⌁ The Lab Dispatch · free · for quants who want to be ahead
AI is rewriting quant finance. Read ahead of it.
The newsletter from inside a working quant lab: what’s actually holding up in AI × quantitative finance, the research papers worth your hours, and the experiments — including the failures — most publications would never print.
Written by one researcher who builds this stack daily, not a content team recycling the same ten threads.
What you’ll actually get
01 · the valueAI for quant finance, minus the hype
Agentic research pipelines, LLMs inside the research loop, uncertainty-aware ML — what holds up when real money-shaped decisions depend on it, and what is conference vaporware. The lab builds this stack every day; you get the distilled verdicts.
The papers worth your hours
Each issue, the top new research in quant finance and ML — read with a rigor auditor’s eye. The claim, the catch (leakage, multiplicity, impossible costs), and the verdict: adopt, salvage, or avoid.
Dispatches from inside the lab
Experiments, theorems, launches — and the obituaries. A one-person lab that publishes its failures is rarer than alpha; you read what died and exactly why, so you don’t have to repeat it.
Things you can run on Monday
Decision rules, monitors, checklists and code lifted straight from the lab’s own stack — decay detection, leakage tests, uncertainty gates — built to be stolen for your own work.
- →One big idea — AI × quant finance, argued properly, with the evidenceessay
- →Three papers — verdicts: adopt / salvage / avoid, with the one-line reasontriage
- →One lab dispatch — an experiment, a launch, or an obituaryin public
- →Tools & links — runnable, not decorativecode
Written for people
who ship models
02 · who it’s for
Know what the machines are doing
A working practitioner’s view of how AI is changing systematic investing — useful for judging managers, tools, and claims.
Steal methods, skip dead ends
Pre-triaged papers, documented failure modes, and the uncertainty machinery the lab publishes — straight into your workflow.
The AI-native research stack
How a one-person lab automates research, review and monitoring with agents — architecture included.
Learn how it’s really done
The gap between textbook backtests and research that survives contact with reality, narrated as it happens.
The people who stay ahead read the primary sources before everyone else’s thread about them. Issue № 001 is being written now.
Get issue № 001 →