display.dev vs Mediator.ai

Display.dev is a micro-SaaS that solves a surprisingly annoying problem in agentic workflows: sharing AI-generated reports and dashboards securely inside a company. Claude, Cursor, and other agents increasingly produce polished HTML artifacts—analysis dashboards, design mockups, research reports—but sharing them means either copy-pasting into a doc tool or using Claude's built-in publish feature, which creates public URLs accessible to anyone on the internet. Display.dev fixes this with a single command: `dsp publish ./report.html`. The artifact lands at a permanent URL gated by Google, Microsoft, or company email authentication. Viewers sign in with their existing credentials; no account creation required on their end. The platform also surfaces inline comments back to the agent, meaning your agent can read feedback and iterate—closing a loop that previously required manual copy-paste between viewers and the AI tool. Pricing is simple: free tier for 10 gated artifacts, Solo at $15/month for unlimited, Pro at $49/month with SSO and audit logs, Enterprise at $499/month for large orgs. It also integrates with Claude Desktop via MCP, making it the kind of tool that becomes invisible infrastructure for teams already deep in agentic workflows. With Product Hunt ranking it #5 today and 134 upvotes, it's clearly striking a chord.

Mediator.ai applies LLMs and Nash bargaining theory to real-world disputes, generating agreements that both parties would accept — including solutions neither side had imagined independently. The process is private by design: each party separately describes their position, priorities, and constraints. The AI then generates multiple candidate agreements, scores each one against both parties' stated needs, and iteratively refines proposals until reaching an optimal solution. Use cases range from founder equity disputes and contractor payment conflicts to shared housing arrangements and inheritance disagreements. The system's key insight is that human negotiation is systematically bad at identifying the entire solution space — we anchor on positions, not interests. By modeling both parties' utility functions simultaneously, the AI can find Pareto-optimal outcomes that pure adversarial negotiation often misses entirely. With 159 Hacker News points, the response was genuinely enthusiastic — and the concept is hard to dismiss. Nash bargaining as a formalism has decades of academic credibility; what's new is making it accessible via natural language input. The pricing isn't published yet and the team is small, but the application domain (legal, HR, personal disputes) is enormous if they can nail trust and confidentiality.