Deepcon
Get the most accurate context for coding agents
Details
- External ID
- 45839378
- Source
- HN
- Company
- —
- Product
- Deepcon
- Website domain
- deepcon.ai
- Launched
- Nov. 6, 2025
- Cohort
- —
- Upvotes
- 7
- Upvotes percentile
- 0.37882096069869
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
I got frustrated watching Claude Code fail at using modern APIs (ask it about GPT-5 and it says it doesn't exist). Existing solutions like Context7 dump thousands of tokens of irrelevant docs into context. So I built DeepCon.How it works:- Crawled 10,000+ official docs using agentic browser automation and structured them hierarchically- Query decomposer breaks down requests, searches in parallel, then merges only relevant context- Returns just what's needed: 2.4x fewer tokens than Context7Results on our benchmark:DeepCon achieved 90% accuracy vs Context7's 65% on real-world tasks with modern AI frameworks. Without any MCP context, Sonnet 4.5 scored 0%.It's an MCP tool. You can just plug it into Claude Code/Cursor and suddenly they understand the latest libraries and APIs.GitHub benchmark: https://github.com/opactorai/context-benchService: https://deepcon.aiBuilt this because I needed it. Would love feedback!
Enrichment
- Theme
- AI agent frameworks and developer tools
- Vertical
- Horizontal
- Function
- Dev tools
- Audience
- Developer
- AI stance
- AI feature
- Project type
- Commercial product
- Normalized one-liner
- context optimization for coding agents
- Manually corrected
- False
Could you build this?
Partial The MCP integration and token delivery are standard, but continuously crawling, parsing, chunking, and indexing documentation across 10,000+ libraries with high accuracy requires a dedicated crawler fleet and vector/hybrid search pipeline.
What it would actually take: Building Deepcon requires an automated web scraping pipeline (e.g., Playwright/Puppeteer workers) that monitors thousands of documentation sites for updates, extracts clean Markdown/code, and removes site boilerplate. The retrieval backend requires a hybrid search pipeline (dense embeddings via vector DB + sparse BM25/reranking) and query decomposition logic to select precise snippets without exceeding agent token budgets.
Discussion
2 comments analyzed.
Concerns raised: Value proposition unclear
Feature requests: YouTube video demonstrating the product
Competitors
Other products that read as similar to this one — 206 launches clear the similarity bar, closest 8 shown.
Attention rank: #137 of 207 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 2 days after the earliest competitor.
- A Context Registry for AI coding agents · hn · 2026-09-03 · 9 upvotes · similarity 0.58
- ContextPool · ph · 2026-04-13 · 179 upvotes · similarity 0.49
- UltraContext · hn · 2026-01-21 · 21 upvotes · similarity 0.49
- Context Gateway · hn · 2026-03-13 · 97 upvotes · similarity 0.48
- Context Mode · hn · 2026-02-25 · 84 upvotes · similarity 0.46
- Deep Work Plan · ph · 2026-09-14 · 6 upvotes · similarity 0.46
- sctxx · github · 2026-09-10 · 19 upvotes · similarity 0.43
- Agentmemory · ph · 2026-05-16 · 322 upvotes · similarity 0.43
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a dev tools tool for Sales yet.