Category: MVP

I built a CLI tool intended to standardize local development setup across microservices. The promise: one command—dev bootstrap—that discovers services, generates .env files, and starts containers via Docker Compose. In demos, it was magical. In real teams, it broke in 40% of setups due to bespoke scripts, Compose version drift, OS differences, and odd edge cases. The MVP automated too much, too early, and eroded trust.

This article explains what I built, why it failed, and how I would rebuild the MVP around a clear compatibility contract and a validator-first workflow that earns trust before automating.

The Context: Diverse Stacks, Fragile Automation

Microservice repos evolve organically. Teams glue together language-specific tools, local caches, custom scripts, and different container setups. A tool that tries to own the entire “bootstrap and run” flow without a shared contract is brittle.

What I Built (MVP Scope)

• Discovery: Scan repos for services via file patterns.
• Env Generation: Infer env keys from docker-compose.yml and sample .env.example files; produce unified .env.
• Compose Orchestration: Start all services locally with one command.
• Opinionated Defaults: Assume standard port ranges and common service names.
• Metrics: Time to first run, number of successful bootstraps per team.

Launch and Early Results

• Solo demos worked spectacularly.
• Team pilots revealed fragility: custom scripts, non-standard Compose naming, and OS-specific quirks caused frequent failures.
• Trust dropped quickly; teams reverted to their known scripts.

Why It Failed: Over-Automation Without a Contract

I tried to automate the whole workflow without agreeing on a small, stable contract that teams could satisfy. Without a shared “dev.json” or similar spec, guessing env keys and start commands led to errors. Reliability suffered, and with dev tools, reliability is the MVP.

Root causes:

• Inference Errors: Guessing configurations from heterogeneous repos is error-prone.
• Hidden Assumptions: Opinionated defaults clashed with local reality.
• No Validation Step: Users couldn’t see or fix mismatches before automation ran.

The MVP I Should Have Built: Validate and Guide

Start with a minimal compatibility contract and a validator that helps teams conform incrementally.

• Contract: Each service exposes a dev.json containing ports, env keys, and start command.
• Validator CLI: dev validate checks conformance, explains gaps, and suggests fixes.
• Linter: Provide a linter for dev.json with clear error messages.
• Guided Setup: Generate .env from dev.json and start one service at a time.
• Telemetry: Track validation pass rate, categories of errors, and time to first successful run.

How It Would Work (Still MVP)

• Step 1: Teams add dev.json to each service with minimal fields.
• Step 2: Run dev validate; fix issues based on actionable messages.
• Step 3: Use dev env to generate environment files deterministically.
• Step 4: Start one service with dev run service-a; expand to orchestration only after a high pass rate.

This builds trust by making the tool predictable and by exposing mismatches early.

Technical Shape

• Schema: dev.json with fields { name, port, env: [KEY], start: "cmd" }.
• Validation Engine: JSON schema + custom checks (port conflicts, missing env keys).
• Compose Adapter: Optional; reads from dev.json to generate Compose fragments rather than infer from arbitrary files.
• Cross-Platform Tests: Simple checks for OS differences (path separators, shell commands).

Measuring Trust

• Validation Pass Rate: Percentage of services passing dev validate.
• First Successful Run: Time from install to one service running.
• Error Categories: Distribution helps prioritize adapters and docs.
• Rollback Incidents: Track how often teams abandon the tool mid-setup.

Onboarding and Documentation

• Quick Start: Create dev.json with a template; run dev validate.
• Troubleshooting: Clear guides for common errors with copy-paste fixes.
• Contracts Over Recipes: Emphasize the compatibility contract and why it exists.

Personal Reflections

I wanted the “it just works” moment so much that I skipped the steps that make “it just works” possible: a shared spec and a validator. Dev teams reward predictability over magic; trust is the currency.

Counterfactual Outcomes

With a validator-first MVP:

• Validation pass rate climbs from ~40% to ~80% in two months.
• Time to first successful run drops significantly.
• Teams adopt the tool gradually, and orchestration becomes feasible.

Iteration Path

• Add adapters for common stacks (Node, Python, Go).
• Introduce a dev doctor command that diagnoses OS and toolchain issues.
• Expand the contract only as needed; resist auto-inference beyond the spec.

Closing Thought

For dev tools, the smallest viable product is a trust-building tool: define a minimal contract, validate it, and guide teams to conformance. Automate only after reliability is demonstrated. Magic is delightful, but trust is what sticks.

The product was a habit-building app focused on sleep: wind-down routines, gentle alarms, and a simple educational library. The launch was exciting—we onboarded ~500 users via two TikTok creators. Engagement was strong in the first week thanks to streaks and badges. But adherence to core routines lagged, and by week three, many users were checking in without actually following the behaviors that mattered. The MVP drove taps, not change.

This article breaks down the design, what didn’t work, and how I would rebuild the MVP around personalization, adaptive scheduling, and a coach-like loop that respects real-life constraints.

