Five products. One conviction. Feedback makes people — and A.I. — better. Data workflow is the spine.

HR analytics products either trap data in dashboards or silo it across vendors. Cross-cutting concerns — anonymization, metric calculation, segmentation, survey delivery, decision support — get re-implemented per product. The result is brittle, fragmented, expensive, and hostile to combination: each tool is good at its one thing, and useless next to its neighbor.

A central hub (people-analytics-toolbox) plus a roster of production spokes that work standalone and compose. Each spoke produces a discrete artifact; spokes share data schemas and APIs so they pull into packages addressing real analytical needs without integration tax. Public-facing spokes include Calculus (210+ precomputed HR metrics), Conductor (metadata-grounded SQL/Python codegen), Reincarnation (RID/SID adaptive measurement), AnyComp (compensation OS), VOI Calculator (formal decision-theoretic value-of-information), Persona Factory, Survey Factory, and Competency Factory — alongside additional internal production spokes that power packages but stay back-end. Hub layer carries the cross-cutting services — Data Anonymizer (deterministic PII), Survey Engine, Preference Modeler, Segmentation Studio, Decision Support — that every spoke depends on. Standardized reporting and bespoke analytical work both run through the same substrate; a custom engagement is a new package composed from existing spokes, not a new build.

• 01Compartmentalable packages on shared data schemas and APIs — every spoke speaks the same anonymization, metric, segmentation, and survey vocabulary, so spokes compose without integration tax (the architectural conviction underneath the rest)
• 02Reincarnation: cross-study item-response accumulation without confounding — adaptive selection over the full evidence pool, not just the current study
• 03Conductor: metadata-grounded SQL/Python generation (not example-grounded) — the model sees schema, field semantics, and canonical metric definitions
• 04Calculus: precomputed metric materialization so manager-level segmentations are instant rather than dashboard-render-blocked
• 05VOI Calculator: formal Expected Value of Perfect/Sample Information as production software — essentially absent in commercial HR tooling
• 06AnyComp: compensation as one coherent decision surface, not four disconnected screens

Hub plus 20 production spokes plus the cross-cutting hub services. Solo build since 2022. Several spokes deployed at enterprise clients; the platform composition powers both off-the-shelf reporting and bespoke analytical packages.

AI coding tools' productivity claims rest on agent-side measurements — lines produced, tasks completed, time-to-PR. If the Ironies of Automation are operative — operator vigilance falling as agent reliability rises — those measurements systematically overstate net effect. There is no operator-side cockpit catching the loss.

A multi-agent kanban with a completion-block protocol that tracks per-card execution across heterogeneous AI tools. Continuous coordination-event log. Two-phase actor handoff (builder → reviewer) where the second transition requires an artifact only the reviewer can produce. Cross-tool sync via a hub SDK so an operator coordinates Cursor, Claude Code, Replit, and other agents through one board. The continuous production telemetry that runs the C1 risk-compensation field study — a pre-registered test of Bainbridge 1983 in real coding work.

• 01Two-phase actor handoff (builder → reviewer) where the second transition requires an artifact only the reviewer can produce — enforces review without trusting it
• 02Coordination-event log as a research instrument, not just an audit trail — the apparatus for the C1 risk-compensation field study
• 03Completion blocks as a protocol — every assignment ends with structured machine-readable completion, not free-text close-out
• 04Hub-and-spoke sync between heterogeneous AI tools so an operator coordinates Cursor + Claude Code + Replit + custom agents through one board

Private. The operator-side coordination spine for the multi-app portfolio. Live, measuring, instrumented for the C1 field study.

Fantasy products are either marketplaces with shallow projections or hardcore stat tools that do not make a case. The middle — readable intelligence with a point of view — is empty.

A multi-app monorepo: a public newsroom (apps/web), a GM workflow console (gm-console), a strategy portal with cover-art game modes (apps/strategy), and a Python analytics API. Live MFL adapter, projections provider, Monte Carlo strategy engine, weekly LLM-generated stories under editorial discipline.

• 01Newsroom-as-product: weekly LLM-generated stories with editorial structure, briefs, and StoryNumbers
• 02Monte Carlo strategy engine running per-decision (fourth-down, lineup, waivers)
• 03Multi-app monorepo with formal adapter contract for swapping fantasy providers
• 04Magazine-grade cover art system for each game mode (the-pick, the-matchup, survivor, drive-duel, fourth-down-gambit)

Private. Multi-app monorepo with engine, adapters, magazine, and analytics API in active development.

Most baby-naming tools are SEO listicles. They cannot tell you whether a name is rising or fading this month, whether it sounds right next to your other kids, or what its narrative texture actually is.

A naming tool that pulls live trend data, scores phonetic and sibling fit, and surfaces narrative associations from a literary corpus. A calibration loop that learns parent preferences over the session.

• 01Live trend frequency from external data sources, not stale annual rankings
• 02Sibling-set acoustic checks (rhyme, stress, syllable count, ending overlap)
• 03Narrative association layer pulled from curated literary references
• 04Within-session preference calibration so the tool gets sharper as parents use it

Private beta. Decision tool for one of the highest-stakes irreversible decisions parents make.

Image platforms either flatten taste into engagement metrics or hide behind gatekeepers. Neither produces a reading rhythm. Neither learns from you.

385 active works pulled from museum APIs (ARTIC, Met, BnF, Smithsonian, Europeana) with full attribution and license discipline. An adaptive player that learns desire and pool composition. A magazine with original fiction and editorial criticism. Stripe membership. A Chrome extension for attribution capture. A derivative pipeline that produces new transformative works under license.

• 01Reincarnation engine: per-user desire scoring with RID/SID adaptive measurement and visual-rhyme sequencing
• 02Pill paradigm: 80 admin pages collapsed into ~7 named flows, each a typed pill expression
• 03Adaptive authorship platform underneath — Vela is property #1; siblings reuse the substrate
• 04Museum-grade attribution and license discipline as a first-class feature, not a footnote

Live at vela.study. Stripe membership in live mode. Magazine publishing weekly. ~1,300 commits since project start.