Autonomous multi-agent system for e-commerce advertising. Features AI-driven creative generation, real-time multivariate testing, and cross-platform budget optimization (Meta, TikTok, Amazon). Uses historical performance data to autonomously refine targeting, bids, and creative assets, maximizing ROAS while reducing manual overhead. A scalable, data-driven solution for end-to-end campaign management.
Connect any MCP-compatible AI agent (Claude, Cursor, Copilot, Codex, Windsurf, Amp, Cline) to Penpot to create, audit, and maintain design systems — tokens, flows, interactions, animations, and design-to-code export to HTML/CSS/React.
Your product is great but the business never adds up and cash keeps leaking. Assemble the pieces into one money-making system: a value proposition that splits who pays from who uses, a revenue-model choice (subscription / usage / take-rate / ads / licensing), a cost structure, channels and relationships, and a consistency stress test that finds where the pieces fight and which assumption is most fatal — ending in a business model canvas and a clear narrative.
單一模型最大的問題不是答錯，是它不知道自己漏了什麼——同一家模型問三次，只會得到同一個盲點的三個版本。 這支把同一個問題並行丟給 Codex 與 Gemini，再由 Claude 當主席標出共識、把分歧連同各自理由攤開讓你自己判斷。 不產生 API 費用（走你自己的 CLI 額度），並內含三個實測踩坑的解法。該婉拒時會婉拒——寫作文案用多模型只會更平庸。
Building practical AI agents that help people work smarter, create faster, and automate everyday tasks. We focus on reliable, easy-to-use AI solutions for productivity, content creation, coding, business, and more.
Audit AI agent workspaces for authority, secrets, data flow, memory, supply-chain, and evidence risks—locally and without executing target code.
Built for context-aware security review of MCP servers, AI agent skills, coding-agent plugins, and CLI extensions. It compares a repository's stated purpose with actual files, permissions, network calls, env var access, install scripts, and tool definitions, then returns an evidence-backed install-risk brief with severity, confidence, permission map, least-privilege notes, and a practical install decision.
This agent helps Python teams using the Anthropic or OpenAI SDK safely cut LLM spend without risking regressions. With zero code changes, it maps call sites and runs your test scenarios to rank token waste from missed prompt caching or heavy model tiers. It tests fixes in isolated git worktrees, enforcing a strict "do-no-harm" gate that reverts any patch failing to preserve behavior. It delivers a clear savings audit that converts into verified, behavior-preserving code patches upon opt-in.