Biggest issue with this language. But... fairly trivial to implement codegen with gleam/glance[0]. No good libraries do this well right now (e.g. support for discriminated unions).
741 karma · joined October 6, 2021
You're right that mathematically, a function with constant (or no) growth is O(n)and also O(n^2), and O(anything_that_grows_faster).
My use of "O(n) startup" and "O(n^2) startup" is intended to classify the type of business based on its *inherent best-case growth potential or ceiling*.
An O(n) startup in my framework is one whose fundamental business model, market, or structure means its growth, even in its best-case scenario, is capped at roughly linear. It cannot achieve sustained super-linear growth; its upper bound is linear.
An O(n^2) startup is one whose model (e.g., strong network effects) has the potential for super-linear (which I've simplified to n^2) growth as its best-case scenario. It might be underperforming (even flat, and thus also technically O(n) in that moment), but its design allows for a fundamentally different, higher growth ceiling. The whole point is illustrate potential withholding implications or conclusions from its current growth rate, which is necessary at a companies inception.
So, yes, a flat-lining "O(n^2) type" startup would currently show growth that is O(c) (and thus also O(n)). But the point of my labels is to say that an "O(n) type" startup, by its very nature, cannot achieve the n^2 best-case that the other type can, even if both are struggling.
The labels describe the class they have, dictating their asymptotic best-case limit, not just any loose upper bound on current, possibly sub-optimal, performance. The separation I'm arguing for is based on that fundamental difference in their potential trajectory’s ceiling.
If I used Omega this would imply the actual growth rate of the startup would have to strictly be better n or n^2.
> [2] Perhaps choosing a better two functions could more closely explain the growth dynamics of network effects, which could be more exponential. I think the analogy diminishes in value if you try to directly numerically match it to some growth metric.
The reality is that companies often underperform their best case possible growth rate. O(n) and O(n^2) are meant to represent the best possible growth rate which may be practically be underperformed.
You may be thinking about algorithmic analysis where the term "worst case" is used for the upper bound, but here, the upper bound represents the best case. Sort of counter-intuitive but the underlying mathematical notation is properly defined.
Generally if you have some sort of idea of what you want to do, you'll be more successful at it.
You can think about it like we are looking at the concave up portion of the sigmoid only. The early growth phase.
Perhaps they are harder to start, but they are also vastly more likely to succeed to their O(n^2), and this is not only due to the increased barrier to entry.
That's what I mean when I say, founders are more likely to succeed at O(n) companies.
The best part about Purdue is that I can use “top CS school” and “random state school” depending on which sounds better.
I would say markets are approx. zero-sum on small time-scales. And low-beta alpha is better sourced from treating the market as zero-sum. Short-term price movements are primarily driven by the redistribution of wealth between market participants rather than by the creation of new fundamental value, making strategic positioning against other traders more effective for generating uncorrelated returns.
I think the 5 issues they provide under “Cognitive Architectures” are severely underspecified to the point where they really don’t _mean_ anything. Because the issues are so underspeficifed I don’t know how their proposed solution solves their proposed problems. If I understand it correctly, they just want agents (Assistants/Agents) with user profiles (Sims) on an app store? I’m pretty sure this already exists on the ChatGPT store. (sims==memories/user profiles, agents==tools/plugins, assistants==chat interface)
This whole thing is so broad and full of academic (pejorative) platitudes that it’s practically meaningless to me. And of course although completely unrelated they through a reference into symbolic systems. Academic theater.
I also generally am writing REST APIs and doing simple "enterprisey" stuff.
The harder and more valuable part was putting those discussions into focused, clear, essays. I am a much better writer and thinker because of it.
The best way to learn how to think is to think. The way in which the quality of your thinking is best judged is in relation to other people. So just try a take your thoughts and try and put them in other people’s minds.
12GB being considered significant makes me feel good, a return to simplicity. The other day I couldn't provision a VM with less than 512GB of storage...
I can't even play my favorite games without giving up >150GBs...
That combined with my general interest in Linux probably saved me thousands in cloud costs. Linux is a dying art among young software engineers, but alive among young nerds. Even among infra/devops people, maybe if they learned more about linux they wouldn't reach for k8s to solve the simplest problems.
This new release will generate more hype/competition around AI, with data and training increasingly becoming the moat (rather than model level innovations), which will lead to more people needing Nvidia GPUs.
Nonetheless, I really like how competitive OSS is in LLMs compared to other major innovations.
My dad went to Sim Lim Square (in Singapore) back in the 90s and he wanted to take me back when we visited a few years ago. There was a bunch of gaming stuff and they got the 4090 early than the US so I was happy, but he was miffed and disappointed at the lack of components and cheap electronics.