Coding agents are only as good as the context you give them. General models are trained on public code and documentation that is often old, and they usually have no idea what is inside your actual repo, internal wiki, or the exact version of a third party SDK you use. The result is very familiar: you paste URLs and code snippets into the prompt, the agent confidently uses an outdated API or the wrong framework version, and you spend more time verifying and correcting it than if you had written the code yourself. Once models are good enough at generating code, feeding them precise, up-to-date context becomes the bottleneck.
I ran into this pattern first on my own projects when (a few months ago) I was still in high school in Kazakhstan, obsessed with codegen tools and trying every coding agent I could find. I saw it again when I got into YC and talked to other teams who were also trying to use agents on real work.
The first version of Nia was basically “my personal MCP server that knows my repos and favorite doc sites so I do not have to paste URLs into Cursor anymore.” Once I saw how much smoother my own workflow became, it felt obvious that this should be a product other people could use too.
Under the hood, Nia is an indexing and retrieval service with an MCP interface and an API. You point it at sources like GitHub repositories, framework or provider docs, SDK pages, PDF manuals, etc. We fetch and parse those with some simple heuristics for code structures, headings, and tables, then normalize them into chunks and build several indexes: a semantic index with embeddings for natural language queries; a symbol and usage index for functions, classes, types, and endpoints; a basic reference graph between files, symbols, and external docs; regex and file tree search for cases where you want deterministic matches over raw text.
When an agent calls Nia, it sends a natural language query plus optional hints like the current file path, stack trace, or repository. Nia runs a mix of BM25 style search, embedding similarity, and graph walks to rank relevant snippets, and can also return precise locations like “this function definition in this file and the three places it is used” instead of just a fuzzy paragraph. The calling agent then decides how to use those snippets in its own prompt. One Nia deployment can serve multiple agents and multiple projects at once. For example, you can have Cursor, Claude Code, and a browser based agent all pointed at the same Nia instance that knows about your monorepo, your internal wiki, and the provider docs you care about. We keep an agent agnostic session record that tracks which sources were used and which snippets the user accepted. Any MCP client can attach to that session id, fetch the current context, and extend it, so switching tools does not mean losing what has already been discovered.
A lot of work goes into keeping indexes fresh without reprocessing everything. Background workers periodically refetch configured sources, detect which files or pages changed, and reindex those incrementally. This matters because many of the worst “hallucinations” I have seen are actually the model quoting valid documentation for the wrong version. Fixing that is more about version and change tracking than about model quality.
We ship Nia with a growing set of pre-indexed public sources. Today this includes around 6k packages from common frameworks and provider docs, plus package search over thousands of libraries from ecosystems like PyPI, npm, and RubyGems, as well as pre indexed /explore page where everyone can contribute their sources! The idea is that a new user can install Nia, connect nothing, and still get useful answers for common libraries. Then, as soon as you add your own repos and internal docs, those private sources are merged into the same index. Some examples of how people use Nia so far: - migrating from one payments provider or API version to another by indexing the provider docs plus example repos and letting the agent propose and iterate on patches; - answering “how do I do X in this framework” by indexing the framework source directly instead of relying only on official docs that might be stale; - turning an unfamiliar public codebase into a temporary wiki to self onboard, where you can ask structural questions and jump to specific files, functions, or commits; - building a browser agent that answers questions using up to date code and docs even when the public documentation lags behind.
Nia is a paid product (https://www.trynia.ai/) but we have a free tier that should be enough for individuals to try it on real projects. Above that there is a self-serve paid plan for heavier individual use, and organization plans with higher limits, SOC 2, seat based billing, and options for teams that want to keep indexing inside their own environment. For private GitHub repos we can clone and index locally so code does not leave your infrastructure.
We store account details and basic telemetry like query counts and errors to operate the service, and we store processed representations of content you explicitly connect (chunks, metadata, embeddings, and small graphs) so we can answer queries. We do not train foundation models on customer content and we do not sell user data. Moreover, I can see Nia play out in the larger context of the agents space due to the global problem of providing reliable context to those systems. Early signals show that people are already using Nia for healthcare data, cloning Paul Graham by indexing all of his essays and turning him into an AI agent, using Naval’s archive to build a personalized agent, and more.
I would love to get Nia into the hands of more engineers who are already pushing coding agents hard and see where it breaks. I am especially interested in hearing about failure modes, annoying onboarding steps, places where the retrieval logic is obviously wrong or incomplete, or any security concerns I should address. I will be in the thread to answer questions, share more technical details, and collect any brutal feedback you are willing to give!
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?
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.
> 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.
`@Docs` — will show a bunch of pre-indexed Docs, and you can add whatever you want and it’ll show up in the list. You can see the state of Docs indexing in Cursor Settings.
The UX leaves a bit to be desired, but that’s a problem Cursor seems to have in general.
+ as I mentioned above there are many more use cases than just coding.Think docs, APIs, research, knowledge bases, even personal or enterprise data sources the agent needs to explore and validate dynamically.
From a business point of view I am not sure how you get traction without being 10x better than what Cursor can produce tomorrow. If you are successful the coding agents will copy your idea and then people being lazy and using what works have no inventive to switch.
I am not trying to discourage. More like encourage you to figure out how you get that elusive moat that all startups seek.
As a user I am excited to try it soon. Got something in mind that this should make easier.
To be reductionist, it seems the claimed product value is "better RAG for code."
The difficulties with RAG are at least:
1. Chunking: how large and how is the beginning/end of a chunk determined
2. Given the above quote, how much or many RAG results are put into the context? It seems that the API caller makes this decision, but how?
I'm curious about your approach and how you evaluated it.
How does Nia handle project-specific patterns? Like if I always use a certain folder structure or naming convention, does it learn that?
