7 ms·

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

• aetherspawn 6mo ago
  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 6mo ago
    The model was the default gemini-3-flash-preview. Harness was https://www.npmjs.com/package/dirac-cli 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
• phoebe_builds 6mo ago
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• GodelNumbering 6mo ago
  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 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_deepseek_v4_flash_with_some_large_code/ 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
  • deskamess 6mo ago
    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.
    • GodelNumbering 6mo ago
      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
      • embedding-shape 6mo ago
        > 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.
• nthypes 6mo ago
  No CLI? Only VSCode extension?
  • GodelNumbering 6mo ago
    Cli too (you can't run tbench without cli as it runs in an isolated docker env) `npm install -g dirac-cli`
• nthypes 6mo ago
  Can't OpenCode reach the same just developing this as a feature or plug-in? Like anchored edit?
  • mdasen 6mo ago
    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).
• martinald 6mo ago
  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/ 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.
  • GodelNumbering 6mo ago
    Wrt LSP, it uses the default LSP mechanism of the ide provider. 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/tree-sitter/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
    • martinald 6mo ago
      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.
      • tuo-lei 6mo ago
        same issue from the other side. when a human is editing, the LSP fires mid-keystroke and shows bogus errors for a second, whatever. with an agent doing 5 edits in a row, the symbol DB is always behind by one edit, so the next lookup pulls stale references. you can re-index synchronously after each edit but that kills the batching speed.
• Mashimo 6mo ago
  Interesting. Would love a comparison to pi.dev (Not Ohmypi) How does this perform in day to day coding tasks, outside of benchmarks?
  • GodelNumbering 6mo ago
    https://github.com/dirac-run/dirac#-evals https://github.com/dirac-run/dirac#-evals 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
    • howdareme 6mo ago
      Going to assume you didnt capture the data but could you add time taken to completion for each if you have it?
    • messh 6mo ago
      re. bash type tooling-- it doesnt mean an agent cannot use ast: using treesitter cli this should be perfect possible
      • Cilvic 6mo ago
        I assume that this benchmarks where done without any modifications to the default open-sourced harness. treesitter CLI would be an extra plugin for pi-mono, put I'd be equally curious about whether it would accomplish the task.
• bryanhogan 6mo ago
  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?
  • GodelNumbering 6mo ago
    Yes, plan+act mode is one thing I loved about Cline!
• deleted 6mo ago
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• dk970 6mo ago
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• blueTiger33 6mo ago
  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
  • GodelNumbering 6mo ago
    I did limited testing using Sonnet on CC vs Sonnet on Dirac. I could not confirm the costs however
• snqb 6mo ago
  how well does it do on frontier models like Opus 4.6?
  • GodelNumbering 6mo ago
    I have only done functionality testing, no benchmark testing on Opus (decided to pay my rent instead)
• redrove 6mo ago
  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?
  • GodelNumbering 6mo ago
    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
    • redrove 6mo ago
      Yes I'm on ChatGPT Pro (OAuth) and I'm trying to use gpt-5.5-xhigh. That was the issue, 5.4 works just fine. Support for service: priority (GPT /fast mode) would also be cool!
      • GodelNumbering 6mo ago
        Will fix this soon. Please feel free to create a github issue in the meantime.
      • GodelNumbering 6mo ago
        Gpt 5.5 is now fully supported using codex and api
        • redrove 5mo ago
          cool!
• adyavanapalli 6mo ago
  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!
  • GodelNumbering 6mo ago
    A few months ago one afternoon I was very frustrated with how slow Cline was being so decided to look under the hood. Decided to make a couple of changes. Got sucked in. About 70k lines of change, another 40k lines of deletions and two months later, here we are.
    • mring33621 6mo ago
      The best kind of project. I'm trying this today. I've been happily using OpenCode so far.
  • satvikpendem 6mo ago
    I've been looking into local LLMs and new harnesses recently, how good is Pi compared to OpenCode, I'm seeing that it's a lot better? What are the best models and customizations for it to fully utilize it?
    • deleted 6mo ago
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• neonstatic 6mo ago
  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?
  • deviation 6mo ago
    AST (Abstract Syntax Tree) is essentially a search algorithm to better help the agent do it's job.
  • GodelNumbering 6mo ago
    Currently, 14 most popular languages (https://github.com/dirac-run/dirac/tree/master/src/services/tree-sitter/queries https://github.com/dirac-run/dirac/tree/master/src/services/...). Easy to add more languages
• tommy29tmar 6mo ago
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• mdasen 6mo ago
  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?
