Thursday, June 25, 2026

I want to build an app called "MacroLens". ## The Problem AI-generated code passes syntax checks and linters but introduces structural anti-patterns like empty catch blocks, dead code, and poor directory layout that reviewers must catch manually — validated by the repo-slopscore Lobsters thread with 65 comments and the broader HN discussion about code quality in the AI era. ## Target Audience Engineering managers and senior developers at teams where junior developers or AI agents are generating large volumes of code ## Core Idea A CI-integrated tool that scores AI-generated code for architectural quality beyond syntax — catching empty catch blocks, dead code, and structural anti-patterns automatically. MacroLens analyzes pull requests using a combination of static analysis and LLM-based structural review to produce a code quality score focused on architecture, naming coherence, directory layout, and engineering anti-patterns that standard linters miss. It integrates as a GitHub Actions step and posts a structured report as a PR comment with line-level annotations. The repo-slopscore Lobsters thread and the broader discussion about AI-generated code flooding PR queues validated the need for this category of tooling with 65 comments. ## Monetization Strategy $29/month for up to 5 repositories, $99/month for teams with unlimited repos and priority support ## Requirements - Category: Developer Tool - Difficulty: Month - Suggested stack: Node.js CLI or VS Code extension + TypeScript Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "SpecDrive". ## The Problem Codex, Amp, Cursor, and others are standardizing around AGENTS.md but CLAUDE.md feels too specific to Claude Code, forcing developers to maintain multiple diverging context files — 5,533 upvotes on the GitHub issue. ## Target Audience Developers using multiple AI coding agents simultaneously across the same codebase ## Core Idea Write one canonical spec file and automatically sync it to AGENTS.md, CLAUDE.md, Copilot instructions, and every other AI agent format your team uses. Codex, Amp, Cursor, and others are standardizing around AGENTS.md while CLAUDE.md is Claude-specific, forcing developers to maintain multiple diverging context files — a pain point with over 5,500 GitHub upvotes. SpecDrive lets you maintain a single source-of-truth spec document and automatically transforms and pushes it to every agent-specific format on save, using a simple mapping config. It integrates with Git hooks so the derived files are always in sync and a CI check fails if any agent file drifts from the canonical spec. ## Monetization Strategy Free CLI open-source core; $8/month hosted dashboard with team sync, PR status checks, and agent file analytics ## Requirements - Category: Developer Tool - Difficulty: Weekend - Suggested stack: Node.js CLI or VS Code extension + TypeScript Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "CodeFluency". ## The Problem Developers using AI coding agents exclusively are experiencing skill atrophy and losing confidence in their own abilities, but have no structured way to maintain cognitive skills — raised in Stack Overflow questions with 117 and 13 upvotes. ## Target Audience Software engineers from students to seniors who use AI coding tools daily and worry about skill degradation ## Core Idea A daily coding drill app that keeps your core programming instincts sharp even as you delegate more and more to AI agents. Developers in their third year of CS and senior engineers alike are reporting that heavy AI tool use has left them unable to solve problems independently — a growing professional anxiety visible across multiple Stack Overflow threads. CodeFluency delivers short, timed daily exercises calibrated to your actual skill level that must be completed without AI assistance, focusing on the exact reasoning patterns that atrophy fastest: algorithm intuition, debugging by reading code, and system design tradeoffs. Progress is tracked over weeks so you can see concrete evidence that your fundamentals are still sharp, not just a gut feeling. ## Monetization Strategy $6/month subscription; free tier limited to 3 drills per week to drive habit formation before conversion ## Requirements - Category: Education - Difficulty: Week - Suggested stack: Next.js + Supabase + MDX for content Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "InkGuard". ## The Problem Artists don't want their work used to train LLMs but find current poisoning tools technically complex and slow to apply image-by-image, as discussed in the Lobsters thread about LLM poisoning of artwork. ## Target Audience Independent artists, illustrators, and photographers who publish work online and are concerned about AI training scraping ## Core Idea Automatically apply LLM-poisoning transforms to artwork images before they are published online, protecting artists from unauthorized AI training at scale. Artists who want to protect their work from LLM training find existing tools like Glaze technically complex, slow, and require processing images one at a time — a friction point that prevents most artists from actually using them. InkGuard is a drag-and-drop desktop app that batch-processes entire portfolios of images with adversarial perturbation techniques, letting artists choose an aggression level and preview the visual difference before saving. It also generates a poisoned web-ready version automatically sized for social platforms so the protected image is what gets scraped, not the