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Stop writing tests. Start describing them. KaneAI converts natural language into end-to-end tests in seconds. ✔ JIRA / PDF / video uploads ✔ Self-healing UI tests ✔ GitHub PR-based test creation ✔ API & backend validation Modern testing, powered by GenAI Give it a try : Follow #AITesting #KaneAI...

25,653 Aufrufe • vor 8 Monaten •via X (Twitter)

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This self-evolving trading system kills 97% of its own strategies It's called SETS Machine. Nobody writes its strategies. It breeds them, tests them and kills them by itself Observe → hypothesize → mutate → backtest → select → deploy. Then again. Every 5 seconds Each strategy is a grid bot with 8 genes: entry logic, lookback, entry threshold, grid levels, spacing, size multiplier, take-profit, stop Every generation: 96 strategies. 8 random newcomers get injected. 80 offspring are bred from the strongest parents. Everything gets backtested on real BTC candles Then comes the part most "AI trading bots" skip Every strategy has to survive data it has never seen. It trains on 70% of the history and gets judged on the last 30%. Lose money there, draw down more than 10%, or win less than half your trades, and you're dead Only the elite live to the next generation. Everyone else is buried Four species compete at once: momentum, mean reversion, volatility breakout, range grid. Quotas stop one lucky species from wiping out the others The winner gets hot-swapped into a paper-trading grid, sized by Kelly. You watch every fill, every take-profit, every stop in real time This is the loop quants get paid $650K a year to run: take an idea, test it on history, kill it if it doesn't work, repeat Here it never stops No PhDs. No team. No servers And it's fully open source. Engine, backtests, dashboard, tests. No API keys, no build step. It runs right in your browser Click any strategy in the gene pool and see its DNA. Change the seed and grow a completely different evolution It even shows you honestly where it loses to buy & hold. Because a system that hides its losses isn't worth running GitHub:

Shelpid.WI3M

119,516 Aufrufe • vor 22 Tagen

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

46,437 Aufrufe • vor 1 Jahr

the winner of an Anthropic hackathon open sourced his full Claude Code setup - and this is absolute f*cking gold 68 subagents, 286 skills, 94 commands, MIT license ECC turns one lone Claude Code assistant into an entire engineering department a blueprint lands before any build, a failing test lands before any fix, and every change gets read again by a context that never watched it get written • who does what > planning - give it one sentence, get back a plan you approve before a line of code exists > review - a fresh-context pass over your diff, with a separate reviewer per language > build repair - one dedicated fixer per toolchain, PyTorch and CUDA included > security - an OWASP sweep plus a scanner hunting injection holes in your agent config > architecture - catches design mistakes while they're still cheap to undo > domain work - database queries, ML pipelines, e2e tests, docs the security duo is the part almost nobody sets up an external OWASP audit runs four figures and a week of waiting this one finishes on your branch before lunch • what the skills cover > testing - tdd-workflow takes you red to green, eval-harness sits on top of it > language packs - Python, Go, Rust, C++, Django, Laravel, Spring Boot, Next.js > context - search-first reads the docs before writing, iterative-retrieval keeps your repo from flooding the window > shipping - Docker, CI/CD, health checks, rollbacks, migrations > beyond code - writing in your voice, market research, pitch decks fork it, cut it down, have your own version running by tomorrow start with one plan and one rules pack switching on all 286 skills at once is the fastest way to make everything worse whoever wires this in over a weekend spends the next quarter reviewing work instead of typing it

