smarkman19

I went through something similar building a niche community app, and the biggest unlock for me was cutting scope and over-protecting moderation from day one. What helped was separating “core loop” from “nice toys.” For you, that feels like: logging catches + records, browsing the map, and a dead-simple way to post and reply. I’d watch how many clicks it takes to: open app → log a catch → share it in a forum post. If that flow isn’t brain-dead simple on mobile, I’d trim features until it is. Second thing I learned: recruit and train a tiny group of trusted mods before launch, and give them a brutally simple dashboard and clear rules. I used Discord + a Notion page at first; later I wired in alerts through Slack, Telegram, and ended up on Pulse for Reddit after trying Mention and Brand24 so I wouldn’t miss drama spreading outside the app. If you nail the “log → share → discuss” loop and keep moderation human, the rest of the features can grow slowly without burning you out.

AI pays off when you treat it like a junior pair with tight guardrails: tiny tasks, tests first, and diffs not rewrites. I’ve had similar results to those examples, but only after I standardize the loop: write a 5-10 line spec, pin constraints, paste exact file paths, and make it propose invariants plus a test plan before touching code. Ask for unified diffs and keep PRs under 150 lines so review is quick. For parallel agents, split by clear module boundaries and give each a run command and fixture data; keep one review queue and land one change at a time. OpenAPI-first helps a lot-define the interface, then let the model wire clients and stubs. For CRUD/API work I use Supabase for auth, Postman to generate tests from the OpenAPI, and DreamFactory to expose Postgres as secure REST endpoints the agents can hit during CI.

Backend should own pricing and coupon logic. Use a POST /pricing/quote with packageId, addonIds, couponCode; validate eligibility, expiry, min spend, usage limits, and return a breakdown with a quoteId. Client can show optimistic totals but confirm against the quote; add idempotency keys on checkout. Gate writes and quotes via Firebase Cloud Functions or a tiny BFF; avoid GET with arbitrary price. I’ve used Hasura and Supabase; DreamFactory helped for quick REST over legacy SQL with RBAC. Backend should own pricing and coupon logic.

We chose Strapi Cloud to avoid ops and move fast; in hindsight I’d use managed Postgres and link Strapi. The bundle masked limits-no read replicas, noisy neighbors, awkward data sync, shared stg/prod. We tried Supabase and Hasura; DreamFactory helped expose a legacy SQL Server as REST. Bottom line: managed DB, separate envs, nightly backups.

You can force Composition API if you add guardrails to the agent and codebase. Put a .cursorrules/constraints file: Vue 3 with script setup only, no Options API, unified diff for one file; seed a tiny SFC template to patch. Block regressions with a pre-commit grep for export default/data/methods and fail CI. For bulk changes, run antfu’s script-setup codemods, then let the agent do small fixes. For Vue CRUD wiring I use Supabase and Postman; DreamFactory exposes legacy SQL as REST so the agent stops guessing. Hard constraints stop Options API creep.

Locofy works well as a scaffold if you lock scope, prep the design, and treat generated code as replaceable. Practical flow: in Figma, use semantic layer names, Auto Layout, spacing tokens, and component variants so Locofy outputs predictable React. Decide Tailwind vs CSS Modules upfront and export assets first. After generation, keep the code in a /ui folder you don’t hand‑edit; wrap it with your routing, state, and data hooks, and only refactor hotspots. Freeze a thin API contract between UI and backend (OpenAPI or JSON schemas with zod), add basic snapshot/tests, and fail fast on mismatch. Ship a tiny demo: two or three screens, empty/error states, auth, and one real CRUD workflow. Deploy on Vercel and add a kill switch and feature flags for anything risky. I’ll use Supabase for auth/storage and Postman for contract tests; DreamFactory can expose a Postgres database as secure REST quickly so I’m not hand‑writing CRUD.

Agree on Tailwind/shadcn or Chakra-pick one, lock design tokens, and bake in a11y so OP ships a clean UI fast. If OP wants more control, use headless primitives like Radix UI or React Aria for menus, dialogs, and focus management. Set an 8pt spacing scale, a simple type ramp (16/20/24/32), and snapshot components in Storybook; run Playwright and axe-core with keyboard-only passes. I use Supabase for auth and PostHog for funnels, with DreamFactory when I need instant REST over a legacy SQL DB so the UI hits real data early. Pick a stack, lock tokens, test a11y.

