Pydantic AI

Pydantic AI AI

Pydantic AI AI

Pydantic AI AI

Pydantic AI AI

Pydantic AI AI

Pydantic AI AI

Pydantic AI AI

Pydantic AI AI

Pydantic AI AI

Pydantic AI AI

Chat based form filler in natural language

Hi folks, I am building an AI chat based system whose eventual goal is to get answers to all the questions I want to have answered from user in plain language conversation. It’s quite similar to filling out a form, but instead of boxes, it happens through a chatbot. I want to design and build it end-to-end for maximum scalability. I also want to make it feature-rich — for example, the bot should be able to use tools like search in the middle of conversations, read uploaded files /images. If users diverge into different topics, I want to allow that and let bot helps it, but eventually bring things back to where we want to lead them. The system should generate questions based on the user's input and intelligently decide what to ask next. I’m confused about how to build it. I previously built a state machine, but it didn’t perform as expected because out-of-order data coming from users breaks it. I want to explore other tools like LangGraph, but I’m not really sure how to design the overall architecture. I need help designing it in a way that it can be plugged into different systems and reused across products. The data I want to gather is stored in a Pydantic model. I also have a couple of helper functions like web search, DB update functions, and utility functions to extract data from user input, which I can probably wrap into tools. Would love some help figuring out the right architecture and approach for this. submitted by /u/sagar12sagar [link] [comments]

MCP Generator v2.0.0

Built this with Claude/Claude Code — it generates MCP servers from OpenAPI specs, free and open-source on GitHub. A feel days ago I posted a CLI that converts OpenAPI specs into MCP servers. The feedback here was brutal and exactly what I needed. Here's what I actually fixed and shipped based on your comments: The original post got two pieces of feedback that changed the project: "Raw endpoints wrapped as tools is a poor LLM interface pattern" — Fair. The generator now produces a scaffold you're supposed to implement, not ship. Incremental generation (@@mcp-gen:start/end markers) means you regenerate without losing your handler logic. "console.log leaking into stdio corrupts the JSON-RPC stream" — This was a real bug. Fixed with a log() helper that writes to stderr and a safeSerialize() that handles Buffer/Uint8Array as base64 before anything touches stdout. Circular $ref schemas were the next wall — fixed with SwaggerParser.dereference({ circular: "ignore" }) + a visited-Set guard in the schema walker. What shipped in v2.0.0: YAML input (.json, .yaml, .yml, URLs) Python/FastMCP + Pydantic v2 target Incremental generation — re-run the generator without losing custom handlers oneOf/anyOf/discriminator support for complex specs Auth stubs from securitySchemes Interactive CLI mode for first-time users Built-in registry: mcp-gen init --from stripe (10+ APIs: Stripe, GitHub, Slack, OpenAI, Twilio, Shopify, Kubernetes, DigitalOcean, Azure) stdout isolation + safe binary serialization Circular $ref safety Published on npm and pip Use cases: Give Claude instant access to any REST API in under 2 minutes Generate internal API MCP servers for your team Rapid prototyping — have a working server before writing a single handler API-first development — spec first, scaffold second, logic last 2-minute setup: npm install -g mcp-gen mcp-gen init --from stripe --out ./stripe-mcp cd stripe-mcp && npm install && npm start Then add it to claude_desktop_config.json and Claude has full Stripe access. GitHub: https://github.com/ChristopherDond/MCP-Generator npm: https://www.npmjs.com/package/mcp-gen Install: npm install -g mcp-gen Questions? Want to contribute? Drop a comment or check out CONTRIBUTING.md on GitHub: https://github.com/ChristopherDond/MCP-Generator/blob/main/CONTRIBUTING.md Still a lot to do — oneOf edge cases, better binary streaming, more registry entries. If you find a spec it chokes on, open an issue. Thanks for all feedbacks and stars!!! submitted by /u/ChristopherDci [link] [comments]

I built a video production pipeline with Claude - Integrates Live2D, Fish Audio, Sadtalker, and tons of other tools.

