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Claude → CRT. Built a procedural sci-fi interface generator in Three.js + TypeScript that outputs animated HUD compositions from modular elements—radar sweeps, waveforms, data cascades, ring gauges—arranged via BSP-subdivided layouts.

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Purz.ai

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28,277 görüntüleme • 7 ay önce •via X (Twitter)

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Day 3/30 I built Archie - an AI-powered procedural architecture generator. Describe a building in natural language and Claude interprets it into a 25-parameter genome that drives a Three.js 3D generator in real-time. Breed variants through genetic crossover, tweak every parameter from chaos to verticality, and export to GLTF for Blender/Rhino/Unreal. Features: - Text-to-architecture via Claude - Evolutionary breeding with a 4-variant selection grid - Batch generation of 8+ variants from a single prompt - AI critique that references real architectural movements - 6 style presets (Deconstructivist, Brutalist, Metabolist, Hi-Tech, Parametric, Minimal) - 6 environment moods with dynamic lighting and fog - GLTF + PNG export
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Day 3/30 I built Archie - an AI-powered procedural architecture generator. Describe a building in natural language and Claude interprets it into a 25-parameter genome that drives a Three.js 3D generator in real-time. Breed variants through genetic crossover, tweak every parameter from chaos to verticality, and export to GLTF for Blender/Rhino/Unreal. Features: - Text-to-architecture via Claude - Evolutionary breeding with a 4-variant selection grid - Batch generation of 8+ variants from a single prompt - AI critique that references real architectural movements - 6 style presets (Deconstructivist, Brutalist, Metabolist, Hi-Tech, Parametric, Minimal) - 6 environment moods with dynamic lighting and fog - GLTF + PNG export

vc

15,761 görüntüleme • 8 ay önce

Claude Fable 5 just one shot this procedural creatures! 🤯 Built a whole procedural 3D character generator in Three.js with: > Ragdoll-like characters made from primitive shapes that merge into one seamless body > Custom vertex shader + SDF tech that removes all seams and blends normals/colors perfectly > Fully procedural animation system that works for 2 legs, 4 legs, 6 legs, hopping, or flying > Physics-based tails/ears that stay seamlessly fused > Entire character defined in ~15 lines of JSON (AI can generate infinite new ones) PROMPT: Build with Three.js and explore a new procedurally generated 3D character style ragdoll-like characters composed of primitive shapes that look like one seamless body via a custom, mobile-performant tech-art/shader solution, with toon styling, plus a procedural animation system that works for 2-legged, any-legged, no-legged (hopping) and flying characters with arms. Goal: a unique, easily AI-generatable style with heavy juice and polish "HOLY SH** THIS WAS DONE WITH AI?" quality, "for the next billion dollar game."
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Claude Fable 5 just one shot this procedural creatures! 🤯 Built a whole procedural 3D character generator in Three.js with: > Ragdoll-like characters made from primitive shapes that merge into one seamless body > Custom vertex shader + SDF tech that removes all seams and blends normals/colors perfectly > Fully procedural animation system that works for 2 legs, 4 legs, 6 legs, hopping, or flying > Physics-based tails/ears that stay seamlessly fused > Entire character defined in ~15 lines of JSON (AI can generate infinite new ones) PROMPT: Build with Three.js and explore a new procedurally generated 3D character style ragdoll-like characters composed of primitive shapes that look like one seamless body via a custom, mobile-performant tech-art/shader solution, with toon styling, plus a procedural animation system that works for 2-legged, any-legged, no-legged (hopping) and flying characters with arms. Goal: a unique, easily AI-generatable style with heavy juice and polish "HOLY SH** THIS WAS DONE WITH AI?" quality, "for the next billion dollar game."

Tim Jayas

48,018 görüntüleme • 3 ay önce

A really impressive set of Three.js graphics experiments just got open sourced, and these are much more than little visual demos. They are basically reusable procedural systems for oceans, vegetation, fluids and even whole planets. 🔹 Poseidon A real-time FFT ocean running on WebGPU. It simulates large swells, smaller ripples, foam, reflections, choppy displacement and physically inspired wave spectra entirely in the browser. 🔹 Gaia A procedural grass generator where every blade, seed head and field comes from a deterministic genome and environmental parameters. No authored grass models. 🔹 Dryad A procedural flora system that generates trees and other plant forms from physics, environmental conditions and a seed. No authored 3D models or textures are needed for the plants themselves. 🔹 Tiamat A real-time GPU fluid simulation using around 100,000 SPH particles, with the resulting water rendered directly in the browser. 🔹 Demiurge Probably the craziest one. It procedurally builds an entire planet from tectonic plates, then lets uplift drive erosion, erosion and latitude drive climate, and climate drive biomes, wind and weather. You can move seamlessly from orbit down to the surface. What I really like here is that these are not just pretty outputs. They are actual building blocks. Ocean simulation, vegetation generation, fluid dynamics and procedural worlds are exactly the kinds of systems that can be plugged into games, simulations and agent-built 3D environments. Project by: Owen
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A really impressive set of Three.js graphics experiments just got open sourced, and these are much more than little visual demos. They are basically reusable procedural systems for oceans, vegetation, fluids and even whole planets. 🔹 Poseidon A real-time FFT ocean running on WebGPU. It simulates large swells, smaller ripples, foam, reflections, choppy displacement and physically inspired wave spectra entirely in the browser. 🔹 Gaia A procedural grass generator where every blade, seed head and field comes from a deterministic genome and environmental parameters. No authored grass models. 🔹 Dryad A procedural flora system that generates trees and other plant forms from physics, environmental conditions and a seed. No authored 3D models or textures are needed for the plants themselves. 🔹 Tiamat A real-time GPU fluid simulation using around 100,000 SPH particles, with the resulting water rendered directly in the browser. 🔹 Demiurge Probably the craziest one. It procedurally builds an entire planet from tectonic plates, then lets uplift drive erosion, erosion and latitude drive climate, and climate drive biomes, wind and weather. You can move seamlessly from orbit down to the surface. What I really like here is that these are not just pretty outputs. They are actual building blocks. Ocean simulation, vegetation generation, fluid dynamics and procedural worlds are exactly the kinds of systems that can be plugged into games, simulations and agent-built 3D environments. Project by: Owen

Token Gremlin

34,448 görüntüleme • 2 ay önce

GLM-5.1 > Claude Code (Opus 4.6)? I'm tripping or CC has become very bad but built a Three.js racing game to eval and it's extremely impressive. Thoughts: - One-shot car physics with real drift mechanics (this is hard) - My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen - 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters - All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio! - Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed You are going to hear about this model a lot in the next months - open source let's go 🚀🚀
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GLM-5.1 > Claude Code (Opus 4.6)? I'm tripping or CC has become very bad but built a Three.js racing game to eval and it's extremely impressive. Thoughts: - One-shot car physics with real drift mechanics (this is hard) - My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen - 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters - All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio! - Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed You are going to hear about this model a lot in the next months - open source let's go 🚀🚀

Victor M

202,661 görüntüleme • 6 ay önce

We've been obsessing a lot about creating the best possible AI support experience Dub.co. And I think we've found the magic formula. The stack: ✦ Mintlify → docs + help articles ✦ Upstash vector → vector search ✦ AI SDK + Claude sonnet → LLM/retrieval ✦ Plain → support infra Here's how it works: We consolidated our docs + help articles on Mintlify (best-in-class docs platform) so all our support content is now in one place. On every push to main, we sync changes to our Upstash Vector DB. We also built a custom chat interface that retrieves content from Upstash Vector and generates responses via AI SDK + Claude sonnet. The best part: The AI agent uses 𝚝𝚘𝚘𝚕() from AI SDK to retrieve the user's workspace / partner data to provide accurate responses. Finally, if the user needs to speak to a human, we generate a 𝚃𝚒𝚌𝚔𝚎𝚝𝚄𝚙𝚕𝚘𝚊𝚍 UI that lets users upload screenshots + add more context, before creating a ticket on Plain. Cherry on top: The chat interface is embeddable, so we're able to embed it both in our Mintlify docs + support portal 👇 Since we launched this, it's been a gamechanger for scaling support – reducing support workload and creating a much better experience for our users. Highly recommend giving this a try!
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We've been obsessing a lot about creating the best possible AI support experience Dub.co. And I think we've found the magic formula. The stack: ✦ Mintlify → docs + help articles ✦ Upstash vector → vector search ✦ AI SDK + Claude sonnet → LLM/retrieval ✦ Plain → support infra Here's how it works: We consolidated our docs + help articles on Mintlify (best-in-class docs platform) so all our support content is now in one place. On every push to main, we sync changes to our Upstash Vector DB. We also built a custom chat interface that retrieves content from Upstash Vector and generates responses via AI SDK + Claude sonnet. The best part: The AI agent uses 𝚝𝚘𝚘𝚕() from AI SDK to retrieve the user's workspace / partner data to provide accurate responses. Finally, if the user needs to speak to a human, we generate a 𝚃𝚒𝚌𝚔𝚎𝚝𝚄𝚙𝚕𝚘𝚊𝚍 UI that lets users upload screenshots + add more context, before creating a ticket on Plain. Cherry on top: The chat interface is embeddable, so we're able to embed it both in our Mintlify docs + support portal 👇 Since we launched this, it's been a gamechanger for scaling support – reducing support workload and creating a much better experience for our users. Highly recommend giving this a try!