  • GodelNumbering 6mo ago
    I really wish there was! I thought of even creating one but it would be conflict of interest
  • manx 6mo ago
    We probably want to compare the cartesian product of model+harness.
  • culi 6mo ago
    Maybe the future isn't a human-like centralized intelligence but an octopus-like decentralized intelligence where more focus is placed on making the harness itself "smart"
    • dominotw 6mo ago
      That would be counter to AI company goals. They want harness to be dumb and models to be smart so they can sell models.
      • SwellJoe 6mo ago
        https://en.wikipedia.org/wiki/Bitter_lesson https://en.wikipedia.org/wiki/Bitter_lesson History indicates you can't tool and harness your way to effectively competing against a smarter model with more compute.
      • satvikpendem 6mo ago
        Not really. Anthropic for example sells both the harness and the models as a unified kit via Claude Code, it is in their best interest to make sure both parts work as well as possible, via reinforcement learning of previous usage as well for new model performance increases.
• avereveard 6mo ago
  "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.
  • deleted 6mo ago
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  • himata4113 6mo ago
    That's why ARC-AGI-3 doesn't allow the use of a harnesses. The model has to create the harness instead.
    • vova_hn2 6mo ago
      The model is not allowed to create a harness either, I think.
      • himata4113 6mo ago
        it can, it just has to be within the same 'session', but it's mostly limited to scratch notes afaik since there's no python or bash, yah if there's no way to execute code there's no real way to build a harness.
    • grzracz 6mo ago
      Seems completely backwards to me. This is like judging Formula 1 just by the raw power of the engine. The rest of the car has just as much engineering, if not more.
      • 6mo ago
• michelhabib 6mo ago
  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.
• johnwhitman 6mo ago
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• Aeroi 6mo ago
  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 6mo ago
  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.
  • amunozo 6mo ago
    For gpt-5.5, do you use Dirac, too?
• nzoschke 6mo ago
  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.
• builderminkyu 6mo ago
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• scoopdewoop 6mo ago
  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 6mo ago
  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.
  • GodelNumbering 6mo ago
    Good points. 1. I have been trying to benchmark openweights models but keep running into timeouts due to slow inference (terminal bench tasks have strict timeouts that you are not allowed to modify). Posted my frustration here https://www.reddit.com/r/LocalLLaMA/comments/1stgt39/the_frustrating_inference_capacity_issue_with/ https://www.reddit.com/r/LocalLLaMA/comments/1stgt39/the_fru... 2. Done (updated github readme) 3. Yes, on an average the times were shorter, but I did not benchmark it because at random times, the model outputs get slower, so it is not a rigorous benchmark 4. Added info on this too
    • deaux 6mo ago
      1. Good point, didn't know about the timeouts, that's rough for the benchmarks. Though they IMO don't necessarily be "SWE-official" to have value, if the only difference is disabling those. 3. Maybe you could instead provide a measure of output tokens used (including thinking), as that's a reasonable measure for speed. I guess input tokens would be similar unless the AST usage and hashes etc increases them a lot? Seems unlikely.
  • Clueed 6mo ago
    I tried it with minimax 2.7 and it really didn’t like the editing tool; collapsing rather quickly to using sed to edit files. I guess it makes sense that models don’t generalize perfectly to arbitrary tools but are biased to those in its training data, especially for a common operation like editing files. The Gemini family might be a good pick here since it generally underperforms in agentic tasks (due to lack of training data or other reasons) and thus might not have this inherent bias towards specific tools.
• npodbielski 6mo ago
  Ha! I had an idea to do something like that myself over the weekend after trying Junie and Mistral to write some test for my personal project, that took literally hours! because Qwen 3.5 I am using locally can run 10k prompt for 10mins. Which should not be the case if agent would ask really simple questions like: - what tool you need? - what would be parameters for the tool - what method you want to read? instead of sending few kilobytes of build output and waiting for response. Oh well.. Good thing someone already did that!
• gobdovan 6mo ago
  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 https://github.com/ouatu-ro/skill-distillery [1] https://github.com/ouatu-ro/skill-distillery/blob/main/skills/dirac-workflow/scripts/ast_tool.py https://github.com/ouatu-ro/skill-distillery/blob/main/skill...
• sally_glance 6mo ago
  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.