I've been working on a multi-agent AI pipeline that takes a topic (like "Ada Lovelace" or "The Cold War Space Race") and produces a complete, chapter-structured educational YouTube video, 15–20 minutes long. Here's what actually happens when you run it: You give it a persona (think: channel identity, tone, visual style) and a topic. From there, a chain of specialized agents handles everything: Script agents generate a chapter contract (outline + pacing plan), then write full narration for each chapter with timing built in. Asset agents generate matching visuals (images, B-roll) and sound design assets for each scene. Render agents (running on a Windows host with GPU) composite everything — narration audio, visuals, transitions, background music — into a finished video file. Upload agents push the result directly to YouTube with generated metadata. The pipeline is split across two environments: script and asset work runs in a Linux dev container (WSL), while rendering runs on the Windows host to access CUDA and video tooling. They talk over HTTP with a lightweight orchestrator coordinating state. The whole thing is phase-based — every step (W2.1, W4.3, R3.1, etc.) is independently re-runnable, so if your render fails or you want to rewrite chapter 3, you don't start over. Each phase reads and writes typed artifact files (JSON manifests, audio files, image directories) so agents are loosely coupled. It uses Claude as the core LLM for scripting, with structured prompts per persona to keep the voice consistent across episodes. Still early-stage but already producing watchable content. Here are the three major technical challenges and how they're solved: 1. Script Writing via Contract Architecture The core problem: how do you keep a 20-minute AI-written script narratively coherent across chapters written in separate LLM calls? The answer is a narrative contract (W2.1.a) — a validated JSON blueprint generated before any script text is written. It encodes four types of cross-chapter constraints: Threads — story arcs that must open in one chapter and close in another, with a declared payoff type (resolved, tragedy, etc.) Entities — named people/places with a forced first-introduction chapter, preventing retroactive mentions Facts Required — citations chained with dependencies (fact B can't appear until fact A is established) Timeline Anchors — temporal reference points that let non-linear structure (flashback, in-medias-res) stay internally consistent The contract is generated via an Opus → structural validate → Sonnet review loop (up to 3 rounds). Sonnet checks semantic coherence (no orphan entities, threads actually close), while the structural validator runs a Pydantic parse + temporal constraint check. Chapter writers downstream are bound to the contract — they can't invent threads or drop required facts. 2. Research via Fanout The research pipeline doesn't produce one outline — it produces several competing ones and eliminates losers. W1.11.a spins up N parallel OutlineAgent instances, each working from the same research package but on different thesis candidates. Each produces a three-level hierarchy: thesis → chapter arguments → scene beats. W1.12.a runs an independent grounding/revision loop on each branch: Grounding reviewer (Sonnet) flags blocking issues (claims contradicting cited facts) vs. polish issues (real facts exist but uncited) Revision agent applies fixes without restructuring Quality reviewer checks for structural failures (topical chapter lists, collapsed middles, summary endings) Up to 3 revision rounds per branch, all in parallel. W1.13.a runs a single judge agent that scores each refined outline on four axes: Axis Weight What it measures Concept Hook 0.40 CTR potential; title falsifiability Trap Closure 0.30 Protagonist's own logic creates complications (not external events) Opening Momentum 0.15 Cold-open quality — concrete moment vs. credentials/definitions Rewatch Anchor 0.15 One chapter that inverts the opening assumption sharply enough to quote The highest-scoring branch becomes Outline.json. The judge doesn't compare outlines against each other — it scores each independently to avoid anchoring bias. 3. Outline Creation and Evaluation The structural rules for a valid outline are unusually strict, based on observed failure modes: Six structural failure patterns the quality reviewer flags: No Narrative Spine — chapters are reorderable (topical list, not argument chain) Thesis Not Echoed — chapters cover topics instead of advancing the central claim Beats That Are States — "tension builds" instead of "character takes specific action" Vibes Chapter — emotionally evocative prose, vague beats Collapsed Middle — chapters 3–5 repeat the same narrative move Summary Ending — final chapter recaps instead of introducing new consequence Beat-level rules are similarly precise: each beat must name an actor, action, and datab

LLM Documentation accuracy solved for free with Buonaiuto-Doc4LLM, the MCP server that gives your AI assistant real, up-to-date docs instead of hallucinated APIs