The Context: Sleep Behaviors Are Constraint-Driven

People’s lives shape their sleep more than motivation alone. Shift work, small children, travel, and social commitments make “ideal” routines unrealistic. The MVP assumed generic routines suited most people, which backfired. Users wanted guidance tailored to their circumstances, not gamification.

What I Built (MVP Scope)

• Routines: Wind-down steps (dim lights, screen off, breathing exercises), and a gentle wake alarm.
• Streaks and Badges: Gamified adherence with daily streaks and weekly badges.
• Educational Library: Short articles on sleep hygiene.
• Reminders: Fixed-time prompts for wind-down and bedtime.
• Metrics: Daily check-ins, streak length, weekly summaries.

Launch and Early Signals

• Activation was strong: ~70% completed the first wind-down routine.
• Streaks increased check-ins but not adherence to the core behavior (e.g., screens off by 10 pm consistently).
• Users reported “feeling good about tracking,” but didn’t see improvements in sleep quality.

Key complaints:

• “My schedule varies; the app nags me at the wrong times.”
• “Badges don’t help when my kid wakes up at 3am.”
• “Travel breaks my streak, and then I stop caring.”

Why It Failed: Motivation Without Personalization

I gamified behavior without modeling constraints. The MVP treated adherence as a universal routine problem rather than a personal scheduling problem. Without adapting to real life, users ignored reminders or checked in perfunctorily.

Root causes:

• Generic routines: Assumed one-size-fits-most.
• Naive reminders: Fixed times didn’t adjust to late nights or early mornings.
• No segment-specific guidance: Shift workers and new parents have different protocols.

The MVP I Should Have Built: Personalization First, Then Motivation

Start with one segment and tailor deeply. For example, shift workers. Build protocols specific to circadian challenges:

• Protocols: Light exposure timing, nap rules, caffeine cutoffs aligned to shift patterns.
• Adaptive Scheduling: Detect late shifts and adjust wind-down and wake times within guardrails.
• Key Habit Metric: Track one behavior that matters (e.g., screens off by 10 pm four days/week) and correlate with subjective sleep quality.
• Coach Moments: Replace badges with context-aware guidance and weekly plan adjustments.

How It Would Work (Still MVP)

• Onboarding: Ask about shift schedule or parenting constraints; pick a protocol.
• Daily Flow: The app proposes a tailored wind-down and wake plan; adjusts if you log a late night.
• Feedback Loop: Weekly review suggests a small adjustment (e.g., move wind-down earlier by 15 minutes) and explains why.
• Success Metric: Adherence to the key habit and reported sleep quality trend.

Technical Shape

• Scheduling Engine: Rule-based adjustments (if late night logged, push wake by 30 minutes; enforce max shift).
• Signal Inputs: Manual logs initially; later integrate phone usage or light sensor where available.
• Content System: Protocol-specific modules rather than generic tips.
• Data and Privacy: Local storage for sensitive logs; opt-in sync for backups.

Measuring What Matters

• Adherence Rate: Percentage of days the key habit is followed.
• Quality Trend: Subjective sleep quality over time.
• Adjustment Efficacy: Whether weekly plan changes improve adherence.
• Drop-off Analysis: Identify segments with high abandonment to refine protocols.

Personal Reflections

I leaned on gamification because it’s easy to ship and feel good about. But in health, behavior change requires modeling constraints and giving actionable, compassionate guidance. People don’t fail because they don’t care—they fail because life is complicated.

Counterfactual Outcomes

With a tailored MVP for shift workers:

• Adherence to the one key habit increases from ~35% to ~60%.
• Reported sleep quality improves modestly but consistently over six weeks.
• Drop-offs decrease because schedules feel respected and adjustments make sense.

Even small improvements mean real value, because they’re sustainable.

Iteration Path

• Add segments: New parents, frequent travelers.
• Introduce adaptive reminders with more signals (calendar, device usage) with strict privacy controls.
• Layer gentle motivation (streaks) only after personalization works.
• Explore “coach check-ins” via chat prompts for accountability.

Closing Thought

Health MVPs shouldn’t start with gamification. Start with constraints: tailor protocols to one segment, make schedules adaptive, and measure adherence to one meaningful habit alongside perceived quality. Motivation supports behavior; personalization enables it.

The goal was clear: build a dashboard that helps mid-size e-commerce operations teams make better decisions. I connected to Shopify and GA4, assembled cohort retention charts, SKU performance views, and canned insights like “top movers” and “at-risk SKUs”. Pilots at three brands praised the look and speed. Yet, they kept exporting data to spreadsheets to decide what to do. The MVP succeeded at visualization and failed at decision support.

This case study covers what I built, how I launched, where it fell short, and how I would reframe the MVP around a single high-stakes decision with a minimal action interface.

The Context and Hypothesis

Ops managers juggle stock levels, supplier lead times, promotions, and budget constraints. A dashboard can unify signals, but unification isn’t the job. The job is confident decisions: when to reorder, which SKUs to discount, which campaigns to pause. My hypothesis was that fast, opinionated charts would surface the right signals and nudge teams toward better choices.

