Mac memory mysteriously eaten up, and it turns out dozens of orphan node processes were left behind by Feishu's official lark-mcp. The root cause is that its stdio lifecycle only handles data inflow, not pipe closure, so the process doesn't kill itself after the host exits. Upstream issues and PRs were filed but have just sat there unmerged, and the repo hasn't been updated for over a year.

While working with WorkBuddy, I noticed that deleted temp files end up in the Trash, whereas my opencode reaches for rm at the drop of a hat. Digging into that detail led me to discover that macOS 15 already ships with a built-in trash command that can replace rm.

The previous post on the goat setup missed one piece: models like deepseek-v4-flash couldn't pick a thinking depth in /models. Digging in, it turned out the GOAT plan does support it — the problem was that the hand-written models in the custom provider were missing a reasoning capability declaration, so opencode's variant generation logic stopped them at the door. In the end I added reasoning:true to every model under goat and goat-zdr, and testing showed no side effects.

I bought CommandCode's goat plan and wanted to use it as my main plan in opencode. The whole integration takes just two steps: first /connect to save the API key, then declare the provider in opencode.json. The goat plan requires an x-cmd-zdr: 1 header on every request, and testing confirmed you can add that in the config too.

OpenCode Go's monthly allowance is spread across several tables, and the official "5-hour quota" chart doesn't list all the models. So I built a chart page that automatically scrapes the official price table and, using the same methodology as the official one, works out the number of requests available per month for each model and plots it. Hover for details.

DeepSeek Harness got a lot of people excited, but I used it once and didn't keep going; Qwen3.8 27B's capabilities have taken off, but I haven't deployed it locally either. Weighing the urge for new things against the need for stability, OpenCode + Paseo + OpenViking is already smooth enough. Anything new that doesn't offer real, controllable, reliable help is just noise.

One-click deployment of 144 AI employees. I took a look at its docs, and only now do I sort of get it. The claim that it "integrates many Agents" is really because the whole open-source project is built out of standalone Agents, one after another — and there are already over a hundred of them. Each folder holds a single Agent.

`SummaryIndex` is a powerful tool in LlamaIndex for handling global questions. Its core mechanism is to **pass the complete document to the large model as context, leveraging the model's comprehension ability to generate a global summary or answer**. With sensible parameter configuration and a streamlined process, you can get high-quality global answers. In practice, it's a good idea to combine `VectorStoreIndex` and `SummaryIndex` and automatically pick the right query engine based on the type of question, so users get more comprehensive and accurate answers. This dual-engine architecture makes the most of both index types: it can handle specific local questions as well as questions that require reading the whole document to answer, providing solid support for building a high-quality Q&A system.

The Effect of Trae Paired with an Obsidian JournalOver the past 3 months I also used an open-source, uh, project. Something called “Day*” or thereabouts. Ah, once it was running it, uh, monitored m...