LLMs often generate incorrect API calls because their knowledge is outdated. The result is code that looks convincing but relies on deprecated functions or ignores recent breaking changes. Buonaiuto Doc4LLM addresses this by providing free AI tools with accurate, version-aware documentation—directly from official sources. It fetches and stores documentation locally (React, Next.js, FastAPI, Pydantic, Stripe, Supabase, TypeScript, and more), making it available offline after the initial sync. Through the Model Context Protocol, it delivers only the relevant sections, enforces token limits, and validates library versions to prevent mismatches. The system also tracks documentation updates and surfaces only what has changed, keeping outputs aligned with the current state of each project. A built-in feedback loop measures which sources are genuinely useful, enabling continuous improvement. Search is based on BM25 with TF-IDF scoring, with optional semantic retrieval via Qdrant and local embedding models such as sentence-transformers or Ollama. A lightweight FastAPI + HTMX dashboard provides access to indexed documentation, queries, and feedback insights. Compatible with Claude Code, Cursor, Zed, Cline, Continue, OpenAI Codex, and other MCP-enabled tools. https://github.com/mbuon/Buonaiuto-Doc4LLM submitted by /u/mbuon [link] [comments]

Research shows auto-generated context makes AI agents 2-3% worse. I tested the opposite approach.

Hey, I've been building in the AI agent space and kept running into the same problem: agents don't really fail at writing code. They fail at understanding how the project works before they start. So they guess. Where to make changes, what pattern to follow, what files are safe to touch. And that's what causes most bad edits. I came across the ETH Zurich AGENTS.md study showing that auto-generated context can actually degrade agent performance by 2-3%. That matched what I was seeing — dumping more code or bigger prompts didn't help. It just gave the agent more surface area to guess from. So I tried the opposite: what if you only give the agent the stuff it *can't* infer from reading code? Things like: - conventions (how routing/auth/testing is actually done in this project) - constraints (generated files you shouldn't edit, circular deps to avoid) - structural signals (which files have 50+ dependents — touch with care) - git signals (what keeps breaking, what was tried and reverted) I built a CLI (and a few runtime tools so the agent can check itself mid-task) to test this. It scans a repo and generates ~70 lines of AGENTS.md with just that information. No LLM, no API key, runs locally in a few seconds. Then I ran it against real closed GitHub issues (Cal.com, Hono, Pydantic) with a pinned model. Agents with this context navigated to the right file faster, used the correct patterns, and produced more complete fixes. On one task: 136s vs 241s, with a 66% more thorough patch — from 70 lines of context, not the full repo. The surprising part: the biggest improvement didn't come from *adding* context. It came from removing everything that didn't matter. This actually lines up with something Karpathy has been saying recently — that agents need a knowledge base, not just more tokens. That distinction clicked after seeing it play out in practice. I also compared against full repo dumps and graph-based tools, and the pattern held — graphs help agents explore, but project knowledge helps them decide. Curious if others have seen the same thing. Feels like most of the problem isn't "more context," it's the wrong kind. (if anyone's curious, the CLI is called sourcebook — happy to share more, but mostly interested in whether this matches what others are seeing with their agents) submitted by /u/re3ze [link] [comments]

I made a terminal pet that watches my coding sessions and judges me -- now it's OSS

https://preview.redd.it/c1h2wvnv6ptg1.png?width=349&format=png&auto=webp&s=46e935832611acd401bb32eac69e7de615067d4f I really liked the idea of the Claude Code buddy so I created my own that supports infinite variations and customization. It even supports watching plain files and commenting on them! tpet is a CLI tool that generates a unique pet creature with its own personality, ASCII art, and stats, then sits in a tmux pane next to your editor commenting on your code in real time. It monitors Claude Code session files (or any text file with --follow) through watchdog, feeds the events to an LLM, and your pet reacts in character. My current one is a Legendary creature with maxed out SNARK and it absolutely roasts my code. Stuff I think is interesting about it: No API key required by default -- uses the Claude Agent SDK which works with your existing Claude Code subscription. But you can swap in Ollama, OpenAI, OpenRouter, or Gemini for any of the three pipelines (profile generation, commentary, image art) independently. So your pet could be generated by Claude, get commentary from a local Ollama model, and generate sprite art through Gemini if you want. Rarity system -- when you generate a pet it rolls a rarity tier (Common through Legendary) which determines stat ranges. The stats then influence the personality of the commentary. A high-CHAOS pet is way more unhinged than a high-WISDOM one. Rendering -- ASCII mode works everywhere, but if your terminal supports it there's halfblock and sixel art modes that render AI-generated sprites. It runs at 4fps with a background thread pool so LLM calls don't stutter the display. Tech stack -- Python 3.13, Typer, Rich, Pydantic, watchdog. XDG-compliant config paths. Everything's typed and tested (158 tests). Install with uv (recommended): uv tool install term-pet Or just try it without installing: uvx --from term-pet tpet GitHub: https://github.com/paulrobello/term-pet MIT licensed. Would love feedback, especially on the multi-provider config approach and the rendering pipeline. submitted by /u/probello [link] [comments]

