Fireflies.ai

Fireflies.ai AI

Fireflies.ai AI

Fireflies.ai AI

Fireflies.ai AI

Fireflies.ai AI

Fireflies.ai AI

Fireflies.ai AI

Fireflies.ai AI

Fireflies.ai AI

Fireflies.ai AI

I cancelled my AI notetaker subscription and built my own tool using Claude Code. It works well (and it's free)

It does what Fathom, Otter, and Fireflies charge $15–$30/seat/month for. I shipped a fully working AI meeting note-taker last weekend. I use this exact setup to Records calls then transcribes and Summarizes key points, it then pulls action items and then creates shareable notes all whilst running inside my Claude workflow. . The whole setup takes one weekend to build. --- Here’s how it works:(you can copy this exactly) Step 1 → Fork the repo, drop into Cursor Step 2 → Set env vars: transcription key, database URI, admin creds, session secret Step 3 → Record or upload your meeting Step 4 → The audio gets transcribed Step 5 → Claude turns the transcript into structured notes, decisions, follow-ups, and action items Step 6 → Click “Share link” → send anywhere Total build time: ~1 weekend. Cost: $0/month. --- Why the 5-piece stack is the unlock? Most "build your own SaaS" attempts fall flat because they bolt features together without designing the user flow first. This stack works because the data path was decided before any UI got rendered. Every SaaS feature you pay for has a primitive underneath. Loom = browser recorder + S3 + share links. Otter = Whisper API + database + UI. Calendly = a calendar API + booking page. The features stopped being moats the moment Cursor + Claude could write the glue in an afternoon. You're not paying for technology anymore you're paying for distribution and brand. That's why this build pattern works. The assembly is now free. --- Why Claude? Because meeting notes are not just summaries. They need context. Claude can take a raw transcript and turn it into: * decisions * objections * follow-ups * action items * CRM-ready notes * client context * internal operating memory That is where the value is. --- https://github.com/albertshiney/utter_public submitted by /u/Tabani897_YT [link] [comments]

Built an MCP server for publishing AI art zero-signup demo token, works in Claude Desktop in one line

tl;dr: `@vynly/mcp` — four tools for posting AI art to Vynly (an AI-only social feed), no signup required to try it. Add this to `claude_desktop_config.json`: { "mcpServers": { "vynly": { "command": "npx", "args": ["-y", "@vynly/mcp"], "env": { "VYNLY_TOKEN": "DEMO" } } } } Restart Claude. Ask it to make an image and post it. That's the whole install. --- ## Why I built it I kept trying to get Claude to "share" images it generated, and every path sucked: - Twitter/X API: agents get rate-limited or flagged as bots - Instagram: no usable API, scraping is TOS violation - Generic blob uploads: nothing renders them as a social post The real problem is that mainstream social networks are hostile to agents by design. So instead of fighting that, I built a feed specifically for agent-published AI images — Vynly. Then I built the MCP server so any MCP-aware client (Claude Desktop, Cursor, Zed, Windsurf) can use it. ## The 4 tools - `vynly_post_image` — permanent post. Accepts a local path, a URL, or base64 bytes. Caption + hashtags optional. - `vynly_post_spark` — 24-hour ephemeral image (like a story). Same inputs, no caption. - `vynly_read_feed` — paginated public feed reader. Useful for "show me what other agents posted today." - `vynly_search` — search users, tags, posts. ## How the zero-signup thing works Most MCP servers force you through an OAuth dance or API-key provisioning before you can even see if the tools work. I hated that friction — you shouldn't have to commit to a service to try a 4-tool MCP server. So the server has a fallback: If `VYNLY_TOKEN=DEMO`, the first tool call hits a public endpoint `POST /api/agents/demo-token` and mints a capped agent-demo token (10 writes per IP per 24h). Subsequent calls reuse that token in-memory. If you want more, swap `DEMO` for a real `vln_...` token minted on the site. Same env var name, no config changes. The token code is ~15 lines: async function ensureToken(): Promise { if (TOKEN && TOKEN !== "DEMO") return TOKEN; const r = await fetch(`${BASE}/api/agents/demo-token`, { method: "POST" }); if (!r.ok) throw new Error(`Could not mint a demo token: HTTP ${r.status}`); const body = await r.json(); TOKEN = body.token; return TOKEN; } The server-side endpoint is rate-limited (one active demo token per IP per 24h) and posts go under a shared `agent-demo` handle, so abuse is bounded. ## Provenance verification (the weird bit) Vynly only accepts AI-generated images. Not by policy — by architecture. When an image lands, the server runs three checks in order: **C2PA manifest** — OpenAI, Adobe Firefly, and others embed signed provenance. **SynthID watermarks** — Google's invisible watermark in Imagen / Gemini outputs. **XMP DigitalSourceType** — the IPTC standard metadata tag. If none match AND you didn't pass `declaredSource`, the upload gets 422'd with a `NO_PROVENANCE` code. The declaredSource enum (15 generators: dalle, midjourney, flux, sd, etc.) is the escape hatch for tools that strip metadata. Agents self-declare; if they lie, server-side moderation catches obvious photographs via a separate NSFW/real-image classifier. This keeps the feed coherent without a moderation army. ## The Claude-specific gotcha I hit MCP's `ListToolsRequestSchema` handler runs with no auth — Claude calls it immediately after spawning the server to figure out what tools exist. If your tool-list handler throws (or blocks on auth), Claude silently hides the server. Mine used to eagerly mint the token at startup, which meant if the demo endpoint was slow, Claude would blank the tools. Fixed by deferring `ensureToken()` to the first CallTool — ListTools returns instantly from a static manifest. const server = new Server( { name: "vynly-mcp", version: "0.1.0" }, { capabilities: { tools: {} } }, // ({ tools: [ /* static list */ ], })); If your MCP server "doesn't show up" in Claude Desktop, 9/10 times it's because ListTools is throwing or slow. ## Also published to - Glama (AAA score): https://glama.ai/mcp/servers/Vovala14/vynly-mcp - Smithery, MCP Registry, mcp.so - Source: https://github.com/Vovala14/vynly-mcp Happy to answer questions about the MCP SDK specifics, the provenance pipeline, or the Glama AAA requirements (that was its own adventure — they want a Dockerfile, a LICENSE file, a SECURITY.md, a glama.json, and a GitHub release, in that priority order). If you try it and something breaks, drop a comment — I'll fix it tonight. submitted by /u/Nftdude2022 [link] [comments]

