Saturday, May 23, 2026

I want to build an app called "GhostWatch". ## The Problem Job applicants get ghosted by companies with no rejection email while the original job listing stays live for weeks or months, leaving applicants with no way to know if the role is still active or if they've been passed over. ## Target Audience Software engineers and tech workers actively job hunting who are frustrated by ghosting and opaque hiring processes. ## Core Idea Track job applications in real time and get alerts when a listing you applied to is quietly removed or reposted, so you know when you've been ghosted. Job seekers routinely apply to positions and receive no rejection, only silence, while the job listing stays live for months. GhostWatch monitors the URLs of job postings you've applied to, detects when they go offline, get reposted under a new ID, or are modified, and sends you a notification with context. A simple browser extension captures application URLs automatically as you apply, feeding a personal dashboard that gives you closure and strategic insight. ## Monetization Strategy Free for up to 10 tracked applications; $5/mo for unlimited tracking, alerts, and historical 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 "GhostTrack". ## The Problem Job seekers apply to roles and receive only silence, with no way to know if the listing is real, already filled, or a ghost posting kept live indefinitely. ## Target Audience Software engineers and tech workers actively job hunting ## Core Idea Automatically monitor job listings you applied to and alert you when they are edited, reposted, or quietly filled. GhostTrack watches job postings you have applied to and sends alerts when listings change status, get reposted with new dates, or disappear — giving applicants signal about whether a role is genuinely active or a ghost listing. The founder of a similar tool described building it after being ghosted repeatedly and discovering listings stayed live for months with no feedback. Applicants can finally understand whether to keep waiting, follow up, or move on. ## Monetization Strategy Freemium — free to track 5 listings, $6/mo for unlimited tracking and weekly insight reports ## 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 "SlopShield". ## The Problem Developers researching security issues and technical problems on GitHub and forums are encountering AI-generated responses that provide false confidence and spread misinformation. ## Target Audience Developers and technical users who rely on community discussions for accurate security and engineering advice ## Core Idea A browser extension that detects and flags AI-generated content in GitHub issues, discussions, and comment threads. SlopShield analyzes comment threads on GitHub, HN, and forums in real time, highlighting responses that appear to be AI-generated so users can weigh them accordingly. A HN user described discovering that GitHub discussions about malware were filled with AI-generated responses — including one that was verbatim identical to what an LLM had told them privately — creating a dangerous misinformation loop. The extension gives readers context about content authenticity without removing or censoring anything. ## Monetization Strategy Free extension with a $4/mo pro tier for advanced detection models, bulk-flagging, and API access for teams ## 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 "ProveThat". ## The Problem As AI-generated text becomes indistinguishable from human writing, there is no reliable way for readers, employers, or publishers to verify the authorship and creation process of a document. ## Target Audience Writers, journalists, academics, job seekers, and anyone who wants to credibly prove their work is human-authored in an AI-saturated environment. ## Core Idea A writing provenance tool that records every keystroke, edit, and AI interaction in a document so readers can verify how much of it was human-written. With AI-generated text flooding the internet, readers, editors, and hiring managers have no way to verify whether a document was written by a human or an AI. ProveThat works as a writing environment that logs a cryptographically signed, time-stamped edit history including which passages were typed manually, which were AI-suggested, and which were edited post-generation. Authors can share a public provenance link alongside any document to build trust with their audience. ## Monetization Strategy Free for public provenance links; $9/mo for private documents, branded provenance pages, and team accounts. ## 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 "TokenLens". ## The Problem Companies are shocked by AI API bills growing to 3x their SaaS spend with no visibility into which agents, tasks, or employees are responsible for the cost explosion. ## Target Audience Engineering managers and CTOs at startups and mid-size companies who have rolled out AI coding tools team-wide and are struggling with runaway costs. ## Core Idea A real-time AI spend dashboard that shows teams exactly which agents, tasks, and team members are burning tokens so they can cut costs before bills explode. Engineering teams are being caught off guard by AI API bills that balloon to 3x their entire SaaS spend, with no granular visibility into which workflows or employees are responsible. TokenLens integrates with OpenAI, Anthropic, and Google AI APIs to provide per-user, per-agent, and per-task token usage breakdowns with configurable budget alerts and automatic throttling rules. It gives managers the data they need to right-size AI spend without banning tools entirely. ## Monetization Strategy $29/mo flat for teams up to 20 seats; $79/mo for unlimited seats with policy enforcement and Slack/email alerts. ## 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 "SpecForge". ## The Problem Coding agents produce unreliable, drifting output when given vague prompts, but structuring proper specs manually is time-consuming enough that most developers skip it and accept worse results. ## Target Audience Developers using Claude Code, Codex, or similar agentic coding tools for feature development ## Core Idea Turn natural language feature requests into structured AI-ready specs that coding agents can execute reliably without drifting. SpecForge takes a rough feature description and guides developers through a structured decomposition — generating requirements, code analysis, and design documents — then breaks the work into discrete subtasks formatted for optimal consumption by Claude Code, Codex, or other coding agents. Multiple HN threads highlighted that spec-driven development dramatically improves agent output quality, but the workflow requires tedious manual structure that most developers skip. SpecForge automates the spec generation while keeping humans in the loop for approval at each stage. ## Monetization Strategy $12/mo subscription with a free tier limited to 5 specs per month ## 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 "VaultExport". ## The Problem Developers lose access to all past AI coding projects and sessions the moment they unsubscribe from tools like Claude Design, with no export or backup mechanism provided by the platform. ## Target Audience Developers who heavily use AI coding assistants like Claude, Codex, or Cursor and switch between subscriptions. ## Core Idea Automatically backs up and exports your AI coding session projects across Claude, Codex, and Cursor so you never lose work when you unsubscribe or switch tools. Developers losing access to months of AI-generated project history when unsubscribing from tools like Claude Design is a real and painful problem with no current solution. VaultExport runs as a lightweight background service that continuously syncs and snapshots your sessions, files, and conversation history to local storage or a cloud bucket you own. It supports multiple AI coding platforms and provides a unified timeline view of all your projects. ## Monetization Strategy One-time purchase of $19 for the desktop app; optional $5/mo for encrypted cloud backup storage. ## 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 "GlucoPilot". ## The Problem Diabetics go months between endocrinologist visits with no one reviewing their continuous glucose monitor data, leaving them to self-interpret complex trends without clinical support. ## Target Audience Type 1 and Type 2 diabetics who use CGMs and have gaps in clinician coverage, especially tech-savvy self-managers. ## Core Idea A self-hosted AI diabetes management platform that connects your CGM data to personalized insights and coaching when you don't have regular clinician access. Type 1 and Type 2 diabetics frequently go months without a clinician reviewing their glucose data, leaving them to interpret complex CGM trends alone. GlucoPilot ingests data from major CGM devices, runs AI-driven pattern analysis, and surfaces actionable coaching on diet, timing, and insulin trends in plain language. It is fully self-hostable for privacy-conscious users and also offers a managed cloud version for those who want zero setup. ## Monetization Strategy Free self-hosted version; $12/mo cloud-managed plan with automated reports and trend alerts. ## 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 "GuardRail Studio". ## The Problem Self-hosted LLM agents fail unpredictably on agentic tasks without reliability layers, but adding guardrails requires deep infrastructure knowledge most developers lack. ## Target Audience Solo developers and small engineering teams running self-hosted LLMs for agentic workflows ## Core Idea A no-code dashboard for adding reliability guardrails to self-hosted LLMs without writing infrastructure code. GuardRail Studio lets indie developers and small teams configure retry logic, error recovery, context management, and step enforcement for local LLM agents through a visual interface. Inspired by the dramatic accuracy gains shown in the Forge project (53% to 99%), it packages these guardrails as reusable presets for common agentic task types. Teams pay per seat to manage and version their guardrail configs across multiple models and projects. ## Monetization Strategy Freemium SaaS — free for 1 model config, $19/mo for teams with unlimited configs and version history ## 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 "LockBox Export". ## The Problem Users lose access to all their AI-generated projects and sessions the moment they unsubscribe from tools like Claude Design, with no warning or export mechanism. ## Target Audience Developers and creators who use multiple AI coding or design subscription tools ## Core Idea Automatically export and back up your AI coding session projects before you cancel any subscription. LockBox Export monitors your active AI tool subscriptions (Claude, Codex, Cursor, etc.) and automatically snapshots and exports all your projects, conversations, and generated artifacts to your chosen storage destination. A user lost months of Claude Design work simply by unsubscribing, a horror story that resonated widely on HN. The tool sends warnings before billing cycles end and maintains versioned backups so you never lose work to a subscription lapse. ## Monetization Strategy One-time purchase $9 browser extension, plus optional $5/mo cloud backup storage tier ## 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 "GlucoseLoop". ## The Problem Type 1 diabetics go months between endocrinologist appointments with no intelligent review of their CGM data, leaving them to interpret complex glucose patterns alone. ## Target Audience Type 1 