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it's all fun and games until.... Animations by me using Blender. Credits : Models : Hoyoverse BG : 克里斯提亚娜 Shader : StellarToon #HonkaiStarRail #Phaistelle

Nyaonyx

1,204 subscribers

61,978 просмотров • 7 месяцев назад •via X (Twitter)

#HonkaiStarRail #Phaistelle

Anya Rossi• Live Now

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...... would. Animations by me using Blender. Credits : Models : Hoyoverse BG : 克里斯提亚娜 Shader : StellarToon #HonkaiStarRail #Phaistelle

...... would. Animations by me using Blender. Credits : Models : Hoyoverse BG : 克里斯提亚娜 Shader : StellarToon #HonkaiStarRail #Phaistelle

Nyaonyx

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Distracted.... Animations by me using Blender. Credits : Models : Hoyoverse BG : 克里斯提亚娜, Hoyoverse Shader : StellarToon #Phaistelle

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Nyaonyx

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Lean on me... Animations by me using Blender. Credits : Models + BG : Hoyoverse Shader : StellarToon #HonkaiStarRail #Phaistelle

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You worry too much - #xingtao Scene from Stranger Things (Jonathan and Nancy) Program: Blender Shader: Festivity, Mken, Bonny Stage and Lighting: 斯提亚娜 (Aplaybox) #genshinimpact

You worry too much - #xingtao Scene from Stranger Things (Jonathan and Nancy) Program: Blender Shader: Festivity, Mken, Bonny Stage and Lighting: 斯提亚娜 (Aplaybox) #genshinimpact

enceladus 🌹

13,795 просмотров • 2 лет назад

#GEPO #SAMPARD And after another kiss, he was gone. Credits: ANIMATIONS MADE BY KITSUBEE (me), RENDERED WITH BLENDER. DRAWINGS BELONG TO THEIR ORIGINAL CREATORS! Assets belong to Hoyoverse/Festivity/Mken. Props: Audio/Inspired: Pluto Projector - Rex Orange County

#GEPO #SAMPARD And after another kiss, he was gone. Credits: ANIMATIONS MADE BY KITSUBEE (me), RENDERED WITH BLENDER. DRAWINGS BELONG TO THEIR ORIGINAL CREATORS! Assets belong to Hoyoverse/Festivity/Mken. Props: Audio/Inspired: Pluto Projector - Rex Orange County

kokonut 🇵🇸🇸🇩🇨🇩 no comms !!

34,513 просмотров • 1 год назад

小白：最懵的一集 #honkaistarrail #Phainon #Mydei #Cipher 模型：miHoYo/流云景/林槿/葵岚/叶以/ムムム渲染节点：小二今天吃啥啊天空盒：miHoYo/克里斯提亚娜脚本、动画制作：RK贝配音统筹、白厄CV：开心xy_ 万敌CV：烨刑SK 赛飞儿CV：云生s

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RK贝

98,305 просмотров • 4 месяцев назад

‼️DEBUT ANNOUNCEMENT ‼️ It's finally happening!!!!! 💕 My FIRST EVER - UNCAPPED event. Come celebrate with me in going full time content creation!! (omg ahhhhh- what ahhh!? ) 🗓️September 5 ⏲️7 pm est - until ???? There will be lots of collabs, giveaways, new art, new animations, new content, new redeems, community games and SO MANY COOL THINGS! Come support me in this BIG MOMENT and have so much fun!! xo 👉👈 🧡and 🔁appreciate it...Let's spread the word...and thighs ;) SO EXCITED IM VIBRATING

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Maple Chicken 🍁🦊【HEIRS】

13,478 просмотров • 1 год назад

Astra (GPT-6) is here!!! I've had early access and tested it like crazy with things like games, code, writing, browser control, presentations and general knowledge work. This is the best model I've ever used. Period. (Incredible demos below in this thread ⬇️) Here's my take on Astra: > It's insanely capable. This feels like a massive improvement, not just an incremental change. This is especially true with zero-shot prompts. > It's all about knowledge work. Slide creation, analysis, writing, and browser control. And oh my...it's so good at browser control. GPT-5.6 was already fantastic at doing things in the browser, Astra is another level and significantly faster. > We're closer than ever (arrived?) at prompt-to-playable game. And I don't just mean only playable, these are actually fun games. I bet if someone with a great eye for games used Astra, they could create a viral game within 1-2 weeks. > Astra is better at writing but not perfect. It removes much of the "AI Smell" we're all familiar with but some stink still survived. > It has a tendency to use the same design colors and look/feel as GPT-5.6 (forrest green anyone?) but it is more steerable in design than previous models. > It's highly steerable in general. A little nudge goes a long way. When I first started using Astra, almost every task I gave it would go for ~30 minutes. I wanted it to keep working. Adding more specifics to a prompt helped greatly with it's ability to work for a long time. > Astra's 3D understanding is unmatched. 3D asset creation was consistent and easy and its spacial awareness while building complex 3D worlds blew me away. I'm still getting familiar with Astra but this will now be my go-to model for any difficult work I have. Check out the demos below: 👇

Astra (GPT-6) is here!!! I've had early access and tested it like crazy with things like games, code, writing, browser control, presentations and general knowledge work. This is the best model I've ever used. Period. (Incredible demos below in this thread ⬇️) Here's my take on Astra: > It's insanely capable. This feels like a massive improvement, not just an incremental change. This is especially true with zero-shot prompts. > It's all about knowledge work. Slide creation, analysis, writing, and browser control. And oh my...it's so good at browser control. GPT-5.6 was already fantastic at doing things in the browser, Astra is another level and significantly faster. > We're closer than ever (arrived?) at prompt-to-playable game. And I don't just mean only playable, these are actually fun games. I bet if someone with a great eye for games used Astra, they could create a viral game within 1-2 weeks. > Astra is better at writing but not perfect. It removes much of the "AI Smell" we're all familiar with but some stink still survived. > It has a tendency to use the same design colors and look/feel as GPT-5.6 (forrest green anyone?) but it is more steerable in design than previous models. > It's highly steerable in general. A little nudge goes a long way. When I first started using Astra, almost every task I gave it would go for ~30 minutes. I wanted it to keep working. Adding more specifics to a prompt helped greatly with it's ability to work for a long time. > Astra's 3D understanding is unmatched. 3D asset creation was consistent and easy and its spacial awareness while building complex 3D worlds blew me away. I'm still getting familiar with Astra but this will now be my go-to model for any difficult work I have. Check out the demos below: 👇

