deepvaluebettor

Would be interested to hear your opinions on this Joseph - wisdom of the AI crowd! @12Xpert youtu.be/2CUQcU32a-g?si…

Ex-Point72 Proprietary Research Head Kirk McKeown on building edge, alpha decay, & why everything that happened on Wall Street is about to happen on Main Street. Kirk McKeown (8.5 years @ Point72 under Steve Cohen | Built primary research at Glenview under Larry Robbins | Now founder of Carbon Arc @CarbonArcAI) "Alpha rewards those who value assets in a cold way. You want to get it right — not be right." We cover: - How alpha creation differs across multi-manager vs. concentrated shops - The 3 vectors every middle office function must move to justify its existence - Why he worked 6-hour Sundays from 2006-2020 — and the math behind it - The TSMC call that signaled semiconductor cancellations before anyone else knew - What the quant revolution on Wall Street tells us about the AI economy today - His framework: 4 market structures, 9 business models, & why they have rules - The MIT beer game & why every business problem is really an inventory problem - His hot take: a top hedge fund launches an enterprise AI lab in 2026 Highlights: 00:00 Intro 04:47 Tutor vs Glenview vs Point72: how edge differs 12:29 How to build “lift” for PMs: at-bats, hit-rate, sizing 18:44 Building research edge: outwork, read, fieldwork 27:16 Personal moat in 2026: analogs, history, decision trees 40:08 “Main Street becomes Wall Street”: what that actually means 44:30 Carbon Arc thesis: “decimalization” of data market structure 46:43 Why the edge migrates to data plus domain context 51:00 How to win in commoditized research: sample size beats anecdotes 01:03:26 Factorizing everything: themes, market structure, business models 01:08:37 Pruning decision trees: signals, scale points, inventory dynamics 01:14:18 Contrarian 2026 take: hedge funds launching enterprise AI labs 01:23:32 Final question: one habit to build career alpha

NEW ODD LOTS: It's @MichaelSelig on prediction markets @tracyalloway and I talk to the new Chairman of the CFTC about the rise of Polymarket and Kalshi and regulation in this new world where almost anyone can be on almost anything. podcasts.apple.com/us/podcast/new…

Introducing Barnum, or... how I ship hundreds of PRs per week, burn through backlogs, and automatically fact-check documentation. LLMs are incredibly powerful tools. But when we try to use them to drive more complicated refactors or more intricate workflows, their shortcomings are quickly revealed. When their context gets full, they get forgetful, and they can't be relied upon to necessarily do the steps that you ask. They often cut corners. Put simply, having an inherently probabilistic process perform what should be deterministic work necessarily comes at the cost of reliability. And you can't build a complicated workflow off of unreliable foundations. That's where Barnum comes in. Barnum is the missing workflow engine for agents. Rather than having agents be responsible for upholding guarantees (e.g., always lint and commit your changes atomically), agents instead do just what they're good at: reading text and reasoning. Everything else is done deterministically, on the outside, by Barnum. This means that you can build bigger, more involved workflows without sacrificing reliability. Because you can intersperse bash scripts, you save on token usage. The agents performing a micro-task only receive the instructions for that specific task, meaning that context does not get overwhelmed and they don't get forgetful. And because all inputs, outputs, and transitions are validated, the agents can't wriggle out of doing the work. This workflow is essentially a state machine described in a config file. And the best part? The configuration has a JSON schema, so agents are actually really good at writing the workflow! It's already been used to ship hundreds of PRs, run automated refactors, burn through various backlogs, fact-check every statement in documentation, and build a deep-research clone! The attached image is a representation of the workflow that I use to identify and implement automated refactors. I follow this up with a separate workflow that splits each commit into a separate PR, judges the refactor, and potentially completes the refactoring (for example, by modifying call sites if the refactor changed some public API). So go on, give it a try. Check out barnum-circus.github.io, star the repository, and join the Discord! I can't wait to see what you build with it! And I'd love for you to get involved!