Hot take: rogue AI worms taking control of household devices isn’t a threat, it’s the @Sonos app update we’ve been waiting for.
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Head of Macro retweeted
Thrilled to officially launch "Headless Hebbia"
It's already one of our fastest growing product lines, customer reception has been phenomenal!
Head of Macro retweeted
Why is it ‘cancelled’ in the U.K. but ‘canceled’ in the U.S.?
Because we gave them that L in 1776.
Head of Macro retweeted
Announcing Talkie: a new, open-weight historical LLM! We trained and finetuned a 13B model on a newly-curated dataset of only pre-1930 data. Try it below!
with @AlecRad and @status_effects 🧵
I will die convinced Cabo Verde's goalkeeper would have scored with a bicycle kick had the referee allowed the final corner kick
@vozinhapr
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Head of Macro retweeted
I took the @karpathy autoresearch loop and pointed it at markets.
25 AI agents debate macro, rates, commodities, sectors, and single stocks daily. Every recommendation scored against real outcomes. Worst agent by rolling Sharpe gets its prompt rewritten by the system. Keep or revert. Same loop, prompts are the weights, Sharpe is the loss function.
Trained the agents on 18 months of market data. 378 iterations. 54 prompt modifications, 16 survived.
The system learned which agents to trust using Darwinian weights — geopolitical, commodities, and the @BillAckman quality compounder rose to the top.
The agents even figured out their own portfolio manager was the weakest link before we did!
Deployed the trained agents. +22% in 173 days. Best pick: AVGO at $152, held for +128%.
The final prompts are evolutionary products — shaped by market feedback, not human intuition. Now running live with my own capital.
github.com/chrisworsey55/atl…
Part hedge fund, part research experiment :)
I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then:
- the human iterates on the prompt (.md)
- the AI agent iterates on the training code (.py)
The goal is to engineer your agents to make the fastest research progress indefinitely and without any of your own involvement. In the image, every dot is a complete LLM training run that lasts exactly 5 minutes. The agent works in an autonomous loop on a git feature branch and accumulates git commits to the training script as it finds better settings (of lower validation loss by the end) of the neural network architecture, the optimizer, all the hyperparameters, etc. You can imagine comparing the research progress of different prompts, different agents, etc.
github.com/karpathy/autorese…
Part code, part sci-fi, and a pinch of psychosis :)
In a world powered by steam, electricity might looked cool, might look like hype. @Noahpinion points out it took 30 years to redesign factories to ⚡️. Here @gsivulka nails how firms need to use AI to realize its value.
Spoiler: you don’t just give everyone a chat bot.
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Head of Macro retweeted
Powerful new Harvard Business Review study.
"AI does not reduce work. It intensifies it. "
A 8-month field study at a US tech company with about 200 employees found that AI use did not shrink work, it intensified it, and made employees busier.
Task expansion happened because AI filled in gaps in knowledge, so people started doing work that used to belong to other roles or would have been outsourced or deferred.
That shift created extra coordination and review work for specialists, including fixing AI-assisted drafts and coaching colleagues whose work was only partly correct or complete.
Boundaries blurred because starting became as easy as writing a prompt, so work slipped into lunch, meetings, and the minutes right before stepping away.
Multitasking rose because people ran multiple AI threads at once and kept checking outputs, which increased attention switching and mental load.
Over time, this faster rhythm raised expectations for speed through what became visible and normal, even without explicit pressure from managers.
Head of Macro retweeted
Here's proof that Claude Code can write an entire empirical polisci paper.
To validate my claim that AI agents are coming for polisci "like a freight train", today I had Claude Code fully replicate and extend an old paper of mine estimating the effect of universal vote-by-mail on turnout and election outcome...essentially in one shot.
After careful prompting, Claude Code:
(1) Downloaded the old paper's repo and replicated the past results, translating our old Stata Code into Python
(2) Crawled the web to get updated official election data and census data
(3) Ran new analyses extending the results through 2024
(4) Created new tables and figures
(5) Performed a lit review
(6) Wrote a wholly new paper
(7) Pushed the whole thing to a new github repo
The whole thing took about an hour.
This is an insane paradigm shift in how empirical work is done.
It also validates the point that several people including @BrendanNyhan made yesterday---it's going to be especially easy to scale observational research with AI.
Thanks to @alexolegimas, @arthur_spirling , and many others who gave me feedback. .
Claude Code and its ilk are coming for the study of politics like a freight train.
A single academic is going to be able to write thousands of empirical papers (especially survey experiments or LLM experiments) per year. Claude Code can already essentially one-shot a full AJPS-style survey experiment paper (with access to Prolific API).
We'll need to find new ways of organizing and disseminating political science research in the very near future for this deluge.
Head of Macro retweeted
When Shohei Ohtani was a high school freshman, he created a detailed "dream sheet" with one central goal: to be the #1 draft pick for 8 NPB (Nippon Professional Baseball) teams.
It was a 64-cell roadmap based on a framework called the Harada Method.
Here's exactly what Shohei did 👇
1. First, some history.... The Harada Method was created by Takashi Harada, a Japanese junior high track coach. He took a team ranked last out of 380 schools and, using his system, turned them into the #1 team in the region within 3 years. They held that top spot for the next 6 years.
2. You start by placing your main goal in the center of an 8x8 grid. For Ohtani, this was "be the #1 draft pick."
3. Next, you identify 8 critical supporting pillars needed to achieve that goal. These surround the main goal.
Ohtani's 8 pillars were:
• Body
• Control
• Sharpness
• Speed
• Pitch Variance
• Personality
• Karma/Luck
• Mental Toughness
4. You then break down each of those 8 pillars into 8 smaller, actionable tasks or daily routines.
This fills out the entire 64-cell grid, turning a massive dream into a concrete, daily action plan.
To improve his karma, he listed tangible actions like:
• Showing Respect to Umpires
• Picking up trash
• Being positive
• Being someone people want to support
5. The method goes far deeper than just technical skills. It forces you to analyze your weaknesses and build confidence. It also has a highlight on service to others, emphasizing that humility and contributing to your community are essential for personal success.
6. The key to the system is daily execution and accountability. Once the 64-cell chart is complete, you turn the tasks and habits into a daily diary and a "Routine Check Sheet." It’s designed to transform abstract intentions into a measurable, daily practice.
The legend continues!
Shohei Ohtani is the NL MVP for the second straight season!
ALT Shohei Ohtani NL MVP Pictured: Shohei Ohtani smiles while wearing a road gray Dodgers uniform. Below, Ohtani yells while wearing a home white Dodgers uniform. In the lower right corner is Ohtani’s signature in Japanese.
The UK loves to lecture others on governance. Meanwhile, try earning a driving licence: instructor–examiner cartels, black-market test slots, months-long queues. It compares unfavourably to Kenya. This is corruption in all but name—and it’s essential occupational licensing.
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Neom is all Figma, no CAD. Great write up in the FT. I’m planning a trip for the Asian winter games. Holler if interested.
ig.ft.com/saudi-neom-line/
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The performance summaries are also public as reported by mercer. New Mexico’s SIC is historically tiny but since fracking took off on our half of the Permian basin, it’s become a real sovereign wealth fund.
if you ever wonder what VCs are actually offering to LPs in meetings, new mexico’s SIC meetings are public by default
here’s chris sacca pitching lower carbon. 200 views.
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Head of Macro retweeted
We killed this one piece of fake news. It's kind of like in the movies where the humans finally manage to kill one alien monster, and then about 10,000 of them show up.
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