Sunday, June 14, 2026

I want to build an app called "TicketFair". ## The Problem Event-goers and venue operators are deeply frustrated with Ticketmaster's monopoly, hidden fees, and the lack of any real competitor for primary ticket sales. ## Target Audience Independent venue owners, event organizers, and concert promoters who want an alternative to Ticketmaster ## Core Idea An independent event ticketing platform for small and mid-size venues with transparent fees and no monopoly lock-in. TicketFair provides venues and independent event organizers a white-label ticketing solution with fair, transparent fees and no exclusive contracts, directly addressing frustration with Ticketmaster's monopolistic grip. It includes QR-based entry, waitlist management, and simple analytics — all the essentials without enterprise bloat. Monetizes by taking a small flat fee per ticket rather than the predatory percentage fees that make Ticketmaster so hated. ## Monetization Strategy Flat $0.99 per ticket sold plus optional 1% platform fee; premium tier at $49/month for white-labeling and advanced 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 "SlopShield". ## The Problem HN users are frustrated by increasing AI-generated slop polluting online communities and want a dedicated mechanism beyond flagging to identify and filter AI-written posts. ## Target Audience Power users of forums like Hacker News, Reddit, and Twitter who value authentic human discussion ## Core Idea A browser extension that detects and flags AI-generated content across web forums and news feeds so you only read human-written posts. SlopShield runs lightweight on-device classifier checks on forum posts, comments, and articles to surface a confidence score for AI-generated content, letting users filter or dim suspected slop. It addresses the growing frustration in communities like HN where AI-generated noise is drowning out genuine human discussion. Users can tune sensitivity and submit corrections to improve the model. ## Monetization Strategy One-time purchase at $7.99 or optional $3/month for cloud-synced custom filter lists and community-sourced model updates ## 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 report that AI model benchmarks don't capture real-world code quality differences and there's no objective metric for comparing which LLM produces better code for their specific stack. ## Target Audience Engineering teams and indie developers who use multiple AI coding assistants and want evidence-based model selection ## Core Idea Benchmark any LLM on your own codebase and tasks to find which model actually writes the best code for your specific context. ModelBench lets developers submit their real-world coding tasks and automatically runs them against multiple LLMs, scoring results on correctness, style consistency, test coverage, and complexity. It addresses the lack of code quality metrics in public benchmarks and helps teams make data-driven model selection decisions. Results are stored privately and optionally shared as anonymized community benchmarks. ## Monetization Strategy Usage-based pricing — free tier with 10 runs/month, $19/month for 200 runs, $99/month for team plans with shared benchmark 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 "SideShip". ## The Problem Solo developers working on side projects on nights and weekends lack accountability and partnership, causing projects to stall despite having the skills and ideas to execute them. ## Target Audience Software engineers with full-time jobs who are building side projects alone and want lightweight accountability without the commitment of a formal co-founder relationship. ## Core Idea An accountability platform that pairs side-project builders for weekly check-ins, milestone tracking, and async co-working sessions. Many developers with side projects explicitly cite lack of accountability and partnership as the main reason their projects stall, as seen in HN posts seeking weekend co-builders. SideShip matches you with a compatible side-project partner based on tech stack, project type, and time zone, then provides structured weekly async video check-ins, a shared milestone board, and a co-working session scheduler. It removes the loneliness of solo building without requiring a full co-founder commitment. ## Monetization Strategy Free for basic matching and check-ins, $8/month for premium features including priority matching, shared project boards, and co-working room scheduling. ## 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 "SpecKit". ## The Problem Developers working with LLM coding agents want Spec Driven Development but find existing tools like SpecKit and GSD incomplete, with specs drifting out of sync with actual code. ## Target Audience Professional developers using LLM coding agents in production who follow or want to adopt spec-driven development workflows. ## Core Idea A spec-driven development IDE plugin that generates, validates, and keeps your LLM coding specs in sync with your codebase. Developers using LLM coding agents are adopting Spec Driven Development but struggling with spec drift and inconsistency across tools. SpecKit integrates directly into VS Code and JetBrains IDEs to generate structured specs from existing code, flag when code diverges from spec, and auto-update specs after agent-driven changes. It acts as a living contract between you and your AI agents. ## Monetization Strategy Free tier for individuals, $15/month per seat for teams with shared spec repositories and CI/CD integration. ## 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 "CodeGrade". ## The