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).
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.
I actually passed my discrete math class and final a few days ago and got the big O vs Theta vs Omega question right.
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.
Experience != talent. Perhaps a better way to phrase it is that they hire the minimum needing to succeed in a well defined role. Startups aren't afforded this comfort as the roles are not well defined.
I use business, startup, and company interchangeably. Generally I tried to use business for O(n), startup for O(n^2), but I guess I wasn't strict with my usage...
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.
Yeah, almost necessary at a high level (and online), however I play against fish most of the time. I am also 18 and lack connections/capital/time to enter high level games.
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.
Maybe I just don’t understand the article but I really have 0 clue how they go about making their conclusions and really don’t understand what they are saying.
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.
Yeah training data is the major issue. You can try programmatically brute-forcing/generating random well-formed (compilable) data and training on that. Then heuristically pick exogenous programs from the output of the trained model to train a new "generation". The issue is in the possibility of the algorithm generating "exogenous" program and "heuristically" picking them out.
The github social media features are so weird I get around 10 follow requests per week from random people who follow >2k people something off happening there.
[0] https://hexdocs.pm/glance/glance.html