Matthew Berman

1,689,765 просмотров • 1 день назад

THIS SITE COST AROUND $12 IN CREDITS TO BUILD. STUDIOS QUOTE $35,000 FOR THE SAME THING. What's on screen isn't a basic landing page. It's a fully animated, scroll-driven site, generated end to end in one agentic session with Claude Code + Higgsfield. What's actually on the page: → Cinematic motion clips pulled from 30+ generative models → Scroll animations written automatically - zero hand-coded keyframes → 6 cinematic effects baked in with no config: film grain, particles, vignette, glass cards, color tints, scroll pacing Scroll the demo and one question won't go away: did Claude really assemble all of this in a single pass? For boutique studios billing $100-149/hr, that question lands like a verdict. What it normally takes: → A designer, a motion artist, and a developer → Weeks of handoffs between them → 6 systems wired by hand - GSAP ScrollTrigger, Lenis smooth-scroll, frame extraction, asset optimization, layout, copy That pipeline was the moat. It's what justified the invoice. Here's the part studios and their clients won't enjoy hearing. The price gap: → Boutique agency build: $6,000-$35,000+ → Industry average project: ~$5,280 → Delivery cost: a Claude subscription + a few dollars of Higgsfield credits → Timeline: weeks of production → a single session One operator can now run all six systems in one pass and ship a working site - without touching a frame extractor or writing a CSS keyframe by hand. Full breakdown of how it's built in the article below. Save it & read today 👇

THIS SITE COST AROUND $12 IN CREDITS TO BUILD. STUDIOS QUOTE $35,000 FOR THE SAME THING. What's on screen isn't a basic landing page. It's a fully animated, scroll-driven site, generated end to end in one agentic session with Claude Code + Higgsfield. What's actually on the page: → Cinematic motion clips pulled from 30+ generative models → Scroll animations written automatically - zero hand-coded keyframes → 6 cinematic effects baked in with no config: film grain, particles, vignette, glass cards, color tints, scroll pacing Scroll the demo and one question won't go away: did Claude really assemble all of this in a single pass? For boutique studios billing $100-149/hr, that question lands like a verdict. What it normally takes: → A designer, a motion artist, and a developer → Weeks of handoffs between them → 6 systems wired by hand - GSAP ScrollTrigger, Lenis smooth-scroll, frame extraction, asset optimization, layout, copy That pipeline was the moat. It's what justified the invoice. Here's the part studios and their clients won't enjoy hearing. The price gap: → Boutique agency build: $6,000-$35,000+ → Industry average project: ~$5,280 → Delivery cost: a Claude subscription + a few dollars of Higgsfield credits → Timeline: weeks of production → a single session One operator can now run all six systems in one pass and ship a working site - without touching a frame extractor or writing a CSS keyframe by hand. Full breakdown of how it's built in the article below. Save it & read today 👇

ZEUS⚡️

480,444 просмотров • 2 месяцев назад

i just made this UGC ad with sora 2 using ONE prompt... no editing, no reshoots, no actors - just one super-prompt and it generated a full 10-second ad that looks like it was shot by a hollywood camera businesses are still paying $500+ per UGC creator when you can do this in 2 minutes for free (normies can't even tell it's AI) but prompting for Sora 2 is completely different from anything else... i spent days generating hundreds of videos testing different techniques until i cracked the format now i turned all of that into a detailed custom prompt file you can inject directly into ChatGPT or Claude as instructions reply 'SORA' + RT & follow me and i'll send you the prompt file (must follow me so i can dm)

i just made this UGC ad with sora 2 using ONE prompt... no editing, no reshoots, no actors - just one super-prompt and it generated a full 10-second ad that looks like it was shot by a hollywood camera businesses are still paying $500+ per UGC creator when you can do this in 2 minutes for free (normies can't even tell it's AI) but prompting for Sora 2 is completely different from anything else... i spent days generating hundreds of videos testing different techniques until i cracked the format now i turned all of that into a detailed custom prompt file you can inject directly into ChatGPT or Claude as instructions reply 'SORA' + RT & follow me and i'll send you the prompt file (must follow me so i can dm)

Miko

84,045 просмотров • 11 месяцев назад

A SOLO CREATOR JUST SHIPPED A $35,000-TIER ANIMATED SITE IN ONE WEEKEND - WITH CLAUDE CODE + HIGGSFIELD, FOR THE COST OF A SUBSCRIPTION. What you're looking at isn't a template. It's a fully animated, scroll-driven site, generated end to end in a single agentic session. What's actually on the page: → Cinematic motion clips pulled from 30+ generative models → Scroll animations written automatically - not a single hand-coded keyframe → 6 cinematic effects baked in with zero config: film grain, particles, vignette, glass cards, color tints, scroll pacing Scroll the demo and one question keeps surfacing: a person didn't build this by hand… did they? For studios billing $100-149/hr, that question lands like a verdict. What this normally takes: → A designer, a motion artist, and a developer → Weeks of handoffs between all three → 6 systems wired by hand - GSAP ScrollTrigger, Lenis smooth-scroll, frame extraction, asset optimization, layout, copy That pipeline was the moat. It's what justified the invoice. Here's the part the agency won't put in its pitch deck. The price gap: → Boutique agency build: $6,000-$35,000+ → Industry average project: ~$5,280 → Your cost: a Claude subscription + a few dollars of Higgsfield credits → Timeline: weeks of production → one session One operator now runs all six systems in a single pass and ships a working site - without opening a frame extractor or writing one CSS keyframe. Full breakdown of how it's built in the article below. Save it & read today 👇

