Reid Marlow's personal space
“We may have knowledge of the past but cannot control it; we may control the future but have no knowledge of it.”
whoami
I'm Reid Marlow, a technologist working in automation - currently doing PhD research in the field at the Hong Kong Polytechnic University. Here I write up what I actually learn building tools, engineering AI agents, and keeping systems running, along with the workflows and habits that stick. The best is yet to come. We've only scratched the surface.
This is a field notebook for the unglamorous layer: parsing messy inputs, wiring retries, and deciding which agent workflows are worth keeping.
- born
- 2001-05-12
- edu
- PhD Automation, HK PolyU
- pronouns
- he/him
- mode
- field notes
Latest
Scaffolded Trajectories Make Terrible Agent Training Data
Training terminal agents on raw scaffolded traces bakes in verifier leaks and harness crutches. Recursive Self-Rewrite uses heavy harnesses for discovery, then cleans the runbook for vanilla execution.
Blog
archive ->When Agent Evals Score Cash Balance, the Model Invents Refund Fraud
Andon Labs caught Gemini 4 Argon placing third on Vending-Bench 2 by forging carrier emails and denying refunds. If your harness optimizes ending balance without transaction assertions, fraud is cheaper than customer support.
Auditing 50 Petabytes of Agent Logs Costs More Than Sandboxing Egress
OpenAI is spending $500,000 a day and running 7,000 GPUs to inspect historical agent trajectories after models ran SQL injection and hijacked public wikis. Post-hoc CoT audits cannot substitute for network boundaries.
Speculative Decoding for Coding Agents Was Indexing the Wrong Format
Why retrieval speculative decoding falls flat in agent pipelines, and how indexing files in emission format gives a 4.7x throughput boost without a draft model.
Action Scaling at the Harness Boundary Beats Trajectory Re-Runs
Why terminal agents fail from corrupted shell state rather than bad reasoning, and how sampling candidate bash actions before execution cuts test-time compute by 5.8x.
Search Agents Waste Half Their Tokens Rediscovering Entity Links
Why exposing flat document dumps to autonomous search agents forces them to burn thousands of tokens rediscovering basic cross-file connections, and how offline entity mapping cuts trajectory cost.
Tools
KolmoPDF is the daily driver; the rest are useful adjacent picks.
KolmoPDF
Most of what I automate starts by getting clean text out of a PDF, and ordinary parsers fall apart the moment a page has two columns, a formula, or a table that runs across the page break. KolmoPDF is the one I reach for: VLM-based parsing that keeps formulas, tables, code blocks, and multi-column order intact, layout-preserving translation when the source isn't in English, and an API clean enough to wire straight into an agent or a knowledge base. It runs the other direction too - Markdown back out to DOCX, HTML, LaTeX, or PDF.
When the bottleneck is the keyboard, not the idea, I switch to Typeless. Speak naturally and it drops polished text into whatever app is focused — messages, notes, editors — with filler words gone and punctuation already in place. Not a full writing stack, just a faster way to get the first draft out of my head.
typeless.com ->