From the article:

>To manage all this, I built laconic, an agentic researcher specifically optimized for running in a constrained 8K context window. It manages the LLM context like an operating system's virtual memory manager—it "pages out" the irrelevant baggage of a conversation, keeping only the absolute most critical facts in the active LLM context window.

The 8K part is the most startling to me. Is that still a thing? I worked under that constraint in 2023 in the early GPT-4 days. I believe Ollama still has the default context window set to 8K for some reason. But the model mentioned on laconic GitHub (Qwen3:4B) should support 32K. (Still pretty small, but.. ;)

I'll have to take a proper look at the architecture, extreme context engineering is a special interest of mine :) Back when Auto-GPT was a thing (think OpenClaw but in 2023), I realized that what most people were using it for was just internet research, and that you could get better results, cheaper, faster, and deterministically, by just writing a 30 line Python script.

Google search (or DDG) -> Scrape top N results -> Shove into LLM for summarization (with optional user query) -> Meta-summary.

In such straightforward, specialized scenarios, letting the LLM drive was, and still is, "swatting a fly with a plasma cannon."

(The analog these days would be that many people would be better off asking Claw to write a scraper for them, than having it drive Chromium 24/7...)