Mr. Buzzoni

42,455 Aufrufe • vor 21 Tagen

The winner of an Anthropic hackathon open sourced his entire Claude Code setup and this is absolute f*cking gold. 68 subagents, 286 skills, 94 commands, MIT license. ECC turns one Claude Code assistant into a staffed engineering org. The plan lands before the build, the failing test lands before the fix, and every change gets read again by a context that never watched it get written. • Who does what > Planning -> give it one sentence, get a plan you approve before any code exists. > Review -> reads your diff cold, with a separate reviewer per language. > Build repair -> one fixer per toolchain, PyTorch and CUDA included. > Security -> an OWASP sweep, plus a scanner looking for injection holes in your agent config. > Architecture -> kills design mistakes while they're still cheap, long before migrations. > Domain work -> database queries, ML pipelines, e2e tests, docs. That security pair is what almost nobody bothers to set up. An outside OWASP audit runs four figures and a week of waiting this one finishes on your branch by lunch. • What the skills cover > Testing -> tdd-workflow walks red to green, eval-harness sits on top. > Language packs -> Python, Go, Rust, C++, Django, Laravel, Spring Boot, Next.js. > Context -> search-first reads the docs before writing, iterative-retrieval keeps your repo from flooding the window. > Shipping -> Docker, CI/CD, health checks, rollbacks, migrations. > Beyond code -> writing in your voice, market research, pitch decks. Fork it, cut it down, run your own version tomorrow. Start with one plan and one rules pack switching on all 286 skills at once is the fastest route to a worse setup. Whoever wires this in over a weekend spends next quarter reviewing work instead of typing it.

slash1s

50,640 Aufrufe • vor 24 Tagen

google just released 15 AI tools that are completely FREE and can save thousands of $$$ every single monthly. all open-source. MIT licensed. save this in your bookmark." 1️⃣ pomelli ( builds your entire brand identity from just your website URL, then generates on-brand social posts, campaigns, and images. a free jasper + a junior brand marketer. no watermark, no gen cap in beta. 2️⃣ stitch ( describe an interface, get production-ready HTML/CSS/Tailwind + a figma export. google's free figma killer. 350 designs a month without paying a cent. 3️⃣ opal ( build no-code AI mini-apps and multi-step workflows just by describing them in plain english. basically a free n8n with Gemini baked in. no usage caps. 4️⃣ antigravity ( agentic IDE that plans, edits across files, and builds full apps from a single prompt. the "cursor-killer," free tier runs Gemini 3 Pro + Claude Sonnet 4.5. 5️⃣ mixboard ( canva x pinterest for AI. generate and remix images into moodboards, then edit right on the canvas with plain language. free while in beta. 6️⃣ disco ( turns your messy open browser tabs into custom interactive AI apps. competitor tabs become a comparison matrix, travel tabs become an itinerary. zero code. 7️⃣ notebookLM ( upload PDFs, videos, and notes, get instant summaries, mind maps, quizzes, even a podcast of your own material. replaces notion AI + perplexity + readwise. 8️⃣ Learn Your Way ( turns any topic into a personalized, AI-built course. immersive text, audio lessons, mind maps, and quizzes adapted to how you actually learn. free tutoring. 🔟 Google AI Studio ( prototype and ship AI apps in seconds with a free API key and a 1M-token context window. replaces the openai playground + paid API credits. 1️⃣1️⃣ Jules ( assign it a github issue, it spins up a VM, writes a plan, makes the changes, and opens a PR. a free devin. 15 tasks a day. 1️⃣2️⃣ Gemini CLI ( claude-code in your terminal. reads your codebase, runs commands, ships PRs. genuinely open source (Apache 2.0) and free. 1️⃣3️⃣ Code Wiki ( point it at any public github repo, get a living, self-updating wiki with architecture diagrams and a Gemini chat, every section hyperlinked to the code. 1️⃣4️⃣ Firebase Studio ( AI cockpit for your backend and cloud logic. heads up: existing users only, google is winding it down, so don't start a new project here. 1️⃣5️⃣ Gemini Code Assist ( free github copilot: 180k code completions a month + AI code reviews in VS Code, JetBrains, and github. the free tier that actually out-specs copilot. Follow me and turn on 🔔 post notifications.

m0h

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New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!