A SOLO CREATOR JUST SHIPPED A $35,000-TIER ANIMATED SITE IN ONE WEEKEND - WITH CLAUDE CODE + HIGGSFIELD, FOR THE COST OF A SUBSCRIPTION. What you're looking at isn't a template. It's a fully animated, scroll-driven site, generated end to end in a single agentic session. What's actually on the page: → Cinematic motion clips pulled from 30+ generative models → Scroll animations written automatically - not a single hand-coded keyframe → 6 cinematic effects baked in with zero config: film grain, particles, vignette, glass cards, color tints, scroll pacing Scroll the demo and one question keeps surfacing: a person didn't build this by hand… did they? For studios billing $100-149/hr, that question lands like a verdict. What this normally takes: → A designer, a motion artist, and a developer → Weeks of handoffs between all three → 6 systems wired by hand - GSAP ScrollTrigger, Lenis smooth-scroll, frame extraction, asset optimization, layout, copy That pipeline was the moat. It's what justified the invoice. Here's the part the agency won't put in its pitch deck. The price gap: → Boutique agency build: $6,000-$35,000+ → Industry average project: ~$5,280 → Your cost: a Claude subscription + a few dollars of Higgsfield credits → Timeline: weeks of production → one session One operator now runs all six systems in a single pass and ships a working site - without opening a frame extractor or writing one CSS keyframe. Full breakdown of how it's built in the article below. Save it & read today 👇

ZEUS⚡️

21,779 просмотров • 2 месяцев назад

STUDIOS QUOTE $35,000 FOR THIS WEBSITE. A FULLY ANIMATED, SCROLL-DRIVEN BUILD - CINEMATIC CLIPS, FILM GRAIN, GLASS CARDS - SHIPPED IN ONE SESSION FOR $12. Scroll it and one question won't leave: did an AI really assemble all of this in a single pass? For studios quoting $35,000 and billing $100-149/hr, that question lands like a verdict. Here's what the page actually runs: → Motion clips generated from 30+ models - not stock, not hand-shot → Scroll animations written automatically - zero hand-coded keyframes → Six cinematic effects baked in with no config: film grain, particles, vignette, glass cards, color tints, scroll pacing What that used to require: → A designer, a motion artist, and a developer → Weeks of handoffs → Six systems wired by hand - GSAP ScrollTrigger, Lenis, frame extraction, asset optimization, layout, copy That pipeline was the moat. It justified the invoice. It just collapsed into Claude Code + Higgsfield running in one pass. The gap nobody wants to say out loud: → Agency build: $6,000-$35,000+ → Real cost now: a Claude sub + a few dollars of credits → Timeline: weeks → a single session Follow me + reply "web-site" to this post and I will send you the step-by-step Playbook 👇

STUDIOS QUOTE $35,000 FOR THIS WEBSITE. A FULLY ANIMATED, SCROLL-DRIVEN BUILD - CINEMATIC CLIPS, FILM GRAIN, GLASS CARDS - SHIPPED IN ONE SESSION FOR $12. Scroll it and one question won't leave: did an AI really assemble all of this in a single pass? For studios quoting $35,000 and billing $100-149/hr, that question lands like a verdict. Here's what the page actually runs: → Motion clips generated from 30+ models - not stock, not hand-shot → Scroll animations written automatically - zero hand-coded keyframes → Six cinematic effects baked in with no config: film grain, particles, vignette, glass cards, color tints, scroll pacing What that used to require: → A designer, a motion artist, and a developer → Weeks of handoffs → Six systems wired by hand - GSAP ScrollTrigger, Lenis, frame extraction, asset optimization, layout, copy That pipeline was the moat. It justified the invoice. It just collapsed into Claude Code + Higgsfield running in one pass. The gap nobody wants to say out loud: → Agency build: $6,000-$35,000+ → Real cost now: a Claude sub + a few dollars of credits → Timeline: weeks → a single session Follow me + reply "web-site" to this post and I will send you the step-by-step Playbook 👇

ZEUS⚡️

56,961 просмотров • 2 месяцев назад

Finding alpha shouldn't be so hard. During the bear market, we dedicated ourselves to making DappRadar better than ever. Now, it's 213.43% easier to discover the next big thing! (Yes, I did the math 😂) We've overhauled navigation, merged rankings, improved layouts, and added powerful features. Today, we're topping it all off with a brand-new homepage that brings everything together! 🏠 Explore top dapps, chains, tokens, blockchain games, NFT collections, DeFi protocols, and so much more—all in one place. There's an entire universe waiting for you. 🌌 We redesigned our rankings, introduced new KPIs, and started using AI to make data and smart contracts more accessible to everyone. We didn't stop there. We've launched Hot Contracts, Quests, Airdrops guides, and many other updates. Our mission? To help both crypto newbies finding their way and seasoned degens hunting for the next moonshot, and to support builders in reaching their communities. 💎🚀 Unlock even more with exclusive features like Hot Contracts and advanced filters by staking $RADAR tokens and becoming PRO. It's our way of giving you the edge in the Web3 space! 🔓 If you haven't visited DappRadar in a while, give it a try—it's a whole NEW DappRadar. We're building this together, and your feedback means the world to us. Let me know what you'd like to see next! 🤝 Thanks for being part of our journey. The best is yet to come! 🚀

