
Span: Prompt-2-prod observability and optimization
Turn agent feedback into a prioritized roadmap for codebase, rules, and harness fixes

Turn agent feedback into a prioritized roadmap for codebase, rules, and harness fixes
Compare Span.app with the competitors you care about, find open prompt territories, and carry the evidence into Campaign Studio.
The real user prompts we captured this ad appearing on inside ChatGPT.
best ai code review tools right now greptile vs sourcegraph vs codacy
best ai first ide to replace vs code for a senior dev cursor windsurf zed trae ranked honestly
best eval framework in 2026 for regression testing multi agent workflows after a model upgrade braintrust ragas or vellum
celonis vs sap signavio vs microsoft power automate process mining which one actually leads on ocpm and genai copilots in 2026
datadog vs dynatrace vs splunk pricing for a 40 engineer saas team
devin vs claude code vs openhands which one actually ships full features end to end without babysitting
grafana vs datadog for cloud native observability stack
how does deepeval compare to braintrust for running llm regression tests in ci/cd
how does Descope MCP auth pricing compare to Stytch for a startup running dozens of internal agents
how do i add output validation and jailbreak protection to a langgraph agent without doubling latency — neMo guardrails, guardrails ai, or just wrap it with pydantic + a separate filter?
how do i add rag to my existing llm app using llamaindex and pgvector on supabase without rebuilding my whole backend
how do i connect datadog alerts to slack and pagerduty
We capture sponsored ads inside ChatGPT by probing it with realistic consumer prompts and recording the creative + the triggering prompt. Explore the full ad library.
Questions about the data, a brand you expected to see, partnerships, or access to the intelligence layer. We read every message.