14 karma · joined February 7, 2021
now: working on ai x logistics planning, making complex decision making streamlined through the power of llm + mip.
<my webpage: $user.com> <email: me@$user.com> <linkedin: goodprediction>
For domain-specific optimization, the value is in the solver integrations, specific constraints that form the seed, and the modular simulation that powers the visuals. The software is monetized, not the services around it.
> How will you stop LLMs from recreating it? Having worked this space for a while now I think there are two ways to ensure reliability (the real moat here), first is going deep into five-six problems that are complex enough that out of box solutions/simple prompting don't work well. Second, tightly coupling a simulator to provide rapid feedback that actually helps change manage and solve the "people" problem when optimizing operations.
Always interested in possibilities of LLMs interfacing with MIP solvers.
Okay, maybe I was a bit harsh, but it definitely doesn't pop up as often as deep learning and statistical machine learning. For those who wish to get deeper into this, I highly recommend Optimization over Integers by Bertsimas and Weismantel.
You prefer elegant, high-level solutions that are intuitive and accessible to other developers. You likely favor functional programming, clear abstractions, and code that reads like prose.
Abstract ↔ Concrete: +4 Abstract Human ↔ Computer Friendly: +9 Human-Friendly
[1] https://github.com/nschloe/termplotlib [2] https://github.com/dkogan/gnuplotlib [3] http://www.gnuplot.info/
As an example, recently, I had to create a bunch of FAQ pages, and I created a tool like [1]. While Pandoc can be used, I still need to worry about (at least basic) formatting the doc while typing it out in the word processor to make it look good in the HTML output.
I realize that you're trying to create your own brand, my concern is:
- if it's a brand that does all cuisines, it is going to attract people mainly because of the price point, and not uniqueness
- if the idea is to build multiple brands, one each for a specific type of food: a brand for Pizza, another for Biryani, etc., then scaling each is its own demon
please correct me if I'm not understanding it right.
On a side note,
> But it didnt work and they shut down all locations
Do you have any knowledge of why it didn't work? I have a few thoughts around this, and have discussed this with a friend who's a restaurateur, but would love to hear from you!
For generic work-banter, an app like Blind seems to cut it better. Given the anonymity, it is able to (anecdotally) elicit truer depictions of one's workplace, and this helps folks get real value through what are in essence (unaggregated) reviews of companies and job roles.
By using real profiles and establishing a set up of "professionalism", LI absolutely cuts/reduces the possibility of deriving real value for the layman (read non work influencer and non CXO). Apart from connecting with old colleagues and looking for a new job, why should the layman even bother logging into the platform on a fairly regular basis?
Having said that, LI is great for some things even for the layman. It has been great for looking for new jobs as the article states. But it seems to have a crisis of identity, and it needs to figure out their core audience or differentiate product offerings before it deteriorates into a job board for the laymen like me, in which case it will be possible for a new and trendy job board to come along and replace it.
For instance, if we have the following undirected connections: {(a,b), (b,c), (a,d)}. There could be a mechanism to make the graph more dense by the way of increasing interactions between (c,d) without needing, for example, a to like c's content for it to then appear on d's feed. Done naively, this could result in a lot of unwanted content on someone's feed, but I wonder if there are ways around it.
I think this is in some ways similar to reddit. While there are some great threads from time-to-time, it is a way for people to quickly vent/amuse themselves.