Andrew Ng

341,528 Aufrufe • vor 2 Jahren

Seedance 2.5: Some Tips / Thoughts & Prompt Share I knew from the start that the story in this video was a little too ambitious for 30 seconds. Normally, it could easily have been a 2+ minute sequence but I wanted to squeeze it into 30 seconds because there were a few specific things I wanted to test. I used six different character references for this video and wrote the final prompt in Chinese. The council members were actually supposed to speak in an alien language but I think because I asked for English subtitles and wrote the dialogue itself in English, Seedance decided to make them speak English too. Seedance 2.5 follows prompts extremely well. Actually, "extremely strictly" might be a better way to describe it. You need to be very careful with timing. If you ask for too many actions within a very short time window, you may get what feels like a sped-up version of the scene. On the other hand, if you assign too much time to an action that should happen quickly, the result can feel unnaturally slow or almost like slow motion. At least, these are both things I've run into during my tests. The same applies when you use a shot list without timestamps. Depending on how much you ask the model to fit into each shot, the pacing can still end up too fast or too slow. Finding the right balance between the amount of action and the time available is very important. Another thing I noticed is that Seedance 2.5 can be very literal about motion and state transitions. For example, if a character is facing the camera and you want to see their back in the next moment, simply describing the next shot from behind may not be enough. If you don't explicitly say that the character turns around, the model can sometimes morph or transition directly into the rear view without performing the actual turn. Seedance 2.0 would usually handle these things on its own. So it helps to think not only about what you want to see but also how the scene gets there. Once you identify these kinds of issues and explicitly correct them in the prompt, you can usually get a much cleaner result. For scenes with multiple characters, dialogue and a lot of specific details, the 5,000-character prompt limit can also become restrictive in English. Sometimes there simply isn't enough room to describe everything properly. That's where Chinese prompts become useful. My original English prompt clearly needed much more detail, so I rewrote it in Chinese and used the extra space to describe the scene more precisely. You can see the result in the video. Prompt in the replies.

Kōda

17,000 Aufrufe • vor 2 Monaten

I cut Fable 5 token usage 2.5x with just one change! - Before: 5.5 M tokens · 7 errors · $8.94 - After: 2.3 M tokens · 0 errors · $4.17 The final build was the same for both, but the path the agent took wildly differed. In both runs, the agent started with the same thing, i.e., it understood the backend before building anything, like: - Permission policies - Available storage buckets - Auth providers configured - How edge functions are deployed The first run used Firebase, which was built for a human dev using a dashboard. While the dev can read the above state by clicking through tabs, an agent has no dashboard. So it gathered the same info through API calls. And there's no single Firebase call that returned this info. The agent required to query multiple times, and each query over-returned. For instance, when the agent asked how sign-in is configured, Firebase also returned the entire auth surface and every method it supported. This was far more context than what it needed. And it repeated across every part of the backend it inspected. Some states (like which auth providers are active) weren't queryable at all. I provided it myself. Otherwise, the agent would have guessed. Errors further compounded the token usage. When a dev sees "permission denied," they can look at the console and figure out whether it's a rule, a path, or an unauthenticated request. Firebase returned the same string to the agent as well, and it had none of that surrounding context to debug. So it guessed again, picked the most likely cause, and rewrote code, utilizing more tokens. This Firebase setup cost me 5.5M tokens and 7 manual interventions during errors on a full-stack RAG app. But I brought that down to 2.3M tokens and 0 manual interventions by using InsForge as the backend context engineering layer (open-source and self-hostable via Docker). It provides the same primitives as Supabase/Firebase, but structures the entire information layer for agents, instead of dashboards. In one CLI call that consumed ~500 tokens, the agent saw the full backend topology before writing a single line of code. This included auth, database, storage, edge functions, model gateway, micro VMs, and deployment. Also, instead of loading the entire product surface into context on every task, four narrowly scoped skills activated only when relevant to keep cognitive load minimal. And to ensure efficient retries if needed, every CLI operation returned structured JSON with meaningful exit codes, so the agent never guessed what to do next. Here's the InsForge GitHub Repo: (don't forget to star it ⭐) The video below depicts the final build, comparing Firebase and InsForge. To dive deeper, I recently published a full walkthrough building the same RAG app on both backends and inspected them end-to-end. Read it below.

