Friday, July 10, 2026

I want to build an app called "BurnWatch". ## 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, foundation program managers, and engineering leads at companies with open-source programs ## Core Idea An analytics dashboard for open-source maintainers that detects contributor burnout signals before people disappear, not after. GitHub contribution graphs reward streak-keeping and volume, inadvertently pushing contributors toward over-commitment and eventual silent dropout. BurnWatch analyzes commit cadence, PR review response times, and comment sentiment over time to surface early warning indicators for individual contributors and flag maintainers who may be approaching collapse. Teams and foundations pay a monthly fee to monitor their contributor health across all their repositories. ## Monetization Strategy Free for single-repo personal use, $19/month for up to 10 repos, $79/month for organizations with unlimited repos ## Requirements - Category: Health - Difficulty: Month - 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 "ResearchAtlas". ## The Problem Reading research papers requires jumping between multiple tabs to find datasets, code, videos, and peer reviews with no unified interface, as described in the Show HN for an 8.5M paper atlas with 78 upvotes. ## Target Audience Academic researchers, PhD students, and ML engineers who read and track large volumes of papers ## Core Idea An interactive visual map of 8.5 million research papers that connects datasets, code, videos, and peer reviews in one place so you never have to tab-hop again. Researchers reading academic papers must constantly jump between tabs to find the associated dataset, code repository, video presentation, and peer reviews, fragmenting their reading experience. ResearchAtlas provides a unified interactive node graph where clicking any paper surfaces all linked artifacts inline, with semantic neighbors shown visually. A freemium model offers unlimited browsing free with a paid tier for private paper collections, annotation sharing, and citation export. ## Monetization Strategy Freemium — free public browsing, $12/month Pro for private collections, annotations, and citation export ## Requirements - Category: Education - Difficulty: Month - 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 "LocalGLM". ## The Problem Running capable LLMs on slow consumer hardware requires significant manual optimization of quantization and inference settings, as highlighted in the GLM 5.2 Show HN with 828 upvotes and 202 comments. ## Target Audience Privacy-conscious developers and power users who want to run LLMs locally on modest hardware ## Core Idea A one-click desktop launcher that gets powerful open-weight LLMs like GLM running optimally on slow or low-VRAM consumer hardware. Many developers want to run capable open-weight models like GLM 5.2 locally for privacy and cost reasons, but optimizing quantization settings, context windows, and inference parameters for slow machines requires significant manual trial and error. LocalGLM auto-detects hardware specs and applies optimal configuration profiles so users get the best possible performance without any tuning. It is distributed as a free open-core desktop app with a paid tier for automatic model updates, preset profiles for specific use cases, and priority support. ## Monetization Strategy Free open-core with a $7/month Pro tier for curated model profiles, automatic updates, and hardware-specific tuning presets ## 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 "ModelBench". ## The Problem Developers want to replace Claude and GPT with local models for daily coding but have no standardized way to evaluate performance, quality, and latency tradeoffs against their specific real-world tasks, as surfaced in the GLM 5.2 Show HN and model routing HN post. ## Target Audience Developers and teams evaluating local LLM alternatives to reduce cost or improve privacy in their coding workflows ## Core Idea Evaluate any local LLM against your actual real-world coding tasks and get a side-by-side cost, quality, and latency scorecard before switching from Claude or GPT. Developers who want to replace cloud LLMs with local models like GLM or Ollama-hosted models have no standardized way to measure how well a local model performs on their specific real-world prompts and codebases rather than generic benchmarks. ModelBench lets users paste in a set of representative tasks, runs them against multiple local and cloud models in parallel, and produces a scored comparison across output quality, token latency, and estimated cost per task. It is sold as a local desktop app with a one-time purchase and optional cloud model comparison add-on. ## Monetization Strategy One-time $29 desktop app purchase with a $9/month add-on for cloud model API comparison and automatic benchmark updates ## 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 "TodoistSync". ## The Problem Todoist users report multi-year sync failures between iOS and desktop where completing tasks from notifications does not register and Siri-created tasks disappear silently, causing real productivity loss. ## Target Audience Power Todoist users who rely on iOS notifications, Siri integration, and cross-device task management ## Core Idea A background sync watchdog for Todoist that detects and repairs broken iOS-desktop sync so tasks completed from notifications and Siri never silently vanish again. Todoist users have experienced multi-year sync failures where completing tasks from iOS notifications does not register on desktop, Siri-created tasks disappear without trace, and calendar connections silently break — causing real missed deadlines. TodoistSync runs as a local agent that monitors the Todoist API for divergence between the local device state and the server, automatically retrying failed mutations and alerting users when a task was lost. It is sold as a one-time purchase app with a small annual fee for continued API compatibility updates. ## Monetization Strategy One-time $9 purchase with $4/year update subscription