  • GodelNumbering 6mo ago
    Thanks. 1. Context management - Don't bother with pruning unless your API doesn't support caching. Every prune breaks the cache and you lose the 90% discounted caching rate 2. I did some work improving Cline's subagent feature that Dirac inherited. In my experience, not all models are trained effectively to delegate work, so YMMV. A common pitfall to watch is, what happens if one or more subagents get stuck in a loop or for whatever reason don't return? You need a mechanism to control them from the main agent
    • hedgehog 6mo ago
      It depends where you prune and how the specific prefix cache you're targeting works. Pruning or condensing recent items that are unnecessary probably pays for itself.
    • sally_glance 6mo ago
      1. For me pruning is a bit less about cost than performance. Recent research suggests lower context size is nearly always better, and many harnesses implement a sliding window for tool output pruning. Also not every provider supports caching, and if they do it might have expired (especially on restored sessions). 2. That's a good hint, I'm currently only trying with tighter turn and token limits for subagents and an error summary on exceeding them. Not sure how else (besides steering and prompt engineering) to ensure the subagent doesn't go wild...
• adyavanapalli 6mo ago
  I had a chance to look at this and noticed you were sending telemetry to an endpoint you control: https://dirac.run/v1/event https://dirac.run/v1/event. It doesn't seem like you're sending anything obviously sensitive or doing anything in bad faith (though, I do see api errors being sent, which could potentially leak sensitive info), but you gotta admit that that's scary seeing you as the sole dev for this. Plus, it's opt out too. Sorry, it's no go for me.
  • deviation 6mo ago
    Here's all the telemetry: 1. Telemetry to dirac.run/v1/event — Sends machine ID, token usage, model info, events, errors (first 500 chars), and platform info. Hardcoded API key. Defaults to opt-in (setting is "unset", not "disabled"). 2. Feature flags from dirac.run/v1/event/decide — Polls every 60 minutes with your machine ID. Always enabled, independent of telemetry opt-out. No way to disable without code changes. 3. Web tools route through api.dirac.run — Web search and web fetch tools proxy through Dirac's own API server, sending your request content plus system headers (platform, version, machine ID). 4. Model list fetches — Calls OpenRouter, HuggingFace, Groq, etc. for model listings even when using the Anthropic provider.
    • GodelNumbering 6mo ago
      > Web tools route through api.dirac.run This is something that needs to be deprecated entirely. The web fetch tool no longer is used or works. There is nothing even listening at api.dirac.run. This was the result of me stretching my capacity too thin and bulk renaming cline.bot to dirac.run UPDATE (+1h): both Web search and web fetch tools are now nuked.
  • GodelNumbering 6mo ago
    Thanks! Since it is a Cline fork, the telemetry mechanism is inherited. I left it as it might help debug issues. There is no evil purpose behind it nor does it create or store any PII
• deviation 6mo ago
  Nice work. I adopted this to use with my workplace's LLM proxy with a few small changes to the api/config files. Works flawlessly.
• ronin_niron 6mo ago
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• davidkunz 6mo ago
  I would like if some of that functionality is extracted in CLI tools. Then every coding agent can use it.
• anandkrshnn 6mo ago
  Really impressive results. The point about the harness mattering more than the model is spot on — we've seen similar patterns in our own work. One thing that stood out to me is your use of hash-anchored edits + AST-based context selection. We're building something in a similar direction with the Sovereign AI Stack, but with a stronger focus on governance and verification. Curious — did you run into issues with context drift when using AST queries on very large codebases? We found that combining it with incremental symbol DB updates helped a lot. Congrats on the results!
  • all2 6mo ago
    I'd be curious to hear more about your work on the 'Sovereign AI Stack'. I'm also working on a project that prioritizes governance and verification and I'd love to compare notes.
    • anandkrshnn 6mo ago
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• dur-randir 6mo ago
  How do I connect it to a local llama.cpp instance?
  • GodelNumbering 6mo ago
    It supports LMStudio or you can start a local endpoint, then run OPENAI_COMPATIBLE_CUSTOM_KEY="xxx" dirac -y --provider "https://localhost/v1" --model <model_name> "hi..."
• DeathArrow 6mo ago
  Right now, top two harness on Terminal Bench are Codex and Forge Code. I wonder how Dirac compares to them. Forge Code is awesome and I plan to test Dirac, too.
• 2001zhaozhao 6mo ago
  Very cool and interesting direction. I'm interested to see how easy it is to extend the harness's language support.
