Curated build ideaBuild packet and draft write-up
MCP release sprint
Evaluate an MCP release in a working coding-agent integration. Review the official release, test a concrete workflow, and document the result for your team.
Evaluate products for your team: inspect official sources, integration resources, and published implementation evidence. Vendors can launch a product and measure the path from evaluation to adoption.
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Try another search or clear the filters to browse available launches.
Clear filtersUse the current filters to draft a build or document what you find.
Launch intelligence
Connect discovery, evaluation, and implementation evidence in a company workspace. Retained usage requires authorized tracking; launch traffic and saves alone do not establish adoption.
Free company workspaces require a work email. There is no pricing or self-checkout; paid enterprise access is provisioned manually after a sales call and agreement.
Saves show interest. Published projects show implementation claims. Verified usage is labeled separately. Sponsored placements never establish product fit.
Build ideas from public releases
These curated starting points link to primary sources so you can inspect a release, try it in a small project, and record what you find. A listed release does not establish developer adoption.
Evaluate an MCP release in a working coding-agent integration. Review the official release, test a concrete workflow, and document the result for your team.
Evaluate persistent Responses API WebSocket connections with previous_response_id continuation. Test tool calls, connection handling, and latency in a bounded implementation before wider adoption.
Try ACP-compatible coding agents through the AI SDK HarnessAgent interface. Check runtime compatibility, MCP connections, session handling, and sandbox behavior in a working evaluation.
Evaluate Exa web search through AI Gateway. Compare grounding, citation quality, model fallbacks, and request traces against your research workflow.
Evaluate Cloudflare Agents SDK tracing across model calls, tool runs, approvals, token usage, and Workers runtime operations. Record which failures and costs your team can inspect.