Show HN: Unlock the best engineering knowledge in papers for your coding agent

I don't mean to be a jerk, I think this looks cool... but unless I'm missing something this is just a sign up page for something that may or may not exist someday?

From the Show HN rules: "Off topic: blog posts, sign-up pages, newsletters, lists, and other reading material. Those can't be tried out, so can't be Show HNs. Make a regular submission instead."

The best engineering knowledge is locked in research papers. Paper Lantern unlocks it for your code.

Paper Lantern is an MCP server that distills 2M+ CS research papers into the right method for your problem — its tradeoffs, benchmarks, and how to implement it — delivered directly to your coding agent. Works with Claude Code, Cursor, Copilot, any MCP client.

Your coding agent can search for papers, but it's searching the open web — not a purpose-built research index. And even when it finds papers, the hard questions remain: which methods actually matter for your problem? What are the tradeoffs at your scale? What was tried and failed? What should you actually implement? That reasoning lives in papers and it never reaches your code.

EXAMPLE ask your agent to implement chunking for a RAG pipeline. Paper Lantern detects your context — multi-source corpus, accuracy-critical, technical documents — then searches across 2M+ papers and finds 4 from January 2026 that directly apply. It explains each technique in plain language, shows why it matters for your specific setup, synthesizes how they address different pipeline stages, and recommends what to start with and why — with implementation details your coding agent can act on immediately.

One of those papers: a cross-document topic-aligned chunking approach hitting 0.93 faithfulness vs 0.78 for semantic chunking (arxiv:2601.05265). Another: a pruning method that cuts input tokens 76% while improving answer quality (arxiv:2601.17532).

The index covers agent design, RAG and retrieval, LLM inference, fine-tuning, evaluation, search and ranking — hundreds of techniques across applied CS.

BACKGROUND I spent 7 years leading various LLM and RAG teams at AWS Bedrock (IIT-Bombay, Stanford). Paper Lantern started as a research discovery platform - this is the same engine with additional reasoning, now plugged into coding workflows via MCP.

Looking for engineers who use coding agents daily. Happy to answer questions about the search, the synthesis, or the MCP integration.

code.paperlantern.ai

Interesting. A few questions about the search layer: are you using dense retrieval, sparse, or hybrid? At 2M papers, how do you handle the drift between how engineers describe problems ("my RAG pipeline hallucinates on long docs") vs how papers describe solutions ("cross-document coherence in retrieval-augmented generation")? That query-document vocabulary gap is the hard part of academic search.

I’m curious about the mechanics of the MCP integration. When Paper Lantern sends results back to the coding agent, how much context is actually included? Are you returning full paper sections, or more compressed summaries? I’m especially thinking about token budget constraints. If the agent is already operating with a large codebase in its context window, does Paper Lantern’s output compete for that same space?

Appreciate MCP server availability, will help integrate with Claude. I literally have a Notion database where I tag papers by technique and link them to our codebase decisions. It's a nightmare to maintain. Does Paper Lantern keep any kind of project memory - like "for this repo, we previously decided on approach Y because of paper Z" - so the reasoning compounds over time?

You mention the index covers "applied CS" — where does it draw the line? If I'm implementing a time series forecasting model, does it cover the stats/econometrics literature too, or only CS-venue papers? A lot of the best techniques for engineering problems live in adjacent fields. Same question for computational biology, NLP-for-legal, etc.

With the landscape of AI/ ML space changing so rapidly, the ability for coding agents having access to SOTA research in their hand makes me feel that I suddenly would have the ability to solve some of the toughest engineering problems and expedite my journey in contributing in solving problems in new areas which I have always aspired to solve.

How do you evaluate the synthesis quality? Like if I ask about chunking strategies and it recommends method A over method B for my use case — how do you know that recommendation is actually good? Have you run any benchmarks on recommendation accuracy, or is this more of a "trust the retrieval + LLM reasoning" setup?

Amazing work. Really nice that you included actual numbers (0.93 vs 0.78) in your semantic chunking example. When the synthesis recommends a method, does it pull benchmark comparisons across papers so I can see how methods perform on the same metrics? That's the thing that takes me forever to compile manually.