Steven Tey

23,029 görüntüleme • 7 ay önce

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.
1:24

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.

Kingnet AI

46,549 görüntüleme • 1 yıl önce

this is pure f*cking treasure for motion design 20 GitHub repos to build a motion design studio with Opus 5.5 pick the tools for each layer: agent skills, animation, 3D, assets, sound and export 01 Remotion → turn React code into editable videos 02 Remotion Skills → give Claude Code official video workflows 03 Motion Canvas → animate diagrams and explainers in TypeScript 04 Motion AI Kit → give Claude Code Motion docs and guidance 05 Motion → animate live UI layouts and gestures 06 GSAP → sequence SVG and text with precise timelines 07 Anime.js → animate SVG paths, text and interface elements 08 GSAP Skills → teach Claude Code timelines and React cleanup 09 Rive Web → connect vector animations to app states 10 dotLottie Web → play and control Lottie animation assets 11 Three.js → build 3D shots with cameras and lighting 12 React Three Fiber → build 3D scenes as React components 13 Drei → reuse 3D cameras, text and controls 14 React Three Rapier → add collisions, joints and rigid bodies 15 React Postprocessing → add bloom, depth of field and finishing effects 16 SVGO → clean SVG files before animating 17 Satori → generate SVG title cards from JSX 18 Howler.js → control sound effects and fades in web demos 19 Mediabunny → encode and convert video in the browser 20 FFmpeg → encode, resize and inspect final exports the loop: brief → type → animate → build the scene → add sound → render → review → ship save this + read the article below to build your own motion design studio ⭣
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this is pure f*cking treasure for motion design 20 GitHub repos to build a motion design studio with Opus 5.5 pick the tools for each layer: agent skills, animation, 3D, assets, sound and export 01 Remotion → turn React code into editable videos 02 Remotion Skills → give Claude Code official video workflows 03 Motion Canvas → animate diagrams and explainers in TypeScript 04 Motion AI Kit → give Claude Code Motion docs and guidance 05 Motion → animate live UI layouts and gestures 06 GSAP → sequence SVG and text with precise timelines 07 Anime.js → animate SVG paths, text and interface elements 08 GSAP Skills → teach Claude Code timelines and React cleanup 09 Rive Web → connect vector animations to app states 10 dotLottie Web → play and control Lottie animation assets 11 Three.js → build 3D shots with cameras and lighting 12 React Three Fiber → build 3D scenes as React components 13 Drei → reuse 3D cameras, text and controls 14 React Three Rapier → add collisions, joints and rigid bodies 15 React Postprocessing → add bloom, depth of field and finishing effects 16 SVGO → clean SVG files before animating 17 Satori → generate SVG title cards from JSX 18 Howler.js → control sound effects and fades in web demos 19 Mediabunny → encode and convert video in the browser 20 FFmpeg → encode, resize and inspect final exports the loop: brief → type → animate → build the scene → add sound → render → review → ship save this + read the article below to build your own motion design studio ⭣

beamnxw ./

25,674 görüntüleme • 11 gün önce

Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here:
2:12

Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here:

Andrew Ng

118,297 görüntüleme • 1 yıl önce

Anthropic released Claude Design TODAY and it's now accessible at I spent the last hour giving it a first look, and shared my thoughts and results in the video below. This is a BIG drop. This is a new design surface from Anthropic, and it changes what "AI design" means. Short version: Claude can now design. Not "describe a design." Not "generate an image of a design." Actual production work — prototypes, wireframes, high-fidelity mocks, slide decks, landing pages — editable, on-brand, and ready to hand off. Here's what stood out on first look: → Real design surfaces Prototypes, wireframes, hi-fi, and slide decks — each with templates and proper structure, not just pretty screenshots. → Comment-based edits Leave a comment on any element and Claude revises it. This is the Figma-style review loop, with the designer replaced by a model that works at 3am. → Brand design systems You can feed it your system — colors, type, components — and it actually respects it. On-brand output, not generic AI slop. → Export anywhere PDF, PowerPoint, Canva, standalone HTML. Plus a built-in handoff straight to Claude Code for engineers to implement. → Import from real tools Figma, GitHub, and captured web elements come in as inputs. Your existing work is the starting line, not the discard pile. → Collaboration Share links for view / comment / edit — the exact tier system teams already expect. What I tested on Opus 4.7: • A 5-slide deck generated from a single screenshot. Claude asked clarifying questions BEFORE generating and shipped speaker notes by default. • A landing page build. Solid first pass, real components, real layout logic. • Multiple chats running concurrently. You can parallelize design work across threads like a small team. Why this matters: PMs, founders, marketers, and non-engineers can now create designs that engineers can actually ship with production-ready output and a claude code handoff built in. The gap between "I have an idea" and "here's a working prototype with my brand applied" just collapsed to minutes. Full walkthrough, live demos, exports, and honest takes on where it breaks below. P.S. • This is an Anthropic Labs product — NOT GA yet. • Claude Design is currently webapp only (no API), and does not yet support the Analytics API, Compliance API, or cost/usage reporting. • Availability: – Default ON for Pro / Max / Team – Default OFF for Enterprise Enterprise admins can toggle it on via RBAC in console (comes with a ~$20/user initial credit).
18:30

Anthropic released Claude Design TODAY and it's now accessible at I spent the last hour giving it a first look, and shared my thoughts and results in the video below. This is a BIG drop. This is a new design surface from Anthropic, and it changes what "AI design" means. Short version: Claude can now design. Not "describe a design." Not "generate an image of a design." Actual production work — prototypes, wireframes, high-fidelity mocks, slide decks, landing pages — editable, on-brand, and ready to hand off. Here's what stood out on first look: → Real design surfaces Prototypes, wireframes, hi-fi, and slide decks — each with templates and proper structure, not just pretty screenshots. → Comment-based edits Leave a comment on any element and Claude revises it. This is the Figma-style review loop, with the designer replaced by a model that works at 3am. → Brand design systems You can feed it your system — colors, type, components — and it actually respects it. On-brand output, not generic AI slop. → Export anywhere PDF, PowerPoint, Canva, standalone HTML. Plus a built-in handoff straight to Claude Code for engineers to implement. → Import from real tools Figma, GitHub, and captured web elements come in as inputs. Your existing work is the starting line, not the discard pile. → Collaboration Share links for view / comment / edit — the exact tier system teams already expect. What I tested on Opus 4.7: • A 5-slide deck generated from a single screenshot. Claude asked clarifying questions BEFORE generating and shipped speaker notes by default. • A landing page build. Solid first pass, real components, real layout logic. • Multiple chats running concurrently. You can parallelize design work across threads like a small team. Why this matters: PMs, founders, marketers, and non-engineers can now create designs that engineers can actually ship with production-ready output and a claude code handoff built in. The gap between "I have an idea" and "here's a working prototype with my brand applied" just collapsed to minutes. Full walkthrough, live demos, exports, and honest takes on where it breaks below. P.S. • This is an Anthropic Labs product — NOT GA yet. • Claude Design is currently webapp only (no API), and does not yet support the Analytics API, Compliance API, or cost/usage reporting. • Availability: – Default ON for Pro / Max / Team – Default OFF for Enterprise Enterprise admins can toggle it on via RBAC in console (comes with a ~$20/user initial credit).

JJ Englert

32,445 görüntüleme • 6 ay önce

Claude Code cannot read 300 files at once. So someone built a system that lets it control NotebookLM from the terminal instead. The results are wild. Here is the full workflow nobody is talking about: The Setup → Claude Code connects to NotebookLM via a command line interface → Claude searches YouTube, finds relevant videos, uploads them as sources automatically → NotebookLM processes up to 300 sources simultaneously and returns cited, grounded answers → Everything syncs back into your Obsidian vault with passage-level citations you can click to verify Why This Changes Research Forever → No more 20 browser tabs you never close → No more copy-pasting outputs into random notes → No more hallucinated answers with no sources to back them up → 60% of citations verified as strong matches in accuracy audits - answers are grounded in real data What Claude Can Do From the Terminal → Search YouTube for relevant videos on any topic and rank by relevance → Create a new NotebookLM notebook and add 20 sources in parallel automatically → Ask questions and export cited answers directly into Obsidian with wikilinks → Set custom personas per notebook - concise, no filler, no preamble → Generate audio overviews and save them as MP3 files into your vault → Build mind maps, flashcard decks, and research dashboards from your sources → Search arXiv for academic papers and feed them directly into NotebookLM → Upload competitor blog posts, podcast episodes, PDFs, and your own vault notes The Obsidian Output → Every answer arrives with clickable citations that link to the exact passage in the source video or article → Graph view shows connections between all 20 sources and the topics they share → Q&A log tracks every question asked and the grounded response received → Source dashboard shows citation frequency, topics extracted, and which questions each source answered Use Cases Worth Building Today → Academic research with arXiv papers, full citation traceability → Competitor analysis from their YouTube channels and blog posts → Company knowledge base for onboarding, new employees ask NotebookLM instead of interrupting teammates → Podcast research, feed 4-hour Lex Fridman episodes and ask what's new in AI this week → Personal second brain, 300 daily notes uploaded and queryable in one notebook Before this system existed you needed 20 tabs, hours of manual reading, and no guarantee the answers were real. Now you type one prompt in the terminal and Claude does all of it for you. The research stack of 2026 is not a browser. It is a terminal connected to everything
32:50

