Hacker News Reader

Comment by GodelNumbering | Hacker News Reader

Interesting things Dirac does:

1. Uses an optimized version of Hash-Anchored edits for file editing (https://dirac.run/posts/hash-anchors-myers-diff-single-token)

2. Utilizes language's AST to decide what to fetch into context, entirely avoids large code file reads

3. Batches all operations. Does large number of reads/edits simultaneously (you can see a video demo for deepseek-v4-flash here https://www.reddit.com/r/LocalLLaMA/comments/1suhdki/tested_...)

4. Allows the model to execute code to analyze things on the fly, so the model can simply write bash/python/perl script to accomplish things where appropriate

5. A lot of context curation and opportunistic context updates, i.e. put into context anything that you are certain model would ask next

View on HN

I always wondered why AST's were not more of a part in both editing and scoping of changes/parsing code. I thought I read an article where they said 'grep' was just as effective. It kinda made sense for the case they were talking about.

I think we should use ASTS more, not for performance, but for easier code review.

Changes that are primarily code refactorings, like breaking up a large module into a bunch of smaller ones, or renaming a commonly-used class, are extremely tedious to review, both in LLM generated diffs and human-written PRs. You still have to do it; LLMs have a habit of mangling comments when moving code across files, while for a human, an unassuming "rename FooAPIClient to LegacyFooAPIClient" PR is the best place to leave a backdoor when taking over a developer's account. Nevertheless, many developers just LGTM changes like this because of the tedium involved in reviewing them.

If one could express such changes as a simple AST-wrangling script in a domain-specific language, which would then be executed in a trusted environment after being reviewed, that would decrease the review burden considerably.

I believe that with agentic development, the most important constraint we have is human time. Making the LLM better and faster won't help us much if the human still needs to spend a majority of their time reading code. We should do what we can to give us less code to read, without losing confidence in the changes that the LLM makes.

Grep is effective for the most part, except for situations like when you have huge codebases and the thing you're looking for is used in too many places both as symbol and non-symbol.

Another annoying thing about plain grep is, LLMs often end up pulling in bundled packages when using grep where 1 line is large enough to ruin the context window

> Grep is effective for the most part

It's very effective in well-written and well-designed code bases where concepts tend to be relatively well formed to not be named the same as everything else, so grepping for symbols give you good search results.

Projects where the god-object or core concepts are generic names like "Tree", "Node" or other things that are used everywhere, tends to be short of impossible to search with grep and friends.

Happened to have written both a tool and a blog post about the topic. It’s more about the different technical approaches you have in solving the problem but it might still interest you :)

https://www.context-master.dev/blog/deterministic-semantic-c...

Let me know, what you think

This is interesting - I have been working on the same thing, building contextual data, LSP-style.

I saw the tools page where if I understand right, `get-symbol-context` is actually the main useful tool for what you provide? The others seem more metadata it's easy to get already (?) but that tool provides the extra info.

I had been working on exposing mine as more high-level, ie multiple APIs to query different kinds of metadata about symbols, types, etc. But I am still not sure of the best approach, where my thinking was about not overloading the AI with too many different tools. They accumulate quickly.

I definitely share the same sentiment. I don’t want to overload the llm with many tools. Better to have a few opinionated and flexible ones, but yeah, keeping the balance is hard.

I would say the main two tools are get-symbol-context and get-repository-overview. The latter is actually the more complex and sophisticated one. I’m running some graph algorithms to rank the symbols in terms of relative importance based on centrality metrics, I.e. how well connected they are in the symbol graph.

The idea behind that is to allow the llm to infer the general structure and architecture of the project with just one tool call.

I guess you could reach a similar thing if you had some good Agents.md or docs detailing that for your project, but this was more meant to reach that on the fly.

The symbol-context tool is basically a graph query tool (without a dsl or cipher support yet), but yeah here the question is also whether it makes more sense to give the ai the possibility to run cipher queries itself or abstract it away in a thinner api.

The main underlying factor of my tool is however the graph that I’m building and the metadata which can be extracted from that (connections, type of connection, etc. ) :)

Whats the metadata you have in mind?

Metadata: I feel like LSP focuses on human-style things (like locating a symbol) which are useful, but not necessarily exactly what a LLM needs. Instead I want to do things like show the inheritance chain. Is a virtual method overriding something, being overridden later? What is the class / polymorphic situation? My feeling is that this will help understand the shape, plus, help some bugs.

