Reducing compounded round trips with aggressively structured compaction.

Ollama’s new limits and max plans are worrying me, so I’ve been going harder on my harness. (Thanks to everyone who’s signed up for the waitlist so far.) Wanted to share how my v1.10.0 will dramatically save AI inference spend. It’s through aggressively budgeting how many tokens each task is allowed to take before compaction kicks in, and compaction is only within the bounds of that task. The idea is that Rehex is highly structured with strategy, tasks, and a DAG to help organize and basically PM. Given that structure and its native memory, I’m finding that I can aggressively compact what makes it to inference. I’ve been implementing Rehex into my law firm. Which this all is so relevant nowadays, especially when Harvey and other legal tech companies are now scrambling to save tokens. Open models are amazing. Yeah, closed frontier still the best. But considering the insane 10x plus cost efficiency, I’m never going back. But with lesser limits, I’m considering serverless inference alternatives. Anyone else feel this way? Hell, anyone else using serverless for speed? submitted by /u/haseebnqureshi [link] [comments]

Source: r/ollama | 2026-09-30