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

Great job and congrats! Working on my own harness has been one of my favorite side projects in the past couple of weeks, of course I never finish anything... But I'm very interested in your experience with the following:1. Context management - specifically pruning old tool call responses, truncation of tool output and automatic compaction. Those have worked pretty great for me, benefits of reducing context greatly seem to outweigh gains from "remembering" everything. I always leave short summaries though.2. "Subagents" - my latest attempts revolve around not exposing any tools for the main agent at all, except for a run_agent tool where the subagent has access to the classic search/execute/fetch tools. My theory is that if subagents return concise summaries this would automatically keep the parent agent context clean for much longer. Still experimenting though, writing prompts for subagents may also be too far outside of the current training sets.

Very interesting, especially the harness point, how much of performance is in the wrapper tools (when I almost run out of credits, I change my model to a smaller one and try to give it more structured prompts; very often gpt-5.4-mini with structure works better than gpt-5.4 with vibes)This inspired me to start a "skill distillery" [0] where I take good agent workflow ideas and turning them into small, inspectable/installable skills.The first one is dirac-workflow, based on Dirac's structural code workflow. It's not a Dirac clone tho, it has no runtime, persistent AST index, hash-anchor editing engine, or benchmark harness. Just a small AST helper and the workflow discipline as a portable skill.I also dogfooded it on the Dirac repo itself and included a short report.Would appreciate feedback from the original author, if the prompts and tools [1] are representative.[0] https://github.com/ouatu-ro/skill-distillery[1] https://github.com/ouatu-ro/skill-distillery/blob/main/skill...

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.

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.

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.

"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.

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?

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!

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?

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 reads3. 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 appropriate5. A lot of context curation and opportunistic context updates, i.e. put into context anything that you are certain model would ask next