Avi Chawla

113,330 Aufrufe • vor 4 Monaten

Anthropic just changed how they think about Claude 5. Not with a new model. Not with a benchmark. With a completely different philosophy for building AI systems. Most people will miss it. They're still trying to write better prompts. Anthropic is optimizing something else entirely: Context. Here's what every AI builder should learn from it: 1. Stop telling AI exactly what to do. Start telling it what success looks like. Older models needed rigid instructions. Newer models perform better when you define the objective and let them make the decisions. The goal is no longer more control. It's more clarity. 2. Context is a budget, not a storage unit. Every extra sentence competes for the model's attention. A massive context file doesn't make AI smarter. It often makes reasoning worse. The best systems don't load everything. They load only what's relevant. 3. Great tools beat great prompts. Most people spend hours tweaking prompt wording. Anthropic is investing in better interfaces instead. Clear parameters. Structured inputs. Well-designed tools. If the interface removes ambiguity, the model makes better decisions before it even starts reasoning. 4. Show reality instead of describing it. Want a specific coding style? Provide the codebase. Want a certain design language? Share the mockup. Want consistent outputs? Give it tests to satisfy. Concrete references consistently outperform long written instructions. 5. Deliver information when it's needed. Not everything belongs in the initial context. Documentation. Style guides. Verification. Reviews. Load them only when they're relevant. Think of context like RAM, not a hard drive. 6. The real advantage is system design. Prompt engineering isn't disappearing. It's becoming one small part of a much bigger stack. Memory. Retrieval. Context architecture. Tool design. Evaluation. Every improvement compounds. The biggest takeaway from Anthropic's update? The next generation of AI won't be won by the people writing the longest prompts. It'll be won by the people building the smartest systems. Same models. Completely different results.

Evan Luthra

38,751 Aufrufe • vor 2 Monaten

Andrew Wilkinson owns 40+ businesses. He just showed me how he's using OpenClaw, Claude Code and AI agents to run latest business, start new ones, and automate everything. Here's what I learned: 1. In December 2025, something clicked. He started waking up at 3AM with a smile, sitting in terminal with 10 Claude Code tabs open. He hasn't stopped since. He calls it chasing the dragon. 2.He built a full SaaS product called Deep Personality. A 40-minute personality test that generates a 100-page report written like Robert Greene. $20 000 in revenue. Zero employees. The entire business runs on AI agents. 3. He has agents for support, marketing, and dev. When a support ticket comes in, the agent either handles it or sends it to the dev agent. If it's critical, the agent fixes the bug and merges the PR before he wakes up. Then it emails the customer back. 4. His marketing agent is connected to PostHog, manages Meta and Reddit ads, creates ad creative, runs multivariate tests, and sets budgets. He's about to give it a $100 k/month ad budget and see what happens. 5. He forgot his laptop on a trip to Arizona. He ran his entire business from the back of Ubers using OpenClaw. Nobody picked up that every single email was written by AI. 6. His take on vibe coding: the worst part about business is people. Between your vision and execution are 100 people you have to convince. Vibe coding removes all of them. For the first time he can do every part of building a product himself. 7. He was trying to build OpenClaw before OpenClaw existed. Now he uses a tool called Harbor, which is basically a GUI for managing multiple agents. You can see all your agents, their status, knowledge bases, and databases in one place. 8. He built a custom AI for his relationship. He and his girlfriend took 15 psychological tests, put the results into ChatGPT, and asked it to analyze their relationship. It nailed every fight they've ever had. That became the product idea for Deep Personality. 9. His honest take: he spends 50% of his time debugging, 30% improving the setup, and 20% being productive. It's a treadmill. But the 20% that works is so powerful he can't stop. 10. His prediction: we're 3-6 months from being able to hand basic businesses off to AI to run entirely. And pretty soon Anthropic and OpenAI are going to launch AI CEOs. This is an inside look at how a serious operator Andrew Wilkinson is using AI agents in the real world. The good, the bad, the debugging, all of it. Most people don't show you this. Episode is live on The Startup Ideas Podcast (SIP) 🧃 watch

