RTK, a popular tool with over 79,000 GitHub stars that compresses terminal output before AI coding agents read it, has been marketed as a way to cut AI coding costs, with one viral post claiming up to 60% token savings. But independent benchmark testing using Terminal-Bench 2.1 across 1,740 task attempts found mixed results: costs dropped 5% for one model setup but rose 5% for another, contradicting the widely shared savings figures.
quesma.com
· 2026-09-11
A developer outlines a workflow for running local large language models on an Apple MacBook Pro M5 with 48GB RAM, combining Ollama for model management, OpenCode as the development harness, and Docker's sbx sandbox tool for isolation. The setup uses models like Qwen 3.8 27B and Gemma 4 31B, pulled via Ollama and configured through a custom sbx kit for each project.
tensorsandtokens.com
· 2026-09-11
A developer ran the same prompt—building a self-contained sci-fi hangar scene with Three.js, including hovering drones, animated lights, and camera paths—across 10 combinations of AI models (including GLM, Luna, SOL, Astra, and Qwen variants) and coding harnesses like Codex, OMP, OpenCode, and DSH. The test tracked metrics such as completion time, token usage, tool calls, error rates, and whether the model verified its own output by opening the file in a browser and checking screenshots.
alvins82.github.io
· 2026-09-08
GoKawiil