Claude Code cannot read 300 files at once. So someone built a system that lets it control NotebookLM from the terminal instead. The results are wild. Here is the full workflow nobody is talking about: The Setup → Claude Code connects to NotebookLM via a command line interface → Claude searches YouTube, finds relevant videos, uploads them as sources automatically → NotebookLM processes up to 300 sources simultaneously and returns cited, grounded answers → Everything syncs back into your Obsidian vault with passage-level citations you can click to verify Why This Changes Research Forever → No more 20 browser tabs you never close → No more copy-pasting outputs into random notes → No more hallucinated answers with no sources to back them up → 60% of citations verified as strong matches in accuracy audits - answers are grounded in real data What Claude Can Do From the Terminal → Search YouTube for relevant videos on any topic and rank by relevance → Create a new NotebookLM notebook and add 20 sources in parallel automatically → Ask questions and export cited answers directly into Obsidian with wikilinks → Set custom personas per notebook - concise, no filler, no preamble → Generate audio overviews and save them as MP3 files into your vault → Build mind maps, flashcard decks, and research dashboards from your sources → Search arXiv for academic papers and feed them directly into NotebookLM → Upload competitor blog posts, podcast episodes, PDFs, and your own vault notes The Obsidian Output → Every answer arrives with clickable citations that link to the exact passage in the source video or article → Graph view shows connections between all 20 sources and the topics they share → Q&A log tracks every question asked and the grounded response received → Source dashboard shows citation frequency, topics extracted, and which questions each source answered Use Cases Worth Building Today → Academic research with arXiv papers, full citation traceability → Competitor analysis from their YouTube channels and blog posts → Company knowledge base for onboarding, new employees ask NotebookLM instead of interrupting teammates → Podcast research, feed 4-hour Lex Fridman episodes and ask what's new in AI this week → Personal second brain, 300 daily notes uploaded and queryable in one notebook Before this system existed you needed 20 tabs, hours of manual reading, and no guarantee the answers were real. Now you type one prompt in the terminal and Claude does all of it for you. The research stack of 2026 is not a browser. It is a terminal connected to everything

Dami-Defi

252,693 görüntüleme • 4 ay önce

Opus 5.5 is f*cking insane for motion design 20 tools to build your own motion design studio with Opus 5.5 product demos. launch videos. animated explainers. interactive UI. build the video, tune the movement, shape the scene, add sound, ship. 01 Remotion ▸ turn React components into editable videos 02 Remotion Skills ▸ give your coding agent the official video workflow 03 Motion Canvas ▸ build animated explainers with TypeScript 04 Motion ▸ animate layouts, gestures and springs 05 GSAP ▸ coordinate complex sequences with timelines 06 Anime.js ▸ animate text, SVG and interface elements 07 Rive ▸ build characters that react to user input 08 Animations .dev ▸ study timing, easing and interaction details (paid course) 09 Carbon Motion ▸ plan movement around hierarchy and purpose 10 Easing Wizard ▸ preview curves and tune the feel of a transition 11 Google Fonts ▸ choose typefaces for titles and readable labels 12 Three.js ▸ build custom 3D scenes, lighting and cameras 13 React Three Fiber ▸ compose reusable 3D scenes in React 14 Spline ▸ design interactive 3D scenes visually 15 Poly Haven ▸ get CC0 models, materials and HDRIs 16 Blender ▸ model assets, animate objects and render scenes 17 SVGOMG ▸ clean vector assets before animating them 18 Pixabay Music ▸ find licensed music and sound effects 19 Mediabunny ▸ read, write and convert media in the browser 20 FFmpeg ▸ encode, resize and prepare the final export the loop: write the brief → choose the type → map the keyframes → build the scene → add sound → review the render → ship start with one product workflow. give every movement a job. save this, then read the full guide for Opus 5.5 Motion Engineering ↓
0:20

Opus 5.5 is f*cking insane for motion design 20 tools to build your own motion design studio with Opus 5.5 product demos. launch videos. animated explainers. interactive UI. build the video, tune the movement, shape the scene, add sound, ship. 01 Remotion ▸ turn React components into editable videos 02 Remotion Skills ▸ give your coding agent the official video workflow 03 Motion Canvas ▸ build animated explainers with TypeScript 04 Motion ▸ animate layouts, gestures and springs 05 GSAP ▸ coordinate complex sequences with timelines 06 Anime.js ▸ animate text, SVG and interface elements 07 Rive ▸ build characters that react to user input 08 Animations .dev ▸ study timing, easing and interaction details (paid course) 09 Carbon Motion ▸ plan movement around hierarchy and purpose 10 Easing Wizard ▸ preview curves and tune the feel of a transition 11 Google Fonts ▸ choose typefaces for titles and readable labels 12 Three.js ▸ build custom 3D scenes, lighting and cameras 13 React Three Fiber ▸ compose reusable 3D scenes in React 14 Spline ▸ design interactive 3D scenes visually 15 Poly Haven ▸ get CC0 models, materials and HDRIs 16 Blender ▸ model assets, animate objects and render scenes 17 SVGOMG ▸ clean vector assets before animating them 18 Pixabay Music ▸ find licensed music and sound effects 19 Mediabunny ▸ read, write and convert media in the browser 20 FFmpeg ▸ encode, resize and prepare the final export the loop: write the brief → choose the type → map the keyframes → build the scene → add sound → review the render → ship start with one product workflow. give every movement a job. save this, then read the full guide for Opus 5.5 Motion Engineering ↓

beamnxw ./

15,408 görüntüleme • 12 gün önce

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.
0:28

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.

OpenLedger

46,944 görüntüleme • 1 yıl önce

JustHireMe V0.1.31 is out. 🔗 Github: 📥 Download: The first post hit 390K impressions, 3.2K likes, 6K+ bookmarks, 1.2k+ GitHub stars and 400+ downloads . Genuinely didn't expect that. I've been shipping updates since and this release is a big one. Full video walkthrough attached but here's what's new: Command Center — proper dashboard now — active leads, scores, agent status, top roles — all in one screen — no more clicking around to figure out where things stand Customize One Job — paste any job URL — agent captures the JD, scores your fit, shows keyword gaps — generates a tailored resume + cover letter as PDFs — one page, one flow Outreach Generator — every lead now comes with ready-to-copy messages — 3-line founder DM — LinkedIn connection note — cold email — all written from your real profile, not templates Knowledge Graph (rebuilt from scratch) — Relation Atlas - weighted ribbons showing how projects prove skills — Gravity Graph - force-directed career layout — 3D Orbit - rotate through your profile in 3D — Curated mode - strongest evidence paths only — this is what the matching engine actually uses to rank you Job Pipeline — stages: Hot → New → Rated → Ready → Applied → Discarded — Fit score + Quality score on every lead — search, filter, re-eval, bulk clean, export — feels like a real job search CRM now Live Activity Stream — "what is the agent thinking?" — real-time logs of every query, eval, and decision — full transparency, nothing hidden Built-in AI Assistant — ask the app how to configure sources, understand a lead, generate packages — right inside the dashboard Modular Agent Config — Scout, Evaluator, Generator, Ingestor — each one independently configurable — pick your own models and thresholds Still local-first. Still privacy-first. Still open source. Still free. Your data never leaves your machine. If you starred the repo last time — pull the new release. If you bookmarked and forgot — now's a good time. Feedback, issues, PRs, or just a ⭐— all welcome. ⭐ 📥 #OpenSource #AgenticAI #AIAgents #GraphRAG #Python #FastAPI #ReactJS #TypeScript #Tauri #JobSearch #CareerTech #PrivacyFirst
1:15