for API compatibility ## 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 "DRMFinder". ## The Problem Readers wanting DRM-free books have no central marketplace — they must find individual author sites or sift through platforms that mix DRM and non-DRM titles with no clear filter, a gap discussed across multiple reader communities. ## Target Audience Privacy-conscious readers, open-source advocates, and book lovers who want to own their purchases without DRM restrictions ## Core Idea A curated marketplace that surfaces only DRM-free ebooks, letting readers buy directly from authors and indie publishers without sifting through locked titles. Readers who want DRM-free ebooks must hunt across individual author websites, Smashwords, and Itch.io with no single destination that guarantees every title is genuinely DRM-free and purchasable in one transaction. DRMFinder aggregates verified DRM-free titles from participating publishers and authors, takes a small commission on each sale, and lets readers filter by format, genre, and price. Authors and small presses list for free with a percentage revenue share on sales. ## Monetization Strategy 15% commission on each sale; premium author listings at $10/month for featured placement and analytics ## Requirements - Category: Marketplace - Difficulty: Month - Suggested stack: Next.js + Supabase + Stripe Connect 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 "NotionPulse". ## The Problem Notion iOS users report deleted workspaces with no recovery, broken voice-to-text for months, and crashing comments, but feel trapped because migrating years of notes feels impossibly risky without a reliable continuous export tool. ## Target Audience Heavy Notion users who store critical work, personal knowledge bases, or years of notes ## Core Idea A lightweight continuous backup daemon that silently exports your entire Notion workspace every night so a single bug can never erase years of your work. Notion iOS users repeatedly report deleted workspaces, voice-to-text failures, crashing comments, and broken syncing that have persisted for months, but feel trapped because migrating years of notes feels impossible without a reliable safety net. NotionPulse runs as a background service that incrementally exports every workspace to local files and an optional cloud backup, maintaining a 90-day version history. Users pay a monthly subscription for storage and one-click restore previews. ## Monetization Strategy Subscription at $5/month for local-only backup or $9/month for cloud-redundant backup with 90-day history ## 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 "SubRadar". ## The Problem dig cannot automatically search for all subdomains — each subdomain must be queried one at a time, explicitly raised on Software Recommendations Stack Exchange with no satisfactory solution found. ## Target Audience Developers, sysadmins, and security engineers doing DNS audits and reconnaissance ## Core Idea A dig-like CLI tool that automatically enumerates all subdomains of a domain in one command, no manual querying required. Developers and sysadmins routinely need to audit all subdomains of a domain, but dig requires querying each subdomain individually with no built-in enumeration capability. SubRadar combines passive DNS sources, certificate transparency logs, and brute-force wordlists into a single fast CLI tool that outputs structured results in JSON or table format. It is sold as a one-time purchase CLI binary with a paid upgrade for continuous monitoring and alerting. ## Monetization Strategy One-time $19 CLI purchase plus optional $9/month continuous subdomain monitoring SaaS ## 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 "WaypointClip". ## The Problem Researchers manually construct Wayback Machine snapshot URLs when referencing pages that may disappear, and the Web Apps Stack Exchange question about reliably fetching the latest existing snapshot has no clean automated solution. ## Target Audience Researchers, journalists, lawyers, and writers who regularly cite online sources ## Core Idea A browser extension that instantly captures and pins the latest Wayback Machine snapshot of any page you are visiting, with one click. Researchers and writers who reference online sources must manually construct Wayback Machine URLs or navigate the archive interface to find the latest available snapshot of a page, a tedious and error-prone process. WaypointClip adds a toolbar button that fetches and pins the most recent valid snapshot URL for the current page and optionally injects it as a formatted citation. It is monetized through a one-time extension purchase with a small team plan for shared citation libraries. ## Monetization Strategy One-time $5 browser extension purchase with a $12/month team plan for shared citation libraries and bulk archiving ## 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 "VisuAgent". ## The Problem Building AI agents that generate visualizations reliably is very tricky — simple chart specs are unreliable and complex ones fail due to reliance on system defaults, as described in the Microsoft Flint Show HN with 344 upvotes and 135 comments. ## Target Audience Developers and data teams building AI agents that need to produce charts and dashboards ## Core Idea A structured visualization language and sandbox that lets AI agents generate reliable, high-quality charts without hallucinating chart specs. AI agents consistently produce low-quality visualizations because they rely on system defaults and cannot reliably handle complex chart specifications. VisuAgent provides a constrained, declarative visualization grammar purpose-built for LLM output, with a live sandbox where developers can test and debug agent-generated chart specs. Teams integrating data visualization into AI pipelines pay a monthly SaaS fee for API access and higher chart complexity tiers. ## Monetization Strategy Freemium SaaS — free tier for simple charts, paid plans at $29–$99/month for API access and complex chart types ## 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.