〽️

Very interested in what the coming era of highly bespoke software might look like. Example from this morning - I've become a bit loosy goosy with my cardio recently so I decided to do a more srs, regimented experiment to try to lower my Resting Heart Rate from 50 -> 45, over experiment duration of 8 weeks. The primary way to do this is to aspire to a certain sum total minute goals in Zone 2 cardio and 1 HIIT/week. 1 hour later I vibe coded this super custom dashboard for this very specific experiment that shows me how I'm tracking. Claude had to reverse engineer the Woodway treadmill cloud API to pull raw data, process, filter, debug it and create a web UI frontend to track the experiment. It wasn't a fully smooth experience and I had to notice and ask to fix bugs e.g. it screwed up metric vs. imperial system units and it screwed up on the calendar matching up days to dates etc. But I still feel like the overall direction is clear: 1) There will never be (and shouldn't be) a specific app on the app store for this kind of thing. I shouldn't have to look for, download and use some kind of a "Cardio experiment tracker", when this thing is ~300 lines of code that an LLM agent will give you in seconds. The idea of an "app store" of a long tail of discrete set of apps you choose from feels somehow wrong and outdated when LLM agents can improvise the app on the spot and just for you. 2) Second, the industry has to reconfigure into a set of services of sensors and actuators with agent native ergonomics. My Woodway treadmill is a sensor - it turns physical state into digital knowledge. It shouldn't maintain some human-readable frontend and my LLM agent shouldn't have to reverse engineer it, it should be an API/CLI easily usable by my agent. I'm a little bit disappointed (and my timelines are correspondingly slower) with how slowly this progression is happening in the industry overall. 99% of products/services still don't have an AI-native CLI yet. 99% of products/services maintain .html/.css docs like I won't immediately look for how to copy paste the whole thing to my agent to get something done. They give you a list of instructions on a webpage to open this or that url and click here or there to do a thing. In 2026. What am I a computer? You do it. Or have my agent do it. So anyway today I am impressed that this random thing took 1 hour (it would have been ~10 hours 2 years ago). But what excites me more is thinking through how this really should have been 1 minute tops. What has to be in place so that it would be 1 minute? So that I could simply say "Hi can you help me track my cardio over the next 8 weeks", and after a very brief Q&A the app would be up. The AI would already have a lot personal context, it would gather the extra needed data, it would reference and search related skill libraries, and maintain all my little apps/automations. TLDR the "app store" of a set of discrete apps that you choose from is an increasingly outdated concept all by itself. The future are services of AI-native sensors & actuators orchestrated via LLM glue into highly custom, ephemeral apps. It's just not here yet.

📢 TaxGPT Raises $4.6M to Build the First AI Tax Co-Pilot for Accounting and Tax Firms. Backed by Rebel Fund, Mangusta Capital, Y Combinator, and Launch, TaxGPT increases the productivity of CPAs by 10x by automating research, client communication, and document collection.

some people didn’t get my apple post about execs smiling this morning, so let me break it down a bit. the r1 news is massive for apple—like, generationally massive. apple bet early on model commoditization. they didn’t blow capex on some me-too race, didn’t overreact, didn’t pivot into panic mode. they just played it steady, focused, controlled. they bet on *edge inference*. the models don’t live in the cloud; they live on the device. & that strategy is now unequivocally proven directionally accurate. in a few years, your iphone, ipad, mac, homepod, whatever will likely be running an llm natively. it’ll be fast. private. agi-level good, potentially. apple will own that end-to-end experience: the ux, the model, the silicon, the hardware. & when everything is deeply optimized, that’s where apple thrives. no one else plays this game better. when models commoditize, apple wins bc the value shifts from the model itself to how it’s deployed, integrated, & leveraged. this was google’s moonshot with pixel which would’ve left apple high & dry. in few years every apple device will ship with agi on board just like every tesla shipped will come with a “driver” on board.

Using light as a neural network, as this viral video depicts, is actually closer than you think. In 5-10yrs, we could have matrix multiplications in constant time O(1) with 95% less energy. This is the next era of Moore's Law. Let's talk about Silicon Photonics... 1/9