diabetic adults, particularly those who are technically inclined or underserved by the healthcare system ## Core Idea A self-hosted AI companion that fills the gap between endocrinologist appointments for Type 1 diabetics using CGM data. GlucoseLoop connects to continuous glucose monitors and uses local AI models to surface personalized patterns, flag concerning trends, and suggest questions to bring to the next clinical visit — without sending sensitive health data to third-party servers. The GlycemicGPT founder described going months without clinical review and being forced to build their own tool, highlighting a real gap for the millions of T1D patients between appointments. The product monetizes through a one-time self-hosted setup fee and optional managed cloud hosting for less technical users. ## Monetization Strategy One-time $49 self-hosted license or $12/mo managed cloud hosting with automatic CGM sync ## 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 "CloudPulse". ## The Problem Companies have no advance warning before cloud providers suspend or throttle their accounts, leading to sudden outages with no transparency or recourse as seen in the Railway/GCP incident. ## Target Audience Startups and small engineering teams running production workloads on major cloud providers ## Core Idea Real-time monitoring and alerts for cloud provider account suspension risk based on spend patterns and policy changes. CloudPulse watches your GCP, AWS, and Azure accounts for anomalous billing spikes, policy flag patterns, and provider-side signals that historically precede account suspension or throttling — giving companies advance warning before a Railway-style outage hits. The Railway GCP incident sparked widespread discussion about the opacity of cloud providers when they suspend high-profile customer accounts with no explanation. Small and mid-size companies that depend on a single cloud provider are most vulnerable and have no early warning system today. ## Monetization Strategy $29/mo per cloud account monitored, with a 14-day free trial ## 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 "AI Cost Sentinel". ## The Problem Engineering teams have no visibility into aggregate AI tool spend until monthly invoices arrive, causing surprise budget overruns that lead to abrupt cancellations affecting developer productivity. ## Target Audience Engineering managers and CTOs at companies with 5–100 developers using multiple AI coding subscriptions ## Core Idea Track, forecast, and enforce team-level AI tool spending limits before your monthly bill becomes an emergency. AI Cost Sentinel aggregates usage across Claude, Codex, Cursor, and other AI subscriptions at the team level, providing per-developer spend dashboards, budget alerts, and usage forecasts before bills spiral out of control. A company described their Claude bill hitting nearly 3x their entire SaaS spend before anyone noticed, forcing them to abruptly cut all AI tool access. Sentinel prevents the binary outcome of no-limits-then-sudden-cancellation by giving managers gradual, actionable visibility. ## Monetization Strategy $15/mo per team, flat rate, with a free tier for teams under 3 developers ## 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 "GuardRail Studio". ## The Problem Self-hosted LLM agents fail unpredictably on agentic tasks, with base 8B models achieving only ~53% success rates, and existing reliability solutions require heavy infrastructure or are tightly coupled to specific models. ## Target Audience Indie developers and small engineering teams running self-hosted LLMs for automation and agentic workflows. ## Core Idea A no-code reliability layer that wraps any self-hosted LLM agent with guardrails, retry logic, and error recovery to push task completion from ~50% to 99%. Developers running local or self-hosted LLMs for agentic tasks constantly struggle with brittle, unreliable outputs. GuardRail Studio provides a visual dashboard to configure domain-agnostic guardrails, step enforcement, and automatic error recovery without touching model internals. Teams pay per seat or per agent deployment, making it accessible for small teams who can't afford enterprise observability stacks. ## Monetization Strategy Freemium with free tier for 1 agent; $29/mo for up to 5 agents; $99/mo for unlimited agents and team features. ## 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 "ScaleCoach". ## The Problem Musicians learning scales and chords lack tools that clearly show how they interact across multiple instruments, with existing tools either too basic or too overwhelming without a progressive complexity system. ## Target Audience Beginner to intermediate musicians learning music theory who play guitar, piano, or other fretted/keyed instruments. ## Core Idea An interactive music theory learning tool that teaches scales, chords, and their relationships across multiple instruments with adaptive exercises and a complexity dial. Musicians learning theory struggle to find tools that connect scales and chords practically across different instruments without overwhelming them or being too simplistic. ScaleCoach offers an interactive fretboard and keyboard that visualizes scale-chord relationships in real time, with a complexity toggle that progressively reveals music theory depth as the learner advances. It supports guitar, piano, bass, and cello with exportable chord charts and daily practice challenges. ## Monetization Strategy Free core tool; $5/mo for adaptive exercises, progress tracking, and printable chord/scale reference sheets. ## Requirements - Category: Education - Difficulty: Weekend - 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 "SpecForge". ## The Problem Teams sharing AI coding tool subscriptions have no structured way to create and manage