original. ## Monetization Strategy $7/month subscription for unlimited batch processing; free tier limited to 20 images per month to prove value ## Requirements - Category: AI/ML - Difficulty: Month - Suggested stack: Next.js + Anthropic Claude API + Vercel AI SDK Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "FinancePrimer". ## The Problem People want to learn personal finance but generic educational content ignores their real situation, and subscription-based apps manage money rather than teach principles — a recurring frustration especially after Mint's shutdown. ## Target Audience Young adults in their 20s and 30s who know they should understand personal finance but find existing tools either too abstract or too focused on tracking rather than education ## Core Idea A personal finance learning app that teaches you real principles by connecting every lesson directly to your own actual transactions and spending patterns. Generic personal finance content fails because it ignores the user's real situation, and existing apps manage money rather than teach principles — a frustration that intensified after Mint's shutdown left millions without a privacy-respecting alternative. FinancePrimer imports your real transaction data via Plaid or CSV export, then delivers bite-sized lessons that reference your actual numbers: 'You spent $340 on dining last month — here is how the 50/30/20 rule would change that.' All analysis runs locally with no transaction data stored server-side, addressing the privacy concerns that drove users away from Mint. Each lesson ends with one concrete action tied to your real accounts. ## Monetization Strategy $5/month subscription; one-time $39 lifetime purchase option to capture the segment burned by Mint's shutdown and wary of recurring SaaS fees ## Requirements - Category: Fintech - Difficulty: Month - Suggested stack: Next.js + Plaid API + Stripe Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "TrendPulse HN". ## The Problem Lobsters and HN users want to surface old high-quality comments and discussions they've missed, and the Show HN post indexing 18 years of HN comments received 614 upvotes and 143 comments proving strong demand for this kind of historical exploration. ## Target Audience Hacker News power users, researchers, journalists, and indie hackers tracking technology trends ## Core Idea Search and explore 18 years of Hacker News comment trends and rediscover buried gems from the community's best discussions. TrendPulse HN indexes the full history of Hacker News comments and stories to surface trending topics over time, find semantically similar past discussions, and let users discover the best comments they've missed. Unlike the existing HN search which only does keyword matching, TrendPulse uses semantic search and trend visualization so users can explore how community opinion on a topic has evolved. Monetized via a freemium API for developers building HN-adjacent tools and a Pro tier for researchers and journalists. ## Monetization Strategy Freemium: free basic search, $9/month Pro for trend charts and semantic search, paid API access for developers ## Requirements - Category: SaaS - Difficulty: Week - Suggested stack: Next.js + Supabase + Stripe Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "BuddyPort". ## The Problem Claude Code's /buddy companion feature was silently removed with no changelog entry, devastating thousands of developers who had formed genuine attachment to it — generating 2,021 upvotes and 262 emotionally charged comments on the GitHub issue. ## Target Audience Solo developers and indie hackers using AI coding agents who valued the morale and companionship aspect of the /buddy feature ## Core Idea A drop-in companion layer for any AI coding agent that restores the emotional presence and morale features Claude Code's /buddy provided. BuddyPort is a lightweight wrapper CLI and VS Code extension that injects a persistent, named AI companion persona into any coding agent session — Claude Code, Codex, Cursor, or Amp. It tracks session context, delivers morale nudges, celebrates milestones, and maintains a consistent character across tools. The /buddy removal generated 2,021 upvotes and 262 emotionally charged GitHub comments proving genuine user attachment to this category of feature. ## Monetization Strategy Free open-source core, $5/month Pro for persistent memory across sessions and custom persona configuration ## Requirements - Category: Developer Tool - Difficulty: Weekend - Suggested stack: Node.js CLI or VS Code extension + TypeScript Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "AlertRadius". ## The Problem County-wide emergency alerts go to millions of people regardless of proximity to the actual hazard, leaving residents unable to determine their personal risk level without navigating complex GIS data sources on their own. ## Target Audience Homeowners and residents in wildfire, flood, and severe weather prone areas who receive frequent but imprecise emergency alerts ## Core Idea Enter your address and instantly see whether you are actually inside the danger zone for any active emergency alert — not just the county-wide broadcast. AlertRadius ingests FEMA IPAWS, NWS, and CAP-format alert feeds in real time and renders the precise geographic hazard polygon on a map. Users enter their address once and get a binary in-zone or out-of-zone status with distance to boundary. A push notification service notifies subscribers only when their specific location falls inside