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At the end of the month, I go through the time-honored tradition of closing my books. I start by opening Mercury, which is my banking platform, to make sure that all my transactions are properly categorized. Auto-categorization rules handle the predictable stuff pretty well. But I'm constantly working with new contractors, you know, tutors and researchers and videographers, and I'm also trying new tools. Manually categorizing all of these transactions would add a couple of hours of overhead every single month. So instead of going through them one by one, I have Command, which is Mercury's built-in AI, take a stab at all of them at once. Command proposes a category for each transaction and provides its rationale. I just review, I fix anything that's off, and I approve. And once all this work is done in Mercury, it syncs everything with QuickBooks. And Command's judgment calls are genuinely good. It does the obvious things like looking at the vendor, but it also investigates who on my team made the purchase and looks at notes and memos to build up as much context as possible. This is just one of the ways you can use Command to automate the backend of your business. To learn more, go to mercury.com slash command. Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column NA members FDIC. AI-generated responses and suggested actions may vary and are not guaranteed.

As I've gotten to know the folks at Jane Street, I've noticed that a lot of them have physics backgrounds. I recently got a chance to talk to Jed Thompson, who was a particle physicist before he was a trader, about how his physics training helps them with his work at Jane Street. I think very few Jane Street traders or researchers come in with any finance background or any trading background. When I used to be in physics, something that I would say is I almost never do a calculation without already having a pretty good guess at the answer. In trading, I think the same is true. These things are fundamentally models for how the world is behaving. You can build good intuition by seeing patterns over and over again and come to a point where you're mostly asking the right question from the beginning, which short-circuits a lot of the work. So even if you don't have a finance background, or for that matter, a physics background, you should still consider applying. Go to janestreet.com slash twerkash to learn more.

Crusoe gave us early access to their serverless fine-tuning product, which lets you fine-tune open models without having to deal with infra or provisioning. I thought it'd be cool to try fine-tuning a question generator using the transcripts of my old interviews. The models have gotten so good that if they had all my research and prep and they could look at a conversation so far, they could ask a next question better than I would. Crusoe made the implementation super straightforward. I just uploaded the data, picked an open model, and started the run. I didn't have to touch any of the hyperparameters. Crusoe's Applied AI team maintains optimal recipes for each model, so I just set everything on auto. When the run finished, I deployed it as a self-server endpoint and built an eval for my team. I had them choose the best next question out of three anonymized choices, one that was produced by the base model, one that was produced by the fine-tuned model, and one that I actually asked. Fortunately, my team preferred my actual questions about two-thirds of the time. Hopefully, this benchmark doesn't saturate. And in the remaining cases, they almost always preferred the fine-tuned model over the base model. Serverless inference is live now, and serverless fine-tuning goes live next week. Learn more at crusoe.ai.com.

I recently wrote this blog post where I speculated that sample efficiency during training actually hasn't improved that much over the last few years. And rather, we've just dramatically improved and widened the data distribution. And I was having dinner with friends recently, and then I had this idea of how you could get some empirical information on this question. There's this nano-GPT speedrun where people compete to train Karpathy's GPT-2 baseline to a fixed loss with less and less compute. The training data is frozen, so I wondered if the loss curves over time of each record could tell you roughly how fast sample efficiency is improving. So I pulled out my phone, I dumped this idea into a voice note in the cursor app, and I went back to dinner. And then I got a notification about 15 minutes later, the cursor agent had cloned the modded nano GPT repo, it had analyzed all the loss curves for all the records, and it had estimated that sample efficiency had been improving about two to five X every single year. Of course, this is very naive and circumstantial evidence, but it inspired me to start writing a full post with a friend where we investigate this question using many different methods. And the friction really mattered here. The idea would have just floated away if I wasn't able to just kick off the investigation right then and there with the Cursor app. If you want to try Cursor's iOS app, go to cursor.com slash thewarkesh.

I grew up in India till I was eight. In addition to English, I also speak Gujarati. Since Google just released Gemini 3.5 Live Translate, I thought it'd be fun to put it to the test in this mid roll. Gemini 3.5 Live Translate automatically detects more than 70 different languages and translates them in almost real time into the target language. It live translates your original speed and format while speaking. I visited China back in 2024, and I remember thinking that this trip would have been so much more productive if I could have live translated the conversations I was having with researchers and random people I met on the street. Now we have that technology. So if you're building an app that needs live translation, you should 100% check out Gemini 3.5 Live Translate. It's available now via the Gemini Live API and in AI Studio. Go to ai.studio/live to get started.

I don't think people appreciate the kinds of things that these models can just go handle for you when you equip them with a good harness like Cursor. For example, I started publishing my episodes on Bilibili for a burgeoning Chinese audience, but everything I upload there needs the sponsored segments cut out. Normally, that would have meant asking my editors to go back through all the old episodes, cut out the ads, and re-export everything. But in about just as much time as it would have taken me to send them that Slack message, I can just tell Cursor to do it instead and spare them. For research for the podcast, I have a whole repo where I've put every single book and paper relevant to prepping for any of the recent episodes. I've been able to hodgepodge everything because the Cursor harness is extremely good at helping the model figure out exactly what information to pull, whether that's from my repo or from the web, in order to answer the questions I have while doing research. So whatever you happen to be working on right now, just try pointing Cursor at it. Go to cursor.com/lorkash to get started.

As the podcast has grown, I've had to deal with more and more operational overhead. Take paying bills. In the past, contractors would just email me their invoices. Every few weeks, I'd dig through my inbox, create a folder with all the bills, and manually pay each one. At this point, I just give everybody an email address that goes straight to Mercury, which is my banking platform. Whenever anybody sends an invoice to that address, Mercury automatically downloads it, scans it, and extracts all the relevant information — things like the contractor name, address, payment amount, invoice number, and due date — and then uses all of this to create a draft payment. Mercury then stores a list of these drafts for me to review. I just go through this list and double check that I've been billed correctly. I don't have to track anything or enter any information myself. Mercury does all the fundamental things for your business extremely well, and it puts them all in one place. If you want to learn more, go to mercury.com. Mercury is a fintech company, not an FDIC insured bank. Banking services provided through Choice Financial Group and Column NA, members FDIC.

Anybody sends an invoice to that address, Mercury automatically downloads it, scans it, and extracts all the relevant information, things like the contractor name, address, payment amount, invoice number, and due date, and then uses all of this to create a draft payment. Mercury then stores a list of these drafts for me to review. I just go through this list and double check that I've been billed correctly. I don't have to track anything or enter any information myself. Mercury does all the fundamental things for your business extremely well, and it puts them all in one place. If you wanna learn more, go to mercury.com. Mercury is a fintech company, not an FDIC insured bank. Banking services provided through Choice Financial Group and Column NA members FDIC.