Mine's a simple BM25 index for code keyword search (I use it alongside serena-mcp) and for some use cases the speeds and token efficiency are insane.
https://gitlab.com/rhobimd-oss/shebe#comparison-shebe-vs-alt...
Vaporware.
Select your coding agentCursor Installation method Local Remote Runs locally on your machine. More stable. Requires Python & pipx.
Create API Key test Create Organization required to create API keys
i can not create api key? the create button is grey and can not be pressed.
I just made a video (2) on how I prompt with Claude Code, ask for research from related projects, build context with multiple documents, then converge into a task document, shared that with another coding agent, opencode (with Grok or GLM) and then review with Claude Code.
nocodo is itself a challenge for me: I do not write or review code line by line. I spend most of the time in this higher level context gathering, planning etc. All these techniques will be integrated and available inside nocodo. I do not use MCPs, and nocodo does not have MCPs.
I do not think plugging into existing coding agents work, not how I am building. I think building full-stack is the way, from prompt to deployed software. Consumers will step away from anything other than planning. The coding agent will be more a planning tool. Everything else will slowly vanish.
Cheers to more folks building here!
1. https://github.com/brainless/nocodo 2. https://youtu.be/Hw4IIAvRTlY
I started out with coding agents specifically because it came from personal pain of how horrible they are with providing up to date context.
There are a lot of ways of how you can interpret agentic rag, pure rag, etc
re local, I do local for certain companies!
In our internal benchmark on bleeding edge SDK and library features, Nia produced the lowest hallucination rate among the context providers and search tools we tested (context7, exa code, etc), and I wrote up the setup and results in a separate blog post: https://www.nozomio.com/blog/nia-oracle-benchmark
w nia the agent can dynamically search, traverse, and validate information outside the local project so it never hallucinates against out-of-date or incomplete sources.
"Don't be snarky."
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
[see https://news.ycombinator.com/item?id=45988611 for explanation]
We don't do a perfect job of this, because (1) Launch HN coaching is on top of our main jobs running HN and we only have so many hours; and (2) startup founders' priority is working on their startup (as it should be!). They only have so many cycles for reworking everything to suit HN's preferences, which are idiosyncratic and at times curmudgeonly or cynical. Curmudgeons and cynics can't be convinced in the first place so it's not a good idea for a founder to put too much time into indulging them.
Some of what you're saying here boils down to that their home page shouldn't have any marketing tropes at all (e.g. testimonials, companies-using-us, etc.). I don't like those tropes either, but this is an example of what I mean by an idiosyncratic preference. Companies do that kind of thing because, obviously, it works. That's how the world is. The only thing that you accomplish by angrily blaming a startup founder for doing standard marketing is to make the discussion dyspeptic and offtopic. And yes, I do use the word "dyspeptic" too much :)
> Curmudgeons and cynics can't be convinced in the first place so it's not a good idea for a founder to put too much time into indulging them.
I'd say we're just not convinced by marketing lingo and puffery. I was convinced by the simple README containing code and transparent evidence that a fellow HNer put up in their personal capacity, so maybe you can direct the Nia team to that as an example of how to properly convince curmudgeons and cynics.
Personally my tastes are much the same as yours, but we're asking for too much if we want startups to stop doing normal marketing.
What's the gif supposed to tell me? It's supposed to demo the product and give me a feel for its capabilities. But it just flits around and goes so fast, offers zero explanation for anything, it just leaves me disoriented. So at minimum, this needs captions and it needs to go about 2x slower. But really, this one GIF should not be the most substantive element on the first page relating to the actual product and what it does. Trust lowered.
Moving on to the "company carousel", which is trying to say "these other companies trust us so you should too". They're trying to ride on the reputations of Stanford, Cornell, Columbia, UPenn, Google, etc. as a sort of pseudo-endorsement, because they cannot post real endorsements from these institutions, because they do not exist (doesn't YC have legal counsel to tell them this is illegal?). How are engineers using Nia at Stanford? We don't know, Nia will not say, likely because no one at Stanford is using it in any real capacity that is impressive enough to put on the front page of the website. If they were, then why wouldn't Nia tell us about that rather than just flashing the Stanford logo? So the logo suffices, and I guess the more logos the better. Trust lowered.
Next the investor list: who is this for and what does it communicate? It appears to be a list of Chiefs, VPs, Co-Founders, and various funds who are deemed to be "world class", which is just another parade of logos but for a different audience, likely other investors who know these people. Maybe this speaks to some people in terms of the project having a solid financial backing but that's a smokescreen to distract you from the fact there's no actual business plan here aside from running on the VC treadmill and hoping to get acquired by one of your customers and/or investors. Trust lowered.
Then we get to the Twitter parade, which is a third instance of "just trust us bro". And it includes such gems as "Can confirm, coding agents go hard" and "go try Nia, go into debt if you have to". Testimonials are for products I can't try myself, this seems like something that can be demoed, so why isn't it? Why did they opt to devote all this space to show a bunch of random people saying random uninteresting things about their product, rather than use the space to say more interesting things about their product? Because the testimonials are a distraction from the actual product. Trust lowered.
Again, I'm left asking: Why do I have to listen to and trust these other people if the technology is so good? Why am I halfway down the page reading this thing, and I've yet to hear any specifics about how this thing works or what it does for me. I was told other people are using it but not how, I was told other people invested in it but not how much, and I was told some companies are maybe using it but not in what capacity.
So in summary, this page is: "Look how shiny! You trust us. No really, you can trust us! Seriously, look at all these people, who say you can trust us, you seriously can! Now give us money."
So to answer your question:
> what would be the correct way to do it according to your checklist.
Don't do any of the things that were done, and instead lead with the product. Prove all claims made. If a claim can't be proven don't make it. Stand behind your technology rather than testimonials.