GREG ISENBERG

144,005 Aufrufe • vor 5 Monaten

Dean Koontz has published more than 140 novels, 74 works of short fiction, and sold more than 500 million books. Simply put, he’s one of the most prolific writers alive today. Some highlights from our chat: 1. Dare to love the English language. 2. Characters come alive when they're given free will. Instead of constraining them in an outline, let them go where they want. You know they’re alive once they start surprising you. He says: “I give the characters free will like God gave it to us.” 3. Everything a writer believes about life and death, culture and society, relationships and the self, God and nature will wind up in their books. A writer’s body of work, therefore, reveals the intellectual and emotional progress of its creator, and over time, becomes a map of their soul. 4. To think you understand the world is to be foolish in the extreme. The world is too complex for us to understand it. To see reality clearly is to be utterly enchanted by its staggering complexity. 5. Where should you look? Well, the supernatural enters the world in mundane ways, and rarely the great and glorious flashes of drama. 6. Dean writes his novels page-by-page, and doesn’t move onto the next page until he nails the existing one. There’s no messy first draft. Because of that, he’s basically done with his novels once he finishes the final page. 7. Where does a unique writing voice come from? Three places: style, perspective, and a philosophy of life. 8. Be skeptical of conventional wisdom. There’s an encyclopedia of common wisdom in publishing. All of it is common and none of it is wise. You have to become aware of that, go your own way, and just stick with it because there are so many ways you can be sent wrong based on "that's the way we always do it." 9. The aesthetic plainness of contemporary writing (and culture at large) is crushing our souls. 10. Contemporary fiction is suffering from plainness in particular. It started when writers started imitating Hemingway (who stripped his prose down but kept the mystery and underlying strangeness of the world by implication). But the imitations that came later stripped the prose down while also removing the underlying depth that made Hemingway so great. 11. Koontz Law of Writing #1: Never go inside more than one character's mind in a scene. Each one should come from a singular viewpoint. 12. Koontz Law of Writing #2: Metaphors aren't meant to dazzle readers, but to seduce them into a more intimate relationship with the story. 13. Koontz Law of Writing #3: Metaphors and similes describe a scene more colorfully than a chain of adjectives — while reinforcing the mood. The point is that you can create depth by describing things metaphorically instead of using blunt adjectives. That’s what poetry does: it uses words to say more than the word itself says, which creates a mood. 14. Great prose doesn't come from piling on adjectives. It comes from finding the perfect metaphor that does triple duty: describes the scene, reinforces the mood, and reveals something about the character. 15. The goal is for metaphors not to pop out like showmanship, but to flow into the music of the language. 16. Develop an ear for the musicality of language. 17. A book can succeed with a mediocre plot if the characters are compelling. Character is the center of good fiction. If the characters work, the story works. 18. From the afterword of his book, Watchers: “We have within us the ability to change for the better and to find dignity as individuals rather than as drones in one mass movement or another. We have the ability to love, the need to be loved, and the willingness to put our own lives on the line to protect those we love, and it is in these aspects of ourselves that we can glimpse the face of God; and through the exercise of these qualities, we come closest to a Godlike state.” I've shared the full conversation with Dean Koontz below. The YouTube video link is in the replies, and so are the links to Apple and Spotify.