So a query on a symbol would:

* Return its type declaration, not (just) location (and I'm considering some kind of summary version where it pulls in the ancestors too, so you directly see everything it has available not just the actual declaration, because leaf nodes in inheritance often don't add much and the key behaviour is elsewhere)

* Return info about inheritance, the shape of how this modifies other code and other code modifies it.

With variations when the symbol is a variable, a type, etc etc. I'm currently using treesitter for this, to bypass LSP and (for the language I'm working on) build a full symbol table and more, to get something closer to the LSP info you mention in your blog but not limited to what LSP makes available. I don't want to rely on a LSP server; I think first-class support per language is better. It's probably possible to generate this with a set of LSP calls, perhaps, but it might take some heuristics and guesswork and... :/

I do have a graph of file-level dependencies, but not yet a graph of what calls what at the symbol or type or method level. And while I build an index of all symbols I haven't yet sorted that by count.

I get the sense we're thinking along similar lines, with slightly different approaches?

Edit: if you would like to chat on this, I'm up for it! You can find me at my username at gmail (easy to lose emails there due to volume and spam!) or my profile has my website which has my LinkedIn (horribly, more reliable :D)

That sounds great, thanks for sharing your thoughts!

It sure sounds like we have similar things in mind. I basically try to build the proper graph representation of the code during runtime, so all caller/callee relationships plus type inheritance chains etc. This is basically what I call a semantic code graph in the blog post.

From the things I tried with tree-sitter I think I would have a hard time achieving the same because by nature tree-sitter can only make educated guesses on real connections and will run into problems, if things are named ambiguously.

But yeah, will definitely reach out and am looking forward to chatting :) Hope I find the time during this week!

It's not intuitive to humans, even after learning parsing theory. I can do basic name refactorings. I've even written neovim plugins to do 1 specific thing with the AST (dfs down and delete one subtree which I understand). Those are fine.

I would not be comfortable doing an on-the-fly "rewrite all subtrees that match this pattern" kind of edit.

It seems like a tool that's good for LLM's though.

"rewrite all subtrees that match this pattern" works really well in jetbrains, they call it structure search-and-replace.

I just realized that the fact that LLMs work so well for me in Clojure might be partly because of the clojure-mcp tools. They provide structural browsing and editing.

Has anybody thought about encoding AST tokens as LLM tokens, similar to how different words can have different meanings and that's reflected in their embedding?

Language keywords are almost definitely individual tokens. But I think you mean more than that. Basically replacing identifiers with special tokens as well. It’s worth a shot but there’s some practical problems.

Immediate downside is that mapping variable name to token and back would probably require indexing the whole codebase. You’d need a 1:1 mapping for every name that was in scope, and probably need to be clever about disambiguating names that come in and out of scope.

...I've said this a few times, and sometimes I get downvoted for it sometimes I do not... This is what happens when you only hire CS people with no real world engineering experience. Sure they can build ML models, but I see how they improve upon them after years, and its always some really old "lesson learned" elsewhere in the industry. There's a thousand projects that make things like Claude Code use less tokens, and edit more efficiently, and nobody at Anthropic or Codex implements a single one of these approaches.

It screams inexperience building real software. If I were anthropic I'd hire devs for Claude Code who arent just AI builders, but tool builders, who care about UX and systems.

Building ML model training and serving infrastructure is real-world engineering. Nevermind the user-facing apps and supporting services.

> Sure they can build ML models, but I see how they improve upon them after years, and its always some really old "lesson learned" elsewhere in the industry. There's a thousand projects that make things like Claude Code use less tokens, and edit more efficiently, and nobody at Anthropic or Codex implements a single one of these approaches.

They have fully internalized the bitter lesson; the result is they get better returns improving the next model over squeezing out performance from the current one.

> Building ML model training and serving infrastructure is real-world engineering. Nevermind the user-facing apps and supporting services.

Looking at Anthropics status info for the last 90 days only serves to prove that they aren't hiring the right people for the right roles.

> They have fully internalized the bitter lesson; the result is they get better returns improving the next model over squeezing out performance from the current one.

Sure, but there's so many things they could be doing that don't require tweaking the model directly to improve it, the community builds all sorts of tools that improve Claude Code directly, and yet nobody at Anthropic takes any initiative in those directions, it feels like either they don't care about building user-facing software, or they don't have any UX experience.

> Looking at Anthropics status info for the last 90 days only serves to prove that they aren't hiring the right people for the right roles

Look at any[1] dashboard over the past 6 months. It's less about the people working there an more about what leadership is demanding, industry-wide: Productivity* is the only metric that matters now - measured by how quickly teams under them can squirt out new features. Leadership desperately need a win because of the amounts invested, so stability becomes what it is now. Nothing to do with the rank and file, though it's Engineering that will be blamed when the time comes, I hope the multitudes of CTOs are earning enough to justify them being sacrificed to appease shareholders.

