of GPT 5.6 who knows, but it's never made mistakes for me until now, so I was surprised. I really want to test Fable/Mythos 5 on my codebase but honestly, it cost frikkin' $2.47 for this stupid 1 shot Tamagotchi test! So the only way that's feasible for me right now is to use the Claude Max plan and use it for the 2 weeks we have it until it goes away on 22nd June. Anyway it would be interesting to get your views. Who do you think did it the best... If you want me to test anything else let me know. Each model received the same prompt template and identical task/spec, with only the lane name and target route changed. E.g.: {LANE} = T1/T2/T3/T5/T6 {ROUTE} = /t1 /t2 /t3 /t5 /t6 {LANE_LOWER} = output path label like t1, t2, etc. The Prompt: Build `Chasbinder Pet Lab {LANE}` as a model-lane benchmark for `chasbi.uk`. Target lane: - Public route: `{ROUTE}/` - Title must include `Chasbinder Pet Lab {LANE}`. - This model is competing under the same brief as the other fresh lanes. Do not mention that this is a placeholder or a previous version. Context: - This is a public-safe static browser game. Do not include private/personal data, secrets, real family details, or network calls. - The challenge is to make a small finished indie-feeling Tamagotchi/pet-lab game, not a demo, landing page, or reskin. - It should be strong enough to compare fairly against the Fable/Mythos-style V4 lane and the SoRa/Codex T7 lane. Return ONLY one complete HTML document. No markdown, no explanation. Hard constraints: - Single self-contained `index.html`. - HTML, CSS, vanilla JS only. - No external fonts, libraries, images, audio, tracking, or network calls. - Mobile-first but polished on desktop. - Must work as a static file under `https://chasbi.uk{ROUTE}/\`. - Use `localStorage`, versioned save data, migration/reset if corrupt. - Include export/import/reset debug controls. - Do not use `eval`, alerts for normal gameplay, or browser permissions. - Keep total file reasonably compact; aim under 120KB if possible. - Use stable layout dimensions so controls do not jump on mobile. Game direction: - Core fantasy: Chasbinder is a tiny digital guardian living in a warm terminal-garden. The world is losing its "memory lights"; the player raises Chasbinder, sends him on short expeditions, restores rooms, and unlocks story chapters. - Keep Tamagotchi care at the center, but add a real story loop and difficulty. - Should be playable in one sitting for 5-10 minutes and still progress over days. Required systems: - Pet stats: hunger, thirst, energy, hygiene, mood, trust

OpenAI just declared 'chat is dead' and is turning ChatGPT into a superapp - what does this mean for how we use AI?

A senior OpenAI employee told the Financial Times that chat is dead as the company prepares the biggest ChatGPT overhaul since launch. The plan is to turn it into a superapp with Codex coding tools, AI agents, and third-party integrations like Canva and Booking.com. This confirms what a lot of us have been feeling - pure chat interfaces have diminishing returns. The buzz is shifting toward agents that do things rather than chatbots that talk. OpenAI is also filing for IPO (confidential S-1 filed June 8) alongside publishing their AGI roadmap called Built to Benefit Everyone. Some interesting angles: The superapp pivot means ChatGPT competes more directly with Claude desktop app and Codex They are moving from reactive Q&A to proactive agents that learn your needs over time Third-party integrations suggest a platform play, not just a product Codenamed Aria, the overhaul starts rolling out in weeks The real question is whether users actually want a superapp. People liked ChatGPT because it was simple. Making it a kitchen sink could fragment the experience. On the other hand, if agents really deliver on automating workflows, the chat-only interface was always going to be a stepping stone. What do you think? Is this the natural evolution of AI interfaces or are they fixing something that wasnt broken? submitted by /u/ArtSelect137 [link] [comments]