Thursday, June 11, 2026

I want to build an app called "SlopRadar". ## The Problem AI-generated slop is flooding online communities and news sites, and existing moderation tools (like HN's flag button) are too blunt — users want a dedicated, nuanced way to identify and filter AI-generated low-quality content. ## Target Audience Heavy readers of HN, Reddit, and online publications who are frustrated by declining content quality from AI slop ## Core Idea A browser extension that scores online articles and posts for AI-generated content so you can prioritize real human writing. SlopRadar runs lightweight AI-detection heuristics on web pages as you browse, adding a subtle confidence score indicator to posts on HN, Reddit, Medium, and news sites. Users can filter feeds to hide low-scoring content and contribute to a community signal layer that improves detection over time. It addresses the growing frustration with AI slop flooding discussion forums and news aggregators without meaningful moderation tools. ## Monetization Strategy Free extension with optional $3/mo Pro for advanced filtering rules, custom blocklists, and cross-device sync ## 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 "TrustTicket". ## The Problem Independent venues have no viable alternative to Ticketmaster and are forced to pass on high percentage fees to fans, but every competitor that has tried to enter the market has either failed or ended up as a resale-only platform. ## Target Audience Independent music venues, comedy clubs, local sports teams, and event organizers with under 5,000 capacity ## Core Idea A white-label ticketing platform for independent venues that charges fair flat fees instead of Ticketmaster's percentage-based gouging. TrustTicket gives small and mid-sized venues a fully branded ticketing page, QR-code scanning app, and payout dashboard with a flat $0.75 per-ticket fee rather than a percentage of face value. It includes built-in fraud prevention, mobile ticket wallets, and an optional resale marketplace where venues control the resale price ceiling. The goal is to give the long tail of venues a credible Ticketmaster alternative without the lock-in. ## Monetization Strategy Flat $0.75 per ticket sold plus optional $49/month white-label branding subscription ## 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 "AlertRadius". ## The Problem County-wide emergency alerts go to millions of people regardless of their actual proximity to the hazard, leaving recipients unable to determine their personal risk level and forcing them to hunt across multiple official sources for precise geographic data. ## Target Audience Homeowners, renters, and families in disaster-prone regions (wildfire, flood, tornado zones) who want precise, actionable alerts ## Core Idea Turn vague county-wide emergency alerts into precise, address-specific risk assessments for your exact location. AlertRadius ingests government emergency alert polygons (NWS, FEMA, local agencies) and overlays them against a user's saved addresses to provide a personalized risk score and plain-language action summary — not a county-wide blast. Users subscribe to specific hazard types, receive notifications with mapped radius visualizations, and can check any address instantly. Inspired by the frustration of receiving unhelpful mass alerts during real emergencies. ## Monetization Strategy Free for 1 address with basic alerts; $3.99/mo for unlimited addresses, SMS alerts, and evacuation route planning ## Requirements - Category: Health - Difficulty: Week - Suggested stack: Next.js + Supabase + PWA + Chart.js Please help me build this step by step. Start with: 1. A project structure and initial setup 2. The core data models 3. The main feature implementation 4. A simple but polished UI Keep it lean — MVP first, ship fast. Use modern best practices and make it production-ready.

