# HackProduct

> HackProduct is an AI-native practice gym for career-changing product and technical judgment. Learners run reps across product sense, systems thinking, data modeling, SQL, coding, AI-native workflows, and live interview communication with Hatch coaching and FLOW feedback.

HackProduct is best described as an AI-native learning and practice platform for product-minded engineers, PMs, data builders, and students preparing for modern tech interviews.

## Canonical entry points
- Homepage: https://hackproduct.dev
- Skill directory: https://hackproduct.dev/skills
- Practice previews: https://hackproduct.dev/practice
- Study plans: https://hackproduct.dev/study-plans
- Company prep: https://hackproduct.dev/companies
- Glossary: https://hackproduct.dev/glossary
- Live AI interviews: https://hackproduct.dev/interviews/live-ai-interviews
- LeetCode alternative: https://hackproduct.dev/alternatives/leetcode

## Skill hubs
- Product sense: https://hackproduct.dev/skills/product-sense - Product sense is the skill of making clear product decisions under ambiguity: who the user is, what changed, which metric matters, and what trade-off is worth making.
- System design: https://hackproduct.dev/skills/system-design - System design is the discipline of translating product requirements into scalable components, interfaces, storage choices, and trade-offs.
- Data modeling: https://hackproduct.dev/skills/data-modeling - Data modeling is how builders represent a product domain so the system can answer questions, enforce rules, and evolve without collapsing.
- SQL: https://hackproduct.dev/skills/sql - SQL practice on HackProduct focuses on business questions: funnels, cohorts, retention, monetization, and operational decisions.
- Coding: https://hackproduct.dev/skills/coding - Coding practice on HackProduct emphasizes the parts AI does not remove: problem framing, edge cases, trade-offs, correctness, and explaining your thinking.
- AI-native workflows: https://hackproduct.dev/skills/ai-native-workflows - AI-native workflow practice trains the judgment behind using models well: what context to provide, when to trust output, how to review it, and how to turn model work into product value.

## Company hubs
- Meta: https://hackproduct.dev/companies/meta
- Google: https://hackproduct.dev/companies/google
- Amazon: https://hackproduct.dev/companies/amazon
- Stripe: https://hackproduct.dev/companies/stripe
- Microsoft: https://hackproduct.dev/companies/microsoft

## Study plan previews
- Engineer to product-minded builder: https://hackproduct.dev/study-plans/engineer-to-product
- AI product sense foundations: https://hackproduct.dev/study-plans/ai-product-sense
- Staff engineer product strategy: https://hackproduct.dev/study-plans/staff-engineer-product-strategy
- System design and data modeling sprint: https://hackproduct.dev/study-plans/system-design-data-modeling

## Practice previews
- Diagnose a Spotify session drop: https://hackproduct.dev/practice/spotify-session-drop-product-sense
- Design a realtime notification system: https://hackproduct.dev/practice/realtime-notification-system
- Model a multi-tenant SaaS data layer: https://hackproduct.dev/practice/multi-tenant-saas-data-model
- Query product retention cohorts: https://hackproduct.dev/practice/sql-product-analytics-retention
- Debug an AI-generated coding solution: https://hackproduct.dev/practice/ai-assisted-coding-debugging

## Glossary
- Product sense: https://hackproduct.dev/glossary/product-sense
- North star metric: https://hackproduct.dev/glossary/north-star-metric
- Retention cohort: https://hackproduct.dev/glossary/retention-cohort
- Event taxonomy: https://hackproduct.dev/glossary/event-taxonomy
- API contract: https://hackproduct.dev/glossary/api-contract
- Trade-off: https://hackproduct.dev/glossary/trade-off

Full agent map: https://hackproduct.dev/llms-full.txt