What I Built (MVP Scope)

• Connectors: Shopify and GA4 via API, nightly sync.
• Visualizations: Cohort retention, SKU performance, revenue breakdowns, promotion impact timelines.
• Canned Insights: “Top movers”, “churn risk SKUs”, “low inventory alerts”.
• Minimal Customization: A few filters and saved views; no deep modeling in MVP.
• Metrics: Usage frequency, time on dashboards, number of saved views.

Launch and Early Feedback

Three pilot brands used the product for four weeks. Feedback themes:

• “It looks great and loads fast.”
• “The alerts surface useful things, but we still have to figure out what to do.”
• “We export to Sheets to calculate reorder quantities and sanity-check assumptions.”

Usage patterns: dashboards opened often early in the week, exports spiked mid-week. The product was a browsing tool, not a decision tool.

Why It Failed: Artifact vs Outcome

I confused the artifact (charts) with the outcome (confident action). Teams didn’t need more visibility; they needed to make a specific decision with grounded assumptions and execution hooks.

Root causes:

• Generic insights: Labels like “churn risk SKUs” were opaque without context and actions.
• Missing assumptions: Lead times, supplier reliability, safety stock rules weren’t modeled.
• No action interface: The moment of decision required leaving the product.

The MVP I Should Have Built: One Decision, End-to-End

Pick a single high-stakes decision and build the smallest viable loop from signal to action. For e-commerce ops, the best candidate is often: “When to reorder top SKUs?”

Scope it tightly:

• Inputs: Historical sales velocity, current inventory, lead time, supplier reliability score, desired service level.
• Calculator: Safety stock, reorder point (ROP), and recommended order quantity.
• Editable Assumptions: Inline edits for lead time, reliability, service level; show impact immediately.
• Action Hook: “Create PO draft” or “Send Slack for approval.” Close the loop.
• Metrics: Stockout reduction, expedited shipping cost reduction, decision cycle time.

How It Would Work (Still MVP)

• One view: “Reorder Decisions”.
• Each SKU row shows: current stock, forecasted demand over lead time, ROP, recommended quantity.
• Hover reveals assumptions with quick edit fields.
• A single button: “Draft PO” (pre-fills supplier, quantities) or “Request Approval”.
• Logs decisions: who approved, when, and any overrides.

No more dashboards for browsing; just a focused assistant for one decision.

Technical Shape

• Data Sync: Keep nightly sync for demand forecasting; add a lightweight on-demand refresh for top SKUs.
• Modeling: Simple demand forecasting (moving average or exponential smoothing). Reliability score derived from late deliveries ratio.
• Assumptions Store: Per-SKU overrides, supplier-level defaults.
• Action Integration: Export PO draft (CSV or API to existing tools) and/or Slack webhook for approvals.
• Audit Trail: Minimal event logging for decisions to enable learning.

Measuring the Right Outcomes

Instead of generic usage metrics, measure decision outcomes:

• Stockouts: Reduce frequency and duration for top SKUs.
• Expedited Shipping: Track spend and aim to reduce by X%.
• Decision Cycle Time: Time from “alert” to “approved PO”.
• Override Rate: How often recommendations are changed; investigate why.

These demonstrate real value, not just engagement.

Onboarding and Habits

Onboarding should anchor in the decision loop:

• Import top SKUs and suppliers.
• Set lead times and desired service levels.
• Walk through one recommended reorder and draft a PO.
• Schedule a weekly review focused on this view.

Avoid generic tours. Guide the user through making a real decision with their data.

Personal Reflections

I built what I enjoy: crisp charts and fast loads. Pilots appreciated it, but the job wasn’t to browse—it was to decide and act. The MVP missed the bridge from insight to execution.

I also underweighted assumptions. Without editable assumptions, teams can’t build confidence—they’ll export to a spreadsheet where they control the variables.

Counterfactual Outcomes

With the decision-centric MVP, after two months:

• Stockouts on top 20 SKUs drop by ~25%.
• Expedited shipping costs decrease by ~15%.
• Decision cycle time drops from days to hours.
• Teams trust recommendations because they can tweak assumptions inline.

Even if the modeling isn’t perfect, the loop from signal to action is valuable—because it’s faster and structured.

Iteration Path

Once the core loop works:

• Expand to “discount decisions” and “promotion scheduling” with similar action hooks.
• Add confidence intervals and explainability for forecasts.
• Integrate with inventory systems for automatic PO creation where feasible.
• Introduce team workflows: approvals, comments on decisions.

Each new decision gets its own minimal loop; resist the urge to add browsing dashboards that don’t close a loop.

Closing Thought

Analytics MVPs fail when they stop at visualization. To prove value, pick one decision and build the smallest viable action interface around it, with editable assumptions and an execution hook. Confidence—not charts—is the product.