Show HN: OSS Agent I built topped the TerminalBench on Gemini-3-flash-preview

Harness was https://www.npmjs.com/package/dirac-cli

Since Dirac is Cline's heavily modified fork, it supports all models Cline supported, including Qwen and all popular open/closed models

As a matter of fact, I am trying to run terminal bench 2.0 using some OSS models at the moment but the slow inference speeds are causing tasks to timeout

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

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

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.

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.

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

Let me know, what you think

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 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?

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)

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!

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.

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.

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.

> 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.

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.

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

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

Congratulations, great work.

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.)

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

https://github.com/jahala/tilth

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.

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

Curious to know if this has been an issue with your AST approach on larger projects?

The hash line based numbering is very interesting too (though I see on Opus 4.5+ far far fewer editing errors).

I've often thought that even if model progress stopped today, we'd still have _years_ of improvements thru harness iteration.

For AST, it uses tree-sitter WASMs (ships them with the package), and maintains queries (https://github.com/dirac-run/dirac/tree/master/src/services/...)

To keep performance fast, it stores the symbols DB (using sqlite) in the workspace's directory and incrementally updates it based on timestamps. Then it uses this DB to resolve symbol queries

Like even "full" Visual Studio and Resharper have issues with this. Eg, you start editing file x, 'intellisense' runs, says there are loads of errors... because you haven't finished editing yet.

How does this perform in day to day coding tasks, outside of benchmarks?

README has eval of 8 tasks over 7 agents (including both pi and omp). Pi-mono costs second lowest across the 8 tasks (after Dirac) but occasionally misses produces incomplete changes.

Interestingly, 2 tasks where pi missed some changes both were the tasks that benefitted from AST symbol understanding (e.g. find all instances of things that refer to this symbol and change those things). Since pi relies on bash type tooling, it missed some occurrences

Any ideas?

In my tests, it worked using gpt-5.4 for me and I assumed gpt-5.5 is not available to me because I am on the free plan

Do you have the subscription that allows 5.5? If so, I can look into what changed in API. Sorry I rarely use openAI so it is a bit of an untrodden path

That was the issue, 5.4 works just fine.

Support for service: priority (GPT /fast mode) would also be cool!

Is there a leaderboard out there comparing harness results using the same models?

History indicates you can't tool and harness your way to effectively competing against a smarter model with more compute.