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Models improve where data flows. ⬟ The Playground lets builders share prompts, datasets, and execution tips. Reusable prompt sets. Community-tested configurations. Build once. Share signal.

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23,343 görüntüleme • 9 ay önce •via X (Twitter)

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Imagine OptimAI Data Network is a giant library that AI Agents use to learn and work. 📚 But here’s the twist, instead of one person deciding what goes in the library, everyone in our community can help pick, check, and improve the books (data). That’s what OptimAI DataDAO is: + DAO? It stands for "Decentralized Autonomous Organization", fancy words for a club where we all make the rules together, no single boss in charge. Like a playground game where kids vote on the fun! + It’s how we, the community, decide together which data is accurate, useful, and fair for AI to use. + The more you contribute, the stronger our network gets, and the more value we all share. You're building the future! Stay tuned - we’re building something that will change how AI learns. BUIDL with us:

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Start to explore 3rd person shooting game simulation by using Minimax H3. Here is what I have found: 01. A lot of fun. 02. Comparing to other models, H3 has a better understanding of the game mechanic. - Great 03. Be careful with prompt "QTE". Many models would treat QTE section as a CG scene. I think that is because of the training data. - Great 04. Prompt fine tuning needed. I began with my own prompts with many challengeable elements: QTE, 3rd person camera movement, high kick and execution kill. In the end, I used ChatGPT to refine the prompts by describing the fight scene as a more sequential, step-by-step progression. -Good PS: the character in this demo is XiXi ( from Error My Love ) Will share more examples later. Great work MiniMax Design (H3)

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Jim HuiHui

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I know a lot of people hate on AI. I get it. Be creative. But for those of use who can't draw or animate, this is more of a quick and cheap fix. and there's nothing wrong with that. I wish others were more open about sharing what they've learned using AI and not hoard their knowledge like it's worth gold. The only way we can learn and improve is if others share what they've learned. I understand people are protective of what prompts they use. Ok fine, don't share them. But you can't even give a tip? Like "add this phrase to the prompt for better [fill in the blank]" or "use [blank] for a more realistic atmosphere]. Just pisses me off. I'm happy to share all I've learned with anyone who askes. Same happened in the 3d printing community with crushable printed houses. No one wanted to share anything. So I gave up on that. #sizetwitter #giantess #AIart #giantessai #Sora2 #soraArt

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Carly {Amazingly Unavoidable}

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This is one of the games made with GPT-6 Astra that I found on Combos Fun. I’ve put the playable link below. For anyone building games in Codex, there’s another useful part here: getting your game online and finding people to play it. Combos CLI lets you publish from Codex to Combos, share a link with friends, and put your game in a community where others can discover and play it. You can use image and audio models through the same account too.

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Donut and Devotions. Every other Thursday I meet with the guys during lunch. I open up the shed/meeting room and try to create a quiet space where they can relax, take a deep breath and reflect on what is good. We share where we are feeling unrest in our lives. We read from the wisdom found Proverbs and then we spend time in prayer. Afterwards the kids have access to the field to throw and hangout together. It’s the highlight of my week. Connect and build community. That’s the business we are in.