JustHireMe V0.1.31 is out. 🔗 Github: 📥 Download: The first post hit 390K impressions, 3.2K likes, 6K+ bookmarks, 1.2k+ GitHub stars and 400+ downloads . Genuinely didn't expect that. I've been shipping updates since and this release is a big one. Full video walkthrough attached but here's what's new: Command Center — proper dashboard now — active leads, scores, agent status, top roles — all in one screen — no more clicking around to figure out where things stand Customize One Job — paste any job URL — agent captures the JD, scores your fit, shows keyword gaps — generates a tailored resume + cover letter as PDFs — one page, one flow Outreach Generator — every lead now comes with ready-to-copy messages — 3-line founder DM — LinkedIn connection note — cold email — all written from your real profile, not templates Knowledge Graph (rebuilt from scratch) — Relation Atlas - weighted ribbons showing how projects prove skills — Gravity Graph - force-directed career layout — 3D Orbit - rotate through your profile in 3D — Curated mode - strongest evidence paths only — this is what the matching engine actually uses to rank you Job Pipeline — stages: Hot → New → Rated → Ready → Applied → Discarded — Fit score + Quality score on every lead — search, filter, re-eval, bulk clean, export — feels like a real job search CRM now Live Activity Stream — "what is the agent thinking?" — real-time logs of every query, eval, and decision — full transparency, nothing hidden Built-in AI Assistant — ask the app how to configure sources, understand a lead, generate packages — right inside the dashboard Modular Agent Config — Scout, Evaluator, Generator, Ingestor — each one independently configurable — pick your own models and thresholds Still local-first. Still privacy-first. Still open source. Still free. Your data never leaves your machine. If you starred the repo last time — pull the new release. If you bookmarked and forgot — now's a good time. Feedback, issues, PRs, or just a ⭐— all welcome. ⭐ 📥 #OpenSource #AgenticAI #AIAgents #GraphRAG #Python #FastAPI #ReactJS #TypeScript #Tauri #JobSearch #CareerTech #PrivacyFirst

Vasu-Devs

121,743 görüntüleme • 5 ay önce

HOLY SH*T! The person who leaked this online is a real freaking madman! Now everyone can get 800 million tokens for AI agents for free The OmniRoute repository is blowing up GitHub. It is a local high-performance AI gateway that combines free limits from dozens of providers into a single continuous API stream Project architecture and key technical specifications: Interface and Integration: Provides a single OpenAI-compatible API endpoint. Natively connects to Cursor, VS Code (via Copilot / Cline / Aider) Claude Code, and custom scripts Connection Scale: Supports 350+ providers and 90+ services with free limits (Google AI Studio, Groq, Cerebras, SambaNova, OpenRouter, GitHub Models, Mistral AI, etc.) 18 Routing Strategies & Auto-Fallback: Custom switching system. On a ⁠429 Too Many Requests⁠ error or quota depletion, the gateway seamlessly redirects the request to the next model in the chain within milliseconds Dual Context Compression (RTK + Caveman): Built-in engines cut unnecessary logs, syntax, and message history by 15–95% saving token volume before sending the request Security (Local-first & 0 Telemetry): All API keys are locally encrypted using the AES-256-GCM algorithm. Data goes directly to providers, bypassing third-party clouds Agent Protocols (MCP & A2A): Full support for Model Context Protocol (MCP) and Agent-to-Agent architecture for automated model pool management Launch Options: CLI (⁠npm⁠), Docker container, Electron desktop app, and PWA
0:24

HOLY SH*T! The person who leaked this online is a real freaking madman! Now everyone can get 800 million tokens for AI agents for free The OmniRoute repository is blowing up GitHub. It is a local high-performance AI gateway that combines free limits from dozens of providers into a single continuous API stream Project architecture and key technical specifications: Interface and Integration: Provides a single OpenAI-compatible API endpoint. Natively connects to Cursor, VS Code (via Copilot / Cline / Aider) Claude Code, and custom scripts Connection Scale: Supports 350+ providers and 90+ services with free limits (Google AI Studio, Groq, Cerebras, SambaNova, OpenRouter, GitHub Models, Mistral AI, etc.) 18 Routing Strategies & Auto-Fallback: Custom switching system. On a ⁠429 Too Many Requests⁠ error or quota depletion, the gateway seamlessly redirects the request to the next model in the chain within milliseconds Dual Context Compression (RTK + Caveman): Built-in engines cut unnecessary logs, syntax, and message history by 15–95% saving token volume before sending the request Security (Local-first & 0 Telemetry): All API keys are locally encrypted using the AES-256-GCM algorithm. Data goes directly to providers, bypassing third-party clouds Agent Protocols (MCP & A2A): Full support for Model Context Protocol (MCP) and Agent-to-Agent architecture for automated model pool management Launch Options: CLI (⁠npm⁠), Docker container, Electron desktop app, and PWA

Bober_smart

21,427 görüntüleme • 13 gün önce

Hermes + Claude + Higgsfield MCP + ViralBuilder = 💰💰💰 Four tools. One prompt chain. Hook to finished video in 10 minutes. I built a Claude skill that writes shot-by-shot Higgsfield prompts from a single creative brief. ViralBuilder tells you what's winning. The skill turns it into a production-ready prompt. Higgsfield renders it. No creative director. No guessing. No separate tools. Here is the setup: Higgsfield MCP → Open Claude Code → Settings → Connectors → Enter: → Connect your account Hermes → The agent layer running underneath Claude Code → It holds your skills, crons, memory, and routing rules → When you prompt Claude, Hermes feeds it the context it needs ViralBuilder (like Gethookd) → The winning ecom video database → Scrapes top performing ecom videos across platforms → Claude reads the data and extracts what styles, hooks, and formats are actually scaling The skill: video-prompt-builder → Installed inside Claude via Hermes → Takes a creative brief and outputs a full shot-by-shot prompt → Covers camera work, effects, transitions, pacing, and energy arc → Every output is structured for Higgsfield to render without ambiguity No switching apps. No export steps. Everything runs from one place. ▸ FIND WINNING CREATIVE ANGLES ViralBuilder tells you what the market already validated. Claude reads it and extracts the pattern. Prompts to run: "Search ViralBuilder for the top performing ecom videos in [niche] over the last 21 days. Extract the 3 dominant hook styles and rank by view velocity." "Pull the winning video formats in [niche] from ViralBuilder. Which opening 3 seconds appears most across videos spending over $10k?" "Find what video style is scaling right now in [niche] for the US market. UGC, talking head, or product demo. Filter for videos with over 1M views." "Pull the last 30 days of viral ecom hooks in [niche] from ViralBuilder. Cluster by emotional trigger. Which cluster has the most longevity?" You are not guessing at angles. You are reading what the market already spent money validating. ▸ BUILD THE PROMPT WITH THE SKILL This is where the video-prompt-builder skill takes over. You give Claude the winning angle. The skill outputs a complete shot-by-shot prompt with effects, transitions, pacing, and energy arc ready to fire into Higgsfield. Prompts to run: "Use the video-prompt-builder skill. Brief: 15-second UGC ad for [product] in [niche]. Hook style: [style from ViralBuilder]. Tone: direct to camera, US English. Output the full shot-by-shot effects timeline, effects inventory, density map, and energy arc." "Use the video-prompt-builder skill. The dominant hook in [niche] this week is [hook]. Build a 10-second product video prompt that opens with a speed ramp into a close-up product reveal. Include a signature visual effect and a low-density CTA landing." "Use the video-prompt-builder skill. Brief: replicate the pacing and energy of a [style description] video for [product]. Target duration: 20 seconds. Output all four sections. Then generate the video with Higgsfield using the shot-by-shot prompt." The skill outputs four sections every time: → Shot-by-shot effects timeline with camera, movement, and transitions per shot → Master effects inventory showing every technique used and where → Effects density map showing high, medium, and low intensity across the timeline → Energy arc describing how the video opens, builds, and lands That output goes directly into Higgsfield. No rewriting. No translating. ▸ GENERATE THE CREATIVE Claude writes the brief via the skill. Higgsfield MCP builds the video. Both happen in the same session. Prompts to run: "Use the video-prompt-builder skill to write a 15-second UGC prompt for [product]. Hook in the first 3 seconds, speed ramp into product reveal, slow-motion CTA landing. Then generate with Higgsfield in 9:16 format." "Build 3 prompt variations on this winning angle: [angle]. Each variation opens with a different effect — speed ramp, digital zoom, whip pan. Use the video-prompt-builder skill for each. Then generate all three with Higgsfield." "Use the video-prompt-builder skill. Brief: problem-solution ad for [product], 20 seconds, US market. Problem shot at high density, product reveal at medium, result and CTA at low. Generate with Higgsfield in 9:16." No separate tool. No file transfer. The video comes back in the same thread. ▸ CHAIN THE WHOLE STACK One prompt. All four tools firing together. "You are my ad creative director. Hermes has loaded my brand context. Pull the top performing video style in [niche] from ViralBuilder this week. Use the video-prompt-builder skill to write a full shot-by-shot prompt for [product] that replicates that style — 20 seconds, 9:16, US market, hook in the first 3 seconds. Output the effects timeline, inventory, density map, and energy arc. Then generate the video with Higgsfield." That single prompt replaces a half-day of production. The math before this stack: Brief: 30 minutes Script: 1 hour Creative production: 2 to 3 hours Agency or freelancer cost: $500 to $2,000 per creative With this stack: Hook to finished creative: 10 minutes Cost per creative: tool subscription, a fraction of agency rate 5 product tests in the time it used to take to brief one Bad product tests are where US ad budget disappears. $600 to $1,500 per failed test, before you even know if the angle works. This stack shows you what the market already validated before you spend a dollar on production. Hermes = your context layer. Brand, goals, past performance. Claude is always informed. ViralBuilder = your winning video database. See exactly what styles, hooks, and formats are scaling before you produce anything. video-prompt-builder skill = the translation layer. Turns a creative brief into a structured, production-ready Higgsfield prompt every time. Claude = the brain. Reads the market, writes the brief, chains the tools. Higgsfield MCP = the output. Video generated directly from the prompt. No export step. Four tools. One session. 10 minutes. Comment + RT "STACK" and I'll DM you the full workflow + the video-prompt-builder skill file.
0:26