1. If your company has a SEV or SLA dashboard, look at it and compare the levels before and after mandated AI-productivity pushes by management.

Anchor based editing requires injecting new anchors to the context, and dirac does so via a diff. So how is this more efficient (token-wise) than search and replace?? Even at a single token per hash. Also, code is read more than written so these just add up. I experimented once with stable anchors, albeit longer than a single token, and found it a downgrade.

My conclusion is that the efficiency dirac sees comes mainly from showing file skeleton by default

I'm not sure one way or another but I've been using a related tool called Tilth by another poster here. It doesn't do anchor-based editing, but it does do syntax-aware search and will e.g. report the line range for function definitions, provide file outlines with line numbers on a file name match, etc.

https://github.com/jahala/tilth

This seems really good...going to test it :)

ohh this is really nice :) testing it

I have six patches that I will at some point upstream, the main bug/surprise is the .gitignore behavior is not what's documented, but even without it seems to work quite well.

> My conclusion is that the efficiency dirac sees comes mainly from showing file skeleton by default

how hard do you think it would be to bring this optimization to oh-my-pi and opencode? I am testing dirac and it's very cool but the tooling isn't there yet comparing to oh-my-pi in terms of UX.

Would love some more feedback on this. Where do you think are major gaps?

Thinking back, I might have jumped the gun here. I can't objectively evaluate UX without spending more time with the tool. I'll try to daily drive it a bit before I can form an opinion.

> Batches all operations. Does large number of reads/edits simultaneously...

I wasn't sure what this meant, so I looked at the source. It seems to be referring to tool APIs being designed around taking multiple targets as a list parameter, instead of hoping the model makes appropriately parallel tool calls. (This matches my experience btw, models are reluctant to make a large number of parallel calls simultaneously, and this seems more pronounced with weaker models.)

I think Anthropic may have mentioned this first, this pattern is also something my custom agent's tools are designed around, pretty sure I picked it up from them.

For the hash-anchored edits, sharing here Can Bölük's original post about the idea https://blog.can.ac/2026/02/12/the-harness-problem/

> Utilizes language's AST to decide what to fetch into context,

Does that mean that it's only going to work with certain langauges for which it has parsers available?

It uses tree-sitter wasms. Currently, 14 languages are available (https://github.com/dirac-run/dirac/tree/master/src/services/...)

The agent would work even without a language parser, just that the AST-based functionalities won't work

Yes

Is there a complete list of the tools somewhere? I'm interested in how you chose to expose the AST specifically. In my own harness attempts I wanted to keep the number of tools absolutely minimal and briefly experimented with including an AST lib to use via an execute_python tool (plus some examples in the system prompt). Results were mixed though, with most models preferring ripgrep.

It would be really cool to do a causality investigation to determine which one of these boosts it so much / quantify how much each matters. Who knows, they may all interact in a sum-is-greater-than-parts way that only improves the score when shipped altogether.

Instead of burning tokens on SOTA models, why not use a dirt-cheap specialised model for file editing?

Where the SOTA model just makes a cheaper model to make edits, and it does so.

Yeah I also believe that there are plenty of efficiency gains available by using different models for different tasks. Reasoning models such as opus should only be used for the main planning and decision flows, but sub operations (exploring, applying edits etc etc) could be delegated to smaller and cheaper models. You also end up with a much smaller context for the main big model

Did you consider incorporating ast-grep or gritql?

Congratulations, great work.

Can't speak for OP but I tried providing ast-grep in the execution context of an execute_bash tool, but even with pretty aggressive steering most models just don't seem to use it a lot. More expensive/SOTA models or higher reasoning increases the chances but lowers speed and raises cost. Maybe due to training bias for exploration tasks?

Yes, I've tried this passive approach too and didn't dig much further after that. I thought maybe they'd figured out something more intentional in the prompting to enable these kinds of approaches.

I have a hunch model proficiency for a given CLI tool very much correlates with how many StackOverflow answers and blog entries providing examples for it there are...

My sense is that we're at a tipping point where instruction following is getting good enough to disrupt these old habits

Not really, but interested in trying them out for a future version, especially gritql.

How are the two token anchors chosen when the initial 1700 single token anchors run out? I'm assuming just a 2 word combination from the 1700.

That's correct

Reply on news.ycombinator.com