Tuesday, June 23, 2026

I want to build an app called "PixelKit". ## The Problem Sports fans and smaller leagues have no affordable, delightful way to broadcast live game state online beyond expensive video streams, and an 8-bit baseball gamecast received strong positive reception proving the demand. ## Target Audience Amateur and semi-pro sports leagues, fan site operators, sports hackathon builders ## Core Idea An embeddable 8-bit live gamecast widget any sports league or fan site can drop into their page in minutes. PixelKit provides a JavaScript widget and API that converts live sports data feeds into retro pixel-art gamecasts similar to the baseball project that earned 260 upvotes and 139 comments on HN. League operators and fan sites configure their sport, team colors, and data source then embed a single script tag. Monetization comes from a per-event API call model so small leagues only pay when games are live. ## Monetization Strategy Free up to 500 events/month, $29/month for unlimited events and custom branding removal ## Requirements - Category: E-commerce - Difficulty: Month - Suggested stack: Next.js + Shopify API or 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 "FairEntry". ## The Problem Independent venues and event organizers are trapped in Ticketmaster's ecosystem because no viable direct-sale alternative exists — every competing platform only offers resale inventory that feeds back into Ticketmaster's infrastructure, a massive public frustration. ## Target Audience Independent music venues, comedy clubs, theater operators, and local event organizers with under 2,000-seat capacity who are priced out of or locked into Ticketmaster ## Core Idea A direct-sale ticketing platform for independent venues that charges a flat fee per ticket instead of percentage-based fees that scale with ticket price. Independent venues are trapped in Ticketmaster's ecosystem through exclusive contracts and hidden percentage fees, while every competitor that has tried to enter the market offers only resale inventory that still routes through Ticketmaster's infrastructure. FairEntry provides venues with a white-label primary-sale ticketing page, QR-based entry scanning, and flat $1.50 per ticket fees regardless of face value, making it economically viable for small and mid-size venues to defect from the incumbent. Fans see full price transparency at checkout with no surprise service fees. ## Monetization Strategy Flat $1.50 per ticket sold plus optional $99/month venue subscription for white-label domain, advanced analytics, and season pass management ## 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 "WeightWatch". ## The Problem With more traffic moving off-web and into LLMs, individuals have no systematic way to audit what AI models know about them or track how that knowledge changes across model releases — validated by 469 upvotes and 247 comments on the Show HN 'Are You in the Weights' post. ## Target Audience Public figures, executives, researchers, journalists, and privacy-conscious individuals who want to monitor their AI footprint ## Core Idea Track what frontier AI models know about you and get notified when your digital footprint changes across model releases. As more traffic shifts from the open web into LLM responses, individuals and organizations have no systematic way to audit what AI models know about them or track how that knowledge changes across releases. WeightWatch runs structured recognition probes against multiple frontier and open models in parallel, clusters the responses to build a knowledge profile, and alerts you when a new model release changes what it says about you. It builds on the 'Are You in the Weights' concept but adds persistent monitoring, delta alerts, and structured removal request templates. ## Monetization Strategy $9/month for individuals with monthly scans across 10+ models; $49/month for organizations with continuous monitoring, team profiles, and removal request tooling ## 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 "GlobalPulse". ## The Problem Live global public data (ships, aircraft, weather, cyber, hazards) exists in completely separate tools with no unified view for correlating events across domains. ## Target Audience OSINT researchers, journalists, maritime analysts, and corporate risk teams ## Core Idea One live map correlating ships, aircraft, weather, cyber events, and public hazards so analysts can finally see cross-domain patterns. GlobalPulse aggregates publicly available real-time data streams — AIS ship positions, ADS-B flight data, NOAA weather, Shodan-indexed cyber events, and USGS hazards — onto a single 3D globe with an event timeline and correlation engine. The Metiq Show HN (147 upvotes, 42 comments) proved demand for unified global data but stopped at visualization; GlobalPulse adds alert rules so users are notified when correlated events match a pattern they define. Designed for OSINT researchers, journalists, and risk analysts who today juggle a dozen separate browser tabs. ## Monetization Strategy Free read-only public view, $29/month for custom alert rules and data export ## 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 "BurnSignal". ## The Problem Open-source contribution graphs gamify over-commitment, and maintainers have no tool to detect when contributors are heading toward burnout before they burn out or disappear — raised in a 1,789-upvote GitHub issue on the isaacs/github repo. ## Target Audience Open-source project maintainers, OSPO managers at companies with significant open-source footprints, and community managers for large developer communities ## Core Idea A GitHub App that detects early warning signs of contributor burnout by analyzing commit cadence, review patterns, and issue sentiment before maintainers disappear. Open-source maintainers and contributors frequently over-commit until they burn out silently, damaging both their wellbeing and project continuity, but existing contribution graphs actively incentivize this harmful pattern. BurnSignal