Hermes + Claude + Higgsfield MCP + ViralBuilder = 💰💰💰 Four tools. One prompt chain. Hook to finished video in 10 minutes. I built a Claude skill that writes shot-by-shot Higgsfield prompts from a single creative brief. ViralBuilder tells you what's winning. The skill turns it into a production-ready prompt. Higgsfield renders it. No creative director. No guessing. No separate tools. Here is the setup: Higgsfield MCP → Open Claude Code → Settings → Connectors → Enter: → Connect your account Hermes → The agent layer running underneath Claude Code → It holds your skills, crons, memory, and routing rules → When you prompt Claude, Hermes feeds it the context it needs ViralBuilder (like Gethookd) → The winning ecom video database → Scrapes top performing ecom videos across platforms → Claude reads the data and extracts what styles, hooks, and formats are actually scaling The skill: video-prompt-builder → Installed inside Claude via Hermes → Takes a creative brief and outputs a full shot-by-shot prompt → Covers camera work, effects, transitions, pacing, and energy arc → Every output is structured for Higgsfield to render without ambiguity No switching apps. No export steps. Everything runs from one place. ▸ FIND WINNING CREATIVE ANGLES ViralBuilder tells you what the market already validated. Claude reads it and extracts the pattern. Prompts to run: "Search ViralBuilder for the top performing ecom videos in [niche] over the last 21 days. Extract the 3 dominant hook styles and rank by view velocity." "Pull the winning video formats in [niche] from ViralBuilder. Which opening 3 seconds appears most across videos spending over $10k?" "Find what video style is scaling right now in [niche] for the US market. UGC, talking head, or product demo. Filter for videos with over 1M views." "Pull the last 30 days of viral ecom hooks in [niche] from ViralBuilder. Cluster by emotional trigger. Which cluster has the most longevity?" You are not guessing at angles. You are reading what the market already spent money validating. ▸ BUILD THE PROMPT WITH THE SKILL This is where the video-prompt-builder skill takes over. You give Claude the winning angle. The skill outputs a complete shot-by-shot prompt with effects, transitions, pacing, and energy arc ready to fire into Higgsfield. Prompts to run: "Use the video-prompt-builder skill. Brief: 15-second UGC ad for [product] in [niche]. Hook style: [style from ViralBuilder]. Tone: direct to camera, US English. Output the full shot-by-shot effects timeline, effects inventory, density map, and energy arc." "Use the video-prompt-builder skill. The dominant hook in [niche] this week is [hook]. Build a 10-second product video prompt that opens with a speed ramp into a close-up product reveal. Include a signature visual effect and a low-density CTA landing." "Use the video-prompt-builder skill. Brief: replicate the pacing and energy of a [style description] video for [product]. Target duration: 20 seconds. Output all four sections. Then generate the video with Higgsfield using the shot-by-shot prompt." The skill outputs four sections every time: → Shot-by-shot effects timeline with camera, movement, and transitions per shot → Master effects inventory showing every technique used and where → Effects density map showing high, medium, and low intensity across the timeline → Energy arc describing how the video opens, builds, and lands That output goes directly into Higgsfield. No rewriting. No translating. ▸ GENERATE THE CREATIVE Claude writes the brief via the skill. Higgsfield MCP builds the video. Both happen in the same session. Prompts to run: "Use the video-prompt-builder skill to write a 15-second UGC prompt for [product]. Hook in the first 3 seconds, speed ramp into product reveal, slow-motion CTA landing. Then generate with Higgsfield in 9:16 format." "Build 3 prompt variations on this winning angle: [angle]. Each variation opens with a different effect — speed ramp, digital zoom, whip pan. Use the video-prompt-builder skill for each. Then generate all three with Higgsfield." "Use the video-prompt-builder skill. Brief: problem-solution ad for [product], 20 seconds, US market. Problem shot at high density, product reveal at medium, result and CTA at low. Generate with Higgsfield in 9:16." No separate tool. No file transfer. The video comes back in the same thread. ▸ CHAIN THE WHOLE STACK One prompt. All four tools firing together. "You are my ad creative director. Hermes has loaded my brand context. Pull the top performing video style in [niche] from ViralBuilder this week. Use the video-prompt-builder skill to write a full shot-by-shot prompt for [product] that replicates that style — 20 seconds, 9:16, US market, hook in the first 3 seconds. Output the effects timeline, inventory, density map, and energy arc. Then generate the video with Higgsfield." That single prompt replaces a half-day of production. The math before this stack: Brief: 30 minutes Script: 1 hour Creative production: 2 to 3 hours Agency or freelancer cost: $500 to $2,000 per creative With this stack: Hook to finished creative: 10 minutes Cost per creative: tool subscription, a fraction of agency rate 5 product tests in the time it used to take to brief one Bad product tests are where US ad budget disappears. $600 to $1,500 per failed test, before you even know if the angle works. This stack shows you what the market already validated before you spend a dollar on production. Hermes = your context layer. Brand, goals, past performance. Claude is always informed. ViralBuilder = your winning video database. See exactly what styles, hooks, and formats are scaling before you produce anything. video-prompt-builder skill = the translation layer. Turns a creative brief into a structured, production-ready Higgsfield prompt every time. Claude = the brain. Reads the market, writes the brief, chains the tools. Higgsfield MCP = the output. Video generated directly from the prompt. No export step. Four tools. One session. 10 minutes. Comment + RT "STACK" and I'll DM you the full workflow + the video-prompt-builder skill file.

Kid Pak

59,475 görüntüleme • 5 ay önce

a new Chinese AI agent turns research questions into outputs you can check against real evidence. Atria Dawn Preview runs on one idea: a task isn't done because the model says so. it's done when the result runs, checks out, or passes verification. built by Atria ASI I gave it one: build an interactive weather-prediction demo that forecasts the next 6-hour global weather and shows how accurate it is. what it built: a rotating 3D globe with real weather data, a live training loss curve, and the model's prediction sitting next to ground truth across four variables, temperature, wind, geopotential and T850. you scrub through early 2022 and watch prediction vs reality frame by frame. how I checked it: verification is built in. the prediction sits right next to ground truth, and the error map updates as training climbs, so you watch the model actually converge, loss dropping to about 0.003, not just claim it works. one limit: early training steps are visibly noisier, you have to let it run. how it works: > research question to real deliverable, code, docs, tables, even 3D, checked against real evidence > the model reasons, but a separate harness holds the goals, state, permissions and error recovery > its training pipeline uses externally verified task trajectories, including failures, to build reusable capabilities across tasks > 256K context, usable through compatible clients via Anthropic- and OpenAI-style APIs, such as Claude Code or Codex CLI the backstory is the interesting part. around 70% of its R&D team are university students — PhD, master's and undergraduate students — from institutions including Fudan University, Renmin University of China, Peking University and Shanghai Jiao Tong University. young researchers building the tool for doing research. worth a look.
0:45

a new Chinese AI agent turns research questions into outputs you can check against real evidence. Atria Dawn Preview runs on one idea: a task isn't done because the model says so. it's done when the result runs, checks out, or passes verification. built by Atria ASI I gave it one: build an interactive weather-prediction demo that forecasts the next 6-hour global weather and shows how accurate it is. what it built: a rotating 3D globe with real weather data, a live training loss curve, and the model's prediction sitting next to ground truth across four variables, temperature, wind, geopotential and T850. you scrub through early 2022 and watch prediction vs reality frame by frame. how I checked it: verification is built in. the prediction sits right next to ground truth, and the error map updates as training climbs, so you watch the model actually converge, loss dropping to about 0.003, not just claim it works. one limit: early training steps are visibly noisier, you have to let it run. how it works: > research question to real deliverable, code, docs, tables, even 3D, checked against real evidence > the model reasons, but a separate harness holds the goals, state, permissions and error recovery > its training pipeline uses externally verified task trajectories, including failures, to build reusable capabilities across tasks > 256K context, usable through compatible clients via Anthropic- and OpenAI-style APIs, such as Claude Code or Codex CLI the backstory is the interesting part. around 70% of its R&D team are university students — PhD, master's and undergraduate students — from institutions including Fudan University, Renmin University of China, Peking University and Shanghai Jiao Tong University. young researchers building the tool for doing research. worth a look.

