A transparent account of using Claude as primary development and research infrastructure — not as a writing assistant, but as an autonomous collaborator running across four simultaneous, production-grade projects.
The data corpus is the input. The product is what comes out: peer-relative disclosure signals that identify when a company's risk language, sentiment, or forward guidance diverges meaningfully from its direct competitors — producing actionable outputs mapped to specific audiences: credit risk, equity research, systematic trading.
| Project | Domain | Claude mode | What was built | Status |
|---|---|---|---|---|
| HomeGadgets.ca | Consumer / data | Code — Autonomous | Full-stack platform, 39 scrapers, cross-retailer price matching, merchant click analytics, bilingual (EN/FR), affiliate layer
Next.js · FastAPI · Supabase · Vercel · Render · Cloudflare |
Live |
| BasketBrain.ca | Grocery / data | Code — Autonomous | 19 grocery chains, 40K+ prices, postal-code comparison, cart optimizer, store locator
Shared codebase and backend with HomeGadgets. |
Live |
| ContextQuant.com | AI / quantitative finance | Research — Analytical | 8 data pipelines, 4 NLP approaches, peer-relative signal framework, 15 hypotheses tested, walk-forward validation, working paper, 32-institution commercialization target list
Python · PostgreSQL · FinBERT · SEC EDGAR · FRED · FEC · local GPU |
Deployment |
| MerchantLink.ca | Fintech / B2B SaaS | Strategy — Architectural | Full BRD, 5-layer system architecture, regulatory framework (PIPEDA, FCAC open banking), competitive positioning, UI prototypes for merchant onboarding, consumer redemption flow, and bank integration
Hypothesis validation first. Backend when bank partner is secured. |
Pre-pilot |
Three distinct modes: autonomous code execution · quantitative research design · strategy and architecture synthesis.
All technical work runs through Claude Code — a command-line agent operating directly on the codebase. Prompts are drafted in Claude Chat, then pasted into Code with a standing instruction to proceed without clarifying questions. Multiple Code instances run in parallel when workstreams are independent.
Claude Chat handles diagnosis, planning, and prompt engineering. For ContextQuant it served as a research design partner across 15 signal hypotheses and 10 years of out-of-sample validation. For MerchantLink it synthesized regulatory framing, stakeholder mapping, and system architecture for a product deliberately built without a backend until a bank pilot is secured.
Each project required a different professional profile. Full-stack development is one rate. Quantitative finance research is another. Fintech strategy consulting is a third. The table below uses Toronto market rates for each discipline.
| Project | Professional equivalent | Basis | Estimated range (CAD) |
|---|---|---|---|
| HomeGadgets.ca + BasketBrain.ca 69K lines · 392 commits · 22 days · bilingual · full affiliate + analytics layer | Senior full-stack developer | 500–600 hrs @ $85–100/hr + design, DevOps, QA, PM | $165K–$200K |
| ContextQuant.com 8 pipelines · 4 NLP approaches · 15 hypotheses · 10-yr OOS validation · working paper · commercialization strategy | Quant researcher / data scientist | 6–12 month engagement @ $150–250/hr consulting rate | $150K–$300K |
| MerchantLink.ca Full BRD · 5-layer architecture · PIPEDA + FCAC regulatory framework · competitive analysis · UI prototypes · bank outreach | Fintech strategy consultant | Discovery engagement @ $200–400/hr (boutique / Big 4) | $50K–$100K |
| Combined estimated value across all four projects | $365K–$600K | ||
Strict separation prevents conflating architecture decisions with implementation. Strategy sessions produce prompts. Code sessions produce commits.
Before any architecture decision, Code is asked for facts first. The pattern "ask before architecting" has prevented several costly misdirections.
Clarifying question loops destroy momentum. Prompts are written with sufficient context for Code to make sensible defaults and report what it decided.
Each project maintains a canonical .md file. Every session begins by reading it. Nothing operationally important lives only in a chat window.
When tasks don't share state, multiple Code instances run simultaneously — the closest analog to managing a small development team.
AI handles execution. The operator retains all structural decisions: what to build, for whom, and why. Judgment is not delegated.
This is not a thesis about AI capability — it is a working experiment conducted by a single operator building from scratch, testing hypotheses, and shipping products entirely in spare time. No agency. No full-time team. No venture funding. Four ventures running concurrently, each at a different stage, each using AI differently as its engine.
What makes this worth documenting is not the output volume. It is what becomes possible when the cost of building stops being the binding constraint — and strategy, judgment, and taste become the scarce resources.
Last updated March 2026 · Claude Max (Anthropic) · Next.js · FastAPI · Supabase · Vercel · GitHub Pages · NVIDIA RTX 3070
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