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Jim Shapiro

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I just built a Claude prompt library that runs your entire DTC marketing operation 🤯 100+ prompts organized by function: competitor research, creative briefs, ad copy, hooks, landing pages, performance analysis, customer review mining, and more. Perfect for DTC brands and agencies who are still prompting Claude from scratch every time they open a new chat, rewriting the same context, and getting generic output that sounds like every other AI-generated ad. This prompt library eliminates the entire loop: → Competitor Research: scrape and analyze competitor ads, extract winning hooks, map creative strategies, build competitive battlecards → Creative Briefs: generate data-backed briefs from ad performance, write iteration briefs, new concept briefs, test plans → Ad Copy & Hooks: 20 hooks across 10 frameworks, full ad copy variations, persona-specific angles, fatigue-busting rewrites → Landing Pages: audit any landing page against DR best practices, clone high-converting advertorial structures, write product page copy → Performance Analysis: audit Google Ads accounts, find wasted spend, build visual dashboards, weekly narrative reports → Customer Intelligence: mine reviews for ad copy language, extract objections, find unexpected use cases, build persona cards from real data → SEO & Content: find keyword gaps, write content in your brand voice, optimize product listings for AI shopping (ChatGPT, Gemini) → Email & SMS: launch sequences, weekly newsletters, abandoned cart flows, post-purchase nurture No more blank-page prompting. No more re-explaining your brand every session. No more generic AI output that sounds like a template. What you get: →100+ copy-paste prompts organized by the 8 functions DTC teams actually run →Every prompt pre-loaded with the context structure Claude needs to give you real output →Prompts that reference your brand voice, your ICPs, and your real data — not generic placeholders →A living library you can customize once and reuse across every campaign I put together the full prompt library as a single downloadable playbook: organized by section, ready to copy-paste into Claude today. Want it for free? > Like this post >Comment "PROMPTS" And I'll send it over (must be following so I can DM)

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🚀 Introducing EgoExo Forge - built on top of Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tuned

Pablo Vela

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Ritual Community Builders Program Ritual is launching a global builders network + research collective at the frontier of onchain AI. What it’s about: •Bringing together developers, researchers, and educators exploring AI, cryptography & blockchain as one discipline. •Covering cutting-edge topics: zkML, SMPC, verifiable inference, algorithmic ethics. •Builders don’t collect certificates, they run experiments, build proofs, and share code that moves the field forward. •Connecting academia, industry teams, and global dev communities to lower barriers for building AI-native protocols. Why it matters: • Turn theory into practice with paper-to-code workshops, demos, and technical deep dives. • Encourage ethical, privacy-preserving, and transparent AI development on-chain. • Make advanced AI knowledge accessible through bootcamps, study circles & multilingual content. • Unite Ritual’s ecosystem partners, research teams, and independent builders under one vision: open, provable AI. How to join: •Fill out the application form. •Applications reviewed in 1–2 weeks. •Selected builders join an onboarding call. •Receive toolkit, curriculum roadmap & access to bi-weekly sessions. •Start contributing: research, workshops, prototypes, or community activities. Apply now. 👉 Link in comments. Ritual Foundation

Ritual Community Builders Program Ritual is launching a global builders network + research collective at the frontier of onchain AI. What it’s about: •Bringing together developers, researchers, and educators exploring AI, cryptography & blockchain as one discipline. •Covering cutting-edge topics: zkML, SMPC, verifiable inference, algorithmic ethics. •Builders don’t collect certificates, they run experiments, build proofs, and share code that moves the field forward. •Connecting academia, industry teams, and global dev communities to lower barriers for building AI-native protocols. Why it matters: • Turn theory into practice with paper-to-code workshops, demos, and technical deep dives. • Encourage ethical, privacy-preserving, and transparent AI development on-chain. • Make advanced AI knowledge accessible through bootcamps, study circles & multilingual content. • Unite Ritual’s ecosystem partners, research teams, and independent builders under one vision: open, provable AI. How to join: •Fill out the application form. •Applications reviewed in 1–2 weeks. •Selected builders join an onboarding call. •Receive toolkit, curriculum roadmap & access to bi-weekly sessions. •Start contributing: research, workshops, prototypes, or community activities. Apply now. 👉 Link in comments. Ritual Foundation

Keith (❖,❖) , ✧ᴗ✧

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𝕏 is the most important platform in the world. It’s where the world thinks out loud in real time. News breaks here. Ideas are challenged here. People from every country and every walk of life can share what they see, what they know, and what they believe, all in real time. 𝕏 is like the global collective consciousness of humanity… a place where billions of thoughts, conversations, experiences, and ideas all come together. No other platform has had a bigger impact on my life. It has given me the freedom to share my thoughts, build a community, learn from the most intelligent people around the world, and have a voice from anywhere on Earth. Looking back now, Elon buying this platform for $44B was pricelss.

