Alternatives
Products that do what Docdex does
Structural code indexer with deterministic LLM context
- 1
- 2
- 3
- 4DT
I got tired of open file.docx → wait 8 seconds → close Word just to read a document, so I built a terminal-native Word viewer! What it does: * View `.docx` files directly in your terminal with (mostly) proper formatting * Tables actually look like tables (with Unicode borders!) * Nested lists work correctly with indentation * Full-text search with highlighting * Copy content straight to clipboard with `c` * Export to markdown/CSV/JSON Why I made this: Working on servers over SSH, I constantly hit Word docs I needed to check quickly. The existing solutions I'm aware of either strip…
2025 · github.com
- 5
- 6IA
Hello HN, I am building Docmost, an open-source collaborative wiki and documentation software. It is an open-source alternative to Confluence and Notion. I have been working on it for the past 12 months. This is the first public release (beta). The rich-text editor has support for real-time collaboration, LaTex, inline comments, tables, and callouts to name a few. Features - Collaborative real-time editor - Spaces (Teamspace) - User permissions - Groups - Comments - Page history - Nested pages - Search - File attachments You can find screenshots of the product on the website. Website:…
2024 · github.com
- 7WT
After working with LLMs for long enough, I found myself wanting a lightweight utility for doing various small tasks to prepare inputs, locate information and create evaluators. This library is two things: a very simple model and utilities that inference it (eg. fuzzy deduplication). The target platform is CPU, and it’s intended to be light, fast and pip installable — a library that lowers the barrier to working with strings semantically. You don’t need to install pytorch to use it, or any deep learning runtimes. How can this be accomplished? The model is simply token embeddings that are…
2024 · github.com
- 8DD
Jul 2026 · github.com
- 9DC
Jul 2026 · github.com
- 10
- 11DU
We've built this over the last few weeks to leverage vector search and LLMs (this is backed by GPT-3.5, though we're also testing Flan-T5) to answer question over large sets of documents with references. Currently, we've ingested the documentation for React and some key adjacent libraries (Redux, React-Redux, React-Router, MUI). This allows you to ask various natural language questions and the output is hopefully a relevant answer with code examples if applicable, while sourcing the original docs whenever possible. We're working on adding up more documentations and have more "general"…
2023 · docasker.com
- 12
- 13CT
I've been building a tool that changes how LLM coding agents explore codebases, and I wanted to share it along with some early observations. Typically claude code globs directories, greps for patterns, and reads files with minimal guidance. It works in kind of the same way you'd learn to navigate a city by walking every street. You'll eventually build a mental map, but claude never does - at least not any that persists across different contexts. The Recursive Language Models paper from Zhang, Kraska, and Khattab at MIT CSAIL introduced a cleaner framing. Instead of cramming everything into…
Feb 2026 · github.com
- 14DT
Think interactive grep for text files, PDFs, DOCXs, etc, but word/token based instead of regex and line based, so you can search across lines easily. Docfd aims to provide good UX via integration with common text editors and PDF viewers, so you can jump directly to a search result with a single key press. --- I originally wrote this tool to help me dig through text/markdown notes, since very often I want to search for a phrase of sorts that may span across multiple lines, but constructing the corresponding multiline regex is a bit painful (especially painful if I want to account…
2024 · github.com
- 15
- 16LD
I was inspired by a recent tweet by Andrej Karpathy, as well as my own experience copying and pasting a bunch of html docs into Claude yesterday and bemoaning how long-winded and poorly formatted it was. I’m trying to decide if I should make it into a full-fledged service and completely automate the process of generating the distilled documentation. Problem is that it would cost a lot in API tokens and wouldn’t generate any revenue (plus it would have to be updated as documentation changes significantly). Maybe Anthropic wants to fund it as a public good? Let me know!
2025 · github.com
- 17IJ
Hi HackerNews, Lately, I have seen an explosion in posts offering paid APIs/services to get unstructured data into LLMs (i.e. langchain extract, ragflow, unstructured, unstract, just to name a few) and I have been largely disappointed by them, either because they fail to implement multimodal support, fail to give good context for "really tricky" PDFs / Word docs / Powerpoints, or are just plain difficult to use. In light of all these posts I figured I'd share my solution that has been working smoothly for me and my clients. I put it up on GitHub for free so you can check it…
2024 · github.com
- 18
- 19IG
Hi HN! I'm excited to share a project I've been working on for the past year: Docland. It is an API documentation browser that generates documentation on demand (through compilation, not LLMs) for Java packages. Instead of relying on Javadoc, the built-in doc generator, I created the engine from scratch to give the documentations a modern look, build fast search indexes, and enable link resolution to other packages. I built Docland because I constantly found it frustrating to locate and view API definitions when programming. You'd have to Google the function/class name, skip all the SEO…
2024 · docland.io
- 20TC
Hi HN, I built a CLI for uploading documents and querying them with an LLM agent that uses search tools rather than stuffing everything into the context window. I recorded a demo using the CrossFit 2025 rulebook that shows how this approach compares to traditional RAG and direct context injection[1]. The core insight is that LLMs running in loops with tool access are unreasonably effective at this kind of knowledge retrieval task[2]. Instead of hoping the right chunks make it into your context, the agent can iteratively search, refine queries, and reason about what it finds. The CLI handles…
2025 · github.com
- 21AV
The word "index" originally came from how humans retrieve info: book indexes and tables of contents that guide us to the right place in documents. Computers later borrowed the term for data structures: e.g., B-trees, hash tables, and more recently, vector indexes. They are highly efficient for machines; but abstract and unnatural: not something a human, or an LLM, can understand and directly use as a reasoning aid. This creates a gap between how indexes work for computers and how they should work for models that reason like humans. PageIndex is a new step that "looks back to move forward".…
Oct 2025 · github.com
- 22CI
This is a project I recently worked on at Definitive. We’re hoping to make it easier to semantically query (changing) source code files. We’d love to see people use it as a building block. If you have any suggestions please leave a comment or open a GitHub issue!
2023 · github.com
- 23LA
G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…
2023 · github.com
- 24DA
I think everyone has already read Karpathy's Post about LLM Knowledge Bases. Actually for recent weeks I am already working on agent-native knowledge base for complex research (DocMason). And it is purely running in Codex/Claude Code. I call this paradigm is: The repo is the app. Codex is the runtime. During my daily working life, I have tons of office documents with knowledge from all teams, and as an IT Architect, I need to combine them altogether to handle complex deep research (which normal LLM definitely could not help). That is the originally reason I built DocMason, and I am…
Apr 2026 · github.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →