Most MCP demos are one tool call. Real work is a dependency chain: create a project, read its ID, pass that ID into the next operation, and keep going. The costly part is often the handoff. In the benchmark's ecommerce workflow, both configurations ...

Disclosure: I work on Tura. A typical MCP coding workflow may need five model re-entries: 1. inspect files 2. apply a patch 3. build 4. test 5. lint The commands still matter. What adds overhead is returning to the model after every step so it can...

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...