𝕏 is the most important platform in the world. It’s where the world thinks out loud in real time. News breaks here. Ideas are challenged here. People from every country and every walk of life can share what they see, what they know, and what they believe, all in real time. 𝕏 is like the global collective consciousness of humanity… a place where billions of thoughts, conversations, experiences, and ideas all come together. No other platform has had a bigger impact on my life. It has given me the freedom to share my thoughts, build a community, learn from the most intelligent people around the world, and have a voice from anywhere on Earth. Looking back now, Elon buying this platform for $44B was pricelss.

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❔ A Question for Our Community 👇 Would you rather build a trading strategy yourself or refine one suggested by AI? 🤖 Strategy creation always starts with an idea. The real work begins later: shaping the logic, adjusting parameters, defining risk, and deciding whether the setup is worth going live. AI can speed up the starting point. Backtesting shows whether the idea actually holds up. 💡 Want to test both paths before execution? Backtest your ideas here 👉 💬 Share how you usually approach strategy building.

GT Protocol

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Introducing a Major Update on AIOZ AI V1: AI Challenges Are Here! AIOZ AI Challenges is a collaborative space where AI builders and tech enthusiasts can create, submit, and explore innovative models tackling real-world problems. This isn’t just a leaderboard — it’s a launchpad for hands-on learning, community collaboration, and potential rewarded innovation. Challenge One: "Face Anti-Spoofing" Starts Now This challenge dives deep into advanced computer vision—tasking you to detect spoofing attacks like printed photos, video replays, and 3D masks, which are critical threats to real-world biometric systems. Rewards: Upload your model to AIOZ AI V1 and unlock a chance to receive a share of the 4,500 AIOZ token challenge rewards. Full Challenge Details & Submission Guidelines: Build your model on AIOZ AI—the Web3 collaborative marketplace powering the DePIN Ecosystem for AI.

AIOZ Network

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Building AI agents is finally simple — and Airia is leading the way. I’ve been testing Airia AI , enterprise AI orchestration platform that unifies every model, workflow, and data source into one secure environment. Whether you’re a developer, analyst, creator, or enterprise leader, Airia makes it incredibly easy to build powerful AI agents — without wrestling with multiple tools or complex integrations. Using the no-code builder, you can drag-and-drop actions, connect data, choose your LLM, and launch an agent in minutes. Then run it live, publish it, and even share it with the Airia Community, home to 2,500+ pre-built agents you can use or remix. If you want to automate workflows, prototype faster, or explore real enterprise AI use cases, Airia is the place to start. 👉 Build your first agent today: 👉 Explore the community: #Airia #AgenticAI #AIOrchestration #AIAgents #AIWorkflow #DigitalTransformation

Building AI agents is finally simple — and Airia is leading the way. I’ve been testing Airia AI , enterprise AI orchestration platform that unifies every model, workflow, and data source into one secure environment. Whether you’re a developer, analyst, creator, or enterprise leader, Airia makes it incredibly easy to build powerful AI agents — without wrestling with multiple tools or complex integrations. Using the no-code builder, you can drag-and-drop actions, connect data, choose your LLM, and launch an agent in minutes. Then run it live, publish it, and even share it with the Airia Community, home to 2,500+ pre-built agents you can use or remix. If you want to automate workflows, prototype faster, or explore real enterprise AI use cases, Airia is the place to start. 👉 Build your first agent today: 👉 Explore the community: #Airia #AgenticAI #AIOrchestration #AIAgents #AIWorkflow #DigitalTransformation

Adarsh Chetan

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GPT Image 2 + Seedance 2.0 on mitte.ai Jump Rope Choreography I’m still testing different storyboard approaches. I don’t think there’s a single “correct” storyboard system. You should use a storyboard style that fits the specific scene and type of video you want to create. Even though current video models don’t follow storyboards 100% yet, I think future versions will dramatically improve in this area and storyboard usage will become essential. Right now, you can already generate useful storyboards just by describing your idea. Soon, I’ll also share a prompt system I’ve been building that seems to work well for different storyboard types. You can find the Seedance 2.0 prompt used for this video in the replies.