I got tired of 3 AM PagerDuty alerts, so I built an AI agent to fix cloud outages while I sleep. (Built with GLM-5.1)

If you've ever been on-call, you know the nightmare. It’s 3:15 AM. You get pinged because heavily-loaded database nodes in us-east-1 are randomly dropping packets. You groggily open your laptop, ssh into servers, stare at Grafana charts, and manually reroute traffic to the European fallback cluster. By the time you fix it, you've lost an hour of sleep, and the company has lost a solid chunk of change in downtime. This weekend for the Z.ai hackathon, I wanted to see if I could automate this specific pain away. Not just "anomaly detection" that sends an alert, but an actual agent that analyzes the failure, proposes a structural fix, and executes it. I ended up building Vyuha AI-a triple-cloud (AWS, Azure, GCP) autonomous recovery orchestrator. Here is how the architecture actually works under the hood. The Stack I built this using Python (FastAPI) for the control plane, Next.js for the dashboard, a custom dynamic reverse proxy, and GLM-5.1 doing the heavy lifting for the reasoning engine. The Problem with 99% of "AI DevOps" Tools Most AI monitoring tools just ingest logs and summarize them into a Slack message. That’s useless when your infrastructure is actively burning. I needed an agent with long-horizon reasoning. It needed to understand the difference between a total node crash (DEAD) and a node that is just acting weird (FLAKY or dropping 25% of packets). How Vyuha Works (The Triaging Loop) I set up three mock cloud environments (AWS, Azure, GCP) behind a dynamic FastApi proxy. A background monitor loop probes them every 5 seconds. I built a "Chaos Lab" into the dashboard so I could inject failures on demand. Here’s what happens when I hard-kill the GCP node: Detection: The monitor catches the 503 Service Unavailable or timeout in the polling cycle. Context Gathering: It doesn't instantly act. It gathers the current "formation" of the proxy, checks response times of the surviving nodes, and bundles that context. Reasoning (GLM-5.1): This is where I relied heavily on GLM-5.1. Using ZhipuAI's API, the agent is prompted to act as a senior SRE. It parses the failure, assesses the severity, and figures out how to rebalance traffic without overloading the remaining nodes. The Proposal: It generates a strict JSON payload with reasoning, severity, and the literal API command required to reroute the proxy. No Rogue AI (Human-in-the-Loop) I don't trust LLMs enough to blindly let them modify production networking tables, obviously. So the agent operates on a strict Human-in-the-Loop philosophy. The GLM-5.1 model proposes the fix, explains why it chose it, and surfaces it to the dashboard. The human clicks "Approve," and the orchestrator applies the new proxy formation. Evolutionary Memory (The Coolest Feature) This was my favorite part of the build. Every time an incident happens, the system learns. If the human approves the GLM's failover proposal, the agent runs a separate "Reflection Phase." It analyzes what broke and what fixed it, and writes an entry into a local SQLite database acting as an "Evolutionary Memory Log". The next time a failure happens, the orchestrator pulls relevant past incidents from SQLite and feeds them into the GLM-5.1 prompt. The AI literally reads its own history before diagnosing new problems so it doesn't make the same mistake twice. The Struggles It wasn't smooth. I lost about 4 hours to a completely silent Pydantic validation bug because my frontend chaos buttons were passing the string "dead" but my backend Enums strictly expected "DEAD". The agent just sat there doing nothing. LLMs are smart, but type-safety mismatches across the stack will still humble you. Try it out I built this to prove that the future of SRE isn't just better dashboards; it's autonomous, agentic infrastructure. I’m hosting it live on Render/Vercel. Try hitting the "Hard Kill" button on GCP and watch the AI react in real time. Would love brutal feedback from any actual SREs or DevOps engineers here. What edge case would break this in a real datacenter? submitted by /u/Evil_god7 [link] [comments]

