Monday, May 25, 2026

I want to build an app called "GhostTracker". ## The Problem Job seekers apply to listings that stay live for months with no response, unable to tell if the role is real, filled, or a ghost listing used for data collection. ## Target Audience Software engineers and tech workers actively job hunting ## Core Idea Monitor job listings you've applied to and get alerted when they're quietly removed or refilled — exposing ghost jobs in real time. GhostTracker lets job seekers paste in links to job postings they've applied for and automatically monitors those listings for changes — status updates, removal, or reposting under a new URL. It exposes the 'ghost job' phenomenon where companies leave dead listings up for weeks or months, wasting applicants' time and hope. Monetized via a freemium model with free monitoring for up to 10 listings and a $5/month plan for unlimited tracking plus weekly market insights. ## Monetization Strategy Freemium: free for 10 listings, $5/month for unlimited tracking and insights ## 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 "PodSkip". ## The Problem Podcast listeners are constantly annoyed by ads but existing ad-blocking apps are either paid, iOS-only, or require manual chapter marking — there's no free cross-platform solution with automatic AI-based detection. ## Target Audience Podcast listeners who consume multiple shows daily and are frustrated by repetitive ad breaks ## Core Idea Cross-platform podcast player that automatically detects and skips ads using on-device AI. PodSkip is a free podcast app that uses a lightweight on-device ML model trained on thousands of ad segments to detect and seamlessly skip podcast advertisements in real time — no crowdsourced chapter markers required. It works across all RSS-based podcasts without requiring host cooperation, and improves with each listen through passive feedback. Available on iOS and Android with a clean, minimal interface and offline support. ## Monetization Strategy Free with optional $2.99/mo premium for offline downloads, sleep timer, and speed controls; app is the loss leader driving word-of-mouth growth ## Requirements - Category: AI/ML - Difficulty: Week - 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 "GhostJob Radar". ## The Problem Job seekers apply to listings that stay live for months with zero response — ghost jobs that companies post to appear active or build a pipeline without intent to hire imminently. ## Target Audience Software engineers and tech workers actively job hunting ## Core Idea Track which job listings are real vs. zombie postings before you waste hours applying. GhostJob Radar scrapes and monitors job listings across major boards, flagging postings that have been live unusually long, have no recent applicant activity signals, or match known ghost-posting patterns. Job seekers paste a listing URL or paste a batch of links and get a freshness score, estimated real-applicant competition, and alerts when a listing finally closes or re-posts. A browser extension auto-annotates listings on LinkedIn and Indeed in real time. ## Monetization Strategy $9/mo for unlimited tracking and extension access; free tier limited to 5 listings ## 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 "SideHatch". ## The Problem Finding side project collaborators is extremely inefficient — developers resort to posting on random subreddits or newsletters with little structure or matching quality. ## Target Audience Solo developers, indie hackers, and recent graduates looking to build side projects with others ## Core Idea A structured matchmaking platform for developers who want to find collaborators for side projects — not just post and pray on Reddit. SideHatch replaces the chaotic subreddit posts and newsletters where developers try to find side project collaborators, offering a structured profile system with skill tags, availability, project type preferences, and async compatibility scoring. Users specify what they're building, what skills they need, and how many hours per week they can commit, then get matched with compatible builders. Revenue comes from a free tier for basic matching and a $6/month premium tier for priority matching, project showcasing, and messaging. ## Monetization Strategy Freemium: free basic matching, $6/month for priority placement, messaging, and project showcase page ## 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 "SpecForge". ## The Problem Developers using AI coding agents get poor results without structured specs, and are independently building their own spec-driven workflows from scratch. ## Target Audience Developers using Claude Code, Codex, Cursor, or similar AI coding agents on non-trivial projects ## Core Idea A lightweight spec-driven development tool that turns requirements into structured task breakdowns Claude Code and other agents can execute reliably. SpecForge provides a structured workflow layer on top of AI coding agents: it takes a feature description, guides the user through generating requirements, design specs, and subtask decompositions, then outputs agent-ready task files that reduce hallucination and improve output quality. Multiple HN posts show developers independently converging on spec-driven development as a best practice for working with agents, but there's no dedicated tool. Monetized as a web app with a free tier and a $12/month pro plan for team collaboration features. ## Monetization Strategy Freemium: free for solo use, $12/month pro for team sharing, version history, and agent integrations ## 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 "HumanWritten". ## The Problem Readers and publishers can't tell how much of online content is AI-generated, and writers have no credible way to prove their work is authentically human. ## Target Audience Newsletter writers, bloggers, journalists, and technical content creators who want to signal authenticity to their audience ## Core Idea A writing transparency tool that shows readers exactly how much of a document was human-written versus AI-assisted, with keystroke-level proof. HumanWritten is a document editor that records the writing process — keystrokes, edits, AI-assisted sections — and generates a shareable transparency report showing the human contribution breakdown for any document. As AI-generated content floods tech blogs, newsletters, and professional documents, readers and publishers increasingly want provenance signals. Monetized via a $8/month subscription for writers who want to build trust with their audience, and a $25/month API plan for publishers who want to add verification badges to submitted content. ## Monetization Strategy $8/month for individual writers, $25/month API plan for publishers; freemium with watermarked reports ## 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 "ForgeGuard". ## The Problem Self-hosted and API LLM agents fail unpredictably on agentic tasks, with vanilla models scoring as low as 53% on complex tool-calling tasks without reliability guardrails. ## Target Audience Indie hackers and small engineering teams building LLM-powered agents and automations ## Core Idea Drop-in reliability layer that boosts your self-hosted LLM agent success rates from mediocre to production-grade. ForgeGuard is a hosted SaaS wrapper around open-source guardrail techniques that adds retry nudges, step enforcement, error recovery, and context management to any self-hosted or API-based LLM tool-calling workflow. Developers connect via a simple SDK and immediately get observability dashboards showing agent task completion rates, failure modes, and guardrail interventions. No ML expertise required — it works out of the box with OpenAI, Anthropic, and local models. ## Monetization Strategy Freemium: free up to 10k agent calls/month, then $29/mo for 100k calls, $99/mo for 1M calls ## 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 "GuardRailKit". ## The Problem Self-hosted and API LLMs are unreliable for agentic tasks without guardrails, but building those guardrails requires deep expertise most teams don't have. ## Target Audience AI engineers and indie developers building LLM-powered agents and tools ## Core Idea Drop-in reliability middleware for self-hosted LLMs that boosts agentic task success rates with zero model fine-tuning. GuardRailKit is a hosted SaaS wrapper and SDK that brings production-grade guardrails — retry nudges, step enforcement, error recovery, and context management — to any self-hosted or API-based LLM. Inspired by open-source projects showing an 8B model jump from 53% to 99% on agentic tasks with guardrails, this product packages that reliability layer for teams who can't build it themselves. Revenue comes from a usage-based API pricing model with a generous free tier to drive adoption. ## Monetization Strategy Usage-based pricing at $0.001 per guardrailed call, free up to 10,000 calls/month ## 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 "GlucoseCompanion". ## The Problem Diabetics often go months between endocrinologist visits with no one reviewing their CGM data, leaving them to interpret complex glucose patterns alone. ## Target Audience Type 1 and Type 2 diabetics using continuous glucose monitors who want actionable insights between clinical appointments ## Core Idea AI-powered diabetes self-management app that bridges the gap between endocrinologist visits using CGM data and personalized insights. GlucoseCompanion connects to continuous glucose monitors and insulin pump data to provide pattern analysis, meal impact tracking, and AI-generated insights for people with diabetes who go weeks or months without clinical review. Inspired by a Type 1 diabetic engineer who built their own tool during a gap in care, this app democratizes the kind of data interpretation usually reserved for clinic visits. Monetized via a $9/month subscription with a free tier for basic CGM viewing. ## Monetization Strategy $9/month subscription; free tier with basic CGM sync and 30-day history ## 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 "NoteGraph". ## The Problem People take lots of notes but never find time to organize them, so the value quietly disappears — notes become a write-only graveyard. ## Target Audience Knowledge workers, researchers, and developers who take notes compulsively but struggle with organization ## Core Idea Automatically organizes your messy notes into a living knowledge graph using local AI. NoteGraph runs a local LLM pipeline that ingests notes from any source (plain text, Obsidian, Notion export, voice memos) and classifies, links, and consolidates them into a searchable knowledge graph — no manual tagging required. It runs entirely on-device for privacy, with a clean visual graph UI to explore connections and surface forgotten insights. A daily digest email shows you what knowledge resurfaced and what notes were merged or linked overnight. ## Monetization Strategy $8/mo subscription for cloud sync and mobile app; local-only version free forever ## 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 "InterviewDojo". ## The Problem Experienced developers with 8-10 years of real-world expertise consistently fail tech interviews