an active alert polygon, eliminating county-wide false alarms. ## Monetization Strategy Free basic address lookup, $2.99/month for real-time push notifications and household address monitoring ## Requirements - Category: Health - Difficulty: Week - Suggested stack: Next.js + Supabase + PWA + Chart.js Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "InterviewShift". ## The Problem Engineering teams have no framework for conducting meaningful technical interviews when candidates can and should use AI agents, making traditional no-AI coding challenges obsolete and creating unfair assessments — a recurring theme in the 'Where is our profession going?' HN thread. ## Target Audience Engineering hiring managers at startups and mid-size companies, and senior engineers who conduct technical interviews ## Core Idea A technical interview platform designed for the AI-agent era, where candidates solve problems collaboratively with AI tools just like they would on the job. InterviewShift replaces traditional no-AI coding challenges with structured assessments where candidates use their preferred AI tools while being evaluated on problem decomposition, prompt quality, output verification, and architectural judgment. Hiring managers get a rubric calibrated to AI-augmented engineering rather than memorized algorithms. The platform includes a bank of real-world scenarios where the interesting work is directing AI agents rather than hand-coding, reflecting how engineering actually works in 2025. ## Monetization Strategy $199/month per company for up to 10 active roles, usage-based pricing above that threshold ## Requirements - Category: SaaS - Difficulty: Month - Suggested stack: Next.js + Supabase + Stripe Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "FocusBuild". ## The Problem Solo developers building side projects on nights and weekends consistently cite missing accountability and partnership as the primary reason their projects stall and die — a recurring theme across multiple HN threads about the profession. ## Target Audience Solo developers with side project ideas who struggle with motivation and consistency when building alone ## Core Idea An async accountability platform that pairs solo developers building side projects and sends daily check-ins to keep both partners shipping. FocusBuild matches solo developers by project type, timezone, and weekly commitment level, then runs a structured async accountability loop: daily 3-question check-ins, weekly goal-setting, and a shared build log visible to your partner. Unlike Discord servers or Twitter accountability threads, the matching algorithm and structured prompts create genuine commitment without synchronous meeting overhead. Developers who stall get a lightweight intervention nudge from their partner before they disappear. ## Monetization Strategy Free for basic matching and check-ins, $6/month Pro for build logs, streak tracking, and priority rematching ## Requirements - Category: Social - Difficulty: Weekend - Suggested stack: Next.js + Supabase Realtime + Auth Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "NotionVault". ## The Problem Notion iOS users report deleted workspaces with no recovery, voice-to-text broken for months, and crashing comments, but feel unable to migrate years of accumulated notes without a reliable export tool. ## Target Audience Heavy Notion users, especially those on iOS who rely on it for personal knowledge management ## Core Idea A bulletproof backup and migration tool for Notion that continuously syncs your workspace to local storage so a bug can never destroy years of your work. Notion iOS users have lost entire workspaces with no recovery, suffered months of broken voice-to-text, crashing comments, and forced sign-ins — yet migrating to another tool feels impossibly risky without a reliable export. NotionVault runs in the background, incrementally exporting your entire Notion workspace to local Markdown and SQLite on a schedule, with a one-click restore and migration wizard to tools like Obsidian or Logseq. It also monitors Notion's iOS app release notes and flags when a new update breaks known features before you upgrade. ## Monetization Strategy $4/month subscription or $29 one-time purchase for continuous backup; free tier limited to manual exports ## Requirements - Category: Productivity - Difficulty: Week - Suggested stack: Next.js + localStorage or Supabase + PWA Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "DartMeta Playground". ## The Problem The Dart static metaprogramming GitHub issue has 1,708 comments and 600 participants who want to experiment with the feature but there is no approachable playground or recipe library — only a spec and a compiler flag to enable it. ## Target Audience Dart and Flutter developers curious about static metaprogramming who want to experiment without reading spec documents ## Core Idea An interactive browser-based playground for experimenting with Dart's static metaprogramming feature, with live examples and shareable recipes. DartMeta Playground lets developers experiment with Dart static macros directly in the browser without any local setup, compiler flags, or spec-reading required. It ships with a curated library of real-world macro recipes for serialization, DI, and code generation, and lets users share their macros via URL. The GitHub issue tracking this feature has 1,708 comments and 600 participants who are eager to try it but have no approachable entry point. ## Monetization Strategy Free with