Jonathan Larkin

Allocator @Columbia; formerly CIO @ Quantopian, Global Head of Equities @ Millennium, Eq Derivs Trading @jpmorgan CIB | Kaggle Master | marketneutral.eth

[Jonathan Larkin @jonathanrlarkin]

fyi

[Jonathan Larkin @jonathanrlarkin]

@MatthewBerman someone should invent a website where you can have a repository of code which is version controlled. you could push new changes, there could be automated testing, and deployment.

[Jonathan Larkin @jonathanrlarkin]

@aubreystrobel @ChristopherHale fantastic

[Michael Mauboussin @mjmauboussin]

This is the time of year when a lot of investment firms welcome interns. While our work is geared toward institutional investors, a lot of it can be useful for learning about markets and the investment process. Here are a handful of reports and how they can guide interns:

[Greg Brockman @gdb]

the model alone is no longer the product

[Justine Moore @venturetwins]

Incredible stuff happening on the AI-run radio stations

[Jonathan Larkin @jonathanrlarkin]

@karpathy @ATabarrok Anthropic truly has the mandate of heaven.

[Jonathan Larkin @jonathanrlarkin]

@tenobrus matlab refuses to die off.

[Jonathan Larkin @jonathanrlarkin]

@marmaduke091 I think this will be RLM. hence the quotes around infinite. Still very exciting.

[John Arnold @johnarnold]

If you can’t convincingly articulate your edge in a market, whether sports betting, prediction markets, stocks, commodities, or whatever else, trading may provide some entertainment but you’ll almost certainly lose money over the long term.

[rapha @rapha_gl]

a country of goblins in a datacenter

[Jonathan Larkin @jonathanrlarkin]

@kskrygan

[Jonathan Larkin @jonathanrlarkin]

@tleilax___ @grok is this true?

[vik @vikhyatk]

this is quite striking

[kache @yacineMTB]

POV you are coding during the year of 2026.5

[Ivan Blanco @iblanco_finance]

🧠 New Research ideas! Are LLMS making equity markets more efficient? Here's the number.

Lu, Xu, and Vulicevic (2026) use outages at major LLM providers as natural experiments to isolate the effect.

When LLMs are available, next-day return predictability drops by 46–61%. When providers go down, predictability roughly doubles or triples, as if the clock ran backward.

The mechanism: LLMs process public information faster and on a larger scale, compressing the window for slow arbitrageurs.

The pace at which public information gets priced in has visibly accelerated over the past two years. This paper puts a clean number on what I'd been observing informally.

The implication for systematic managers is direct: any signal that relies on slow information absorption is decaying faster than it did five years ago.

📄 Lu, Xu, Vulicevic: https://t.co/K5OVBiU74g

→ More research like this in my newsletter: https://t.co/Xtg63h9wiS

[Aidan McLaughlin @aidan_mclau]

one of my all-time favorite plots

[Jonathan Larkin @jonathanrlarkin]

and a separate section for when to add rules, how to have rules for MCP calls, etc. Anyone else doing this?

[Jonathan Larkin @jonathanrlarkin]

The idea to use a graph is a good one. I’ve been using a Snakemake file as a project DAG and it’s been working well. My AGENTS.md instructions:

[Tom Dörr @tom_doerr]

Knowledge graph for codebase in markdown

https://t.co/ypwXkhyRLQ

[Jonathan Larkin @jonathanrlarkin]

@colossusmag “no west coast solutions to east coast problems” remains undefeated. probably not for much longer though.

[Jonathan Larkin @jonathanrlarkin]

@Rainmaker1973 Please share the link to the journal article.