This looks neat, we certainly need more ideas and solutions on this space, I work with large codebases daily and the limits on agentic contexts are constantly evident. I've some questions related to how I would consume a tool like this one:
How does this fare with codebases that change very frequently? I presume background agents re-indexing changes must become a bottleneck at some point for large or very active teams.
If I'm working on a large set of changes modifying lots of files, moving definitions around, etc., meaning I've deviated locally quite a bit from the most up to date index, will Nia be able to reconcile what I'm trying to do locally vs the index, despite my local changes looking quite different from the upstream?
great question!
For large and active codebases, we avoid full reindexing. Nia tracks diffs and file level changes, so background workers only reindex what actually changed. We are also building “inline agents” that watch pull requests or recent commits and proactively update the index ahead of your agent queries.
Local vs upstream divergence is a real scenario. Today Nia prioritizes providing external context to your coding agents: packages, provider docs, SDK versions, internal wikis, etc. We can still reconcile with your local code if you point the agent at your local workspace (cursor and claude code already provide that path). We look at file paths, symbol names and usage references to map local edits to known context. In cases where the delta is large, we surface both the local version and the latest indexed version so the agent understands what changed.
Cursor promises to do this[0] in the product, so, especially on HN, it'd be best to start with "why this is better than Cursor".
> favorite doc sites so I do not have to paste URLs into Cursor
This is especially confusing, because cursor has a feature for docs you want to scrape regularly.
The goal here is not to replace Cursor’s own local codebase indexing. Cursor already does that part well. What Nia focuses on is external context. It lets agents pull in accurate information from remote sources like docs, packages, APIs, and broader knowledge bases
Absolutely insane that we celebrated coding agents getting rid of RAG, only with the next innovation being RAG
Not exactly just RAG. The shift is agentic discovery paired with semantic search.
Also, most of the coding agents still combine RAG and agentic search. See cursor blog about how semantic search helps them understand and navigate massive codebases: https://cursor.com/blog/semsearch
The context problem with coding agents is real. We've been coordinating multiple agents on builds - they often re-scan the same files or miss cross-file dependencies. Interested in how Nia handles this - knowledge graph or smarter caching?
hey! knowledge graphs are also used at runtime but paired with other techniques, since graphs are only useful for relationship queries.
The pendulum swings back.
This is happening over and over and over. The example of prompt engineering is just a form of protocol. Context engineering is just about cache management. People think LLMs will replace programming languages and runtimes entirely, but so far it seems they have been used mostly to write programs in programming languages, and I've found they're very bad interpreters and compilers. So far, I can't really pick out what exactly LLMs are replacing except the need to press the individual keys on the keyboard, so I still struggle to see them as more than super fancy autocomplete. When the hype is peeled away, we're still left with all the same engineering problems but now we have added "Sometimes the tool hallucinates and gaslights you".
Having this RAG layer was always another thing to try for me. I haven't coded it myself, and super interested if this gives a real boost while working with Claude. Curious from anyone who have already tried the service, what's your feedback? Did you feel you're getting real improvements?
Wouldn’t call it just RAG though. Agentic discovery and semantic search are the way to go right now, so Nia combines both approaches. For example, you can dynamically search through a documentation tree or grep for specific things.
We call it agentic RAG. The retriever is an agent. It’s still RAG
Which would be much better than the techniques used in 2023. As context windows increase, combining them becomes even easier.
There are a lot of ways of how you can interpret agentic rag, pure rag, etc
Hard to follow gif of the thing working without explanation: check
Carousel of a bunch of random companies "using" the product without an indication how or in what capacity: check
List of a bunch of investors, as if that's meaningful to anyone who will use this product rather than people who would invest in it: check
The audacity to ask for actual money for a product that barely exists and is mostly a wrapper around other technology, who are investing in you: check
Claims of great internal success without any proof: check
Testimonials from random Twitter accounts who may or may not be bots or paid, who knows: check
To try it or even get a sense of how it works or what it is, you have to sign up: check
Congrats! Looks like you're set up for a trillion dollar valuation in the AI space!
To be less flippant and more constructive: if you're going to say the thing reduces hallucinations and provides 10x speedups in development, you need to provide proof immediately, or stop making the claim, otherwise there's 0 credibility for this product.
super smart. congrats on the launch!
I love Nia, keep it up Arlan
thank you haha!
Love it.