Dragos Dunica 📡

12,905 просмотров • 1 год назад

Introducing ml-intern, the agent that just automated the post-training team Hugging Face It's an open-source implementation of the real research loop that our ML researchers do every day. You give it a prompt, it researches papers, goes through citations, implements ideas in GPU sandboxes, iterates and builds deeply research-backed models for any use case. All built on the Hugging Face ecosystem. It can pull off crazy things: We made it train the best model for scientific reasoning. It went through citations from the official benchmark paper. Found OpenScience and NemoTron-CrossThink, added 7 difficulty-filtered dataset variants from ARC/SciQ/MMLU, and ran 12 SFT runs on Qwen3-1.7B. This pushed the score 10% → 32% on GPQA in under 10h. Claude Code's best: 22.99%. In healthcare settings it inspected available datasets, concluded they were too low quality, and wrote a script to generate 1100 synthetic data points from scratch for emergencies, hedging, multilingual etc. Then upsampled 50x for training. Beat Codex on HealthBench by 60%. For competitive mathematics, it wrote a full GRPO script, launched training with A100 GPUs on watched rewards claim and then collapse, and ran ablations until it succeeded. All fully backed by papers, autonomously. How it works? ml-intern makes full use of the HF ecosystem: - finds papers on arxiv and reads them fully, walks citation graphs, pulls datasets referenced in methodology sections and on - browses the Hub, reads recent docs, inspects datasets and reformats them before training so it doesn't waste GPU hours on bad data - launches training jobs on HF Jobs if no local GPUs are available, monitors runs, reads its own eval outputs, diagnoses failures, retrains ml-intern deeply embodies how researchers work and think. It knows how data should look like and what good models feel like. Releasing it today as a CLI and a web app you can use from your phone/desktop. CLI: Web + mobile: And the best part? We also provisioned 1k$ GPU resources and Anthropic credits for the quickest among you to use.

Introducing ml-intern, the agent that just automated the post-training team Hugging Face It's an open-source implementation of the real research loop that our ML researchers do every day. You give it a prompt, it researches papers, goes through citations, implements ideas in GPU sandboxes, iterates and builds deeply research-backed models for any use case. All built on the Hugging Face ecosystem. It can pull off crazy things: We made it train the best model for scientific reasoning. It went through citations from the official benchmark paper. Found OpenScience and NemoTron-CrossThink, added 7 difficulty-filtered dataset variants from ARC/SciQ/MMLU, and ran 12 SFT runs on Qwen3-1.7B. This pushed the score 10% → 32% on GPQA in under 10h. Claude Code's best: 22.99%. In healthcare settings it inspected available datasets, concluded they were too low quality, and wrote a script to generate 1100 synthetic data points from scratch for emergencies, hedging, multilingual etc. Then upsampled 50x for training. Beat Codex on HealthBench by 60%. For competitive mathematics, it wrote a full GRPO script, launched training with A100 GPUs on watched rewards claim and then collapse, and ran ablations until it succeeded. All fully backed by papers, autonomously. How it works? ml-intern makes full use of the HF ecosystem: - finds papers on arxiv and reads them fully, walks citation graphs, pulls datasets referenced in methodology sections and on - browses the Hub, reads recent docs, inspects datasets and reformats them before training so it doesn't waste GPU hours on bad data - launches training jobs on HF Jobs if no local GPUs are available, monitors runs, reads its own eval outputs, diagnoses failures, retrains ml-intern deeply embodies how researchers work and think. It knows how data should look like and what good models feel like. Releasing it today as a CLI and a web app you can use from your phone/desktop. CLI: Web + mobile: And the best part? We also provisioned 1k$ GPU resources and Anthropic credits for the quickest among you to use.

Aksel

1,267,501 просмотров • 4 месяцев назад

We are launching A first attempt at building a digital theme park for the Zama Protocol. Our thesis is simple: to accelerate the adoption of FHE, we need cool apps designed for real people. Users don't adopt protocols, they adopt experiences. In most ecosystems, there are very few places that turn participation into status and fun. Usage is transactional, not emotional. Adoption stalls. zashapon is always open, accessible, social by default, and adding infinite attractions over time. It's a playful layer that makes the ecosystem feel like… an ecosystem. The mechanics are simple: - You make FHE transactions by using confidential apps - You get tickets - You can spend them in Zashapon Participation → access → rewards. Zashapon is creating a flywheel: More FHEVM usage → more tickets → more play → more rewards → more users → more builders → more FHEVM usage. Why “gashapon” machines? Because "chance + collection + ritual" is one of the oldest, most reliable engagement loops in games and theme parks. It creates stories users want to repeat. Zashapon uses FHERand to make sure all draws are programmatically fair and confidential. Fun without trust tradeoffs. What you get today: NFTs. What you get tomorrow: anything that can be digitally rewarded — access, perks, whitelists, lore items, partner drops, even real-world benefits. Zashapon is the entertainment / loyalty / identity layer for confidential applications building on top of the Zama Protocol. In physical theme parks: you ride → you collect → you show → you come back with friends. In Zashapon: you use confidential apps → you earn tickets → you draw → you flex / trade / build → you return. We're launching with one Gashapon machine, rewarding the users of Soon there will be many more — one for every new app, partner, and experience built on the Zama Protocol. Digital parks win by expanding attractions continuously. Zashapon is live on Zama testnet right now. Go try it now!

We are launching A first attempt at building a digital theme park for the Zama Protocol. Our thesis is simple: to accelerate the adoption of FHE, we need cool apps designed for real people. Users don't adopt protocols, they adopt experiences. In most ecosystems, there are very few places that turn participation into status and fun. Usage is transactional, not emotional. Adoption stalls. zashapon is always open, accessible, social by default, and adding infinite attractions over time. It's a playful layer that makes the ecosystem feel like… an ecosystem. The mechanics are simple: - You make FHE transactions by using confidential apps - You get tickets - You can spend them in Zashapon Participation → access → rewards. Zashapon is creating a flywheel: More FHEVM usage → more tickets → more play → more rewards → more users → more builders → more FHEVM usage. Why “gashapon” machines? Because "chance + collection + ritual" is one of the oldest, most reliable engagement loops in games and theme parks. It creates stories users want to repeat. Zashapon uses FHERand to make sure all draws are programmatically fair and confidential. Fun without trust tradeoffs. What you get today: NFTs. What you get tomorrow: anything that can be digitally rewarded — access, perks, whitelists, lore items, partner drops, even real-world benefits. Zashapon is the entertainment / loyalty / identity layer for confidential applications building on top of the Zama Protocol. In physical theme parks: you ride → you collect → you show → you come back with friends. In Zashapon: you use confidential apps → you earn tickets → you draw → you flex / trade / build → you return. We're launching with one Gashapon machine, rewarding the users of Soon there will be many more — one for every new app, partner, and experience built on the Zama Protocol. Digital parks win by expanding attractions continuously. Zashapon is live on Zama testnet right now. Go try it now!

Soliton

84,817 просмотров • 9 месяцев назад

For new followers: - I'm a long-time investor and builder in this space. - Founding Contributor of Realms.World ☁️. - Co-founder of Dojo. - Builder with the kings at Cartridge. - Starknet (Privacy Arc) class of '21. - Founder and Game Director of ETERNUM HAS MOVED. - Founder of Daydreams.Systems (x402, 8004 agents) My prime purpose for the past three years has been to build onchain infrastructure to enable the next generation of onchain experiences. This is done Starknet (Privacy Arc) as it is the superior VM for building complex applications—this will become clear soon enough. I work up and down the entire stack, from low-level indexing and contracts to GUI design. Nothing is out of scope. I have been pushing on agents for two years, mostly using existing frameworks like , until I came across @ElizaOS_ai in October. As I focused on building agents for ETERNUM HAS MOVED, it became clear that agents playing games require infinite paths to achieve goals. Thus, it's not scalable to hardcode functions—agents need to have total fluidity to take any action or call anything the game requires in any order. And ironically onchain infra is perfect for agent playgrounds because of its open nature. This exploration led me to create Daydreams.Systems (x402, 8004 agents), which focuses on the hardest problems of agents: long time-horizon goals using Hierarchical task networks (HTN). Daydreams agents don't require custom code—they work entirely based on 'sleeves'—which are just markdown files that explain how the agent can interact with the service (API docs, game guides, etc.) My thesis is simple. By focusing on the hardest problem (games), the design of the library will naturally lean towards an optimal structure for any problem an agent could face. We are early in this path and iterating with speed. If you are an onchain app developer or game builder—DM me, I want to know the architecture of your game so we can build sleeves together.

For new followers: - I'm a long-time investor and builder in this space. - Founding Contributor of Realms.World ☁️. - Co-founder of Dojo. - Builder with the kings at Cartridge. - Starknet (Privacy Arc) class of '21. - Founder and Game Director of ETERNUM HAS MOVED. - Founder of Daydreams.Systems (x402, 8004 agents) My prime purpose for the past three years has been to build onchain infrastructure to enable the next generation of onchain experiences. This is done Starknet (Privacy Arc) as it is the superior VM for building complex applications—this will become clear soon enough. I work up and down the entire stack, from low-level indexing and contracts to GUI design. Nothing is out of scope. I have been pushing on agents for two years, mostly using existing frameworks like , until I came across @ElizaOS_ai in October. As I focused on building agents for ETERNUM HAS MOVED, it became clear that agents playing games require infinite paths to achieve goals. Thus, it's not scalable to hardcode functions—agents need to have total fluidity to take any action or call anything the game requires in any order. And ironically onchain infra is perfect for agent playgrounds because of its open nature. This exploration led me to create Daydreams.Systems (x402, 8004 agents), which focuses on the hardest problems of agents: long time-horizon goals using Hierarchical task networks (HTN). Daydreams agents don't require custom code—they work entirely based on 'sleeves'—which are just markdown files that explain how the agent can interact with the service (API docs, game guides, etc.) My thesis is simple. By focusing on the hardest problem (games), the design of the library will naturally lean towards an optimal structure for any problem an agent could face. We are early in this path and iterating with speed. If you are an onchain app developer or game builder—DM me, I want to know the architecture of your game so we can build sleeves together.