because interview performance is a separate skill that doesn't correlate with job competence, and existing prep tools don't diagnose the specific failure modes. ## Target Audience Mid to senior software engineers preparing for job interviews, especially those re-entering the market ## Core Idea Practice realistic tech interviews with an AI interviewer that adapts to your actual weak spots. InterviewDojo conducts mock technical interviews via voice and code editor, then analyzes your responses to identify specific gaps — not just 'practice more LeetCode' but pinpointing whether you struggle with problem decomposition, communication, time complexity explanations, or specific data structures. After each session it generates a personalized drill plan and tracks improvement over time. Unlike generic platforms, it simulates the pressure and ambiguity of real interviews rather than clean textbook problems. ## Monetization Strategy $19/mo for unlimited mock interviews and personalized coaching plans; $4.99 one-time for a single deep-analysis session ## 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 "SideProjectMatch". ## The Problem Finding collaborators for side projects is painfully inefficient — people post in scattered subreddits and newsletters with no matching system, leading to low-quality connections and projects that never start. ## Target Audience Indie hackers, solo developers, and technical founders looking for co-builders ## Core Idea Tinder for side project collaborators — swipe to find your co-founder for the weekend. SideProjectMatch lets indie hackers post their project ideas with required skills and time commitment, then algorithmically matches them with complementary developers, designers, and marketers. Unlike subreddits or newsletters, matches are mutual and skill-verified — you only see people who want to work on your type of project and have skills you actually need. A built-in async workspace with weekly commitment check-ins keeps projects from dying after the first call. ## Monetization Strategy Free to join; $6/mo for premium visibility, unlimited matches, and project workspace features ## 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 "GlucoseCoach". ## The Problem Diabetics often go months between endocrinologist visits with no clinician reviewing their CGM data, leaving them to interpret complex glucose patterns alone with no actionable guidance. ## Target Audience Type 1 and Type 2 diabetics using continuous glucose monitors, especially those with infrequent clinical access ## Core Idea AI-powered CGM data interpreter that gives diabetics actionable insights between doctor visits. GlucoseCoach connects to continuous glucose monitors (Dexcom, Libre) and wearables to analyze patterns, flag anomalies, and generate plain-English weekly reports that patients can actually understand and act on — no endocrinologist required. It uses local AI to give personalized meal and activity recommendations based on the user's own glucose response patterns, not generic guidelines. A shareable report PDF can be sent to a doctor ahead of appointments to make visits more productive. ## Monetization Strategy $12/mo subscription; potential B2B licensing to telehealth providers and employer wellness programs ## 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 "SpecForge". ## The Problem Developers using coding agents get much better results with spec-driven development but creating quality specs, requirements docs, and subtask decompositions manually is time-consuming and most teams skip it. ## Target Audience Solo developers and small teams using AI coding agents like Claude Code, Codex, and Cursor ## Core Idea Generate structured specs, requirements docs, and subtask breakdowns from a single product idea sentence. SpecForge takes a plain-English description of what you want to build and produces a full spec-driven development package: requirements document, architecture decisions, phased subtask breakdown, and acceptance criteria — formatted for direct use with Claude Code, Codex, or any coding agent. It learns from your codebase context (paste your tech stack and existing files) to generate specs that match your actual architecture rather than generic boilerplate. Export to Markdown, Notion, or Linear tickets in one click. ## Monetization Strategy $15/mo for unlimited spec generation; free tier for 3 specs/month; team plans at $40/mo for 5 seats ## 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 "DiffPilot". ## The Problem Developers reviewing large LLM-generated diffs find git+delta too limiting; there's no purpose-built tool for reviewing agent-written code at scale. ## Target Audience Solo developers and small engineering teams using AI coding agents like Claude Code, Codex, or Cursor ## Core Idea AI-native code review tool built specifically for reviewing LLM-generated diffs at scale. DiffPilot is a local-first diff review tool that goes beyond git+delta by offering structured annotation, AI summarization of large LLM-generated patches, and pattern detection for common agent mistakes. As developers review more and more code written by coding agents, standard diff tools feel limiting — DiffPilot fills that gap with context-aware review flows. Monetized as a desktop app with a one-time purchase or subscription for team features. ## Monetization Strategy $29 one-time purchase for individual license, $15/month per seat for team features ## 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 "MeetupPulse". ## The Problem Local tech meetup scenes are dying in major cities but there's no visibility