Sunday, July 5, 2026

I want to build an app called "FluentBack". ## The Problem Language learners have no easy way to convert authentic native audio into structured vocabulary practice and shadowing exercises — the Show HN post on this concept received 91 upvotes and 37 comments confirming strong demand for an automated solution. ## Target Audience Intermediate and advanced language learners, polyglots, immersion-method practitioners ## Core Idea Turn any native audio or podcast into structured shadowing exercises and vocabulary flashcards in one click. FluentBack accepts a URL or audio file, transcribes it, aligns the transcript word-by-word to the audio, and generates a spaced-repetition deck with native-speed audio clips for each new vocabulary item. Learners can loop any sentence at 50–100% speed for shadowing practice directly in the app, with the foreign text revealed progressively to prevent passive reading. The entire pipeline runs automatically with no manual segmentation, solving the core friction of turning authentic media into structured study material. ## Monetization Strategy Free for 60 minutes of audio per month; $9/month unlimited with offline deck export ## 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 "LidFree". ## The Problem Engineers are physically propping their MacBook lids half-open in cafés and parks because closing the lid suspends AI agents mid-task, validated by a wave of posts and a 124-upvote Show HN about this exact problem. ## Target Audience Developers using AI coding agents on MacBooks ## Core Idea Keep your Mac awake with the lid fully closed so AI agents never get interrupted mid-task. LidFree is a lightweight macOS menu bar utility that prevents sleep when the lid is closed, activating automatically only when a monitored AI agent process (Claude Code, Codex, Cursor, etc.) is actively running and deactivating the moment the process exits. Unlike blunt tools like Amphetamine, LidFree ties wakefulness to actual agent activity so your battery is never needlessly drained. Engineers carrying MacBooks around cafés and parks to babysit half-open lids finally get a clean fix. ## Monetization Strategy One-time purchase $7 on Gumroad or Mac App Store; free trial with 3 activations ## Requirements - Category: Productivity - Difficulty: Weekend - 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 "SlopscanCI". ## The Problem AI-generated code passes syntax checks and linters but introduces structural anti-patterns with no automated CI detection, as validated by the repo-slopscore Lobsters thread with 65 comments — while separately, clean human code is being falsely flagged as 90-99% AI-generated by naive detection tools, a professionally damaging false positive described on Stack Overflow with 18 upvotes. ## Target Audience Engineering teams using AI coding assistants, open-source maintainers reviewing AI-assisted PRs ## Core Idea Automatically detect AI-introduced structural anti-patterns in pull requests before they merge. SlopscanCI is a GitHub Action and CLI that analyzes code diffs for structural slop — empty catch blocks, dead code paths, inconsistent abstraction levels, and poor directory organization — that pass linters and syntax checks but degrade long-term codebase health. Unlike AI-detection tools that produce embarrassing false positives on clean human code, SlopscanCI flags specific structural patterns rather than trying to guess authorship. Teams get actionable PR comments with the exact lines and patterns to fix. ## Monetization Strategy Free for public repos; $19/month per private repo or $99/month for org-wide access ## 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 "GlintHealth". ## 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 and 202 comments on the isaacs/github issue. ## Target Audience Open-source maintainers, prolific contributors, engineering managers overseeing open-source teams ## Core Idea Give open-source contributors a private burnout dashboard so they can see warning signs before they vanish. GlintHealth connects to a contributor's GitHub account and analyzes commit timing, PR review frequency, issue response latency, and session length trends to build a private burnout risk score visible only to them. When patterns match known burnout trajectories — late-night marathon sessions followed by sudden silence — the contributor receives a gentle nudge with context about what the data shows and links to healthy pacing resources. Maintainers can optionally invite trusted teammates to share aggregated (never individual) team health signals without exposing personal data. ## Monetization Strategy Free for individual contributors; $12/month per maintainer for team health dashboards and early-warning alerts ## 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 "DiceForge". ## The Problem Tabletop gamers and game designers cannot calculate or visualize probability distributions for complex custom dice formulas involving conditional expressions — explicitly flagged on Software Recommendations Stack Exchange with no viable tool found for anything beyond simple additive rolls. ## Target Audience Tabletop RPG game designers, dungeon masters, board game developers, math-curious hobbyists ## Core Idea Plot the full probability distribution of any dice formula — including conditionals, minimums, and explosions — in seconds. DiceForge accepts complex tabletop RPG dice expressions including conditional logic, keep-highest, exploding dice, and chained rolls, then renders the complete probability distribution as an interactive histogram with percentile markers. Game designers can compare multiple formulas side by side to balance mechanics, and the shareable URL lets them paste results directly into forum discussions. A simple REPL-style input means no syntax guide is needed for common expressions. ## Monetization Strategy Free web tool; $4/month