David Perell

74,956 Aufrufe • vor 1 Jahr

People have been asking how I'm generating my product videos. Here's a prompt my agent wrote so you can copy its process: # Prompt: make product videos with code and AI agents Copy everything below the line into a coding agent (Claude Code, Codex, etc.). Fill in the `{{…}}` placeholders first. It describes how Inbox Zero produced its landing-page hero, launch films and about 15 docs and app walkthroughs, each costing well under $1 in API calls. --- You are producing product videos for **{{PRODUCT}}** ({{ONE-LINE DESCRIPTION}}). The videos are a landing-page hero (~55s), short launch films (25–40s) and feature walkthroughs (45–110s) for the app and docs. They are made entirely with code: there's no screen recording and no video editor. Quality matters far more than cost. The product's source code is at `{{APP_REPO_PATH}}` and its docs are at `{{DOCS_PATH}}`. The founder or reviewer is {{REVIEWER}}. They give taste feedback, and you turn that feedback into written rules. ## 1. Core idea Every video is an **HTML/CSS/JS page whose every frame is a pure function of time `t`**. Headless Chrome screenshots each frame, and ffmpeg encodes them. This gives you: - Exact, repeatable renders: a copy change means a cheap re-render, not a re-edit. - Parallel rendering, because nothing carries over from one frame to the next. - Real product UI, because the "footage" is real DOM captured from the running app. Don't use Remotion or any other video framework; a ~200-line engine is enough. Don't screen-record: recordings can't be re-timed, re-worded or kept pixel-perfect. ## 2. Set up a private video repo Create a **private** repo (e.g. `{{ORG}}/demo-video`) separate from the product repo. Its layout: ``` AGENTS.md # read-first notes for every agent: pipeline, taste rules, mistakes made README.md # catalog: every video, its branch, length, status, where it's used BRIEF.md # product truths, fictional cast, kit API, end-card and taste rules EXPLAINER.md # brief for the walkthrough format LAUNCH.md # brief for launch films MUX.md # hosting IDs for every published video engine.js # renderAt(t), addScene, spring/easing helpers, word-by-word captions render.mjs # parallel Playwright frame renderer → ffmpeg # voice + music mix with ducking and loudness normalisation judge.mjs # audio judge (picks the best TTS take) vjudge.mjs # video judge (reviews a whole render) # contact sheet from sampled frames kit/app/ # REAL app screens captured as DOM snapshots (see §3) kit/explainer.js # overlays: step captions, callouts, highlights, camera, cursor, typing, toasts, # and mocks of third-party UIs (Gmail, Outlook, Slack, …) kit/endcard.js # shared end card music/ # shared music bed(s) tools/ # upload scripts (hosting), helpers ``` **One branch and one git worktree per video** (`git worktree add -b explainer-foo ../demo-explainer-foo main`). `main` holds only shared things: the kit, the briefs and the tools. Merge `main` into a video branch to pick up kit fixes. Put kit fixes found while making a video in a separate commit so they can be cherry-picked to `main`. Commit each video's web encode, poster, `.vtt` captions and a `VARIANT.md`. That file holds the script, the timings, chapter markers, the claim checks, and any docs-vs-UI discrepancies found. Git-ignore masters and intermediate files. Keep `AGENTS.md` alive. Every time the reviewer gives feedback, or an agent makes a mistake, add a one-line rule there. This file is what makes the tenth video good on the first try. ## 3. Capture the real UI kit 1. Run the app locally with a **seeded database of fictional data**: one consistent cast (a main user plus 4–5 contacts and a fictional company), and realistic but fake emails, events and numbers. Use local emulators for third-party APIs where possible. 2. Write a capture script (Playwright) that visits each screen and state the videos need. It saves the **rendered DOM plus inlined CSS** as a standalone HTML snapshot, plus a PNG for reference. Inbox Zero ended up with ~140 states. 3. Missing states can be **derived** from the nearest snapshot by editing the DOM to match the real component's source (text, classes), never invented. 4. **Audit every snapshot for leaks** before committing: real names, emails, avatars, API keys, and the capture machine's **timezone and locale**. A real city name once leaked through a timezone picker. 5. Document the states, selectors and how to recapture in `kit/app/README.md`. ## 4. Write the script (accuracy first) - Read the docs page and **the app source** for the feature. Every label, button and step on screen must match the product. Where the docs and the UI disagree, the UI wins; log the discrepancy in `VARIANT.md` and fix the docs afterwards. (This produced a steady stream of real docs fixes and bug reports.) - Check claims in code, not just in the docs. For example, does feature X actually use data Y? - Keep a list of **product truths** in `BRIEF.md`: supported platforms ("always say Gmail *and* Outlook"), trial terms, and what is early-access. - A walkthrough has a 1–2s product payoff up front, then numbered steps (one caption each), one result shot, and a minimal end card. - **Intro messaging** (hero and launch) must say what the product *is* in the first line. Write several candidate openers and let the reviewer pick. ## 5. Build the timeline - `engine.js`: scenes are DOM subtrees shown and hidden with `display:none` (not `visibility`, because visible children leak through a hidden parent). All motion is computed from `t` with springs and easing. - **Render purity is mandatory**: no ` no randomness without a seed, no state carried between frames. Workers start mid-timeline. - Camera moves are CSS transforms on the snapshot, and so are cursor paths, typing and highlights. - Preview live with `npx serve .