I want to build an app called "SlopFilter". ## The Problem Online communities on HN and Reddit are increasingly flooded with AI-generated posts, and users have no reliable tool to distinguish authentic human content from AI slop. ## Target Audience Heavy readers of tech news, Reddit power users, and journalists who rely on authentic online discourse ## Core Idea A browser extension that scores and visually flags AI-generated content on Reddit, HN, and news sites so you can read with confidence. SlopFilter uses lightweight on-device heuristics and an optional cloud model to estimate the probability that any piece of online content was AI-generated, surfacing a discreet badge next to posts and comments. Users can set their own sensitivity threshold and contribute to a crowd-sourced training dataset. It works within MV3 constraints and includes a 'slop report' weekly digest of which communities are most affected. ## Monetization Strategy Free extension with optional $3/month Pro for advanced ML scoring and cross-site 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 "QuotaWatch". ## The Problem Developers using Claude Code and other AI tools have no unified way to monitor quota consumption across multiple services, leading to unexpected interruptions and wasted work. ## Target Audience Indie hackers, freelance developers, and power users of AI coding assistants ## Core Idea A cross-platform dashboard that tracks and visualizes your AI coding tool usage quotas in real time. QuotaWatch sits in your menu bar and aggregates usage metrics across Claude Code, Cursor, Copilot, and other AI coding tools so you never get surprise rate-limited mid-session. It provides burn-rate projections, daily budget alerts, and a historical usage chart. Developers can set soft and hard limits per tool and receive notifications before hitting quota walls. ## Monetization Strategy Free tier for one tool integration; $5/month Pro for unlimited integrations and smart alerts ## 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 "SkillKeep". ## The Problem Senior engineers are worried they are losing fundamental coding skills by delegating too much to AI tools, but have no structured way to preserve and measure those skills. ## Target Audience Mid-to-senior software engineers who use AI coding assistants daily ## Core Idea Daily coding challenges that target the specific skills you use AI for most, so you never lose your edge. SkillKeep analyzes your AI assistant usage patterns (via logs or self-reported habits) to identify which coding skills you've outsourced, then generates personalized daily exercises to keep those muscles sharp. It tracks skill decay over time and gives you a personal 'atrophy score' for each competency area. Think Duolingo but for software engineers worried about skill regression from AI over-reliance. ## Monetization Strategy Freemium: free for 3 skill tracks; $8/month for unlimited tracks, progress reports, and team dashboards ## 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 "FocusWait". ## The Problem Developers waste or distract themselves during the long idle periods while AI coding agents run tasks, losing focus and missing an opportunity to understand what is being built. ## Target Audience Developers using AI coding agents for substantial tasks lasting more than a few minutes ## Core Idea Turns dead time waiting for AI agents to finish coding tasks into structured micro-learning sessions on exactly what the agent is building. FocusWait hooks into your coding agent's task queue and, when an agent starts a long-running job, automatically surfaces bite-sized explanations, documentation excerpts, and quiz questions about the libraries and patterns the agent is using. This keeps developers in the loop on their own codebase and prevents the context-switching and distraction that happen when agents run for minutes at a time. It integrates with Claude Code, Cursor, and custom MCP setups. ## Monetization Strategy Free for solo use; $7/month Pro for custom learning tracks and integration with team agent setups ## 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 "FinanceBites". ## The Problem Most people lack basic personal finance knowledge but generic finance apps and courses feel irrelevant because they are not tailored to the user's actual financial situation and habits. ## Target Audience Young professionals aged 22-35 who want to improve their financial literacy without committing to long courses ## Core Idea A microlearning app that teaches personal finance through 5-minute daily scenarios personalized to your real spending categories. FinanceBites connects (optionally) to your bank account via read-only Plaid integration to identify your actual spending patterns, then generates personalized daily lessons on budgeting, investing, debt paydown, and tax optimization that are relevant to your real financial situation. Each session ends with a one-tap action item you can complete in under two minutes. Progress is tracked with a streak system and a financial health score that updates monthly. ## Monetization Strategy Freemium: free for core lessons; $6/month Pro for bank sync, personalized action items, and advanced investing modules ## 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 "FlowGuard". ## The Problem Developers lose their flow state while waiting for slow AI coding agents, leading to distraction, multitasking, and loss of deep focus that previously defined their work. ## Target Audience Software engineers and indie hackers using AI coding agents like Claude Code, Codex, or similar tools daily ## Core Idea Stay in deep work mode while AI agents code for you by turning wait time into structured micro-learning. FlowGuard monitors your AI coding agent sessions and detects idle/waiting periods, then surfaces contextual learning content, code reviews, or documentation relevant to what the agent is building. It prevents the doom-scrolling context-switch that kills productivity when waiting for slow agents like Claude. Includes focus session analytics and a dashboard showing how much deep work time you reclaimed. ## Monetization Strategy Freemium SaaS — free for basic idle detection and focus tracking, $9/mo for contextual learning content, integrations, and analytics ## 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 "AgentFence". ## The Problem AI agents operating on production systems (Postgres, Kubernetes, cloud