analyzes contribution velocity, after-hours commit frequency, review sentiment drift, and unacknowledged effort ratios to generate a weekly burnout risk score for each contributor. Maintainers receive private, compassionate nudges to check in with at-risk contributors before they silently disappear. ## Monetization Strategy Free for public repositories with up to 5 contributors; $12/month for private repos and teams; $49/month for organizations with multiple repos and aggregate health dashboards ## 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 "AgentsMD". ## The Problem Codex, Amp, Cursor, and others are standardizing around AGENTS.md but CLAUDE.md feels too specific to Claude Code, forcing developers to maintain multiple diverging context files — 5,500 upvotes on the GitHub issue. ## Target Audience Software developers using two or more AI coding agents on the same codebase ## Core Idea Maintain one canonical codebase context file and auto-sync it to every AI coding agent's proprietary format. AgentsMD watches your repository for a single AGENTS.md source-of-truth and automatically generates and updates CLAUDE.md, Copilot instructions, Cursor rules, and Amp context files whenever the source changes. It ships as a CLI with a CI integration so the files are always in sync without manual copy-pasting. Developers stop maintaining five diverging instruction files and agents stop hallucinating stale context. ## Monetization Strategy Free CLI, $9/month for team sync dashboard and private repo support ## 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 "PrivacyMesh". ## The Problem AWS Bedrock and other platforms introduced mandatory data-retention policies for high-capability AI models, alarming developers who need to send sensitive or proprietary data through LLM APIs without it being retained. ## Target Audience Developers and teams using cloud LLM APIs for code generation or document processing with sensitive data ## Core Idea A local proxy that automatically detects and redacts PII from prompts before they reach any cloud LLM API. PrivacyMesh runs as a lightweight local process that intercepts HTTP requests to OpenAI, Anthropic, and other LLM APIs, scans for personal names, emails, phone numbers, credit card patterns, and custom regex rules, then substitutes reversible placeholders before sending. Responses are de-anonymized on the way back so the developer sees real output. It integrates with Claude Code and Cursor via a one-line config change and requires zero code changes to existing workflows. ## Monetization Strategy Free open-source core, $19/month for team policy management dashboard and audit logs ## 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 "PenTest Proxy". ## The Problem SMEs and mid-market companies cannot access AI-assisted pen testing because mainstream models are over-censored and enterprise cyber models are gated, leaving them exposed — validated by 92 upvotes on the Show HN post about a post-trained pen test model. ## Target Audience SME security teams, independent penetration testers, and bug bounty hunters who cannot afford enterprise AI security contracts ## Core Idea An uncensored, self-hosted AI assistant purpose-built for SME security teams who need offensive and defensive cybersecurity help without enterprise gatekeeping. Mainstream AI tools like Claude and GPT refuse routine offensive security tasks, while Anthropic and OpenAI's cyber-focused models are locked behind enterprise contracts that SMEs cannot access. PenTest Proxy runs a post-trained local model specifically fine-tuned for penetration testing workflows, providing SMEs with AI-assisted vulnerability discovery without requiring enterprise agreements. It ships as a Docker container with a web UI and supports integration with Burp Suite and common recon tools. ## Monetization Strategy $49/month per team subscription with a 14-day free trial; self-hosted Docker license at $299/year for air-gapped environments ## 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 "SnapRedact". ## The Problem Developers are accidentally pasting personal or sensitive data into cloud AI tools, creating compliance and privacy risks — a recurring concern in AWS Bedrock data retention discussions and general AI workflow threads. ## Target Audience Developers, lawyers, HR professionals, and anyone pasting sensitive documents into ChatGPT, Claude, or similar tools ## Core Idea A browser extension that automatically detects and redacts PII from your clipboard before you paste it into any AI tool. Developers and knowledge workers routinely paste emails, documents, and code containing personal data, passwords, and confidential information into cloud AI services, creating serious compliance and privacy risks. SnapRedact intercepts clipboard content the moment you press paste on any AI chat interface, highlights detected PII in a preview, and lets you approve or auto-redact before the content reaches the cloud. It runs entirely client-side with no data leaving the browser. ## Monetization Strategy Freemium extension with free PII detection and $6/month Pro tier for custom redaction rules, team policy sync, and audit logging ## 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 "AlgoReflex". ## The Problem Developers using AI coding agents exclusively are experiencing skill atrophy and losing confidence in their own abilities, but have no structured way to maintain cognitive skills — raised directly in the Stack Overflow question about avoiding LLMs as a developer. ## Target Audience Senior software engineers and engineering managers who use AI tools daily but want to preserve and demonstrate their independent coding abilities ## Core Idea Daily algorithm challenges that deliberately prevent AI assistance, helping developers maintain their core problem-solving skills in the age of coding agents. Senior engineers are noticing skill atrophy as AI tools handle more of their daily coding work, but simply knowing this is happening does not provide a structured