into which communities are declining or tools to help organizers coordinate a revival. ## Target Audience Tech community organizers, developer advocates, and company DevRels trying to engage local engineering communities ## Core Idea A city-level tech community health tracker that aggregates local meetup activity, flags dying scenes, and helps organizers revive them. MeetupPulse scrapes Meetup.com, Eventbrite, and Luma to build a real-time map of tech community activity by city, flagging areas where meetup frequency is declining and surfacing actionable data for organizers who want to restart local events. The HN discussion about NYC meetups dying despite being a major tech hub reveals a real gap in community visibility and coordination tooling. Revenue comes from a free public dashboard and a $29/month organizer plan with attendee analytics, co-promotion tools, and sponsor matching. ## Monetization Strategy Free public city dashboards; $29/month organizer pro plan with analytics and sponsor matching ## 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 "Codiff Pro". ## The Problem Developers reviewing large amounts of LLM-generated code find standard git diff tools inadequate — the volume and nature of AI-written changes require a different review workflow than human-written code. ## Target Audience Developers using Claude Code, Codex, Cursor, or other coding agents who review their own or teammates' AI-generated PRs ## Core Idea A smarter local diff reviewer purpose-built for the era of LLM-generated code. Codiff Pro is a desktop app that makes reviewing large AI-generated diffs fast and safe — it groups changes by semantic intent, highlights potentially dangerous patterns (auth changes, SQL queries, API surface changes), and lets you annotate sections for follow-up. It integrates with git and supports side-by-side review with inline AI explanations of what each chunk does and why it might be risky. Designed specifically for developers who are shipping large volumes of agent-written code and need a human review layer that scales. ## Monetization Strategy $12/mo per developer; free for open source projects; team plans at $8/seat/mo ## 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 "TokenWise". ## The Problem AI coding agents burn enormous numbers of tokens on inefficient codebase navigation like grep fallbacks and full file reads, with no visibility into where costs are going. ## Target Audience Developers and small teams using Claude Code, Codex, or other LLM-powered coding agents who pay per token ## Core Idea A real-time token usage dashboard for AI coding agent sessions that helps developers cut costs by identifying wasteful search and file-reading patterns. TokenWise sits as a proxy layer between your IDE and the LLM API, logging every token exchange during agent sessions and visualizing where tokens are being burned — grep fallbacks, redundant file reads, bloated context windows. Inspired directly by the pain point of agents consuming massive tokens inefficiently when navigating large codebases, it gives developers actionable data to configure their agents more efficiently. Priced as a $10/month SaaS tool with a local self-hosted option. ## Monetization Strategy $10/month hosted SaaS; free self-hosted open-source version to drive adoption ## 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 "DigitalWill". ## The Problem People have no plan for their digital assets, passwords, crypto, and online accounts after death — a growing problem as more wealth and identity lives online, with no simple consumer solution. ## Target Audience Tech-savvy adults 30+, crypto holders, and anyone with significant digital asset accumulation ## Core Idea Securely store and automatically deliver your digital assets, passwords, and final wishes to the right people when you die. DigitalWill lets users create an encrypted vault containing account credentials, crypto wallet keys, important files, personal messages, and instructions for digital accounts. A dead man's switch system — requiring periodic check-ins — triggers a verified delivery process to designated beneficiaries through a multi-party verification system that prevents premature access. It also generates a plain-language digital estate checklist and integrates with services like Google Takeout to pre-package data exports. ## Monetization Strategy $4/mo or $39/yr per user; one-time legacy plan at $99 for lifetime storage ## 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 "CloudExitAlert". ## The Problem Small companies get their cloud accounts suspended without warning or public explanation, as seen with the Railway/GCP incident that left teams scrambling. ## Target Audience Startup founders, DevOps engineers, and indie hackers running production infrastructure on AWS, GCP, or Azure ## Core Idea Real-time monitoring and early warning system for cloud provider account suspension and service degradation events. CloudExitAlert watches for signals that your cloud provider account may be at risk — unusual billing flags, API error pattern spikes, support ticket response degradation — and alerts you before a full suspension occurs like what happened to Railway's GCP account. It also aggregates community-reported incidents from platforms like HN and status pages, giving small teams and startups the institutional awareness that only large enterprise customers usually have. Priced as a SaaS subscription per cloud account monitored. ## Monetization Strategy $19/month per cloud account monitored, with a 14-day free trial ## 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.