Elasticsearch

Elasticsearch AI

Elasticsearch AI

Elasticsearch AI

Elasticsearch AI

Elasticsearch AI

Elasticsearch AI

Elasticsearch AI

Elasticsearch AI

Elasticsearch AI

Elasticsearch AI

Automated the boring parts of content creation

I've been making content for a while and the tooling situation is genuinely annoying. Every platform wants a subscription. Runway is $35/mo for video only. InVideo locks everything behind their editor. Buffer/Later for scheduling is another $15-20. You end up paying $80-100/mo for a pipeline that you don't even fully control. So I built something and just open sourced it. It's a set of Claude Code slash commands. You type /content:create, answer a few questions (or just give it a topic and let it run), and it takes the whole thing from brief → script → image/video generation → scheduled post. No GUI, no subscription, just your Claude Code session and a few API keys. The pipeline: Images: Gemini Flash for free illustrative images, fal.ai Flux for character-consistent stuff Video: KlingAI through fal.ai (~$0.42 per 5s clip vs $35+/mo for Runway) Voice narration: Chatterbox Turbo running locally (GPU-accelerated if you have one, falls back gracefully if not) Scheduling: self-hosted Postiz → publishes to YouTube, X, LinkedIn simultaneously The thing I'm actually proud of: an AutoResearch loop that pulls your post analytics after each publish cycle and automatically rewrites your generation prompt toward what's actually performing The zero monthly floor thing matters if you're doing this casually. Some months I post a lot, some months I don't. Paying $35/mo when you post twice that month feels bad. Setup is: copy a .claude/ folder into your project, set your env vars, run /content:status to verify everything's connected. That's it. It's rough in places — the Postiz self-hosting setup is genuinely annoying (needs Temporal + Elasticsearch, not just Redis + Postgres like the docs imply). I documented the painful parts in the README including a LinkedIn OAuth patch you have to apply manually because their default scopes require Pages API approval most people don't have. Anyway, code's there, MIT licensed, might be useful to someone. https://github.com/arnaldo-delisio/claude-content-machine submitted by /u/arnaldodelisio [link] [comments]

I built a persistent memory MCP server for Claude Code — v1.0 ships with 17 tools, hybrid search, contradiction detection, and a visual memory graph

Six months ago I posted about giving my Claude Code agent persistent memory. The response was "cool, but can it detect when I change a decision? Does it work with Cursor? Can I search by exact keywords?" Built all of it. Here's what v1.0 ships: 17 MCP tools — save, recall, search, forget, list, export, import, ingest, index, migrate, compact, stats, profile, related, session start/summary, health 3 search modes: Vector (cosine similarity, HNSW index) — finds by meaning Keyword (full BM25, same algo as Elasticsearch) — finds by exact terms Hybrid (70% vector + 30% BM25) — best of both, what I use daily Contradiction detection — saves "we use PostgreSQL", you later save "switched from PostgreSQL to CockroachDB", it automatically creates a supersedes relationship and deprioritises the old one. No config needed. Importance scoring — explicit saves > auto-captures, decisions > conversation, with 347-day exponential decay so recent context surfaces first. Knowledge graph — extracts file paths, functions, classes, packages, URLs, env vars from every memory. Ask "what memories mention auth.ts?" via memory_related. Visual memory graph — memento serve → localhost:7007. D3.js force-directed graph, nodes colored by tag, edges showing relationships. Same URL serves a REST API mirroring all 17 tools. Production resilience — circuit breaker, write-ahead log (crash recovery), LRU cache for embeddings. Multi-IDE — Claude Code, Cursor, Windsurf, OpenCode. Shared memory store across all four. Chrome extension — right-click any page or selection to save it. While building v1.0, Memento was running on itself. It captured 2,191 memories across 27 sessions silently — 1,905 from auto-capture hooks, 206 from session summaries, 79 explicit. 53MB of engineering context, searchable next session. Everything runs locally. No cloud. No API keys. No telemetry. Setup: npx memento-memory setup GitHub: https://github.com/sanathshetty444/memento Docs: https://sanathshetty444.github.io/memento/ Full writeup: https://medium.com/@sanathshetty444/it-remembered-9e7d10f444ff submitted by /u/AltruisticPizza7271 [link] [comments]

I built an MCP server that connects Claude Code to 2M+ research papers - it stops defaulting to what it "knows" and starts using published, benchmarked and latest methods.

Claude Code is great at implementation but terrible at "what's the best approach for X." It searches the web and gets blogs and Stack Overflow. Meanwhile there are papers from this month with benchmarked methods that would've saved me hours of trial and error. I used Claude Code to build Paper Lantern - an MCP server that connects any coding agent to 2M+ CS and 43M+ biomedical research papers. Instead of guessing at approaches, Claude Code now searches real literature, finds relevant methods, and gets implementation-ready guidance grounded in actual research. The boost in practice: "implement chunking for my RAG pipeline" - without Paper Lantern, Claude Code picks a standard chunking approach from its training data. With it, it finds 4 papers from this month, one showing 0.93 faithfulness vs 0.78 for the standard method, another cutting tokens 76% while improving quality. It synthesizes across pipeline stages and knows where to start. How Claude Code built it: Embedding pipeline: Qwen3-Embedding on AWS g5 instances, USearch HNSW index, LMDB cache for 2M+ CS papers FastAPI MCP server with multi-query generation and synthesis Elasticsearch BM25 indexing across the full corpus ALB routing for api/mcp subdomains on AWS Claude Code was my entire engineering team on this. Solo founder, built the whole thing in Claude Code sessions. Free to try, no paid tier: code.paperlantern.ai Curious what problems you'd throw at this - what engineering decisions are you making this week where research would actually help? submitted by /u/kalpitdixit [link] [comments]