solution. AlgoReflex serves short, timed algorithm challenges through a locked-down interface that detects and blocks AI tool usage, forcing genuine cognitive engagement. Each session ends with a skill-decay score showing which problem-solving abilities are weakening over time so developers can focus their practice. ## Monetization Strategy $12/month subscription with a generous free tier of 3 challenges per week; team plans at $8/seat/month for companies running internal skill retention programs ## 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 "IncidentGroup". ## The Problem On-call engineers face alert storms where dozens of related alerts fire simultaneously with no tool to automatically group them into incidents or suggest root causes, forcing manual correlation under pressure. ## Target Audience On-call engineers, SREs, and DevOps teams at companies running microservices architectures where correlated failures produce cascading alert floods ## Core Idea Automatically clusters related alert storms into a single incident with an AI-generated root cause hypothesis so on-call engineers start triaging in seconds, not minutes. On-call engineers are overwhelmed by alert storms where dozens of correlated alerts fire simultaneously from different monitoring tools, forcing them to manually correlate signals at 3am. IncidentGroup ingests webhooks from PagerDuty, Datadog, Grafana, and similar tools, uses semantic clustering to group related alerts into a single incident timeline, and generates a plain-English root cause hypothesis ranked by confidence. Engineers get one Slack notification with the grouped incident instead of fifty individual pages. ## Monetization Strategy $29/month for small teams up to 5 engineers; $99/month for unlimited engineers with runbook auto-generation and postmortem drafting ## 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 "MemoryRecall". ## The Problem Claude Code sessions containing valuable debugging and architecture context are siloed on whichever developer's machine they happened on, with no way to share or search them across a team. ## Target Audience Engineering teams using Claude Code or other agentic coding tools ## Core Idea Give Claude Code a persistent, searchable project memory that survives across sessions and is shareable with your whole team. MemoryRecall stores summaries, architectural decisions, and debugging context from every Claude Code session in a local vector database and surfaces relevant snippets automatically at session start. Unlike the individual local memory shown in the Recall Show HN, MemoryRecall syncs sessions across team members so institutional knowledge isn't siloed on one laptop. A VS Code extension and CLI surface the right context at the right time. ## Monetization Strategy $0 solo, $15/seat/month for team sync and search ## 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 "FounderFunnel". ## The Problem Technical founders repeatedly build high-quality SaaS products they are proud of but have no marketing skills and no clear path to finding their first customers, resulting in abandoned projects. ## Target Audience Solo technical founders and indie hackers who have launched or are about to launch a product ## Core Idea A structured go-to-market coaching tool that turns a polished technical product into a launch plan with real acquisition channels. FounderFunnel guides technical founders through a step-by-step GTM workflow: defining an ICP from their feature set, identifying three testable acquisition channels, writing five cold outreach templates, and scheduling a two-week launch sprint. Each step uses the founder's actual product description as context so advice is specific rather than generic. The HN 'What are you working on?' thread (312 upvotes, 1,144 comments) is full of builders who have shipped products but are paralyzed at the marketing step. ## Monetization Strategy $29 one-time purchase for the full workflow, optional $15/month for live cohort accountability group ## 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 "StripeWatch". ## The Problem Founders get their Stripe accounts unexpectedly frozen or face forced biometric ToS updates with no warning, losing access to their revenue with no recourse and no prior signal that anything was wrong. ## Target Audience Early-stage SaaS founders and indie hackers using Stripe as their primary payment processor ## Core Idea Monitor your Stripe account health and get early warnings before your account gets flagged, frozen, or force-upgraded. StripeWatch continuously checks your Stripe account against a checklist of known risk triggers — accepting pre-seed funds through invoices, high refund rates, suspicious velocity spikes, and ToS-violating business description language — and sends Slack or email alerts with specific remediation steps before Stripe acts. It also monitors Stripe's ToS changelog and maps each change to which of your current practices it affects. Founders who discovered too late that routine behaviors like accepting investor payments through Stripe invoices can cause permanent account closure are the primary audience. ## Monetization Strategy $12/month subscription with a 14-day free trial ## 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 "TikZFlow". ## The Problem Academics coding TikZ figures by hand must endlessly tweak coordinates and recompile, wasting hours on layout instead of content — validated by 295 upvotes and 58 comments on the Show HN TikZ editor post. ## Target Audience Academic researchers, PhD students, and professors who write LaTeX papers and need publication-quality figures ## Core Idea A WYSIWYG visual editor for TikZ figures that generates clean LaTeX code without manual coordinate tweaking. Academics and researchers spend hours hand-coding TikZ figures, tweaking coordinates and recompiling repeatedly to get diagrams right. TikZFlow provides a drag-and-drop canvas that outputs valid TikZ LaTeX, eliminating the compile-tweak-recompile loop. Users can start from templates for common