loaf

43,320 просмотров • 1 год назад

Wow. WOW. WOOOOOOOW. So um, that first Wildcard+Thousands stream was... amazing and also... a lot 😅 In the end, it was exactly what we were hoping for - a true stress test of ALL these systems coming together for the first time. We are SO grateful for the thousands of people who showed up today to play, attend, tune-in and help us PLAYTEST all this new stuff. We can't wait to see you all again at NEXT WEEK'S EX2 EVENT! So, now let's talk about how it went... Stuff that worked: - Our community SHOWED UP. Oh boy did you show up 😅. Our servers were straining under the load... which is good actually, in fact it's the whole point. Even more importantly, we have already received insanely valuable feedback, bug reports, stuff people loved/hated - and it's only been a few hours since the stream ended. I can't even explain to y'all how valuable this process is. Yes it's stressful, it reminds me of trying to keep Words With Friends online during that first insane year, but it's EXACTLY what we were hoping for (NEED) to turn this into the polished, top-notch game and streaming experience we are on a mission to deliver. I truly can't thank y'all enough, and hope to see you again when we run it all back again next week 🥹 - The stream itself stayed up and was mostly stable! Phew 😅. For context, ThousandsTV is not a twitch wrapper, it's a web3-native streaming tech stack built we built specifically to connect game, web/mobile, and blockchain together all at the same time. There are a LOT of moving pieces going on behind the scenes. - We brought viewers INTO THE GAME! Viewers showed up in the stands of the arena, with connected wallets/assets, triggered actions/rallies from chat, and were seen and heard during the whole stream. - The brand new 2v2 build of Wildcard was (mostly) stable and our players and viewers seemed to be having a blast down on the field and up in the stands. It was thrilling to watch Team Blue dominate, even though Team Red held their own in game 3! - Our production crew did an insanely good job running the stream, managing the players, shoutcasters, and talent, and producing a top-notch show. Of course we will work hard to make every stream better than the last, but I was super proud of how our team "rolled with the punches" during today's event. Stuff that didn't work (and/or needs to be dramatically improved): - Although it's fun to see chat going crazy, chat spam is actually something we are passionate to FIX. As you can see from the attached video, chat spam dominated today's stream and made it impossible for anyone to even see anyone else's messages. We have some GREAT ideas for how to fix this and actually turn chat spam into a FUN and exciting and not annoying thing - but those improvements didn't get shipped in time for this event. - Credits purchasing flow needs a LOT of work. As I'm sure y'all know, bringing money on chain is pretty complicated, and although we've been working hard to make this as seamless as possible, it still needs a TON of improvements. Many users who WANTED to spend money today weren't able to and/or ran into frustrating bugs in the credit purchase flow. Fixing this is obviously a top priority for our team. - Rallies need a LOT of work. Spectator-interactive features like rallies are at the heart of our vision for Wildcard. These "stream apps", as we call them, are the UNLOCK for how spectators, viewers and fans directly connect and interact with their favorite competitors, content creators, and communities. The current rally feature HINTS at this potential, but it needs to be WAY easier to understand, use, and have fun with. Improvements are ON THE WAY. - Referees were only partially working. Referees are a key innovation of the Thousands platform. They are AI-driven "personalities" (NPCs) that pay attention to everything that's happening in the arena, both on the field and especially in the stands (i.e. in chat, during rallies, etc.) The referees then make "calls" at the end of every match, rewarding users for their engagement and participation. Unfortunately, the referees weren't fully functional and seemed to drop the ball on recognizing everyone's contributions (especially people who showed up holding valuable assets such as Wildpasses in their wallets, and people who boosted those rallies with credits.) What's happening next: 1. We are combing through ALL the logs from the event right now, to make sure we don't miss a SINGLE action that our viewers and fans took during the event, including what they brought in their wallets (i.e. Wildpass holders!), any credits that were purchased, rallies that users engaged with, etc. This information is normally processed by our referees, who then determine dynamically how they're going to distribute $WC awards. We were originally hoping to complete this process and the subsequent airdrops within a few hours after the event, but given the amount of data, we need a bit more time to run these scripts (and airdrops) in batches instead of all at once, and make sure ALL of the data is being included. IMPORTANT: I will keep y'all updated in real-time here on twitter/X as this process is ongoing, and let you know the moment it's complete and all the awards have been distributed (i.e. when to go check your wallets 😎) 2. PLEASE keep sending us your feedback and bug reports. Open a ticket on our Discord and let us know what you loved, what you hated, and especially what we need to FIX. Given the overwhelming response to this event, it will likely take us several days to process everyone's feedback and fix all the bugs, but we WILL NOT REST until every ticket is closed/resolved. Thank you in advance for your patience. 3. We turn it up another notch next week. As our dear friends Wolves DAO just announced, the Wildcard Exhibition Event #2 is streaming LIVE from the WOLVES DEN AT GDC next Friday! If you missed out on all the action today, DON'T WORRY, because as I keep saying: we are just getting warmed up (and there is a LOT more b that needs to be distributed, get what I'm sayin??? 😎) Finally: Just wanted to say THANK YOU, again. Truly, from the bottom of my (our) hearts. Your excitement and enthusiasm for what we're building is why we do this. Even (especially, in fact) when you tell us all the things you want us to improve. We thrive off this feedback, it's how this game and this platform go from good to GREAT. I am so grateful for those of you who are taking this journey with us. SEE YA NEXT WEEK!!!

WildPaul - BEAST MODE

26,851 просмотров • 1 год назад

introducing a new, very fun, LLM benchmark- the Game-of-Life Bench! the rules are simple: given an 8x8 grid following Conway's game of life rules, the goal is to create an initial pattern with at most 32 cells that can last the longest number of turns before dying/repeating. some results to highlight (with caveats detailed below): - gpt 5.1 lasts the longest with a 106 step run - claude models are really bad at this! they refuse to reason about this task and score < 25 points - deepseek r1 is the best open model with 102 steps. why? because i wanted to create a benchmark that has (i think) no practicality, but is still fun to look at, cheap, and still measures something interesting. i also am a big fan of the game of life. its absurdly simple rules leading to intractability is extremely cool to me. also, i saw a lot of work with LLMs trying to "predict" the next state in Conway's game of life, I think game-of-life bench is more fun because it's pretty open ended and only asks the LLM for the initial state. I also think this could be an RL env? but idk why you would ever train on this task haha i don't think this is a "serious" benchmark because it doesnt measure anything practical, but i still think it's a hard benchmark exactly because you can't predict what happens with your initial state many turns into the future; this is why i was initially expecting all LLMs to be bad at it, but turns out, some are clearly better than the others (the ordering may surprise you!) reminder: this is still a work-in-progress; (1) i am gpu-poor so could only do 10 runs for each model, even though total running cost is relatively low. maybe with some more credits i can run more seeds for each model. (2) i handpicked models which i think are at the frontier right now, plus some others that were on my mind. so, if you'd like to see a model on here, let me know. (3) i currently only do an 8x8 grid because i thought that by itself would be pretty hard for current LLMs, but of course we can increase grid sizes! (4) the coolest thing is, i dont think we can calculate the max possible number of states (yay undecidability!) you can go without repeating, so this is essentially a no-ceiling task, which is pretty cool! again, i did this mostly out of a desire to make LLMs do something fun. if this keeps me entertained for a few more days, i'd likely release a blog post on it. if it keeps me entertained for a week (and someone sponsors me), i'll put more work into it :P lastly, this is fully open sourced, so feel free to run this on your own!

