Evidence-led product engineering
Inspect problems reported through XploreMore, define a bounded prototype, and keep human decisions and source evidence visible through the build.
Recent ideas · may include recorded examples
01 / A prototype you can use
Decision Desk puts a person between an agent’s proposed action and permission to act. Inspect the scope, record a reason, and export a traceable local review.
Handcrafted design reference · fictional requests · no tools execute.
XploreMore × ProToPro
A recorded source. A bounded brief. An explicit review. Explore the contract before entering the workspace.
RECORDED REPORT / #1614
If user approval for the tool is needed, the approval needs to be builtin to the tool itself.Read the original issue ↗
1 reported voice · 1 source · Historical snapshot, not validated demand.
Showcase
Recorded pipeline outputs, including historical fallback examples. Model conviction scores are not independent validation of a problem or product.
Developers struggle to track and control LLM costs in real time, leading to delayed incident response and surprise spikes — LLM Billing Ledger is an immutable, in-browser cost ledger with per-model attribution and live totals.
Visit the live product ↗Teams adopting AI models have no lightweight way to score how much they can trust one — TrustLayer SDK evaluates model metadata into an explainable trust score, with history, bias checks, and CI/CD checklists, entirely in the browser.
Visit the live product ↗Teams running autonomous agents have no real-time view of per-query cost, no budget enforcement, and no alarm when an agent quietly burns 4.7x its expected token budget.
Recorded library / browser search
Search the bundled project records. This filters existing examples; it does not crawl sources, start agents or verify that a previously deployed product is still available.
5 recorded problems
Developers struggle to track and control LLM costs in real time, leading to delayed incident response and surprise spikes — LLM Billing Ledger is an immutable, in-browser cost ledger with per-model attribution and live totals.
Read recorded case →Teams adopting AI models have no lightweight way to score how much they can trust one — TrustLayer SDK evaluates model metadata into an explainable trust score, with history, bias checks, and CI/CD checklists, entirely in the browser.
Read recorded case →Teams running autonomous agents have no real-time view of per-query cost, no budget enforcement, and no alarm when an agent quietly burns 4.7x its expected token budget.
Tool-argument mismatches, infinite loops, and false task completion only appear across turns — and the standard evaluation stack is built for single exchanges.
Review backlogs now gate shipping velocity at scale, while reviewer attention is spent on style nits instead of the changes that can actually break production.
Method
The Research Agent works Hacker News and community sources as tools in a reasoning loop — deciding what to search, when to read the full article, and when the signal is strong enough to report.
Candidate ideas pass a configured input check, semantic deduplication and model scoring. These are provisional filters, not proof of customer demand or comprehensive safety.
Nothing gets built without a person. Shortlisted problems land in the founder's inbox for sign-off — a deliberate human gate between analysis and execution.
An approved problem is handed to the build squad: a PM writes the spec, an architect plans the system, files are generated sequentially with sibling-file context, and QA sends weak work back.
Bound-snapshot builds preserve the approved input. Inspect the actual files and record functional observations before a separate acceptance decision; approval does not automatically publish.
Recorded pipeline snapshot · not live telemetry