APIs) pose serious security risks — teams need granular, auditable permission controls to safely allow agent access without enabling destructive actions. ## Target Audience Engineering teams and DevOps engineers using AI agents in production workflows or incident response ## Core Idea A security firewall and permission policy manager that keeps your AI coding agents from touching production systems they shouldn't. AgentFence sits between your AI agents and your infrastructure, enforcing granular read/write policies per resource — databases, Kubernetes clusters, cloud APIs — with a visual policy editor and full audit log. It lets teams safely give agents access to production for investigation without risking destructive operations. Supports Claude Code, Codex, and any MCP-compatible agent via a lightweight proxy. ## Monetization Strategy SaaS subscription — $49/mo per team up to 5 agents, $149/mo for unlimited agents and SSO/compliance features ## 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 "TicketSplit". ## The Problem Ticketmaster maintains a near-monopoly on event ticketing through exclusive venue contracts, hidden fees revealed only at checkout, and lock-in mechanics — independent venues and fans have no viable transparent alternative. ## Target Audience Independent venue operators, local event organizers, and concert-goers tired of Ticketmaster's fees and lock-in ## Core Idea A transparent, fee-first ticketing platform for independent venues and event organizers that doesn't lock attendees into a monopoly. TicketSplit lets small venues and independent event organizers sell tickets directly with fully disclosed, flat-rate fees shown upfront at checkout. No junk fees revealed at the last step, no inventory held hostage, and no forced account creation. Organizers get a simple dashboard, customizable event pages, and payouts within 48 hours — a direct answer to the Ticketmaster frustration documented widely in communities. ## Monetization Strategy Flat 2.5% + $0.50 per ticket sold, clearly shown upfront; no monthly fees for organizers ## 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 "SkillPulse". ## The Problem Experienced developers are noticing skill atrophy as AI tools handle more of their work — they lack visibility into which capabilities are degrading and have no structured way to maintain their engineering fundamentals. ## Target Audience Mid-to-senior software engineers who use AI coding tools daily and are concerned about long-term skill preservation ## Core Idea A personal coding skills tracker that ensures AI-assisted developers don't lose their core engineering fundamentals. SkillPulse analyzes the code you write with and without AI assistance to identify skill areas where you're becoming dependent, then serves targeted practice challenges in those weak spots. It integrates with your IDE to passively monitor patterns, surfaces a weekly 'skill decay' report, and offers spaced-repetition exercises for areas showing atrophy. Designed for senior engineers who fear losing hard-won expertise to AI delegation. ## Monetization Strategy Subscription $12/mo or $99/yr; team plans at $8/seat/mo for engineering managers wanting team skill health dashboards ## 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 "PressMill". ## The Problem Generating PDFs from HTML is painful — headless Chrome in Docker, broken page flows, content spilling across table boundaries, and CSS hacks that differ across environments are a constant developer headache. ## Target Audience Backend developers, indie hackers, and SaaS teams that need to generate invoices, reports, or documents programmatically ## Core Idea Generate pixel-perfect PDFs from a clean markup language purpose-built for print, not hacked from HTML. PressMill provides a hosted API and CLI for converting a simple, print-native markup language into professional PDFs without headless Chrome, Docker, or CSS hacks. It handles page breaks, flowing tables, multi-column layouts, and citations natively. Developers get an SDK and AI-friendly syntax so coding agents can generate documents reliably without fighting browser rendering quirks. ## Monetization Strategy Usage-based API pricing — free tier 100 PDFs/mo, then $0.02/PDF; $29/mo flat for up to 2,000 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.

I want to build an app called "AgentGuard". ## The Problem Developers giving AI agents access to production systems like Postgres and Kubernetes have no fine-grained permission layer to prevent accidental or malicious destructive actions. ## Target Audience DevOps engineers, platform teams, and startups using AI agents in production workflows ## Core Idea A zero-config security firewall that wraps AI coding agents with granular permission policies before they touch production systems. AgentGuard intercepts tool calls from coding agents like Claude Code or OpenClaw and enforces declarative allow/deny rules for database queries, API calls, and filesystem writes. It provides a live audit log, anomaly detection, and one-click rollback suggestions when an agent does something unexpected. Designed for teams who need AI agents to access real infrastructure without giving them the keys to the kingdom. ## Monetization Strategy $29/month per developer seat; enterprise contracts with SSO and compliance reporting ## 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 "PaperFlow". ## The Problem Developers generating PDFs from HTML face a nightmare of headless Chrome Docker containers, CSS hacks, and content that incorrectly flows across page boundaries. ## Target Audience Backend developers and SaaS products that generate invoices, reports, or contracts programmatically ## Core Idea An AI-friendly PDF generation API that takes structured markup and produces pixel-perfect, paginated documents without headless Chrome. PaperFlow accepts a clean declarative markup language (similar to the approach described in Papermill Press) via REST API and returns production-ready PDFs with proper page breaks, table handling, headers, and footers. It eliminates the Docker overhead, CSS hacks, and page-overflow bugs that plague HTML-to-PDF pipelines. Developers get SDKs for Node, Python, and Ruby plus a visual preview playground. ## Monetization Strategy Usage-based: free tier for 100 PDFs/month; $19/month for 2,000 PDFs; $99/month for 20,000 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.