Problem Developers observe significant quality differences between LLM-generated code but there is no standardized metric for AI code quality in existing benchmarks to help them choose the right model. ## Target Audience Software engineers and engineering teams who regularly use multiple LLMs for coding and want to objectively compare their output quality. ## Core Idea A benchmark and scoring dashboard that objectively measures the real-world code quality of different LLM models so you can pick the right one. Developers frequently complain that existing AI benchmarks don't reflect actual code quality attributes like readability, maintainability, and correctness on real tasks. CodeGrade runs your own codebase snippets and task types through multiple LLMs, scores them on objective quality metrics, and builds a personalized leaderboard. It helps teams make data-driven decisions about which model to use for specific coding tasks. ## Monetization Strategy Free for 10 evaluations/month, $19/month for unlimited evaluations and team dashboards. ## 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 "LaunchRadar". ## The Problem Indie hackers build complete SaaS products but have no idea how to market them or find users, lacking insight into what worked for similar products at launch. ## Target Audience Solo founders and indie hackers who have built a product and need actionable marketing guidance without hiring a growth team. ## Core Idea Automatically analyze your newly launched SaaS product's positioning and suggest marketing copy, target audiences, and channels based on what's working for similar launches. Indie hackers frequently build great products but struggle with marketing and distribution after launch. LaunchRadar crawls recent product launches from Product Hunt and Show HN, clusters them by category and tech stack, and surfaces what positioning, copy styles, and channels drove traction for comparable products. When you input your product details, it generates customized go-to-market recommendations backed by real launch data. ## Monetization Strategy One-time $49 launch analysis report or $29/month for ongoing competitor tracking and positioning updates. ## 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 "PhiloSpark". ## The Problem Parents whose children ask deep philosophical questions cannot find quality, age-appropriate philosophy content online, resorting to manually prompting AI tools with specific workflows each time. ## Target Audience Parents of curious children aged 6-14 and homeschooling educators looking for structured critical thinking and philosophy curriculum. ## Core Idea A kid-friendly philosophy learning platform that turns big questions into age-appropriate interactive lessons and discussions. Parents and educators struggle to find quality philosophy content for children, often resorting to custom ChatGPT prompts to generate ad-hoc explanations. PhiloSpark offers a curated library of age-graded philosophy articles and Socratic dialogue exercises covering ethics, logic, and metaphysics. Each lesson ends with open-ended questions designed to spark real family conversations, with a parent companion guide included. ## Monetization Strategy $7/month family subscription for unlimited access, with a free tier of 5 articles/month to drive organic word-of-mouth. ## 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 "AgentWatch". ## The Problem Developers suspect AI providers deliberately degrade agent performance before new model releases but have no tooling to detect, measure, or prove this pattern of behavior. ## Target Audience Engineering teams and indie developers who rely heavily on AI coding agents in production and need to detect performance regressions quickly. ## Core Idea A monitoring dashboard that tracks LLM agent performance degradation over time and alerts you when your agents get dumber before a model update. Developers have noticed that coding agents seem to perform worse in the weeks leading up to new model releases, suspecting vendors throttle performance to make new models look better by comparison. AgentWatch logs baseline performance metrics for your agents, runs lightweight daily benchmarks, and alerts you when response quality or speed degrades significantly. It creates an audit trail of model behavior changes across providers so you have data to back up your observations. ## Monetization Strategy $19/month for individual developers monitoring up to 3 agents, $79/month for team plans with shared dashboards and Slack/PagerDuty alerting. ## 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 "FlowLock". ## The Problem Developers report losing their flow state and ability to work deeply because agentic coding tools like Claude are slow and interrupt concentration, leaving them distracted between agent responses. ## Target Audience Software engineers using AI coding agents who want to maintain deep focus and productivity ## Core Idea An AI coding companion that manages context-switching and keeps you in flow state while waiting for slow agents. FlowLock detects when your AI coding agent is processing and intelligently queues your next tasks, surfaces relevant documentation, or triggers micro-focus sessions so you never lose momentum. It integrates with Claude Code, Codex, and other popular agents via their CLIs. Built for developers who've lost their deep work habits to the async nature of agentic coding. ## Monetization Strategy Freemium SaaS — free for solo use, $9/month for advanced task queuing, distraction blocking, and multi-agent orchestration ## 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 "LaunchStack". ## The Problem Indie hackers and new founders lack reliable data on what tech stacks real products are being built with, relying instead on marketing content and opinion pieces. ## Target Audience Indie hackers, solo founders, and developers deciding on tech stacks for new products ## Core Idea Instantly see what tech stack any indie product was built with, sourced from real Product Hunt and Show HN launches. LaunchStack crawls new product launches from Product Hunt, Show HN, and similar platforms to catalog the real-world tech stacks indie hackers are shipping with, giving founders data-driven answers to 'what should I build with?' It surfaces trends like which hosting providers, frameworks, and databases are gaining or losing share among new launches. Validated by StackScope on HN which received strong community interest for exactly this use case. ## Monetization Strategy Free for basic browsing; $8/month for API access, email digests of trending stacks, and advanced filtering by category or launch date ## 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 "FocusBuild". ## The Problem Solo developers report struggling to make consistent progress on side projects due to lack of accountability, partnership, and the isolation of building alone on nights and weekends. ## Target Audience Solo developers, indie hackers, and side-project builders who want accountability and community without joining a full accelerator ## Core Idea An accountability platform that pairs side-project builders for async check-ins, milestone tracking, and mutual motivation. FocusBuild matches solo developers and indie hackers who want a accountability partner for nights-and-weekends projects, enabling async weekly check-ins, shared milestone boards, and lightweight pair-motivation sessions. It directly addresses the recurring HN complaint of building in isolation without partnership or accountability. Includes a public 'building in public' feed so members can gain audience while staying accountable. ## Monetization Strategy Free for basic matching and check-ins; $6/month for priority matching, milestone analytics, and private group accountability pods ## 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 "AgentAudit". ## The Problem Developers suspect AI coding agents perform worse in the weeks before new model releases as vendors quietly tweak parameters, but have no tooling to detect or verify this degradation. ## Target Audience Professional developers and engineering teams who rely heavily on AI coding agents and need reliability guarantees ## Core Idea Monitor, log, and compare your AI coding agent's performance over time so you always know if it's gotten worse before a model release. AgentAudit runs lightweight automated test tasks against your configured coding agents daily, tracking output quality, latency, and behavioral consistency so you can detect when a model has been quietly degraded before a new version ships. It validates the widely-reported but unconfirmed phenomenon of agents getting dumber before new model releases. Developers get alerting when their agent's benchmark score drops below a threshold, enabling informed decisions about switching providers. ## Monetization Strategy Free for one agent and five daily test tasks; $15/month for multi-agent monitoring, custom test suites, Slack/email alerts, and historical trend reports ## 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 "FlowLock". ## The Problem Developers report losing their flow state while waiting for slow coding agents, leading to distraction and inability to work deeply as they did before agentic AI coding tools. ## Target Audience Software engineers using AI coding agents like Claude Code, Codex, or similar tools who struggle to stay focused during agent wait times. ## Core Idea An AI coding companion that detects when you're waiting on agents and fills the dead time with focused micro-tasks. FlowLock monitors your agentic coding sessions and identifies when you're stuck waiting for slow AI agents like Claude to respond. Instead of letting you drift to social media, it surfaces small, well-scoped tasks from your backlog that fit the wait window. It learns your focus patterns over time and helps you maintain deep work even in an async AI-assisted workflow. ## Monetization Strategy Freemium with a $9/month Pro plan for advanced focus analytics, custom task routing, and integrations with Jira/Linear. ## 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 "LockNote". ## The Problem Users are frustrated by having to unlock their phone every time they need to view or add a quick note, especially during activities like grocery shopping. ## Target Audience Android users who rely on quick-capture note apps and find the unlock friction annoying in daily use ## Core Idea A lock-screen and notification-panel note app for Android so you never have to unlock your phone just to check a list. LockNote lets you view and add notes directly from your Android lock screen and notification shade, eliminating the friction of unlocking your phone every time you need to check a grocery list or jot something down. It supports pinned sticky notes, checklists, and voice memos all accessible without authentication. Proven demand exists from a developer who built a similar tool out of personal frustration that received strong HN engagement. ## Monetization Strategy Free with a one-time $2.99 unlock for premium features like multiple lists, custom widgets, and cloud backup ## 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 "Verso". ## The Problem Writers