Show HN: OSS Agent I built topped the TerminalBench on Gemini-3-flash-preview
Scored 65.2% vs google's official 47.8%, and the existing top closed source model Junie CLI's 64.3%. Since there are a lot of reports of deliberate cheating on TerminalBench 2.0 lately ( https://debugml.github.io/cheating-agents/ ), I would like to also clarify a few things 1. Absolutely no {agents/skills}.md files were inserted at any point. No cheating mechanisms whatsoever 2. The cli agent was run in leaderboard compliant way (no modification of resources or timeouts) 3. The full terminal bench run was done using the fully open source version of the agent, no difference between what is on github and what was run. I was originally going to wait for it to land on the leaderboard, but it has been 8 days and the maintainers do not respond unfortunately (there is a large backlog of the pull requests on their HF) so I decided to post anyways. HF PR: https://huggingface.co/datasets/harborframework/terminal-ben... It is astounding how much the harness matters, based on this and other experiments I have done.
Discussion Highlights (20 comments)
aetherspawn
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?
GodelNumbering
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
nthypes
No CLI? Only VSCode extension?
nthypes
Can't OpenCode reach the same just developing this as a feature or plug-in? Like anchored edit?
martinald
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.
Mashimo
Interesting. Would love a comparison to pi.dev (Not Ohmypi) How does this perform in day to day coding tasks, outside of benchmarks?
bryanhogan
If I understand correctly, this is a heavily improved Cline fork? Does that mean features such as plan and act mode are also still there?
blueTiger33
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
snqb
how well does it do on frontier models like Opus 4.6?
redrove
I keep trying to use dirac-cli with codex and it won't work: Error: Codex API error: Codex API request failed: 400. Any ideas?
adyavanapalli
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!
neonstatic
I am a bit confused. What languages does it help with? You mention AST manipulation, so I am assuming it's not universally applicable, e.g. to Rust?
mdasen
It's really interesting how much the AI harness seems to matter. Going from 48% via Google's official results to 65% is a huge jump. I feel like I'm constantly seeing results that compare models and rarely seeing results that compare harnesses. Is there a leaderboard out there comparing harness results using the same models?
avereveard
"astounding how much the harness matters" is the right read and it should be the lasting one. the model is rentable, the prompts are rentable, the benchmark numbers are mostly a function of the harness around them. swapping Gemini for Sonnet underneath the same harness has a smaller bench delta than swapping the harness around the model. the cheating-agents post you linked is the same observation through a different lens, the harness is what's being measured, the model is just the substrate. that said context management seem to be solving today model problems, more than being an universal property, and will probably be obsoleted a few model generations down the road, as tool obsoleted RAG context injection from question embeddings.
michelhabib
woow, looks very good. I'm wondering if you do any optimizations for cli in general, since you're not using MCP. I'm building my own CLI for AI Agents, and was always concerned with context rot.
Aeroi
harness definitely makes a difference for the benchmarks. I ran my agent Camera Search against a few benchmarks and was able to beat Opus 4.7. I created a real world benchmark, for mining, oil&gas, construction ect. called FieldOps-bench and it basically proves that vertical agents and specialized harness, tool, systems outperforms SOTA models alone still.
kha1n3vol3
I am using dirac with Kimi 2.6 for refactoring a rust codebase. I have a Clean Architecture design which is being reinforced. The scope of work is laid out in a Beads epic with sub-issues. The planning was done with gpt5.5, and gpt5.5 is checking the work is complete. I have found that dirac is more productive on large codebase refactoring than OpenCode which actually trashed the .rs file and had to revert the code.
nzoschke
I’ve haven’t had great experiences with Gemini for coding yet. I’m doing reasonably simple full stack Go apps. Tried Gemini-ClI, antigravity, Pi. The problems I’ve experienced are less adept at picking the right bash commands to build and test the Go app, and not following idiomatic Go or code base patterns for changes. A skill hasn’t helped much. Will need to try this and open code next.
scoopdewoop
The Hash-anchor edit guy! Sincerely great idea, I used it in my own toy harness to good effect. I just checked this out, never tried it before, and its great! Clearly a well-iterated design with good choices made. It is so refreshing to see real FOSS and not a grift. Simple openrouter api key, and I'm going. This is what I'm using from now on. You are doing the best work in this space.
deaux
1. Would be good to benchmark at least one other model from a different family to see if it indeed generalizes. Minimax 2.7 seems a good candidate to keep it affordable. Until then we can't really tell if it's just overfit on Gemini 3 Flash. 2. Until then your landing page needs to mention all the numbers are just from running on Gemini 3 Flash. Currently there's no mention at all of Gemini. 3. Assuming that cheaper also means faster in this case where model is equal? If so, then why not add this to the benchmarks to highlight another advantage - time until completion of the tasks. If it's the opposite and it takes longer (seems unlikely), then it would be transparent to note this. 4. Would be good to note if it does or does not support skills, (nested) AGENTS.md, MCP and so on for people considering migrating.