premium for saved formula libraries, PDF export, and embeddable widgets for game documentation sites ## 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 "MailDig". ## The Problem Gmail silently routes messages to Trash rather than Spam due to conflicting filter rules, and users struggle to write syntactically correct body-matching filter search strings — both pain points appear across multiple Web Apps Stack Exchange questions with no clear solution. ## Target Audience Power Gmail users, developers subscribed to mailing lists, small business owners ## Core Idea A visual Gmail filter builder that auto-corrects syntax and stops your emails from silently landing in Trash. MailDig provides a point-and-click interface for constructing syntactically correct Gmail search strings, with real-time validation and plain-English explanations of what each filter will actually do. It detects conflicting filter rules that cause Gmail to route messages to Trash instead of Spam or Inbox, surfacing the conflicts with a visual dependency graph. Users can test filters against their last 30 days of mail before saving, eliminating the trial-and-error that currently makes Gmail filtering unreliable. ## Monetization Strategy Freemium: free for up to 10 filters; $5/month for unlimited filters, conflict detection, and bulk management ## 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 "AccessFlow". ## The Problem Developers working with blind users discover invisible accessibility gaps too late because there is no automated tool that replays real user flows through a screen reader and reports exactly where the experience breaks down — a gap highlighted in the 91-upvote HN post about working with a blind client. ## Target Audience Frontend developers, QA engineers, accessibility consultants, product teams at companies with accessibility obligations ## Core Idea Replay real user flows through a screen reader and get a line-by-line report of exactly where blind users get stuck. AccessFlow records a developer's interaction flow as a Playwright script, then replays it through a headless NVDA or VoiceOver instance and captures every announcement, silence, and navigation dead-end. The output is a structured report with the exact DOM element, the announcement a screen reader user would hear, and the severity of the issue — making invisible accessibility gaps visible before any real user encounters them. Teams integrate it into CI so regressions are caught in the same PR that introduced them. ## Monetization Strategy $49/month per project; free for open-source repos; enterprise pricing for unlimited projects ## 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 "CopilotBlock". ## The Problem Maintainers want to ban GitHub Copilot from reviewing PRs in their repos but there is no built-in mechanism — raised explicitly on Web Apps Stack Exchange where no working solution was found. ## Target Audience Open-source maintainers, engineering leads at companies with AI-free code review policies ## Core Idea A one-click GitHub App that prevents Copilot from auto-reviewing pull requests in your repositories. CopilotBlock installs as a lightweight GitHub App that intercepts Copilot review events and automatically dismisses or blocks them on repositories you configure, with no code changes or manual intervention required per PR. Repository owners set a blocklist once through a simple dashboard and every future PR is protected automatically. It also generates a weekly report showing how many Copilot reviews were blocked, giving maintainers full visibility into AI activity in their codebase. ## Monetization Strategy Free for up to 3 repositories; $6/month for unlimited repositories; $25/month for org-wide enforcement ## 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 "TabLabel". ## The Problem Linux terminal tools like gemini-cli constantly overwrite the terminal tab title to show status, making it impossible for developers to track which tab is which when managing many concurrent AI agent sessions — explicitly raised on Software Recommendations Stack Exchange with no solution found. ## Target Audience Linux developers running multiple AI coding agents simultaneously ## Core Idea Permanently pin custom names to terminal tabs so AI agents can never overwrite them. TabLabel is a Linux terminal wrapper that gives each tab a persistent user-defined label stored independently from the OS window title. When tools like gemini-cli constantly rewrite the terminal title to show progress, TabLabel overlays the user's chosen name in the tab bar so it is always visible regardless of what the underlying process sets. Developers managing dozens of concurrent AI agent sessions across tabs can finally tell them apart at a glance. ## Monetization Strategy Free and open source; optional $5/month hosted config sync for power users ## 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 "OrbitDesk". ## The Problem No software exists 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, flagged on Software Recommendations Stack Exchange with no viable solution found. ## Target Audience Amateur astronomers, planetary scientists, space enthusiasts, educators ## Core Idea Calculate astronomical positions and transits as seen from the surface of any planet using your own ephemeris files. OrbitDesk is a desktop app that lets researchers and enthusiasts compute planetary positions, transits, and mutual occultations as viewed from arbitrary planetary surfaces, ingesting user-provided JPL or VSOP ephemeris files. No mainstream planetarium software supports custom observer locations beyond Earth or user-supplied ephemeris data. OrbitDesk fills this gap with a clean GUI and exportable results for scientific workflows. ## Monetization Strategy One-time purchase $29, with a free tier limited to solar system bodies only ## 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.