and professionals are frustrated with subscription pricing for word processors and want a capable, polished tool they can buy once and own. ## Target Audience Mac users who write regularly and are tired of subscription software, including writers, academics, and knowledge workers ## Core Idea A beautiful, one-time-purchase word processor for Mac with no subscription, no cloud lock-in, and no bloat. A lightweight native Mac word processor focused on distraction-free writing, priced as a single purchase rather than a recurring subscription. It targets the large segment of writers and professionals who are subscription-fatigued from tools like Microsoft Word and Google Docs. The HN post about Verso at $14.99 generated immediate interest, validating clear appetite for this pricing model. ## Monetization Strategy One-time purchase at $14.99–$24.99 on the Mac App Store with optional paid major version upgrades ## Requirements - Category: Productivity - Difficulty: Month - 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 "SpecDraft". ## The Problem Developers using LLM coding agents struggle to communicate intent clearly, and spec-driven development workflows are poorly tooled — users report trying multiple spec generators with mixed results. ## Target Audience Indie hackers and startup developers using AI coding agents who need better structured specs ## Core Idea Turn a rough product idea into a structured, agent-ready spec document in minutes using AI-guided interviews. SpecDraft walks founders and developers through a conversational intake process to produce comprehensive product specs formatted for LLM coding agents like Claude Code or Codex. It captures user stories, edge cases, data models, and acceptance criteria, then exports to Markdown or JSON. Solves the painful gap between 'I have an idea' and 'my agent understands what to build.' ## Monetization Strategy Freemium — 3 free specs/month, $12/month for unlimited specs, team sharing, and agent-specific export formats ## 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 "OfflineVault". ## The Problem Developers and researchers want to reliably access websites and documentation offline, but existing tools are either CLI-only, unreliable, or fail on dynamic JS-heavy sites. ## Target Audience Developers, researchers, and knowledge workers who regularly reference online documentation and want reliable offline access during travel or outages. ## Core Idea A one-click desktop app that snapshots any website, documentation, or web app into a fully browsable offline archive. The strong positive reception of Kage on Hacker News reveals deep user demand for reliable offline web archiving beyond what standard tools provide. OfflineVault wraps this capability in a friendly desktop GUI for Mac and Windows, allowing users to schedule recurring snapshots of documentation sites, reference material, or competitor pages and browse them seamlessly without internet. It includes search indexing across all saved sites and diff alerts when content changes. ## Monetization Strategy One-time purchase at $19.99 with a free tier capped at 5 saved sites, and a $5/month subscription for unlimited snapshots and scheduled updates. ## 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 "PrivacyBedrock". ## The Problem Companies using AI APIs like AWS Bedrock are blindsided by policy changes requiring data sharing with model providers, with no easy way to monitor or understand ToS changes across multiple AI vendors. ## Target Audience CTOs, DevOps engineers, and compliance teams at companies using multiple AI provider APIs who need to stay current on data policy changes. ## Core Idea Get instant alerts and plain-English summaries when your AI provider changes their data retention or sharing policies. The discovery that AWS Bedrock would require sharing data with Anthropic caught many developers and companies off guard. PrivacyBedrock monitors the terms of service, data policies, and usage agreements of major AI providers and sends you digests and alerts when anything changes. It translates dense legal language into clear impact summaries so engineering and compliance teams can respond quickly. ## Monetization Strategy $29/month per workspace covering up to 10 AI providers, with enterprise tiers at $199/month for custom provider monitoring and compliance reports. ## 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 "PaperPress". ## The Problem Developers generating PDFs programmatically suffer from the fundamental mismatch between HTML and print, requiring headless Chrome setups and constant CSS hacks to get acceptable output. ## Target Audience Backend developers and AI application builders who need to programmatically generate high-quality PDFs such as invoices, reports, or contracts. ## Core Idea A markup-to-PDF generator built for AI pipelines that produces pixel-perfect documents without headless Chrome or CSS hacks. Developers generating PDFs from HTML regularly battle headless Chrome Docker setups, broken page flows, and CSS that behaves differently in print. PaperPress provides a clean, AI-friendly markup language and hosted API specifically designed for document generation, handling pagination, tables, headers, and multi-column layouts natively. It integrates directly into LLM output pipelines so agents can produce professional documents without fragile browser rendering. ## Monetization Strategy $0.01 per generated PDF with a free tier of 100 PDFs/month, scaling to volume discounts for high-usage customers at $49/month for 10,000 PDFs. ## 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.