GPT Image 2 + Seedance 2.0 on mitte.ai Jump Rope Choreography I’m still testing different storyboard approaches. I don’t think there’s a single “correct” storyboard system. You should use a storyboard style that fits the specific scene and type of video you want to create. Even though current video models don’t follow storyboards 100% yet, I think future versions will dramatically improve in this area and storyboard usage will become essential. Right now, you can already generate useful storyboards just by describing your idea. Soon, I’ll also share a prompt system I’ve been building that seems to work well for different storyboard types. You can find the Seedance 2.0 prompt used for this video in the replies.

Kōda

69,388 görüntüleme • 5 ay önce

this is f*cking gold A group of Chinese students from Stanford University published an architecture where an agent saying "done" means nothing. only evidence can mark a task complete. three layers: > graph engineering controls states and transitions. no verification evidence, no transition. > loop engineering owns failure: diagnose, repair, retry, re-verify. up to 20 attempts under hard time, token, cost and tool budgets. > the zero-trust harness authorizes every action, isolates execution and writes audit evidence for every path. they tested it across 3,360 executions. verified completion: 56.4% -> 95.0% recovery success: 51.0% -> 93.1% evidence-gated execution: 100% unauthorized capability denial: 140/140 bounded termination: 100% even when the model fails, the framework never lets an unsupported success through. models decide how well. the harness decides whether.

this is f*cking gold A group of Chinese students from Stanford University published an architecture where an agent saying "done" means nothing. only evidence can mark a task complete. three layers: > graph engineering controls states and transitions. no verification evidence, no transition. > loop engineering owns failure: diagnose, repair, retry, re-verify. up to 20 attempts under hard time, token, cost and tool budgets. > the zero-trust harness authorizes every action, isolates execution and writes audit evidence for every path. they tested it across 3,360 executions. verified completion: 56.4% -> 95.0% recovery success: 51.0% -> 93.1% evidence-gated execution: 100% unauthorized capability denial: 140/140 bounded termination: 100% even when the model fails, the framework never lets an unsupported success through. models decide how well. the harness decides whether.

NO1ennn

22,641 görüntüleme • 18 gün önce

While building SEV0, I ran into moments where natural language prompting just wasn't enough - too vague, too indirect. So I started using a method that bridges that gap. I call it Bridged Prompting - a technique where you temporarily step out of the prompt-response loop to manipulate something directly using an AI-generated UI, then step back in. Think of it like GenUI, but more user-driven and transient. AI generates a UI on-the-fly, tailored to your prompt, and lets you manipulate the artifact directly before resuming the conversation. Sure, you could build a full tool, switch tabs, wire it up and round-trip your data. But with Bridged Prompting, the AI just spins up a temporary interface right in the flow. In the video below, I used it to construct the hallway system to closely match the layout of the severed floor. This is something I needed to do for this one project, this one time - I didn't need to make a whole separate app. Bridged Prompting lets you: - Make precise edits visually or structurally - "Hit save" to persist changes to local storage or a backend - Return to your natural language prompt

While building SEV0, I ran into moments where natural language prompting just wasn't enough - too vague, too indirect. So I started using a method that bridges that gap. I call it Bridged Prompting - a technique where you temporarily step out of the prompt-response loop to manipulate something directly using an AI-generated UI, then step back in. Think of it like GenUI, but more user-driven and transient. AI generates a UI on-the-fly, tailored to your prompt, and lets you manipulate the artifact directly before resuming the conversation. Sure, you could build a full tool, switch tabs, wire it up and round-trip your data. But with Bridged Prompting, the AI just spins up a temporary interface right in the flow. In the video below, I used it to construct the hallway system to closely match the layout of the severed floor. This is something I needed to do for this one project, this one time - I didn't need to make a whole separate app. Bridged Prompting lets you: - Make precise edits visually or structurally - "Hit save" to persist changes to local storage or a backend - Return to your natural language prompt

Chris Tate

42,558 görüntüleme • 1 yıl önce