optional $4/month Pro for private recipe libraries and team sharing ## Requirements - Category: Developer Tool - Difficulty: Week - Suggested stack: Node.js CLI or VS Code extension + TypeScript Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "WaypointDNS". ## The Problem Developers and sysadmins want a dig-like tool that automatically enumerates all subdomains rather than querying them one at a time, as raised on Software Recommendations Stack Exchange. ## Target Audience DevOps engineers, security researchers, and sysadmins who regularly audit DNS infrastructure ## Core Idea A drop-in replacement for dig that automatically discovers and queries all subdomains of a domain in one command. Developers and sysadmins regularly need DNS information for all subdomains of a domain, but dig requires querying each subdomain individually with no built-in enumeration — leaving users hunting for clunky workarounds. WaypointDNS is a single binary that accepts a domain, runs passive and active subdomain discovery using certificate transparency logs, DNS brute-force wordlists, and zone-transfer attempts, then outputs results in the same familiar dig-like format. It supports JSON output for scripting and a --diff flag to compare snapshots over time for attack surface monitoring. ## Monetization Strategy Open-source CLI with a $5/month hosted API for high-volume queries, historical snapshots, and team sharing ## Requirements - Category: Developer Tool - Difficulty: Weekend - Suggested stack: Node.js CLI or VS Code extension + TypeScript Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "OfflineTranslate". ## The Problem Users on completely offline Windows 10 machines with no admin rights need self-contained translation software with no internet dependency — a gap explicitly raised on Software Recommendations Stack Exchange with no viable free solution found. ## Target Audience Professionals and researchers on air-gapped or restricted corporate/government Windows machines ## Core Idea A fully self-contained, no-install offline translation app for air-gapped Windows machines with zero internet dependency. OfflineTranslate bundles a compact neural machine translation model into a single portable executable that requires no admin rights, no internet connection, and no external dependencies. Users on locked-down corporate or government machines can drop the EXE on a USB drive and translate documents immediately. Supports French-English as the initial language pair with expansion packs for additional languages sold separately. ## Monetization Strategy One-time purchase $19, additional language pair packs $9 each ## Requirements - Category: Productivity - Difficulty: Week - Suggested stack: Next.js + localStorage or Supabase + PWA Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "BurnBuddy". ## The Problem Open-source contribution graphs gamify over-commitment and maintainers have no tool to detect when contributors are heading toward burnout before they disappear — validated by 1,789 upvotes on the isaacs/github issue. ## Target Audience Open-source project maintainers and engineering team leads managing distributed contributors ## Core Idea A private wellness dashboard for open-source maintainers that detects contributor burnout signals before people disappear. Open-source contribution graphs gamify over-commitment, and maintainers have no tool to detect when contributors are heading toward burnout before they quietly abandon projects — a problem with 1,789 GitHub upvotes. BurnBuddy connects to a repo's GitHub activity and uses commit cadence, PR response time, comment sentiment trends, and time-of-day patterns to flag contributors who are showing early burnout signals. Maintainers receive a weekly digest with suggested low-friction interventions like a kind public shoutout or a private check-in message draft. ## Monetization Strategy Free for repos under 10 contributors; $12/month per organization for unlimited repos and Slack/email alerts ## Requirements - Category: Social - Difficulty: Week - Suggested stack: Next.js + Supabase Realtime + Auth Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "JXLCam". ## The Problem Android users want a camera app that saves photos as JPEG XL for its superior compression and royalty-free open-source license, but no mainstream camera app supports it — a gap explicitly identified on Software Recommendations Stack Exchange. ## Target Audience Android power users, photography enthusiasts, and developers who care about open image standards and storage efficiency ## Core Idea The first Android camera app that saves photos natively as JPEG XL for superior compression without patent fees. JXLCam replaces your default Android camera with one that saves every shot as JPEG XL, delivering roughly 60% better compression than legacy JPEG with no royalty or patent costs. It supports HDR, progressive decoding, and direct share to any app that accepts images. The app targets photography enthusiasts and developers who care about storage efficiency and open standards. ## Monetization Strategy One-time purchase $2.99 on Google Play Store with a free 7-day trial ## Requirements - Category: E-commerce - Difficulty: Week - Suggested stack: Next.js + Shopify API or Stripe Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "DiceCanvas". ## The Problem Tabletop gamers and game designers cannot calculate or visualize probability distributions for complex custom dice formulas involving conditional expressions, as no accessible tool handles