diagram types like neural networks, flowcharts, and state machines. ## Monetization Strategy Freemium with free tier for basic shapes and paid tier ($8/month or $60/year) for advanced templates, export to SVG/PDF, and team sharing ## 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 "SlackInternalPass". ## The Problem Slack shows a 'double-check this link' warning on every internal IP address or private network URL, adding constant friction for engineering teams who share monitoring dashboards and internal service links dozens of times a day. ## Target Audience Engineering teams at companies with internal tooling shared via Slack ## Core Idea A Slack app that whitelists your internal IP ranges so the 'double-check this link' warning never interrupts your engineering team again. SlackInternalPass is a Slack workspace app that intercepts link-unfurl events and silently approves URLs matching administrator-defined CIDR ranges or hostname patterns, eliminating the modal warning that fires on every internal dashboard or monitoring link. Setup takes under two minutes and requires no infrastructure changes. Engineering teams who share dozens of internal service links per day immediately eliminate a constant source of friction without waiting for Slack to add native support. ## Monetization Strategy $4/workspace/month flat fee via Slack App Directory ## 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 "ContextCarry". ## The Problem Claude Code and other AI coding agent sessions containing valuable debugging and architecture context are siloed on whichever developer's machine they happened on, with no way to share or search them across a team — confirmed by the AGENTS.md GitHub issue with 5514 upvotes. ## Target Audience Engineering teams using AI coding agents collaboratively, especially remote teams where context handoffs across time zones are costly ## Core Idea A portable, shareable session journal for AI coding agents that captures decisions, dead ends, and architecture context so any developer can pick up where another left off. Claude Code, Cursor, and other AI coding agent sessions accumulate rich debugging context and architectural decisions that are siloed on the developer's local machine and lost when the session ends. ContextCarry hooks into agent sessions and automatically distills key decisions, rejected approaches, and architectural rationale into a structured markdown journal that lives in the repository. Teammates can onboard to any work-in-progress in minutes, and the journal feeds back into new agent sessions as compressed context. ## Monetization Strategy Free for solo developers; $15/month per team for shared session journals, search across sessions, and Slack/GitHub integration ## 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 "A2AVisualizer". ## The Problem Developers are interested in the A2A protocol but find it hard to understand how to use it practically, with no visual tooling to explore message flows and task states before committing to implementation. ## Target Audience Backend and AI developers evaluating or implementing multi-agent systems ## Core Idea Visually explore and debug Google's A2A agent-to-agent protocol flows before writing a single line of implementation code. A2AVisualizer is a browser-based sandbox where developers can import agent cards, simulate task state transitions, and replay message flows with diff views between steps. It removes the 'I couldn't figure out how to use it practically' barrier that HN commenters cited as the reason they abandoned the protocol. Export any flow as a working TypeScript or Python stub to bootstrap real implementations. ## Monetization Strategy Free tier with public flows, $12/month for private flows and team sharing ## 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 "OakShift". ## The Problem Existing version control systems like Git were designed for humans and lack the speed, parallel operation support, and structured context that AI coding agents need for serious projects. ## Target Audience Developers running AI coding agents like Claude Code, Codex, or Amp on complex codebases ## Core Idea A Git-compatible version control layer optimized for AI coding agents that need fast, parallel branch operations and rich context diffs. OakShift wraps standard Git repositories with an agent-friendly API that supports virtual mounts, concurrent agent workspaces, and structured change summaries designed for LLM context windows. It was inspired by the Oak Show HN which showed strong demand (199 upvotes, 170 comments) for VCS purpose-built for agents rather than retrofitted. Developers keep their existing GitHub remotes and CI while agents get a dramatically faster and safer working environment. ## Monetization Strategy Open-source core, $20/month hosted sync service for teams ## 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 "LandDown Shield". ## The Problem Developers giving AI agents access to production systems have no fine-grained permission layer to prevent accidental or malicious destructive actions, as raised in the Lobsters landdown sandboxing thread. ## Target Audience DevOps engineers, SREs, and platform teams running AI coding agents against live infrastructure ## Core Idea A lightweight sandboxing layer that intercepts and audits dangerous shell operations before AI agents execute them on production systems. As AI agents are given access to real production systems like Postgres, Kubernetes, and cloud APIs, teams have no fine-grained runtime guardrail to prevent accidental destructive actions. LandDown Shield wraps agent shell sessions with policy-based interception, logging every command and blocking operations that match configurable danger patterns. Teams get an audit trail proving what the agent did versus what a human authorized. ## Monetization Strategy Open-core with free self-hosted version and $19/month SaaS tier with centralized audit logs, team policies, and Slack/PagerDuty alerts ## 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.