Build an AI-powered content generation platform for software engineers.
The application should automatically generate 10–15 high-quality social media posts per day focused on:
- Artificial Intelligence
- Machine Learning
- LLMs
- Agentic AI
- Full-stack development
- JavaScript
- TypeScript
- Python
- Cloud
- AWS
- Kubernetes
- Open Source
- Trending GitHub repositories
- Trending AI papers
- Tech news
The primary platforms are:
- X (Twitter)
- LinkedIn
Each generated post should feel human-written, educational, and engaging rather than generic AI-generated content.
Goals:
- Generate selectively every day, with one or two draft windows and no obligation to fill them.
- Research current trends before writing.
- Avoid hallucinated facts.
- Cite sources internally.
- Never duplicate previous posts.
- Maintain a consistent writing style.
- Produce viral-quality content.
Core Features
- User authentication
- Dashboard
- AI post generation
- Trend discovery
- GitHub trending integration
- Hacker News integration
- Reddit integration
- AI paper integration
- Save drafts
- Schedule adaptive draft windows and recommend X/LinkedIn publishing independently from measured results
- Publish to X, but not without my approval, my approval is must
- Publish to LinkedIn, but not without my approval, my approval is must
- Analytics
- Post history
- Search previous posts
- Topic management
- Writing style management
- AI model settings
Workflow:
1. Collect latest news.
2. Collect GitHub trending repositories.
3. Collect AI research papers.
4. Collect Reddit discussions.
5. Cluster topics.
6. Rank by importance.
7. Generate content ideas.
8. Generate hooks.
9. Generate posts.
10. Score posts.
11. Rewrite low-quality posts.
12. Save approved posts.
13. Schedule publishing.
Create specialized AI agents.
Research Agent
- Finds trends.
Summarizer Agent
- Summarizes articles.
Content Planner
- Selects topics.
Writer Agent
- Writes posts.
Editor Agent
- Improves grammar.
Fact Checker
- Verifies claims.
SEO Agent
- Improves discoverability.
Publisher Agent
- Publishes posts.
Analytics Agent
- Learns from engagement.
Writing style:
- Sounds like a senior software engineer.
- Avoid marketing buzzwords.
- Educational.
- Opinionated only when supported by evidence.
- Short paragraphs.
- Uses code snippets where helpful.
- Uses emojis sparingly.
- No clickbait.
- Avoid AI clichés.
Generate:
- Single tweets
- Tweet threads
- Polls
- Quote tweets
- Tips
- Code snippets
- Comparisons
- Hot takes
- Tutorials
Character limit:
280
Optimize for engagement.
Generate:
- Long-form posts
- Storytelling
- Technical breakdowns
- Lessons learned
- Architecture posts
- Career advice
- AI insights
Length:
500–2000 characters.
Note: below is for reference, we must use those technologies which provide free service if number of requests is low, since this app is not for public to use, only I'm going to use it, so the requests wouldn't be that high and there're many platforms that give free access if the requests are low, for example I can use vercel for deployments, Tavily provide free api access if requests are low, supabase provide a free db access which is limited to 500 mb once the storage reaches like 450mb we should have a cron that will wipeout the DB since by that time the trends would have changed so no need to have them stored, instead of S3 we've Cloudflare R2; for AI model I will get Deepseek subscription which is the cheapest of all AI providers, for others we must try to find if there is any other service providing the same for free if the requests are low and are within some limits.
Frontend
- Next.js
- React
- Tailwind
- shadcn/ui
Backend
- FastAPI
or
- Node.js + NestJS
AI
- LangGraph
- LangChain
Database
- PostgreSQL
ORM
- Prisma
Queue
- Redis
- BullMQ
Scheduling
- Cron
Storage
- S3
Authentication
- Better Auth
Deployment
- Docker
Requirements:
- TypeScript everywhere.
- Modular architecture.
- Clean Architecture.
- SOLID principles.
- Repository pattern.
- Dependency Injection.
- Unit tests.
- Integration tests.
- E2E tests.
- Documentation.
- API versioning.
