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
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.
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
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.
"Hey everyone, you know that tech that so many of you mentioned has made your work miserable and you're worried might put you out of a job? I think I made it even better! And I didn't even get paid for it! Hah!"
I haven't tried it, but I'm curious why you decided to implement a whole new harness over just writing extensions in pi. From whatever I've done with pi so far, the extension api is quite extensive. Hash anchored edits, for example, can definitely be implemented in pi. Anyhow, thank you for showing us your project and will be checking it out later. Cheers!
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
Very interesting! I've often thought static analysis could really help agents (I wrote this last summer: https://martinalderson.com/posts/claude-code-static-analysis...), but despite being hyped for LSPs in Claude Code it turned out to be very underwhelming (for many of the reasons that they can be annoying in a "real" IDE, ie static analysis starts firing mid edit and complaining and cached analysis getting stuck).
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.
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
Yes I understand, but do you not have issues that it drifts out of date and confuses the agents (especially on longer running tasks)?
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.
Stared it. will try it later. one question though, to make it simpler for me, in what tasks does this model shine, how do you improve the score?
I already use some skills to cut down CC costs, like caveman, rtk cli and a few others. just want to understand
Assuming you logged in with OAuth, I am guessing you are trying to use gpt-5.5?
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
Sure. Dirac is just a fork of the Cline harness and obviously OpenCode could take the same techniques and implement them. I don't know how difficult it would be to implement them in OpenCode, but given that Dirac and OpenCode are both open source, a future version of OpenCode could always be a re-branded Dirac (I'm sure there are ways to implement Dirac's techniques without having to completely replace OpenCode's underlying code base, but this illustrates that at the extreme, they could clearly just take Dirac in its entirety to get the same results).
Sorry I couldn’t really figure out if this was a harness, a fine tuned model, or both. Can we use Qwen with this for example? Is the performance expected to be better in that case?
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.
Anyone working on this is anti-developer.
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
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.
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
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