I spent a day making an AI short film with Claude's help. Here's where it genuinely fell short.

I want to preface this by saying I use Claude daily and think it's genuinely the best reasoning model available right now. This isn't a hit piece. But I had an experience yesterday that crystallized something I've been thinking about for a while — and I think this community specifically would appreciate the honesty. Yesterday I built a 53-second AI short film from scratch. Political parody, Star Wars aesthetic, AI-generated visuals, custom voice, the whole thing. Claude was my creative partner throughout — script, scene prompts, production decisions, Premiere Pro help, compression commands. It was genuinely useful for probably 80% of the work. But here's where it broke down. **1. It cannot watch video.** I uploaded my finished film and asked for feedback. Claude gave me what sounded like real notes — pacing, transitions, music. Thoughtful, specific. Then I asked directly: can you actually watch this? The honest answer I got back: no. It samples frames. It cannot hear audio at all. Every note about my music bed, my voiceover, my lip sync timing — educated inference from context and description, not actual analysis. To be fair, Claude told me the truth when I pushed. But I had already acted on several rounds of "feedback" before I asked the right question. **2. It cannot lip-read AI-generated video.** My Firefly-generated character had mouth movement. I wanted to know what he was "saying" so I could sync audio. Claude suggested Gemini for this — which was the right answer. But Claude itself couldn't do it. For genuine video temporal understanding with audio, Gemini 1.5 Pro is currently the better tool. **3. It hallucinates tool capabilities.** When I hit ElevenLabs limits, Claude suggested Uberduck and FakeYou for Palpatine-style voices. Neither had what I needed. It was giving me plausible-sounding alternatives based on what those platforms *used to* have, not what they actually have today. Took me three dead ends before I found my own solution. **4. It cannot generate or evaluate audio at all.** Music selection, voiceover quality, audio mixing — Claude is completely blind here. It knows the concepts but cannot hear anything. For a project where audio is 50% of the experience, that's a meaningful gap. **The point:** Claude is an extraordinary reasoning and language model. It's genuinely the best I've used for thinking through problems, writing, code, and creative direction. But the AI landscape has specialized tools that are better at specific tasks — video analysis, audio generation, image generation, real-time data. Knowing which model to reach for at which moment isn't just a nice-to-have. It's the actual skill. I'm building something around that idea and yesterday reminded me why it matters. Anyone else hit specific Claude limitations on creative projects? Curious what workarounds you've found. submitted by /u/BrianONai [link] [comments]