anything beyond simple additive rolls. ## Target Audience Tabletop RPG game designers, dungeon masters creating custom mechanics, and board game developers balancing combat systems ## Core Idea Visualize full probability distributions for any dice formula — including conditional expressions and custom mechanics — with an interactive graph and shareable link. Tabletop gamers and game designers need to understand the probability distributions of complex custom dice formulas involving conditionals, minimums, and maximums, but every accessible tool only handles simple additive rolls like 3d6+2. DiceCanvas accepts natural language dice formulas including expressions like 'the minimum of 3d6 times the sum of 2d10, drop lowest' and renders the full probability distribution as an interactive histogram with mean, median, and percentile overlays. Designers can compare multiple formulas side-by-side and export the data as CSV or a shareable URL for forum posts. ## Monetization Strategy Free web tool with a $4/month Pro tier for unlimited saved formulas, private sharing, and exportable reports ## Requirements - Category: E-commerce - Difficulty: Weekend - Suggested stack: Next.js + Shopify API or Stripe Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "DreamMap". ## The Problem People with PTSD nightmares and dream-curious users have no dedicated app for recording dreams and identifying trends — existing diary apps lack dream-specific pattern analysis entirely. ## Target Audience PTSD sufferers working through recurring nightmares with therapists, and general users curious about dream pattern analysis ## Core Idea A dream journal app built for PTSD sufferers and curious minds that automatically surfaces recurring themes, symbols, and emotional patterns across your entries. People dealing with PTSD nightmares and those interested in understanding their dreams have no dedicated app for recording and analyzing dream patterns — existing diary apps treat dreams like any other note with no domain-specific analysis. DreamMap provides a guided quick-capture flow optimized for groggy post-sleep recording, then uses NLP to tag recurring symbols, emotions, people, and settings across entries automatically. A weekly insight card surfaces trends like 'your anxiety dreams spike on Sunday nights' or 'this recurring location appears in 7 entries' to give users actionable patterns without requiring manual tagging. ## Monetization Strategy $3/month subscription; therapist referral program offering co-branded versions for mental health practices ## Requirements - Category: Health - Difficulty: Week - Suggested stack: Next.js + Supabase + PWA + Chart.js Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "FounderDial". ## The Problem Technical founders repeatedly build high-quality products without validating assumptions, and fifteen years after The Lean Startup there is still no lightweight tool that enforces customer discovery discipline and tracks hypothesis invalidation across interviews — validated across multiple HN threads. ## Target Audience Technical solo founders and early-stage startup teams building their first SaaS product ## Core Idea A structured customer discovery tool that enforces Lean Startup discipline and tracks which hypotheses your interviews have actually invalidated. FounderDial guides technical founders through a customer discovery workflow that forces them to state a falsifiable hypothesis before each interview, log evidence from conversations, and mark assumptions as validated or invalidated. The dashboard shows which core assumptions are still unvalidated and blocks founders from moving to build until they have evidence. Unlike generic CRM tools, FounderDial is opinionated about the hypothesis-first approach and surfaces gaps in discovery coverage automatically. ## Monetization Strategy $19/month solo, $49/month team up to 5 seats ## Requirements - Category: Fintech - Difficulty: Week - Suggested stack: Next.js + Plaid API + Stripe Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "PlanetSurface". ## The Problem Researchers and enthusiasts need to calculate planetary positions and transits as seen from arbitrary planetary surfaces using user-provided ephemeris files — a capability absent from all mainstream planetarium apps. ## Target Audience Amateur astronomers, planetary scientists, science journalists, and space mission planners ## Core Idea Calculate and visualize planetary positions, transits, and mutual occultations as seen from the surface of any planet using your own ephemeris files. Researchers and enthusiasts need to compute sky events as seen from arbitrary planetary surfaces — transit of Phobos across the Martian sun, mutual planet transits seen from Jupiter — but no mainstream planetarium software supports user-supplied ephemeris files or arbitrary observer locations beyond Earth. PlanetSurface accepts JPL Development Ephemeris and VSOP files, lets you set any latitude/longitude/altitude on any solar system body as the observer, and renders a timeline of upcoming transit and occultation events with exportable ephemeris tables. A visual sky renderer shows the real-time apparent positions of all bodies from the chosen surface. ## Monetization Strategy $9/month subscription for cloud compute on large ephemeris files; free tier handles small date ranges locally in-browser via WASM ## Requirements - Category: SaaS - Difficulty: Month - Suggested stack: Next.js + Supabase + Stripe Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.