introducing a new, very fun, LLM benchmark- the Game-of-Life Bench! the rules are simple: given an 8x8 grid following Conway's game of life rules, the goal is to create an initial pattern with at most 32 cells that can last the longest number of turns before dying/repeating. some results to highlight (with caveats detailed below): - gpt 5.1 lasts the longest with a 106 step run - claude models are really bad at this! they refuse to reason about this task and score < 25 points - deepseek r1 is the best open model with 102 steps. why? because i wanted to create a benchmark that has (i think) no practicality, but is still fun to look at, cheap, and still measures something interesting. i also am a big fan of the game of life. its absurdly simple rules leading to intractability is extremely cool to me. also, i saw a lot of work with LLMs trying to "predict" the next state in Conway's game of life, I think game-of-life bench is more fun because it's pretty open ended and only asks the LLM for the initial state. I also think this could be an RL env? but idk why you would ever train on this task haha i don't think this is a "serious" benchmark because it doesnt measure anything practical, but i still think it's a hard benchmark exactly because you can't predict what happens with your initial state many turns into the future; this is why i was initially expecting all LLMs to be bad at it, but turns out, some are clearly better than the others (the ordering may surprise you!) reminder: this is still a work-in-progress; (1) i am gpu-poor so could only do 10 runs for each model, even though total running cost is relatively low. maybe with some more credits i can run more seeds for each model. (2) i handpicked models which i think are at the frontier right now, plus some others that were on my mind. so, if you'd like to see a model on here, let me know. (3) i currently only do an 8x8 grid because i thought that by itself would be pretty hard for current LLMs, but of course we can increase grid sizes! (4) the coolest thing is, i dont think we can calculate the max possible number of states (yay undecidability!) you can go without repeating, so this is essentially a no-ceiling task, which is pretty cool! again, i did this mostly out of a desire to make LLMs do something fun. if this keeps me entertained for a few more days, i'd likely release a blog post on it. if it keeps me entertained for a week (and someone sponsors me), i'll put more work into it :P lastly, this is fully open sourced, so feel free to run this on your own!

Akshit

13,775 просмотров • 6 месяцев назад

HIGGSFIELD + FABLE 5 MADE A SITE THAT LOOKS LIKE A $35,000 STUDIO JOB FOR $12. NOBODY'S CLOCKED IT YET. the cheap giveaway of an AI-built site is always the same: stocky visuals, dead scroll, zero polish. this skips all three. what actually makes it read as $35k: → REAL MOTION, NOT STOCK the clips come from 30+ generative models, matched to your story - not the same three stock loops everyone's already seen. → SCROLL WITH WEIGHT GSAP ScrollTrigger + Lenis give it pacing and feel - pinned sections, scrubbed video, reveals that land. this is the thing that makes expensive sites feel expensive. → THE CINEMATIC LAYER film grain, particles, vignette, glass cards, color tints - baked in, no config. it's the polish agencies charge a designer for. → TASTE IS THE INPUT the AI executes; you direct. point real judgment at the stack and it ships premium. point lazy prompts at it and yeah - you get slop. that part's on you. CONNECT HIGGSFIELD (MCP): add it as a custom connector in Claude Code: - mcp_servers: - higgsfield: - url: " one OAuth flow. Claude generates and pulls clips directly - no exporting by hand. THE MATH: → what a studio charges: $6,000-$35,000+ → what it costs you: a Claude sub + a few dollars of Higgsfield credits → what it takes: 4 people + 3 weeks → 1 operator + 1 session the tools were never the bottleneck. taste was. now taste is the only input left. Follow me, reply "LAUNCH" and I'll send you the full step-by-step Playbook. full breakdown in the article 👇

HIGGSFIELD + FABLE 5 MADE A SITE THAT LOOKS LIKE A $35,000 STUDIO JOB FOR $12. NOBODY'S CLOCKED IT YET. the cheap giveaway of an AI-built site is always the same: stocky visuals, dead scroll, zero polish. this skips all three. what actually makes it read as $35k: → REAL MOTION, NOT STOCK the clips come from 30+ generative models, matched to your story - not the same three stock loops everyone's already seen. → SCROLL WITH WEIGHT GSAP ScrollTrigger + Lenis give it pacing and feel - pinned sections, scrubbed video, reveals that land. this is the thing that makes expensive sites feel expensive. → THE CINEMATIC LAYER film grain, particles, vignette, glass cards, color tints - baked in, no config. it's the polish agencies charge a designer for. → TASTE IS THE INPUT the AI executes; you direct. point real judgment at the stack and it ships premium. point lazy prompts at it and yeah - you get slop. that part's on you. CONNECT HIGGSFIELD (MCP): add it as a custom connector in Claude Code: - mcp_servers: - higgsfield: - url: " one OAuth flow. Claude generates and pulls clips directly - no exporting by hand. THE MATH: → what a studio charges: $6,000-$35,000+ → what it costs you: a Claude sub + a few dollars of Higgsfield credits → what it takes: 4 people + 3 weeks → 1 operator + 1 session the tools were never the bottleneck. taste was. now taste is the only input left. Follow me, reply "LAUNCH" and I'll send you the full step-by-step Playbook. full breakdown in the article 👇