We built an open-source framework for deploying AI agents in production; with built-in Claude Code skills

hey r/ClaudeAI, we just open-sourced Agent2; a production runtime for AI agents built on PydanticAI + FastAPI. what makes it relevant here: the repo ships with built-in SKILL.md files that teach Claude Code how to use the framework. open the repo in claude code and it already knows how to scaffold agents, add knowledge bases, wire up approvals, and debug issues. skills included: - /create-agent — scaffolds a complete agent service - /building-domain-experts — knowledge-backed document processing - /adding-knowledge — R2R collections, per-tenant scoping - /adding-capabilities — pause/resume, approvals, provider routing - /debugging-agents — systematic diagnosis we've processed 4M+ documents with it. $200k+ revenue. bootstrapped. the idea: you describe what you want in claude code, and it builds a production AI agent backend. schema in, API out. → https://github.com/duozokker/agent2 MIT licensed. feedback welcome. submitted by /u/duozokker [link] [comments]

I got tired of Claude hallucinating decimal points in financial CSVs, so I built a 3-layer deterministic MCP Server.

Hey everyone, If you’ve ever tried feeding a 5,000-row CSV, a messy broker trade history, or a bank statement (like Norma 43 or SEC XBRL) directly into Claude's context window, you know the pain. **The Token Tax:** Sending raw B2B formats to a context window burns tokens for no reason. **The Hallucination Risk:** LLMs struggle with strict spatial alignment. One misplaced comma by the AI, and a $100.50 transaction becomes a $10,050.00 disaster. I realized that "LLM-first" is the wrong architecture for structured B2B data. AI agents shouldn't *read* CSVs; they should query a deterministic middleware. So, I built **ETL-D** and just open-sourced the MCP Server for Claude Desktop. **The Architecture (The "Waterfall" approach):** Instead of dumping text to the LLM, when you ask Claude to parse a file, it routes it to the MCP server which processes it in 3 strict layers: * **Layer 1 (Heuristics):** 100% Python (`regex`, `dateutil`, strict structural parsers). If it's a known format, it parses instantly. We just ran a load test: 200 parallel requests hit ~70ms response times with **0 LLM calls**. Zero hallucination risk. * **Layer 2 (Semantic Routing):** If headers are obfuscated, we use a lightweight router to map columns to strict Pydantic schemas. * **Layer 3 (LLM Fallback):** Only triggered for high-entropy "free-text" noise (using Llama 3.3 70b under the hood to enforce JSON schemas). Claude just gets a perfectly clean, flattened JSON array back, ready for actual reasoning. **Try it out:** I just got it approved on the official Anthropic MCP Registry today. You can check out the source code and how to configure it in your `claude_desktop_config.json` here: 🔗 **GitHub:** [pablixnieto2/etld-mcp-server](https://github.com/pablixnieto2/etld-mcp-server) Would love to hear how you guys are handling the "Data Tax" and preventing hallucinations in your own agent pipelines. Any feedback on the architecture is welcome! submitted by /u/PrettyOne8738 [link] [comments]

Theow - Heal your CI automatically with LLMs with 0 clicks and 0 copy pasting context

Theow is an observable, programmatic LLM agent that auto-heals failing Python functions at runtime. Wrap any function withtheow.mark(), and when it raises, theow intercepts the exception, diagnoses it, and retries transparently. Every LLM call, tool execution, and token spend is traced via OpenTelemetry. Zero prompt engineering. Zero code changes beyond the decorator. Initially at my work we were figuring out a way to leverage LLMs in a packaging pipeline to recover the workflow on the fly based on failure. This lead to the development of Theow. Quickly after I realized CI pipelines are basically sequential workflows that is self contained and with enough failure context. So I started using theow decorators to wrap my CI steps and let it automatically heal and create PRs to the feature branch. Its different from solutions like Copilot (which also ties you the platform) because theow lives inside your process and gets triggered on failiure. What this means is that, for example, in an integration test, the LLM has the opportunity to investigate the actual environment and not just work based off of the static error logs. Theow is built on top of pydantic-ai and supports all the providers supported by pydantic-ai. And on top it also supports copilot-sdk so you can also use it with your copilot subscription and the claude-agent-sdk It has observability built-in with logfire, so you can get the LLM telemetry directly in logfire or use your own observability stack. I use it recover my projects CI pipelines and plan to integrate into my workplaces central CI. Here are some actual examples of theow at work (parrot is a test runner bot for CI that uses theow) - Auto-healed lint and unit tests with PR fixes - In-runner investigation and fix suggestions for an integration test Theow is free and open source. Here is the repo - https://github.com/adhityaravi/theow. Happy to get feedback or even more happy to support if you wanna try it on your own workflow submitted by /u/__4di__ [link] [comments]