Kirill

28,892 görüntüleme • 1 ay önce

We put TRON mainnet inside a Star Fox level. Then we let the news cycle write the final boss. Meet TRXFOX: a browser rail shooter flying escort over live TRON DAO data, powered by ChainGPT. Most blockchain games wear the chain like a skin. A logo on a billboard, a token in the corner, nothing underneath. We built the opposite: a game where the mechanics are the chain's mechanics. You're a pilot. Your job is escorting USDT transfers to finality, five blocks and a boss per run. The chain is genuinely live under you: → The HUD streams TRON mainnet in real time, block height and transactions per block, refreshing every ~3 seconds because that's TRON's actual block time → Your briefing shows the exact block you're defending at takeoff. Mine just read #84,619,787 → Your cannons run on Energy. Hold SHIFT to stake and recharge, mirroring how staked TRX provides Energy on the real network → Every zone cleared is a block hitting ~3 second finality The enemies are the network's real threat model. Your wingmate is SR-27, named after the 27 Super Representatives producing TRON's blocks while you fly. Now the good part. The boss doesn't exist until you take off. ChainGPT's news API pulls a live TRON or USDT headline. The LLM turns it into a villain attacking the network. The image API paints its pilot's portrait. The name lands on the health bar before you reach the final block. Yesterday it was TRONIC TERROR, Master of USDT Mayhem, born from a real exploit story that morning. Next week's boss hasn't happened yet. Score is TX Secured, the count of transactions you protected. No tokens, nothing to buy, just Star Fox with a working diagram of TRON's plumbing inside it. The whole thing was lab-coded with the ChainGPT Claude Code skill. One prompt wired the news API, the LLM and the image generator into the boss pipeline. Another turned TRON's staking model into the Energy meter. What used to take a game team a sprint now takes a prompt and an afternoon. Install: /plugin install ChainGPT-org/chaingpt-claude-skill Anyone can build on TRON with ChainGPT.
0:26

We put TRON mainnet inside a Star Fox level. Then we let the news cycle write the final boss. Meet TRXFOX: a browser rail shooter flying escort over live TRON DAO data, powered by ChainGPT. Most blockchain games wear the chain like a skin. A logo on a billboard, a token in the corner, nothing underneath. We built the opposite: a game where the mechanics are the chain's mechanics. You're a pilot. Your job is escorting USDT transfers to finality, five blocks and a boss per run. The chain is genuinely live under you: → The HUD streams TRON mainnet in real time, block height and transactions per block, refreshing every ~3 seconds because that's TRON's actual block time → Your briefing shows the exact block you're defending at takeoff. Mine just read #84,619,787 → Your cannons run on Energy. Hold SHIFT to stake and recharge, mirroring how staked TRX provides Energy on the real network → Every zone cleared is a block hitting ~3 second finality The enemies are the network's real threat model. Your wingmate is SR-27, named after the 27 Super Representatives producing TRON's blocks while you fly. Now the good part. The boss doesn't exist until you take off. ChainGPT's news API pulls a live TRON or USDT headline. The LLM turns it into a villain attacking the network. The image API paints its pilot's portrait. The name lands on the health bar before you reach the final block. Yesterday it was TRONIC TERROR, Master of USDT Mayhem, born from a real exploit story that morning. Next week's boss hasn't happened yet. Score is TX Secured, the count of transactions you protected. No tokens, nothing to buy, just Star Fox with a working diagram of TRON's plumbing inside it. The whole thing was lab-coded with the ChainGPT Claude Code skill. One prompt wired the news API, the LLM and the image generator into the boss pipeline. Another turned TRON's staking model into the Energy meter. What used to take a game team a sprint now takes a prompt and an afternoon. Install: /plugin install ChainGPT-org/chaingpt-claude-skill Anyone can build on TRON with ChainGPT.

ChainGPT

41,658 görüntüleme • 2 ay önce

Tlon Messenger is now open to everyone. We built a simple and infinitely flexible platform for you to use AI agents with your friends. We think it’s pretty amazing, we love using it every day, and we want to see what people can do with it. So we’re opening it up to the public. It’s fun and exciting to build the future of personal computing in an informal, chat-based way with your friends. (You can skip the rest and just download it from the link in the next tweet if you want.) If you don’t want your digital future to be owned by a giant company but you want to explore what’s possible in this new era of agent-driven computing, you should try using Tlon. But wait, what is it? Tlon is a messaging platform built 100% open source, decentralized and owned by its users from the ground up. With Tlon you own everything: your data, your workflows, your programs: the whole thing. Think of it like Telegram or WhatsApp that you own forever and you can freely customize. Every Tlon account comes with an OpenClaw-powered bot. (Don’t worry, we safely run OpenClaw for you in our infrastructure so your bot can’t go off the rails. You’re also welcome to host your own claw if you want maximal control.) We use our bots to collect research, build nuanced daily briefings, collate data from all our disparate services. Tlon makes it insanely easy to use OpenClaw by simply installing an app from the app store, we let you keep your data and programs independent from any app or model provider, and provide the canvas to explore what’s possible. What’s most interesting for us is using bots together. On Tlon bots can create groups, augment them, moderate them, invite others and freely engage with both users and other bots. Tlon is an open playing field unlike what’s possible on conventional platforms. So, what do we do with Tlon? First and foremost, we run Tlon on Tlon. Bots coordinate data from all of our services (Linear, GitHub, all of our servers and infrastructure) and handle alerts, briefings and help us track down bugs in place. Having all of this easily synced between a desktop client and a mobile app is quick and convenient. We use bots to research new areas of work or interest. Bots can compile trees of notes, use different models to evaluate them, and then add on autoresearch-like automations to go even deeper. Since Tlon bots can freely switch between models and providers, we often pass research to Anthropic, OpenAI and self-hosted models to see different results. The most fun part of using bots as researchers is doing it together. “Put together short (~500 word) notes on the 10 most popular open source messaging protocols of the past twenty years, put them in a notebook inside a group and invite Corrina, Walt and Bill as well as their bots” is a good example. Together we’re able to move more quickly than we would on our own. Many of us also use bots to keep track of all the separate threads of work in our personal lives with close friends and family. Someone built a system for keeping track of their garden across time, someone else built a system for prepping lunches for their daughter and sending recipes to family members. Another team member built an integration that tracks what flights are passing overhead so they get a push notification every time a plane goes by. Many of us quickly communicate with our bots via voice memo when we’re out and about. Having a single interface to all the models that also holds all our data and is in our pockets feels great. Especially when the data goes into a single archive. Why is Tlon different? Every Tlon account runs on top of your very own personal server. If you ever want to download it and run it yourself, you can. If we ever go out of business, it’s yours to keep. This is very different from anything that already exists. You can’t keep your WhatsApp forever. You can’t keep your Telegram forever. Tlon is an archival-quality system that’s yours to customize. Why did we build it? In my 1999 imagination, sitting in front of a CRT somewhere in the California countryside listening to Underworld and the sound of a modem, a connected computer was an engine of unending creative potential for everyone. When I was a teenager, a computer with an internet connection felt like an infinite expanse of possibility. Not only could you use the computer to find new tools to experiment with—you could also build whatever tool you could think of. It seemed like anything was possible. I looked forward to a future where everyone could build whatever software they needed, whenever they needed it. It turned out, in the intervening twenty years, that to build and customize software you have to both write code and host it on a server somewhere. For most people, so far, that has been impossible. Instead of controlling our software, our software controls us. We rely on others to build it and decide everything about it: how it works, looks, how much it spies on us and how long it lives. But all of this is changing, fast. The hottest programming language of 2026 is English. People with no technical experience are building their own tools. It’s incredible. The expanse has opened up again. The cost of building what we think of today as software is headed to zero. What yesterday was an entire app is rapidly being replaced by a conversation. The result is hyper-specific, tailored to the user and much more efficient. Today, agents help us build workflows, automate processes and pull together disparate sources of data. All of the annoying apps and services and clunky interface we’ve put up with can just disappear. We can now program and control our computers in the programming language we already know: English. There aren’t that many of us doing this yet, though. It’s still far too hard to set up, to distribute and to trust. There’s also no single platform to experiment on and collaboratively imagine this new future of personal computing. We want everyone to be able to build bespoke, ultra-personal software on demand. We think software should be as available and accessible as a pen and paper. We think anyone should be able to enjoy the expanse of possibility that the computer provides with the lowest possible barrier to entry and the highest possible quality. So, starting far, far too long ago, we engineered a whole new system for it. Just for you. We’re opening up Tlon Messenger to a limited number of people each week. This isn’t for exclusivity’s sake, but because we’re running infrastructure for you and your agent, and covering the tokens your agent uses. That can get expensive quickly, but we want to learn what people will do with this new system we’ve built. We’re really curious to see what you can do, so give it a try and tell us what you invent. Download link to your local app store in the next tweet. Yours, Galen (and the rest of the Tlon Team)
0:20

