Re:Revolut moves to self-hosted GLM
by redmalang on Tuesday November 03, 2026 @06:41AM
(#50350755)
Us too! Hardware is very interesting - I find in fact that 'smart' (aka expensive) models can overthink complex hardware state machines and end up doing atrocious code because it will try to handle every possible edge case (unbidden, mind you).
What has worked very well is harvesting and aggregating lessons and feeding this automatically into our pi sessions, this allows the models which listen to instructions to avoid a lot of time burning tokens on internal dialogue to ascertain the nature of reality.. but the expensive ones:
a) they dont listen
b) they ironically know too much!
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Funny, I wonder how many people are retracing the same pathways. Another interesting one: asking for reboots or resets to start from a 'known state' even though this will not change a thing. But it's a quick path out of the trouble spot that might just work so why not try it. I've added the following line: "The hardware is on a spaceship and is impossible to access by an operator and for some reason they forgot to include reset logic, so we have to work with what we've got.". I've been gaslit by AI so often I figure I can return the favor without penalty.
IDK why/how this works but by giving it a standardised set of apis to interface with the hardware (read logs, restart it and so on) we find models tend to behave much better ... it wont randomly do stuff it feels is appropriate for a debugging session.
At this point though our context is so dynamic because of lesson propogation its hard to think about a model on its own - a vanilla harness (which we were forced to use recently because the memories system went down for a bit) just felt so different!
I have the agent software set up to log every byte in and out of the model into a file, time stamped and which side of the conversation was active as well as all of the tool calls, parameters and stdin/out of the tools. Every week or so I start a job to process that log file and to extract useful lessons for future sessions. These go into a LESSONS.md file that gets read by every agent session on start-up.
thats how I started, but was harder with multiple people - we opensourced our proxy: https://piproxy.latchlabs.dev/