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Oh-My-OpenClaw – agent orchestration for coding, from Discord/Telegram

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Oh-My-OpenClaw – agent orchestration for coding, from Discord/Telegram

I've been using Oh-My-OpenCode (OmO) — a multi-agent orchestration plugin for terminal-based AI coding — and it's great. But being terminal-only kept bugging me: - Can't kick off work from my phone. I'd want to say "implement this feature" while I'm out, but I'd have to open my laptop. - No async workflow. I have to stay attached to the terminal until it's done. - Can't run multiple tasks in parallel. One session, one task, sequentially. - Sometimes I come back and it's just sitting there waiting for a clarification question. - The web UI exists but it's slow. Discord and Telegram are right there — polished, fast, on every device. So I ported OmO's multi-agent patterns to OpenClaw (a chat-platform AI agent framework) and built a plugin that actually integrates with OmO. Then I hit a second set of problems on the OpenClaw side: - The agent tries to do everything itself instead of using sub-agents. - To use OpenCode for coding, I had to explicitly say "open tmux, run opencode, do the work there" every time. - Opus 4.6 as the main model means weak multimodal. I'd have to manually route screenshots and PDFs to Gemini CLI. Oh-My-OpenClaw (OmOC) solves both sides. What it does: 1. Fully async, from any chat platform. Type `/omoc omoc_prometheus` in Discord or Telegram and walk away. Planning → agent dispatch → execution → verification runs autonomously. Works from your phone. 2. 11 specialized agents, auto-orchestrated. Not one model doing everything — a team. Prometheus plans, Atlas orchestrates, Sisyphus-Junior implements, Oracle architects, Momus reviews. Each has a specific mandate and personality. The orchestrator (Atlas) delegates, verifies every result, and never writes code itself. 3. Automatic model routing per task category. Simple search? Sonnet. Complex refactor? Opus. Architecture decision? GPT-5.3 Codex. Visual/UI work? Gemini 3.1 Pro. The right model for the right job, configured in one JSON file. 4. OmO + tmux integration. When coding work comes in, OmOC automatically delegates to OpenCode/OmO running in tmux. No manual "open tmux and run opencode" instructions. Gemini CLI multimodal analysis is also auto-routed through tmux. 5. Todo enforcer tracks every step and warns when work is incomplete. Architecture: Planning (Prometheus/Metis/Momus) → Orchestration (Atlas) → Workers (Sisyphus-Junior/Hephaestus/Oracle/Explore/Librarian) → tmux: OpenCode/OmO (actual coding) → tmux: Gemini CLI (multimodal analysis) The plugin layer (TypeScript) enforces all of this at the code level: - Session-scoped in-memory todo tools — agents must plan before executing - Comment checker — 11 regex patterns kill AI slop comments on sight - Keyword detector — auto-routes to the right workflow based on what you type - Checkpoint system — save/load execution state for crash recovery Two commands. The setup wizard configures all 11 agent personas with your preferred model providers. Works without OmO — you get the full multi-agent orchestration on OpenClaw alone. *Works with OmO* — you get OmO's full power (AST-grep, LSP, 55+ hooks) accessible from chat, no terminal needed. Still early. There's plenty of rough edges. Feedback, issues, and PRs are all very welcome.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: para, gemini · Missing: supports, reddit linkedin, podcasting
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way, para · Missing: mobile apps, ios, entrepreneurs
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, reviews · Missing: plus, intuitive, host
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, ide, io · Missing: https docs, excited, just released
27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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