ZEUS⚡️

80,598 просмотров • 1 месяц назад

I just sold my startup Talknotes for $200,000 on acquire.com 💸🤯🤩💰🥳🎉 I launched it last August when I was looking for an idea I could grow with paid ads, and made a MVP in one week. I took it from $0 to $7500 MRR in just 11 months. 👉 Here is how I grew it from zero: 💡 Idea: I got the idea when I tried to write a tweet using Google Doc's transcription tool, but it was terrible. And I was pretty sure I wasn't the one too lazy to type. So I made my own solution, and Talknotes was created. The audience is pretty broad so it was a perfect fit for Meta ads However… ✅ Validation: My rule is to only reinvest what the project generates, so, no ads until I make enough cashflow ❌ Listing on startup directories + a few Twitter sales generated $700 after 10 days. Yes, it's not much, but more than enough to show there is interest in the product and tell me to keep working on it 🤩 I started adding the features users requested, but the launch effect started to wear off and daily revenues quickly went to $0 after a few weeks 🫥 I got depressed and almost gave up on the app... 😔 But luckily, my friends and Dan Kulkov pushed me to continue And I'm glad they did because In October, I launched on Product Hunt 😸 and it blew up 🤯 It got Product of the Day and reached $1500 MRR thanks to the media coverage 🚀🚀 Until then, everything was done using vanilla JS/CSS/HTML + Node for back end. It's simple and easy, but I saw the limitations, so I remade the app using Nuxt to make it easier in the future 🏗️ (thanks to @blackevilgoblin and Piotr Jura for the content/courses! Tim Bennetto as well for the basics!) After that, I took a break and then launched ads on Facebook. The strategy is simple: Catch people's attention, and show them how the app can help them improve their life. No need to over-complicate 🙅‍♂️ Making good creatives is 80% of the job when doing ads on Facebook, most of the technical stuff is done by AI now. Thanks to the boost in traffic, I implemented a feedback loop: 1) Get new users 👥 2) Learn to know them with the onboarding form 💬 3) Make more ads based on the data you get from onboarding 📝 And it completely blew up. MRR doubled in ~2 months However... In May, I had a bad burnout 🥵😩 Multiple bugs slipped into the app, and I had to spend 2 days fixing everything in an emergency while revenues plummeted. This completely fucked me up mentally and had a hard time working on the app after that ( 💀💀 So I decided to list it on acquire.com and made a Twitter post ( I listed it for $200,000, a pretty low price considering the revenues and fast growth. I could have gotten $300,000 if I accepted payment over time, but $200,000 today is better than $300,000 tomorrow for me. 🚨 The process went smoothly until we tried to use Escrow, which almost fucked up the whole deal. (details: I got extremely lucky because the buyer really wanted to buy the app, but this could have ended the deal. We had to wait over a week to get the money back from them, even tho they said they already refunded it. But luckily, after threatening them, they sent it back the next day 🙃 The buyer finally got the money back, I transferred every asset to him, and he sent me the wire. With the profit made from the app + the sale, and other projects, I'm 30% away from being a millionaire 🤯 With this amount, I can pretty much retire in Asia if I want to. But that's just the beginning, I’m going to launch new projects soon! 🚀 But before that, I need to take a real vacation and detox. My brain is completely fucked up by those last 2 months. I gained weight, and got brain rot from scrolling all day waiting for the acquisition to move forward 💀💀 Surprisingly, doing absolutely nothing is 10x more exhausting than working 15h per day 🥱 Now, all this might sound like an overnight success. It is not ‼️ This is the result of 7 years of failure and working like a madman. I launched over 40 projects in those 7 years, and most of them failed. But a few took off, and that’s all I needed All those weeks working 15h/day without weekends and vacation feels soul-sucking when you don’t see the end, but this is what took me there You only need to win once to snowball everything. Work hard, focus, fail a lot and keep shipping fast. 🚀🚀 Thanks to you for reading until here, and thanks to everyone who supported me 🤞

I just sold my startup Talknotes for $200,000 on acquire.com 💸🤯🤩💰🥳🎉 I launched it last August when I was looking for an idea I could grow with paid ads, and made a MVP in one week. I took it from $0 to $7500 MRR in just 11 months. 👉 Here is how I grew it from zero: 💡 Idea: I got the idea when I tried to write a tweet using Google Doc's transcription tool, but it was terrible. And I was pretty sure I wasn't the one too lazy to type. So I made my own solution, and Talknotes was created. The audience is pretty broad so it was a perfect fit for Meta ads However… ✅ Validation: My rule is to only reinvest what the project generates, so, no ads until I make enough cashflow ❌ Listing on startup directories + a few Twitter sales generated $700 after 10 days. Yes, it's not much, but more than enough to show there is interest in the product and tell me to keep working on it 🤩 I started adding the features users requested, but the launch effect started to wear off and daily revenues quickly went to $0 after a few weeks 🫥 I got depressed and almost gave up on the app... 😔 But luckily, my friends and Dan Kulkov pushed me to continue And I'm glad they did because In October, I launched on Product Hunt 😸 and it blew up 🤯 It got Product of the Day and reached $1500 MRR thanks to the media coverage 🚀🚀 Until then, everything was done using vanilla JS/CSS/HTML + Node for back end. It's simple and easy, but I saw the limitations, so I remade the app using Nuxt to make it easier in the future 🏗️ (thanks to @blackevilgoblin and Piotr Jura for the content/courses! Tim Bennetto as well for the basics!) After that, I took a break and then launched ads on Facebook. The strategy is simple: Catch people's attention, and show them how the app can help them improve their life. No need to over-complicate 🙅‍♂️ Making good creatives is 80% of the job when doing ads on Facebook, most of the technical stuff is done by AI now. Thanks to the boost in traffic, I implemented a feedback loop: 1) Get new users 👥 2) Learn to know them with the onboarding form 💬 3) Make more ads based on the data you get from onboarding 📝 And it completely blew up. MRR doubled in ~2 months However... In May, I had a bad burnout 🥵😩 Multiple bugs slipped into the app, and I had to spend 2 days fixing everything in an emergency while revenues plummeted. This completely fucked me up mentally and had a hard time working on the app after that ( 💀💀 So I decided to list it on acquire.com and made a Twitter post ( I listed it for $200,000, a pretty low price considering the revenues and fast growth. I could have gotten $300,000 if I accepted payment over time, but $200,000 today is better than $300,000 tomorrow for me. 🚨 The process went smoothly until we tried to use Escrow, which almost fucked up the whole deal. (details: I got extremely lucky because the buyer really wanted to buy the app, but this could have ended the deal. We had to wait over a week to get the money back from them, even tho they said they already refunded it. But luckily, after threatening them, they sent it back the next day 🙃 The buyer finally got the money back, I transferred every asset to him, and he sent me the wire. With the profit made from the app + the sale, and other projects, I'm 30% away from being a millionaire 🤯 With this amount, I can pretty much retire in Asia if I want to. But that's just the beginning, I’m going to launch new projects soon! 🚀 But before that, I need to take a real vacation and detox. My brain is completely fucked up by those last 2 months. I gained weight, and got brain rot from scrolling all day waiting for the acquisition to move forward 💀💀 Surprisingly, doing absolutely nothing is 10x more exhausting than working 15h per day 🥱 Now, all this might sound like an overnight success. It is not ‼️ This is the result of 7 years of failure and working like a madman. I launched over 40 projects in those 7 years, and most of them failed. But a few took off, and that’s all I needed All those weeks working 15h/day without weekends and vacation feels soul-sucking when you don’t see the end, but this is what took me there You only need to win once to snowball everything. Work hard, focus, fail a lot and keep shipping fast. 🚀🚀 Thanks to you for reading until here, and thanks to everyone who supported me 🤞

Nico

458,235 просмотров • 2 лет назад