§ 001 / 5090 REPORTS
The 5090 Reports
Weekly benchmark and build logs for production AI agents running on hardware I own. The hook is the 5090. The moat is the operating system around local, private, production agents.
§ 002 / CURRENT RUN
The report includes misses.
The first public run proved RTX 5090 hardware detection and exposed an Ollama runner timeout before a valid tokens/sec row. That failure stays in the notebook.
Tokens/sec
Model x quant
Measured on real local-agent prompts, not synthetic hype demos.
VRAM pressure
Context + cache
What fits, what spills, and what changes after quantization.
Cost curve
Local vs API
Per-workload math for agents that run often enough to matter.
Failure log
Timeouts included
Runner crashes, bad configs, and dead ends stay in the record.
§ 003 / OPERATING RULES
Publish the lab notebook. Do not perform thought leadership.
One new experiment per week, and it must feed the owned-hardware wedge.
No fake benchmark numbers. A failed run is a valid artifact.
No calls, no cold outreach, no retainers, no hourly work.
§ 004 / PRODUCT PATH
Content is the sensor. Product is the output.
The loop is simple: benchmark in public, grow the list, take capped inbound deployment work only when it teaches the product, then ship the repeated tooling as self-serve software.
Phase 0
Instrument the lab
Weekly reports from hardware snapshots, benchmark CSVs, and failure logs.
Phase 1
Distribute artifacts
Three posts per week across LinkedIn, X, and r/LocalLLaMA, all pointing here.
Phase 2
Capped deployments
Inbound-only, async paid R&D for regulated teams that need local AI.
Phase 3
Extract product
Local agent observability, memory, or MCP tooling rebuilt from repeated deployment work.
§ 005 / EMAIL LIST
Get the weekly lab notes.
One artifact-backed note per week. Benchmark table, failure log, architecture diagram, or repo note. No calls.
Want more like this?
AI agent builds, real costs, what works. M-F only when there is something worth sending. No fluff.