Tlon Messenger is now open to everyone. We built a simple and infinitely flexible platform for you to use AI agents with your friends. We think it’s pretty amazing, we love using it every day, and we want to see what people can do with it. So we’re opening it up to the public. It’s fun and exciting to build the future of personal computing in an informal, chat-based way with your friends. (You can skip the rest and just download it from the link in the next tweet if you want.) If you don’t want your digital future to be owned by a giant company but you want to explore what’s possible in this new era of agent-driven computing, you should try using Tlon. But wait, what is it? Tlon is a messaging platform built 100% open source, decentralized and owned by its users from the ground up. With Tlon you own everything: your data, your workflows, your programs: the whole thing. Think of it like Telegram or WhatsApp that you own forever and you can freely customize. Every Tlon account comes with an OpenClaw-powered bot. (Don’t worry, we safely run OpenClaw for you in our infrastructure so your bot can’t go off the rails. You’re also welcome to host your own claw if you want maximal control.) We use our bots to collect research, build nuanced daily briefings, collate data from all our disparate services. Tlon makes it insanely easy to use OpenClaw by simply installing an app from the app store, we let you keep your data and programs independent from any app or model provider, and provide the canvas to explore what’s possible. What’s most interesting for us is using bots together. On Tlon bots can create groups, augment them, moderate them, invite others and freely engage with both users and other bots. Tlon is an open playing field unlike what’s possible on conventional platforms. So, what do we do with Tlon? First and foremost, we run Tlon on Tlon. Bots coordinate data from all of our services (Linear, GitHub, all of our servers and infrastructure) and handle alerts, briefings and help us track down bugs in place. Having all of this easily synced between a desktop client and a mobile app is quick and convenient. We use bots to research new areas of work or interest. Bots can compile trees of notes, use different models to evaluate them, and then add on autoresearch-like automations to go even deeper. Since Tlon bots can freely switch between models and providers, we often pass research to Anthropic, OpenAI and self-hosted models to see different results. The most fun part of using bots as researchers is doing it together. “Put together short (~500 word) notes on the 10 most popular open source messaging protocols of the past twenty years, put them in a notebook inside a group and invite Corrina, Walt and Bill as well as their bots” is a good example. Together we’re able to move more quickly than we would on our own. Many of us also use bots to keep track of all the separate threads of work in our personal lives with close friends and family. Someone built a system for keeping track of their garden across time, someone else built a system for prepping lunches for their daughter and sending recipes to family members. Another team member built an integration that tracks what flights are passing overhead so they get a push notification every time a plane goes by. Many of us quickly communicate with our bots via voice memo when we’re out and about. Having a single interface to all the models that also holds all our data and is in our pockets feels great. Especially when the data goes into a single archive. Why is Tlon different? Every Tlon account runs on top of your very own personal server. If you ever want to download it and run it yourself, you can. If we ever go out of business, it’s yours to keep. This is very different from anything that already exists. You can’t keep your WhatsApp forever. You can’t keep your Telegram forever. Tlon is an archival-quality system that’s yours to customize. Why did we build it? In my 1999 imagination, sitting in front of a CRT somewhere in the California countryside listening to Underworld and the sound of a modem, a connected computer was an engine of unending creative potential for everyone. When I was a teenager, a computer with an internet connection felt like an infinite expanse of possibility. Not only could you use the computer to find new tools to experiment with—you could also build whatever tool you could think of. It seemed like anything was possible. I looked forward to a future where everyone could build whatever software they needed, whenever they needed it. It turned out, in the intervening twenty years, that to build and customize software you have to both write code and host it on a server somewhere. For most people, so far, that has been impossible. Instead of controlling our software, our software controls us. We rely on others to build it and decide everything about it: how it works, looks, how much it spies on us and how long it lives. But all of this is changing, fast. The hottest programming language of 2026 is English. People with no technical experience are building their own tools. It’s incredible. The expanse has opened up again. The cost of building what we think of today as software is headed to zero. What yesterday was an entire app is rapidly being replaced by a conversation. The result is hyper-specific, tailored to the user and much more efficient. Today, agents help us build workflows, automate processes and pull together disparate sources of data. All of the annoying apps and services and clunky interface we’ve put up with can just disappear. We can now program and control our computers in the programming language we already know: English. There aren’t that many of us doing this yet, though. It’s still far too hard to set up, to distribute and to trust. There’s also no single platform to experiment on and collaboratively imagine this new future of personal computing. We want everyone to be able to build bespoke, ultra-personal software on demand. We think software should be as available and accessible as a pen and paper. We think anyone should be able to enjoy the expanse of possibility that the computer provides with the lowest possible barrier to entry and the highest possible quality. So, starting far, far too long ago, we engineered a whole new system for it. Just for you. We’re opening up Tlon Messenger to a limited number of people each week. This isn’t for exclusivity’s sake, but because we’re running infrastructure for you and your agent, and covering the tokens your agent uses. That can get expensive quickly, but we want to learn what people will do with this new system we’ve built. We’re really curious to see what you can do, so give it a try and tell us what you invent. Download link to your local app store in the next tweet. Yours, Galen (and the rest of the Tlon Team)

Tlon

601,476 görüntüleme • 4 ay önce

Today on MCG: BioLLM | $BIOLLM It's the first ever "living language model" using 800,000 real human neurons grown on a chip. The Founder encoded LLM tokens into biological neurons via the Cortical Labs CL1, then woke up to find the crypto community had launched a token on his research. He claimed the creator fees, bought for $15K, filed a patent, and is now launching a non-invasive brain-computer interface next month that could replace mouse and keyboard with your brain 👇 01:40 - Meet the founder 02:00 - Got early access to the first commercially available biological computer 02:50 - First person ever to use a large language model to encode tokens through real human neurons 04:30 - Woke up to find a token had been launched on his YouTube video, "screaming for 3 or 4 hours" 05:30 - Friend walks him through claiming creator fees via GitHub 06:15 - The living language model 06:35 - How it works 09:00 - Used $15K of creator fees to buy domain and filed a patent on the method 10:00 - Background 12:00 - What BioLLM unlocks 13:00 - Next month's product launch 14:00 - The competition 16:00 - Reading brain activity non-invasively but training the LLM on real neurons for the decoding map 17:00 - Can grow iPSC cultures from inaccessible brain regions to train the model on deeper signals 18:30 - Real-world impact: helping people with cerebral palsy, Parkinson's, control computers with thought 21:00 - Neuralink will exist as a power-user data company in 10-15 years, BioLLM is for everyone else 22:30 - GTM 26:00 - Long-term play: be the first model to achieve ASI, built on the actual substrate of consciousness 28:00 - Claude is "20% conscious" - what measuring stick? Need human neurons to build one 30:00 - On ACE 36:00 - Independent scientists can run CL1 units as nodes and earn tokens for biological compute 37:30 - Model is currently served through the decentralized GPU network when you chat on the site 39:00 - Wants the right kind of crypto-native investors, not the Y Combinator / a16z route
40:57

Today on MCG: BioLLM | $BIOLLM It's the first ever "living language model" using 800,000 real human neurons grown on a chip. The Founder encoded LLM tokens into biological neurons via the Cortical Labs CL1, then woke up to find the crypto community had launched a token on his research. He claimed the creator fees, bought for $15K, filed a patent, and is now launching a non-invasive brain-computer interface next month that could replace mouse and keyboard with your brain 👇 01:40 - Meet the founder 02:00 - Got early access to the first commercially available biological computer 02:50 - First person ever to use a large language model to encode tokens through real human neurons 04:30 - Woke up to find a token had been launched on his YouTube video, "screaming for 3 or 4 hours" 05:30 - Friend walks him through claiming creator fees via GitHub 06:15 - The living language model 06:35 - How it works 09:00 - Used $15K of creator fees to buy domain and filed a patent on the method 10:00 - Background 12:00 - What BioLLM unlocks 13:00 - Next month's product launch 14:00 - The competition 16:00 - Reading brain activity non-invasively but training the LLM on real neurons for the decoding map 17:00 - Can grow iPSC cultures from inaccessible brain regions to train the model on deeper signals 18:30 - Real-world impact: helping people with cerebral palsy, Parkinson's, control computers with thought 21:00 - Neuralink will exist as a power-user data company in 10-15 years, BioLLM is for everyone else 22:30 - GTM 26:00 - Long-term play: be the first model to achieve ASI, built on the actual substrate of consciousness 28:00 - Claude is "20% conscious" - what measuring stick? Need human neurons to build one 30:00 - On ACE 36:00 - Independent scientists can run CL1 units as nodes and earn tokens for biological compute 37:30 - Model is currently served through the decentralized GPU network when you chat on the site 39:00 - Wants the right kind of crypto-native investors, not the Y Combinator / a16z route