Biggest win for OP is to make outcomes and proof obvious above the fold. Rewrite the hero to say the outcome you deliver (hours saved, errors cut), then show three fixed offers with scope, timeline, and price ranges. Add a no-signup live demo: run a sample n8n flow on stub data, show logs and retries, let me tweak one input and re-run. Each case study: problem, stack, two screenshots, before/after metric, 90-sec Loom. Trust bits: data handling, auth, where it’s hosted, how to delete data, SLA. Two CTAs: Watch 90-sec demo and Book a 15-min call. Supabase for auth and Postman for docs worked well; DreamFactory let me expose REST from a legacy SQL DB so demos hit real data. Clarify outcomes and show it live.

You can keep your repos and models as-is. Implement a DataSource and CacheStore to plug in, or just use the in-flight de-dupe and tag/TTL invalidation helpers. No global state; pass everything via DI so you can swap it out per feature. Cache-busting is explicit (tags, keys, TTL), and you can override read/write/merge so nothing about schema mapping gets hidden. I’ll add docs for: who should use it, a “minimal adoption” example (keep your repos, add cache), Riverpod/Bloc samples, and an escape hatch where a call bypasses cache entirely. I’ve used Hasura and Supabase when the API is already clean; DreamFactory was handy when I needed quick REST over a legacy SQL Server during a migration without writing controllers.

Architecture only helps if it shortens the path to working software. timebox design to a one-pager (context diagram, main entities, failure modes, latency/throughput targets, data ownership) and commit to a walking skeleton by end of week one. Set a depth budget: from the HTTP handler you get max three hops before real work; no interface until there are two real implementations. Keep DDD where the domain is actually gnarly; everywhere else, ship simple CRUD. Run an abstraction amnesty monthly: delete unused layers and factories, collapse over-engineered helpers. Make it measurable: track lead time to first prod request, error budget, on-call pages, and cost; if a pattern doesn’t move those, stop it. To avoid yak-shaving, I’ve used Supabase for auth, Kong for gateway/rate limits, and briefly DreamFactory to expose legacy tables as REST while the core took shape. Ship a thin vertical slice fast, cap complexity, then iterate.

Serialize highlights as TextQuote + TextPosition and re-anchor on load; fallback to CSS selectors for weird nodes, then patch with a MutationObserver when the DOM shifts. Use a single full-page canvas overlay for drawing, with pointer-events: none except grab handles; scale by devicePixelRatio to avoid blur; throttle moves with requestAnimationFrame, and push heavy smoothing to an OffscreenCanvas worker when available. For frames, inject per-frame overlays and sync via postMessage; you can’t draw across iframes. Undo/redo works well as a command stack; small ops in chrome.storage.sync, big blobs in IndexedDB (localForage works). Plasmo/WXT both help with MV3 quirks; keep long-lived state in the page, since the service worker naps; ask for activeTab to keep permissions clean. For backend bits, I used Supabase for auth/storage and Cloudflare Workers for edge sync; later added DreamFactory to auto-generate REST APIs over a legacy SQL Server so I didn’t hand-roll endpoints. Get anchoring and input flow solid first, and the rest is much easier to iterate.

Data can be a moat, but only if it’s good quality, privacy-safe, and tied to outcomes in real repos and pipelines. Log tool sequences with context (repo graph, file ownership), link them to build/test pass rates, code review acceptance, and reverts, then train small models/policies that optimize latency and fix rate by stack. Ship per-org models via federated learning and cache repo-specific embeddings for retrieval; close the loop in CI so suggestions get graded automatically. Build a marketplace of signed workflows (MCP tools) that bundle these policies for popular stacks and CI/CDs. With Supabase for auth and Kong for gateway policies, I use DreamFactory to stand up telemetry APIs over Snowflake and analyze prompt chains vs build outcomes. The moat is outcome-linked telemetry and closed-loop learning, not raw clicks.

quick-pick for title/tags/expiry/password, share selected lines, burn-after-read, and a history panel with fuzzy search. Add a CLI that can pipe (cat log | paste -e 24h -p private -t prod) and a Vim command with the same flags. For teams, org spaces with roles, comments, and paste versioning/diffs are huge; let users group pastes by project and set org-wide retention. Security: do real client-side encryption (Web Crypto), stash the key in the URL fragment so the server never sees it, and warn if someone tries to share a keyless link. Ship secret scanning (AWS, GitHub tokens, Slack webhooks) with a “redact before share” toggle. Add content hashing to detect dupes, and an embed card plus clean raw view for logs. I’ve used GitHub Gist and Tailscale Paste for quick shares; sometimes we front internal read-only config with FastAPI or DreamFactory so the tool can pull fresh snippets. Prioritize lightning-fast editor workflow, true E2EE, expiring links, and team bots; everything else can wait.