• gchamonlive 6mo ago
  hey there! thanks for the project! I was intrigued with the claims so I wanted to test it myself. First I (vibe)made an AUR package I could use to install it from git source, from master: https://aur.archlinux.org/packages/dirac-cli-git https://aur.archlinux.org/packages/dirac-cli-git Then I went in to see what's what, but I there isn't support for gemini-cli login, and importing from opencode doesn't work, failing with a message "Something went wrong. Could not read API keys from OpenCode config.". `dirac auth --verbose` doesn't seem to do anything. Sorry for reporting it here, but it seems that GitHub is throwing a tantrum again and your issues page's been knocked out. It was able to login with my OpenAI sub though, so let's see how's that. EDIT: headsup, only gpt-5.4 seems to work, gpt-5.4-pro and gpt-5.5-2026-04-23 all throw api error 400, maybe by no fault of your own. OpenAI has been deliberately hindering third-party agents lately, as oh-my-pi ceased to work last week with all gpt models, either throwing an error or having a ludicrously low api rate limit.
  • GodelNumbering 6mo ago
    Hey thanks for doing this! I will be looking into the gpt versions. It doesn't support gemini CLI because google seems to ban users for using it, there was a big controversy about it some time ago so I decided to leave it alone for now. Also, feel free to reach out to me if you want to discuss anything specific
    • GodelNumbering 6mo ago
      UPDATE: chatgpt 5.5 using both codex and API are fully supported now
      • gchamonlive 6mo ago
        Neat! I'll give it a try. It'd be nice to try to mix gpt 5.5 with local qwen3.6 to see if the context and retrieval optimizations can alleviate the context limitations of running such model on a consumer card (I have a single 3090)
• jeff_antseed 6mo ago
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• markboo 6mo ago
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• dbxfb 6mo ago
  Sounds great, I saw your first post but haven't had the time to try it out. It's unfortunate that this is explicitly a fork of Cline but the commit history stops at a gigantic "initial commit". There is not even the cline history + a giant "initial version of Dirac" commit on top, which is a bit sad :)
  • all2 6mo ago
    Importing git history is ugly but do-able. I had to do that at a previous job (splitting a git repo in two pieces or importing commit history from SVN). I can take a look and try to create a PR around this if there is interest.
• hahooh 6mo ago
  why not MCP?
  • GodelNumbering 6mo ago
    Protocol overhead
• _ink_ 6mo ago
  Is it still advisable to use something like codebase-memory-mcp for large codebases, or is Dirac doing fine without that?
  • GodelNumbering 6mo ago
    Dirac doesn't use any of that but the memory part may be something I explore in future
• techpulselab 6mo ago
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• gertlabs 6mo ago
  This is fascinating, and aligned with my experience working on harnesses. I bet there is still significant upside left on the table with that same model. There was a narrative last year by Anthropic that each new model release had them making the harness closer to a simple while loop with tools, but now it seems to be going in the other direction. There's just so much to explore with harnesses. Rolling context windows (instead of compaction) have been very powerful in my work with agentic harnesses, while keeping a persistent high level summary and a detailed automated feedback pipeline (granted, this is easier said than done if you don't have specific, consistent goals for your agent like we do).
• alfiedotwtf 6mo ago
  Would you encourage others to make their own agents? I’ve been looking for a new Emacs side quest, and there seems to be a lot of low-level libraries
• TJ_FLEET 6mo ago
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• Oxlamarr 6mo ago
  The harness point is probably the most important part here. With terminal agents, it often feels like the benchmark is measuring the whole loop: model, prompt, tool interface, retry policy, timeout handling, and recovery from bad shell commands. Do you have a sense of which part contributed most to the jump?
• thinhdev 6mo ago
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• 0xmagic0 6mo ago
  Very interesting! I see it works with different providers via API keys. Does it also support provider subscriptions (like Codex CLI)? And does it have a plugin system similar to OpenCode's?
• Amber-chen 6mo ago
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• ahmad212o 6mo ago
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• amelia-anh-lam 5mo ago
  How well do these gains transfer outside TerminalBench? I’ve seen agents do well on structured evals but fall apart once the environment becomes less constrained (messy files, ambiguous tasks, partial failures, etc.).
• mrwolf 5mo ago
  Thanks for publishing this. I tried it out and it works pretty well, nice job! I had the "feeling" the agent was more effective and accurate, not only cheaper. The only thing that I had issues with is the retry mechanism. For example, when I get error 429 as the endpoint is being rate limited (using openrouter) the session gets stuck and stops. This is the most annoying, and most crippling issue I've encountered with all open source agents. Fixing this would be a huge accomplishment