/* ------------------------------------------------ */
BMD
// build. measure. deploy.
*/
A real shell running in your browser. Type desk to size your GPU, or tap a command. The bench replay streams at the measured rate. It is a replay, not a live model.
pat@5090:~$ desk
Pick the GPU you own. Get the local models that fit it, the quant to use, expected tokens per second, and a copy-paste run command. Measured on real hardware where we have it, estimated everywhere else.
pat@5090:~$ cat latest_sweep.md
THE 5090 REPORTS // the lab notes behind the desk
Each report shows the model, quant, prompt, hardware, speed, VRAM use, and what broke. AgentGuard handles spend limits, loops, timeouts, and rate limits.
Read the 5090 Reportspat@5090:~$ column -t fig1_sweep.tsv
pat@5090:~$ tail -n 1 stderr.log
[WARN] stderr
Changing num_ctx between requests forces a full model reload. On a 26B that is 140 seconds per swap. Pin your context size.
Field note, 2026-07-09
pat@5090:~$ agentguard --status
I use AgentGuard to stop spend, loops, timeouts, and rate-limit failures before a test touches real work.
$ pip install agentguard47pat@5090:~$ ls tools/
12 live / 1 beta from 13 public tools. Open what helps.
$ pip install agentguard47$ pip install agentguard47pat@5090:~$ ps aux | grep agents
Run the model on the target GPU.
Record tokens, latency, VRAM, cost, and failure mode.
Write up the result or ship the tool it required.
pat@5090:~$ tail blog.log
pat@5090:~$ mail -s "subscribe" pat@5090
To: the journal // Subj: subscribe
New benchmark results, failed runs, VRAM checks, and tools. I send an email only when there is a real result.
Four copy-ready tools now, then measured local AI field notes M-F only when there is something worth sending.
Free. One-click unsubscribe. No sponsored placements. Your email is used only for these notes.