spec-driven workflows, leading to inconsistent agent outputs and no accountability for what requirements were actually implemented. ## Target Audience Small engineering teams using shared Claude, Codex, or Cursor subscriptions who want more consistent and auditable AI-assisted development. ## Core Idea A spec-driven development workspace that helps teams using shared AI coding subscriptions create, version, and assign structured specs so agents produce consistent, reviewable output. Teams sharing a single Claude or Codex subscription have no structured way to manage spec-driven workflows, leading to agents that hallucinate requirements or produce inconsistent code across different users. SpecForge provides a collaborative spec editor that decomposes features into requirements, design decisions, and subtask chains compatible with any major AI coding agent. It tracks which specs have been executed, by whom, and what output was produced, creating an auditable development trail. ## Monetization Strategy Free for solo developers; $15/mo per team for collaborative spec management, version history, and agent output tracking. ## 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 "DocSemantic". ## The Problem Developers building apps that need in-browser Word document editing have no good options — existing libraries convert .docx to HTML and lose critical document semantics and formatting. ## Target Audience SaaS developers and indie hackers building document-centric products that require Word compatibility ## Core Idea A browser-based .docx editor that preserves full document semantics and structure instead of converting to lossy HTML. DocSemantic provides a clean, embeddable Word document editor that parses OOXML directly so styles, tracked changes, comments, and document structure are faithfully preserved — solving the long-standing problem where existing browser editors convert .docx to HTML and destroy semantics. Targeted at SaaS builders who need to let their users edit Word documents without building their own rendering engine, it ships as an embeddable React component with a simple licensing model. The open-source .docx editor project on HN highlighted strong developer demand for this capability. ## Monetization Strategy $49/mo developer license for production use, free for open-source projects ## 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 "DiffSense". ## The Problem Developers reviewing large LLM-generated diffs find tools like git+delta limiting and inadequate for the volume and nature of AI-produced code changes. ## Target Audience Software engineers and tech leads at companies using AI coding agents who need to review large volumes of generated code. ## Core Idea A local-first code diff review tool purpose-built for reviewing large volumes of LLM-generated code with inline AI commentary and semantic grouping. As developers ship more and more LLM-generated code, traditional git diff tools like delta feel inadequate for reviewing hundreds of changed files at once. DiffSense groups diff hunks semantically, highlights suspicious or potentially hallucinated logic, and lets reviewers annotate and approve sections in batches. It runs entirely locally with no cloud dependency, making it safe for proprietary codebases. ## Monetization Strategy Free open-source core; $8/mo pro plan for AI-powered semantic grouping, hallucination flagging, and team annotation 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 "DiffLens". ## The Problem Developers reviewing large diffs of LLM-written code find standard git diff tools inadequate — they lack semantic understanding and make it hard to audit intent across hundreds of changed files. ## Target Audience Developers using Claude Code, Codex, or other AI coding agents who must review and audit generated output ## Core Idea A focused local diff review tool built specifically for auditing large volumes of LLM-generated code. DiffLens provides a purpose-built interface for reviewing AI-generated code diffs, going far beyond git + delta by offering semantic grouping of changes, AI-assisted risk flagging, and inline annotation tools. As developers review increasingly large AI-written codebases, generic diff tools become inadequate for understanding intent and catching subtle errors. It runs entirely locally, integrates with any git workflow, and exports annotated review reports. ## Monetization Strategy Free core tool, $8/mo pro tier for AI-assisted risk flagging and team shared annotations ## 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 "CloudRisk Radar". ## The Problem Cloud providers like GCP can suspend customer accounts without warning or public explanation, causing catastrophic outages for businesses with no advance notice or clear recourse. ## Target Audience Startups and small businesses running production workloads on AWS, GCP, or Azure who are worried about account suspension risk. ## Core Idea Monitor your cloud provider account health and get early warnings modeled on real suspension incidents so you never wake up to a surprise outage like Railway did. The Railway/GCP incident exposed a terrifying reality: cloud providers can silently suspend high-profile customer accounts with no warning and no public explanation, causing catastrophic downtime. CloudRisk Radar continuously monitors your cloud account status, billing anomalies, policy changes, and provider-side incidents, cross-referencing them against a database of known suspension patterns and triggers. It alerts you proactively and provides a runbook for emergency mitigation including multi-cloud failover checklists. ## Monetization Strategy $19/mo per cloud account monitored; enterprise tier at $99/mo for multi-cloud monitoring and SLA-backed alerting. ## 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.