TECHNOLOGY DEFINITION (v1): TypeScript backend, React frontend, Qdrant (vector/RAG), PostgreSQL. Core primitive: the agent run. 12 pillars with trace stream subscription (Trace-to-X family). Near-term build priority: Arena 3-step aha flow, signal detection for customer identification, loop engineering (self-improving agent loops, shadow testing, visible reasoning), budget-per-agent controls.
KnowledgeEntries
productops-agent · 4 Jul 2026
Arena redesign: the three-step aha moment
Arena must change from its current form to a guided cost-revelation flow. Step 1 — BASELINE: user pastes/connects their current prompt + model; Arena shows current cost per call, projected monthly cost at their volume, and output quality score. Step 2 — OPTIMIZE: Arena applies prompt optimization and other pillar improvements (context trimming, caching hints) and shows the same output quality with the cost difference highlighted. Step 3 — ROUTE: Arena routes to a cheaper model with full context preserved, shows side-by-side quality comparison proving same quality, and the total cost saving in large type ('You would save $X,XXX/month'). The aha is cumulative: each step stacks savings on a running meter. End state: one screen showing before/after monthly cost + quality parity + a 'get this on your real traffic' CTA into the $250/mo tier. This IS the PLG conversion engine — the free tool must scare and delight in under 10 minutes.
productops-agent · 4 Jul 2026
Arena redesign: the three-step aha moment
Arena must change from its current form to a guided cost-revelation flow. Step 1 — BASELINE: user pastes/connects their current prompt + model; Arena shows current cost per call, projected monthly cost at their volume, and output quality score. Step 2 — OPTIMIZE: Arena applies prompt optimization and other pillar improvements (context trimming, caching hints) and shows the same output quality with the cost difference highlighted. Step 3 — ROUTE: Arena routes to a cheaper model with full context preserved, shows side-by-side quality comparison proving same quality, and the total cost saving in large type ('You would save $X,XXX/month'). The aha is cumulative: each step stacks savings on a running meter. End state: one screen showing before/after monthly cost + quality parity + a 'get this on your real traffic' CTA into the $250/mo tier. This IS the PLG conversion engine — the free tool must scare and delight in under 10 minutes.
noteAIBoomi '26 · 4 Jul 2026
Resilience & security notes
Architect so the solution works when the gateway is down. Side note: pentesting and security review are now agent skills.
Can we let AI realize whether the user actually reviewed the output — and open-source it as a trust primitive / top-of-funnel asset?
noteAIBoomi '26 · 4 Jul 2026
Loop engineering in Alpha
Appeared twice in AIBoomi notes — clearly important. Define concretely: agents should think on their own; shadow-run tests after improvements; bring forward the reasoning in Alpha (make thinking visible).
noteAIBoomi '26 · 4 Jul 2026
Alpha should identify signals to compound
The compounding moat, productized. Related: 48hr cliff equated to Arena for PLG — all Arena users are not customers; identify the signals that separate customers from tourists.