u/chryseobacterium

Here is my case and I'm looking for an specific feedback.

I just completed my motorcycle safety training and waiting for my license endorsement. I have zero motorcycle experience but I feel confident handling the training one, an old Kawasaki Eliminator.

My interest is into Adventure motorcycles. I want to build skill and technique, to do some adventure tours.

My expected progress is: training and license > ADV motorcycle > local ride to build confidence and technique > endurance (2 to 6h riding) > advance motorcycle training > basic motorcycle maintenance > start looking into some tours.

I was decided (on paper) on the Kawasaki KLE500se. Nice bike but too tall. I am 5-10 and although I have seen people shorter than me in taller bikes, I don't want to risk it in a u-turn or low speed. I want to build confidence in a safe way.

So, I also saw the KLR650S and the KTM390X. Both hight are better, but the KLR heavier. Although heavier than the KLE, it felt more manageable. The KTM was light and perfect size.

In regards to engine, I will be following the speed limit, for sure, and both seems on in this aspect.

The KLR seems perfect, especially due to its proven record.

For those with the KLR or that started with a KLR. How was your experience with its weight?

From the last 3 months I have been building and improving my local LLM-orchestrator. It started as a AI calendar assistant, and now is my server AI coordinator, with 4 nodes, tools, and multi agent dispatch. It is a stateless session (main session) that I interact through a WSL terminal or through my dedicated Android app. This session dispatch and is allow to perform some inline tasks.

Its injected preamble is everything. Identity, rules, behavior, tools, instructions, but specially memory.

It has a multi tier level memory, using RAG, and graphiti. I tried with a permanent session that only recycle at midnight, but by the end of the day was sluggish, confessed, and bloated from a long day of messages. Stateless with a well designed preamble (<8k tokens) provides the best context, awareness and trend on conversations.

It has a Today's memory with raw and compression messages that injects in its preamble, a Yesterday's memory with graphiti and summary (only summary inject). A Past memory, the growing based Yesterday files.

Besides it has daily message compression, night introspection, and a context yaml file that it uses at its discretions for reminders that also injects back. For example, a temporary change in a file or server, it writes it here for awareness.

The graphiti memory doesn't inject in the preamble, but it has a direct query tool that pull from graphiti + RAG based on multiple criteria.

Besides, all its agents dispatches and reports back are recorded in the DB and can be query. So, it can look back few weeks for results and correlate with current discussions.

Isn't it what developers do with AI agents? Why it seems to be a major issue with AI and memory? I am missing something?

I am working in a repository for my system, it is a frontier LLM-orchestrator and assistant with full system control.