`, using `?t=17` to start at 17s. ### Taste rules (start with these; add your reviewer's) - **Openers:** light and product-native. No dark gradients, glossy fake app icons with red badges, or captions over blurred clutter. Reach the product payoff within ~2s. - **No recaps:** never repeat earlier points in words or as a montage at the end. - **End cards:** neat and centred, with a logo lockup, one line and a quiet URL. Landing cuts can carry one CTA (e.g. "Try free for 7 days") plus small platform marks. Never "·"-joined copy, and never a button stuffed with text. - **Legibility:** body text ≥ 20px at 1080p, no wide shot of unreadable UI for more than ~1s, no words sliced at frame edges, and captions fully settled ≥ 0.4s before the click they describe. - **Transitions:** no long crossfades between two busy UIs (the double exposure looks muddy). Use a cut, a camera push or a short blur-through. ## 6. Audio - **Voiceover:** a strong TTS model with style instructions (Inbox Zero used Gemini Flash TTS through OpenRouter). Pick one voice per format. Generate **2–3 takes per line** and pick the best with an audio-capable model (`judge.mjs`): it scores pacing, mispronunciations, odd stresses and clipped endings. - **Music:** generate one shared bed per format (Inbox Zero used Lyria). Models ignore the requested length, so detect the beat grid (librosa) and cut or loop at phrase boundaries. - **Mix (` duck the music under the voice with a sidechain, then do two loudnorm passes to −14 LUFS and −1.2 dBTP. - Agents can't listen. Flag music endings and loop points for a human ear-check. ## 7. Render - `render.mjs` splits frames across N Playwright workers. Each one loads the page, sets `t` and screenshots. ffmpeg then encodes a high-quality master (CRF ~12) and a web copy (CRF ~20). - Add a screenshot timeout and retry (e.g. 120s, 5 tries); long renders hit flaky frames. - Support env vars to render a sub-range or a single segment, so fixes don't need a full render. - **On a shared machine:** cap workers (e.g. 3 per agent) and only stop your own processes by PID. Never `pkill -f render.mjs`: it kills other agents' jobs. ## 8. Review before showing anyone - Sample frames into 2×2 contact sheets and **look at them yourself**, zooming in on captions, edges and data. This is the main review. - Use a video-capable model (`vjudge.mjs`, Gemini Flash) for whole-video checks like timing and caption order, but **treat its output as hints**. Video and audio judges hallucinate problems, so verify every claim against frames. - Check: claims match the code, the fictional cast only, no leaks, the legibility rules, the end card. - Show the reviewer the finished mp4s (open the folder for them) along with the `VARIANT.md` discrepancy list. ## 9. Work in parallel with subagents - Once the kit and briefs exist, give **one subagent per video**. Each gets its own worktree and branch, the brief, the relevant docs page, and the instruction to read `AGENTS.md` first. - Each subagent writes the script, builds, renders, reviews and reports back with the mp4 path and the discrepancies found. - The coordinator merges kit fixes into `main`, uploads, and integrates the videos into the app and docs. ## 10. Publish - **Hosting:** use Mux (or similar) rather than YouTube embeds. You get a clean player, no recommendations and auto-subtitles. The upload script sets a public playback policy and generated English captions, and records each ID in `MUX.md`. Pass API tokens via env vars and never commit them. - **In the app:** a feature-page video button or dismissible card. **Lazy-load the player** (`next/dynamic`, `ssr:false`) so it only loads when the dialog opens and adds nothing to the page bundle. - **Docs:** a lazy ` ` at 16:9. - **Landing hero:** track `video clicked / started / progress / completed / closed` with a `video_id`. Don't A/B test the hero video: only the ~10% who click can be affected, so overall-sign-up tests are underpowered. Ship the better cut and compare cuts on watch-through and viewer → sign-up by `video_id`. - **YouTube:** the API locks uploads from unaudited projects to private, so upload in Studio and schedule one video every ~2 days. If an agent automates Studio, set text with `execCommand('insertText')`, type dates with real keystrokes, and **read every value back before clicking Schedule**. - If the product repo is public, keep commit and PR text free of private details. If it mounts private submodules, grep them before removing any shared component. ## 11. Mistakes we already made (don't repeat them) - Invented UI in v1, where the reviewer's first note was "show real UI". → Real DOM snapshots only. - Claims the product doesn't make (e.g. "important emails move to the top" when they're actually labelled). → Check in code. - Taglines the company doesn't use. → Offer options and let the reviewer pick, then record the pick. - A real timezone leaked in a snapshot and was committed to history. → Audit before committing. - Agents killed each other's renders with `pkill`. → Stop processes only by your own PID. - A judge model "found" skips and glitches that weren't there. → Verify against frames. - Removing a shared component broke production through a private submodule. → Grep it first. - A free hosting plan capped the asset count, and the upload script swallowed the error. → Scripts must surface API errors. ## Deliverables per video - [ ] `out/ -web.mp4`, poster, `.vtt`, and chapter markers - [ ] `VARIANT.md`: script, timings, claim checks, docs/UI discrepancies - [ ] An entry in the `README.md` catalog, and its hosting ID in `MUX.md` - [ ] Any new rules learned are added to `AGENTS.md`

Elie Steinbock

19,696 Aufrufe • vor 9 Tagen

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 Aufrufe • vor 1 Jahr