I built a full-stack SaaS in ~10 hours with Claude Code — paste a business name, get a deployed website in 60 seconds

I've been deep in Claude Code for a few months now and just shipped something I think shows what's actually possible with agentic development when you set it up right. Wanted to share the real workflow, not the hype. What I built Site Builder Paste a business name, get a fully deployed website in 60 seconds. It scrapes Google Maps (Playwright + Chromium), writes all the copy (Claude Sonnet), generates images for sections without real photos (Gemini), assembles a React + Tailwind site from 14 components, and auto-deploys to Cloudflare Pages. Live URL returned instantly. Live demo: https://site-builder-livid.vercel.app/ How Claude Code actually made this possible in a day The game-changer: persistent expertise files.** I maintain `expertise.yaml` files per domain (~600-1000 lines of structured knowledge). My WebSocket expert knows every event type, every broadcast method. My site builder expert knows every pipeline step, every model field. These load every session. By session 50, the agent knows your codebase like a senior engineer who's been on the team for a year. Session 1 vs session 50 is honestly night and day. The workflow that compounds: I chain three agents in sequence — Plan (reads expertise + codebase, writes a spec), Build (implements the spec), Self-Improve (diffs the expertise against the actual code, finds discrepancies, updates itself). The system literally audits itself after every build cycle. It catches things like "you documented this method at line 142 but it moved to line 178" or "the builder added a new WebSocket event that isn't in the expertise yet." Parallel agents are the real speed hack. When I need to update docs, scout for bugs, and build a feature — I launch all three simultaneously. Different files, different concerns, results back in minutes. I built four README files in the time it takes to write one. This is the biggest reason ~10 hours was enough for a full production system. Opus for architecture, Sonnet for volume. Pipeline design, multi-agent coordination, tricky debugging = Opus. Content generation, routine code, documentation = Sonnet. Match the intelligence to the task. You wouldn't hire a principal engineer to write boilerplate CSS. The CLAUDE.md rules file is underrated. Mine enforces: Pydantic models over dicts, no mocking in tests (real DB connections), use Astral UV not raw Python, never commit unless asked, read entire files before editing. The agents follow these consistently because they're always in context. I've watched my agent catch itself mid-edit and switch from a dict to a Pydantic model because the rules said so. What went wrong (because it's not all magic): - TypeScript build failures on Railway because `tsconfig.json` was in my root `.gitignore` and never got committed for 2 of 3 templates. Took 3 deploys to figure out. Claude Code found it instantly once I SSH'd into the Railway container and let it look around. - Franchise businesses (chains with multiple locations) break the scraper assumptions. Had to build a whole confidence scoring system — high/low/none — with franchise detection heuristics and editor warning banners. - AI-generated images showed up on deployed sites but were broken in the editor preview. The editor uses iframe `srcdoc` (inlined HTML), so relative paths like `/images/services.png` don't resolve. Had to base64-encode them into the HTML bundle. - TinyMCE required domain registration for every deployed site. Ripped it out and replaced with a plain textarea. Sometimes simpler wins. The stack (10 backend modules, 14 React components, 5 Vue components): - Backend: Python 3.12, FastAPI, Pydantic v2, Playwright - Frontend: Vue 3 + TypeScript + Pinia - Generated sites: React + Tailwind CSS (14 section components) - AI: Claude Opus 4.6 (orchestration) + Sonnet 4.6 (content) + Gemini3.1 Flash (nano banana) - Deploy: Docker + Railway (backend), Vercel (frontend), Cloudflare Pages (generated sites) - Real-time: WebSocket streaming with progress panel This is one of 7 apps in a monorepo called Agent Experts credit (u/indydevdan) ( built on the ACT > LEARN > REUSE pattern. Agents that actually remember and improve. **Now I need help.** The builder works. Sites look like $5K custom builds. The workflow is: find business on Google Maps > generate site (60 sec) > customize in inline editor > sell for $500-$800. But I'm an engineer, not a GTM person. I'm looking for: **Feedback** — what would make this more valuable? What's missing? **GTM partner/advisor** — someone who's launched a SaaS or productized service agency. I need help with pricing model (per-site vs subscription vs white-label), distribution channels, and go-to-market strategy. **Early users** — if you do freelance web development or run a micro-agency, I'd love to let you try it and hear what breaks. DMs open. Happy to share the expertise file patterns with anyone building with Claude Code — the persistent memory approach works regardless of what y