Show HN: I built a social media management tool in 3 weeks with Claude and Codex

Original Article ↗ Hacker News Discussion ↗

AI-assisted development workflow

  • OP built a full-featured social media management tool in ~3 weeks using detailed specs plus two AI coding tools (one for initial implementation, one for review/security).
  • Specs, architecture docs, and style guide were largely AI-drafted then heavily refined over several days.
  • Work was structured into “layers” and parallel “streams” (e.g., content pipeline, providers, media, notifications), with 3–4 agents running in parallel; merging and human review became the bottleneck.
  • High parallelism also hit token/session limits and raised costs.

Where AI coding worked vs broke down

  • Worked well for: Django CRUD, models/views/serializers, Tailwind + HTMX UI, provider modules for well-documented APIs, tests, and cross-file refactors.
  • Failed or was risky for: poorly documented APIs (TikTok), multi-tenant permission logic (data leaks across workspaces), OAuth edge cases, and background job orchestration. These bugs often passed AI-generated tests.
  • Significant time went into UX polish; initial AI-built UI was feature-complete but confusing and inconsistent.

Stack and database debates

  • Discussion around Django + HTMX being “old” vs FastAPI/SvelteKit; some see Django/HTMX as pragmatic and well-documented, ideal for AI agents and solo devs.
  • Database debate: several argue Postgres should be the default for serious apps (strictness, transactional DDL, battle-tested), others say SQLite is enough for this kind of tool, while managed MySQL/Postgres favored for cloud hosting.

Platform APIs and coverage

  • LinkedIn posting appears to use their website/API; details examined via the repo.
  • X/Twitter integration initially omitted due to high API costs; recent per-request pricing may make it feasible, but some question whether it’s worth integrating given declining engagement.
  • Questions about whether automated posting is allowed or throttled; answers: depends on platform and can change, but all major platforms have official posting APIs for developers.

Monetization, cloning, and “SaaS-pocalypse”

  • Several see this as evidence that generic SaaS is easily cloned with AI; sustainable businesses may need:
    • Access to unique data/insights, or
    • Cutting-edge tech outside current training distributions, or
    • Strong distribution/brand.
  • Some argue many users will just “vibe code” bespoke tools instead of adopting generic open source.

Quality, trust, and “vibe coding”

  • Some potential users are wary of a “built in 3 weeks with AI” product for production use, preferring mature proprietary tools.
  • Others note much commercial software is already hurriedly built; AI doesn’t automatically make quality worse.
  • Debate over “vibe coding”: for some it means fully surrendering to the model and ignoring the code; others mean AI-assisted but with human review and testing.
  • Concerns center on multi-tenant bugs, maintenance of AI-generated code, and long-term support; some think AI also makes maintaining legacy systems easier.

Use cases and alternatives

  • Interest from agencies managing many client accounts; confirmed that multiple accounts/clients are supported.
  • Separate desire for a “social media reader” aggregating feeds into a calm, ad-free UI; considered hard to do without violating platform T&Cs, though RSS, open networks (e.g., Mastodon/Bluesky), and scraping tools were mentioned.
  • Some view social media as increasingly bot-driven and toxic; tools like this are seen as both practical and emblematic of that trend.