Entities
Users
Posts
Topics
Sources
Tags
Research
Drafts
Schedules
PublishingJobs
Analytics
Templates
WritingStyles
Models
PromptVersions
Prompt templates should be versioned.
Support:
System prompt
Developer prompt
User prompt
Few-shot examples
Output validation
JSON schema
Automatic retries
Example:
Score each post based on:
Novelty
Accuracy
Hook quality
Readability
Virality
Technical correctness
Engagement potential
Overall score
Reject anything below a threshold (for example, 8.5/10) and regenerate it.
Include integrations such as:
- X API
- LinkedIn API
- GitHub API
- Hacker News API
- Reddit API
- arXiv API
- Google News RSS
- RSS feeds from major engineering blogs
Ask Claude to generate something like:
apps/
packages/
agents/
prompts/
workers/
database/
scripts/
docs/
tests/
Ask Claude to implement in phases instead of trying to build everything at once.
Phase 1 — completed
- Authentication, dashboard, slot generation, screenshots, and manual posting workflow
Phase 2 — completed
- Product-first research, owned-project fact cards, GitHub/RSS and selective external evidence
Phase 3 — completed
- Platform-native X/LinkedIn generation, grounding audits, scoring, and rewrite loops
Phase 4 — completed
- Manual performance snapshots, analytics, and engagement opportunities
Phase 5 — completed
- Generation provenance, controlled growth experiments, bulk metrics, and approval-gated learning
Phase 6 — completed
- Grounded branded technical cards, LinkedIn carousels, visual settings, and media experiments
Phase 7 — completed
- Owned-repository sync, meaningful-change filtering, fact review, and approved evidence sources
Phase 8 — completed
- Manual platform distribution cycles, ranked conversations, relationship tracking, grounded replies, and audience content signals
Phase 9 — completed
- Goal-driven product campaigns, evidence-gated narrative stages, manual orchestration, and campaign analytics
Phase 10 — completed
- Privacy-safe tracked redirects, explicit conversions, full-funnel attribution, and controlled CTA experiments
Phase 11 — completed
- Production health, stage-level operational telemetry, cron monitoring, deployment validation, and checkpoint-safe recovery
Phase 12 — completed
- Deterministic seven-day growth reviews with prior-period comparison
- Exactly three approval-gated continue, reduce, and test decisions
- Safe content-mix changes with stale-review protection and draft-only experiment creation
- Historical review evidence, next-week briefs, and PDF/CSV exports
Phase 13 — completed
- 1h, 24h, 72h, and 7d capture queue for X and LinkedIn
- Comparable-age coverage and deterministic snapshot quality alerts
- Idempotent DevPulse, X, and LinkedIn CSV imports with audit history
- Explicit account follower and profile-view checkpoints
- Weekly-review decisions gated on valid 24-hour cohorts and coverage confidence
Phase 14 — completed
- Deterministic seven-day execution plans generated from persisted weekly reviews
- One product-first anchor per day while remaining slots keep the approved content strategy
- Individual rejection, whole-plan approval, cancellation, skipping, and completion controls
- Approved anchors guide matching slot generation without bypassing draft review or manual publishing
- Passive iCalendar export plus explicit publish and valid-24h measurement confirmation
Phase 15 — completed
- Adaptive generation cadence with two X-oriented daily draft windows by default
- Independent LinkedIn weekly publishing days and per-platform recommendation queues
- Evidence, overall-quality, novelty, project-cooldown, and content-type-cooldown gates
- Intentional slot skips when no candidate clears the configured publishing bar
- Measured posting-hour recommendations with conservative small-sample fallbacks
- Daily manual engagement sequence before publishing, after publishing, and at the 24-hour checkpoint
One feature that can make this stand out from typical AI post generators is to make it research-first instead of prompt-first. Rather than asking an LLM to invent posts, have the system:
- Continuously ingest fresh sources (GitHub, arXiv, Hacker News, Reddit, company engineering blogs, AI news).
- Deduplicate and rank topics by relevance and novelty.
- Generate multiple content angles for each topic (tutorial, opinion, comparison, quick tip, thread, architecture breakdown).
- Learn from engagement data over time to improve future recommendations.