DeepSWE's harness comparison made me curious, so I tried mini-swe-agent myself on a matched set of debugging tasks with GPT-5.6 SOL at High reasoning. !Live Debug / Tokens benchmark screenshothttps://github.com/user-attachments/assets/6b4b8a41-cca1-...

!Real 15-slide deliverablehttps://github.com/user-attachments/assets/f3f269e8-b4f7-4ddf-bfff-6359c120e724 What if an agent spent less time going back to the model, but did much more useful work inside each turn? That is the idea I have been testing...

Disclosure: I maintain Turahttps://github.com/Tura-AI/tura. Here is the simple idea. A normal coding agent may use five separate LLM turns for one predictable workflow. Turn 1 — inspect rg -n "TODO|commandrun|handler" crates/ rg --files crates/run...

Hi, I’m Yu. Earlier this month I released Tura and wrote about why agent harnesses need long-horizon benchmarks, not just one-off demos. Over the past two weeks, I’ve seen more people publish tests of token-saving tools such as RTK and Ponytail. Tha...

Disclosure: I am the maintainer of Tura. I am posting this to ask for candid advice about open-source communication and evaluation, not as an independent review of my own project. Since the beginning of 2026, projects such as RTK, Caveman, and Ponyt...

Disclosure: I maintain Tura, which hosts the full test record. I ran 280+ Codex CLI sessions with GPT-5.6 Sol. In the current build, fewer commands per model turn can mean more turns and more cached-input cost. Most benchmarks favor 5.6 Sol on cost...

Disclosure: I maintain Turahttps://github.com/Tura-AI/tura. Here is the simple idea. A normal coding agent may use five separate LLM turns for one predictable workflow. Turn 1 — inspect rg -n "TODO|commandrun|handler" crates/ rg --files crates/run...

Claims such as “90% less output” or “50% less context” are useful diagnostics, but they do not answer the question that matters for a coding agent: did the complete, verified task become cheaper? I ran a small matched experiment to see how different...