Question the measurement
My LLM judge was flipping a coin a third of the time
What happened when I tested the judge I had trusted for months.
The notebook
Writing
Small thoughts, things I learned the hard way, and ideas that needed more room.
Follow via RSS — every new thought, field note, and essay in one feed.
A few places to start
Follow a fix
Every FP8 GEMM traps on DGX Spark (SM121)
An exact failure, the hardware behind it, and the two-line fix.
Look at the craft
A page transition that breathes
The engineering behind a quieter page transition on this site.
Browse the notebook
5 pieces about Evaluation & measurement
Field note
A per-request speculative.type of "none" still drafted 198 tokens
llama-server accepts per-request speculative.n_max, p_min and type, which makes sweeping a drafter on one load look safe. It is not: the per-request value appends to the launch-time list rather than replacing it, so the echoed settings read speculative.types = none,draft-mtp and every row of a six-configuration sweep was the same configuration. The "off" arm reported 198 drafted tokens. A speculative-decoding baseline needs a server restart, and the echoed settings block is the only thing that reveals it.
Field note
pgrep -f matched my own shell, and nohup did not survive the timeout
Two process-management traps that both look like a crashed inference server. pgrep -f matches the command line of the process doing the grepping, so a stop script can kill its own shell and a wait loop can "confirm" a server stopped when it did not. And nohup guards against SIGHUP, not the SIGTERM a timing-out parent sends to its process group — so a 90-second model load inside a 2-minute command budget is killed at exactly two minutes and looks identical to a load failure. Have the child write its own PID, and use setsid.
Field note
DefaultOOMScoreAdjust=200 in the systemd user manager kills terminal-launched servers first
A production llama-server process was killed by the kernel with nothing in its own log: no assert, no stack trace, just silence, noticed only because /metrics stopped responding. The killed process carried oom_score_adj:200. The cause was not the coding-agent session that launched it — it was a systemd user-manager default, DefaultOOMScoreAdjust=200, which scores every terminal-launched process to die before system services do, even ones many times smaller. That score cannot be lowered after launch by an unprivileged process, so the fix is to run the server as a systemd system service instead, which defaults to OOMScoreAdjust=0.
Field note
Detecting which inference engine owns a port: vLLM vs. llama.cpp
Detection that infers the engine from the port number breaks the moment either engine moves. Moving llama.cpp onto the port normally used by vLLM mislabeled it, and downstream code silently wrote null. /props is a clean discriminator: llama-server serves it, and the vLLM OpenAI-compatible server has no such route and returns 404. A second gotcha in the same detour: docker ps --filter publish= does not match a container using host networking.
Field note
Muse-Glimmer returns empty reasoning_content while the parser demonstrably extracts it
Every completion reports reasoning_content: "" and reasoning_tokens: 0, even when a hundred tokens of reasoning were generated. The parser returns the reasoning when called directly. The defect is somewhere between the two, and I have not found it.