I want to build an app called "AlertStorm". ## The Problem On-call engineers are overwhelmed by alert storms where dozens of related alerts fire simultaneously, with no tool to automatically group them into incidents and suggest a root cause. ## Target Audience SRE teams, DevOps engineers, and startup CTOs managing cloud infrastructure ## Core Idea An open-source AI SRE layer that groups cascading monitoring alerts into coherent incidents and auto-investigates root causes. AlertStorm ingests alerts from PagerDuty, Grafana, Datadog, and other monitoring tools, then uses an LLM agent to correlate related alerts into a single incident, suppress noise, and produce a plain-English root-cause hypothesis with links to relevant logs and metrics. It is read-only by design so it never makes changes to production, making it safe to point at live systems. Teams get a Slack or email digest that replaces alert storms with a single actionable summary. ## Monetization Strategy $29/month per team for up to 5 integrations; $99/month for unlimited integrations and custom runbooks ## 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 "PrivacyShield". ## The Problem AWS Bedrock and other platforms are introducing mandatory data-retention policies for high-capability AI models, alarming enterprises that need to send sensitive data through LLM APIs without it being retained. ## Target Audience CTOs, compliance officers, and developers at regulated companies using cloud AI APIs ## Core Idea An AI API proxy that automatically redacts PII and sensitive data before sending prompts to cloud LLMs, satisfying compliance requirements. PrivacyShield sits between your application and any cloud AI provider, detecting and tokenizing sensitive fields like names, emails, financial data, and health information before the request leaves your infrastructure, then re-hydrating the response. It generates a compliance audit trail showing exactly what was redacted and when, making it easy to satisfy GDPR, HIPAA, and enterprise data-sharing policies like those recently announced by Anthropic on AWS Bedrock. Self-hostable with a managed cloud option. ## Monetization Strategy $49/month for up to 1M tokens proxied; enterprise pricing for unlimited volume with SLA and audit exports ## 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 "MailLane". ## The Problem SaaS companies need embeddable email editors for their users but existing solutions (Stripo, Unlayer, Beefree) have prohibitively expensive licensing costs — especially painful for bootstrapped or early-stage products. ## Target Audience SaaS founders and product teams building platforms that need to offer email template creation to their end users ## Core Idea An embeddable drag-and-drop email template builder SDK at a fraction of the cost of enterprise email editor incumbents. MailLane is a white-label, embeddable React component that gives SaaS products a full-featured drag-and-drop email template builder without licensing expensive platforms like Stripo or Unlayer. It handles responsive layouts, merge tags, and HTML export out of the box, with a usage-based pricing model that makes it accessible to bootstrapped startups. Self-hosted option available for privacy-sensitive customers. ## Monetization Strategy Usage-based SDK licensing — free up to 100 active users/mo, then $49/mo up to 1,000 users, custom enterprise pricing above that ## 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 "PartFinder". ## The Problem PCB designers and hardware engineers waste significant time searching for electronic components because existing distributor search tools are rigid filter-based systems that fail when requirements are complex or component names aren't known exactly. ## Target Audience PCB designers, hardware engineers, and electronics hobbyists who regularly source components for new designs ## Core Idea A natural-language electronic component search engine that finds exact parts matching complex multi-spec requirements instantly. PartFinder lets hardware engineers describe what they need in plain language or structured multi-parameter queries — voltage range, package type, temperature spec, availability — and returns ranked results across Mouser, Digi-Key, LCSC, and distributor APIs with real-time stock and pricing. It understands the way PCB designers actually think about component selection rather than forcing rigid filter-based search. Saves hours of cross-referencing datasheets and checking availability across multiple distributor sites. ## Monetization Strategy Free for basic search; $15/mo Pro for BOM batch search, saved project lists, price alerting, and API access for EDA tool 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 "QuotaDash". ## The Problem Developers using multiple AI coding agents have no unified way to monitor quota usage, token consumption, and costs, leading to surprise billing and productivity interruptions when limits are hit unexpectedly. ## Target Audience Developers actively using multiple AI coding agents (Claude Code, Codex, etc.) who want cost control and visibility ## Core Idea A unified menu bar dashboard to track and manage all your AI coding agent quotas and spending in one place. QuotaDash aggregates quota usage, token burn rates, and cost tracking across multiple AI coding tools — Claude Code, Codex, Gemini, and others — into a single macOS/Windows menu bar app. It alerts you before you hit limits, shows projected monthly costs, and lets you set hard spend caps per agent. Solves the fragmentation of juggling multiple dashboards and surprise billing across AI tool subscriptions. ## Monetization Strategy One-time purchase $12 or $4/mo subscription; upsell to team plans with shared budget alerts ## 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.