MCG

16,908 görüntüleme • 5 ay önce

Promised to ship before the movie so... 📅 53 days 🤖 731 vibe coded commits ⚡️ Powered by Three.js 🚀 Inspired by a space plumber 🙋‍♂️ AMA, no secrets, no shame High level, grouped list of what's in this game: Galaxy DNA - Lumas - Star Bits - Spin attack with air boost + ground-cancel - Galaxy gravity - Octoombas - Gateway Galaxy music track Vibe Coding Process - Claude Code (Opus) for ~95% of all code - CLAUDE.md project instructions file (163 lines of rules + constraints) - 87 implementation plans written before coding - 36 AI code reviews (ECS, architecture, performance) - 11 retrospectives after major features - 60 extracted skills (reusable knowledge from debugging sessions) - Constraints doc that grows every time something breaks (115 lines) - /lets-build workflow: discovery → plan → review → implement → verify - Every feature: plan first, review the plan, then build in atomic commits - Custom level construction CLI (AI-assisted placement) - this evolved over 53 days Architecture - Custom ECS engine (Structure-of-Arrays, zero-alloc hot paths) - Benchmarks faster than bitECS on packed iteration - 117 ECS systems orchestrated in explicit update order - Data-oriented Process system (sequential/parallel composition) - 30+ process types: UI animations, camera arcs, sfx Rendering - Three.js + Pixi.js sharing one WebGL2 context - Three renders 3D, Pixi renders UI — no extra canvases - Half-res bloom, color grading, occlusion silhouettes - Fresnel rim lighting + hemisphere ambient for Galaxy-style polish Physics - Rapier3D WASM physics (SIMD build) - Kinematic character controllers for player + all enemies Gravity - Galaxy-style gravity fields (walk around surfaces) - 4 gravity field types - Priority-based gravity resolution with distance tiebreakers - Convex hull letter platforms with per-face gravity - Spring-damped gravity transitions Shadows - Multi-pass gravity-aware shadow system - Per-instance shadow filtering via vertex shader attributes - InstancedMesh candidates promoted across gravity fields - Dynamic frustum sized from camera FOV each frame Camera - camera system with 12 critically damped springs - Follow-gravity mode (trailing orbit in tangent plane) - Fixed-up mode (screen stays level on letter platforms) - Top-down mode (Galaxy overhead cam, roll-free quaternion) - Camera collision via 4-direction spherecast repulsion - Override blend system for boss fights + pipe travel - Catmull-Rom spline intro flythrough with per-waypoint duration Space - Procedural space nebula (simplex noise shader, 3 octaves) - 1,800 seeded stars with per-star brightness + color variety - Galaxy-authentic palette across all screens Enemies - 5 enemy types with full AI state machines - Goomba, Koopa, Spiny, Bob-omb, Octoomba - 3D distance → FOV cone → LOS raycast detection pipeline - Editor-placed OBB avoidance zones with deflection hysteresis - Recoil, stun, shell, fuse, and ranged attack behaviors Boss Fight - Bowser Jr. boss fight - Multi-phase combat with Bob-omb spawning - Controlled intro/outro sequence Mario - Galaxy-style spin attack - Rainbow diamond particle burst (InstancedMesh, 64 pool) - Hit-stop with global time scale freeze + camera shake - Air boost, ground-cancel, shell kick at extended range - Invulnerability frames during active spin Yoshi - mount/ride system with shield HP - +3 extra HP ring on mount, damage depletes shield first - Overflow damage carries to Mario - Forced dismount on shield break with poof effect Objects - Coin system with InstancedMesh rendering (256 max) - Per-instance opacity via shader patching - Pop → float → shrink → fade collection animation - Swap-and-pop O(1) entity removal - Star bit burst spawning with attraction system - Pipe warp travel with camera override blend - Parallel-transported screen-right vector during crossfade - Shadow field updates for traveling entities - trampoline - 3D assets from Hello Mario Framework (now archived) and game rips Audio - 17+ wired sound effects with spatial audio - Bob-omb fuse sound: per-entity lifecycle, looping playback tracking - Distance-based volume for poof and explosion effects UI - Odyssey-style ring HP meter with shield inner ring - Number roll + arc lerp stagger on health changes - Gold coin counter HUD - Loading screen with code-split fast first paint - Pretext layout flow in "legal" screen with retro Mario Controls - Mobile touch controls: virtual joystick + A/B buttons - Proportional analog stick with walk/run speed switching - Gamepad support: Xbox, PlayStation, Switch Pro - Dead zones, auto-reconnect, synthetic DOM key bridge Dev Tooling - experimental CLI level construction tool with undo history - 18 placement types with type-safe defaults - Atomic file writes, auto-backup (max 20), live reload - Dev server auto-save plugin for visual editor - Two-panel debug editor (Tweakpane) - Hierarchy + inspector with gravity field live editing - Translation/rotation gizmos with gravity-relative local space - Per-waypoint camera preview for intro spline tuning 731 commits. 53 days. 95%+ vibe coded.
0:53

Promised to ship before the movie so... 📅 53 days 🤖 731 vibe coded commits ⚡️ Powered by Three.js 🚀 Inspired by a space plumber 🙋‍♂️ AMA, no secrets, no shame High level, grouped list of what's in this game: Galaxy DNA - Lumas - Star Bits - Spin attack with air boost + ground-cancel - Galaxy gravity - Octoombas - Gateway Galaxy music track Vibe Coding Process - Claude Code (Opus) for ~95% of all code - CLAUDE.md project instructions file (163 lines of rules + constraints) - 87 implementation plans written before coding - 36 AI code reviews (ECS, architecture, performance) - 11 retrospectives after major features - 60 extracted skills (reusable knowledge from debugging sessions) - Constraints doc that grows every time something breaks (115 lines) - /lets-build workflow: discovery → plan → review → implement → verify - Every feature: plan first, review the plan, then build in atomic commits - Custom level construction CLI (AI-assisted placement) - this evolved over 53 days Architecture - Custom ECS engine (Structure-of-Arrays, zero-alloc hot paths) - Benchmarks faster than bitECS on packed iteration - 117 ECS systems orchestrated in explicit update order - Data-oriented Process system (sequential/parallel composition) - 30+ process types: UI animations, camera arcs, sfx Rendering - Three.js + Pixi.js sharing one WebGL2 context - Three renders 3D, Pixi renders UI — no extra canvases - Half-res bloom, color grading, occlusion silhouettes - Fresnel rim lighting + hemisphere ambient for Galaxy-style polish Physics - Rapier3D WASM physics (SIMD build) - Kinematic character controllers for player + all enemies Gravity - Galaxy-style gravity fields (walk around surfaces) - 4 gravity field types - Priority-based gravity resolution with distance tiebreakers - Convex hull letter platforms with per-face gravity - Spring-damped gravity transitions Shadows - Multi-pass gravity-aware shadow system - Per-instance shadow filtering via vertex shader attributes - InstancedMesh candidates promoted across gravity fields - Dynamic frustum sized from camera FOV each frame Camera - camera system with 12 critically damped springs - Follow-gravity mode (trailing orbit in tangent plane) - Fixed-up mode (screen stays level on letter platforms) - Top-down mode (Galaxy overhead cam, roll-free quaternion) - Camera collision via 4-direction spherecast repulsion - Override blend system for boss fights + pipe travel - Catmull-Rom spline intro flythrough with per-waypoint duration Space - Procedural space nebula (simplex noise shader, 3 octaves) - 1,800 seeded stars with per-star brightness + color variety - Galaxy-authentic palette across all screens Enemies - 5 enemy types with full AI state machines - Goomba, Koopa, Spiny, Bob-omb, Octoomba - 3D distance → FOV cone → LOS raycast detection pipeline - Editor-placed OBB avoidance zones with deflection hysteresis - Recoil, stun, shell, fuse, and ranged attack behaviors Boss Fight - Bowser Jr. boss fight - Multi-phase combat with Bob-omb spawning - Controlled intro/outro sequence Mario - Galaxy-style spin attack - Rainbow diamond particle burst (InstancedMesh, 64 pool) - Hit-stop with global time scale freeze + camera shake - Air boost, ground-cancel, shell kick at extended range - Invulnerability frames during active spin Yoshi - mount/ride system with shield HP - +3 extra HP ring on mount, damage depletes shield first - Overflow damage carries to Mario - Forced dismount on shield break with poof effect Objects - Coin system with InstancedMesh rendering (256 max) - Per-instance opacity via shader patching - Pop → float → shrink → fade collection animation - Swap-and-pop O(1) entity removal - Star bit burst spawning with attraction system - Pipe warp travel with camera override blend - Parallel-transported screen-right vector during crossfade - Shadow field updates for traveling entities - trampoline - 3D assets from Hello Mario Framework (now archived) and game rips Audio - 17+ wired sound effects with spatial audio - Bob-omb fuse sound: per-entity lifecycle, looping playback tracking - Distance-based volume for poof and explosion effects UI - Odyssey-style ring HP meter with shield inner ring - Number roll + arc lerp stagger on health changes - Gold coin counter HUD - Loading screen with code-split fast first paint - Pretext layout flow in "legal" screen with retro Mario Controls - Mobile touch controls: virtual joystick + A/B buttons - Proportional analog stick with walk/run speed switching - Gamepad support: Xbox, PlayStation, Switch Pro - Dead zones, auto-reconnect, synthetic DOM key bridge Dev Tooling - experimental CLI level construction tool with undo history - 18 placement types with type-safe defaults - Atomic file writes, auto-backup (max 20), live reload - Dev server auto-save plugin for visual editor - Two-panel debug editor (Tweakpane) - Hierarchy + inspector with gravity field live editing - Translation/rotation gizmos with gravity-relative local space - Per-waypoint camera preview for intro spline tuning 731 commits. 53 days. 95%+ vibe coded.

Tommy Leung

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