nborwankar

Seems ironic to be working for a company without integrity, drawing a salary ie your livelihood from such an organization while believing somehow your personal integrity is not already impacted by doing so. The two seem incompatible to an outside observer.

Can you share what kinds of problems were conducive to hyperbolic embeddings in your experience. Also, separately, are you saying companies are using these in practice but don’t talk about them because of the advantage they give? Or am I reading too much into your last sentence.

They have vacuum-cup like pads on their feet?

+1 for Mathematica’s documentation system. It is the best of any software doc, OSS/free or closed/commercial. By far.

This is related to a classic relational database problem called “bill of materials problem” - suggest you Google that. Also look for book on Graphs and networks in SQL by Joe Celko where he goes into modeling graphs in SQL tables. Naive formulations will lead to deeply nested joins which balloon in computational cost.

Gives a whole new meaning to “manufactured consent“.

If it could support the pgvector extension it would be a super fast vector database with all the power of Pg - the relational aspect brings the ability to add and query using rich domain specific metadata usually contained in relational databases.

+1 on fastmail

I didn’t take a year fully off but last year was slow in my consulting work so I did a deep dive on the emerging LLM area.

One recommendation - get a beefy Mac laptop so you can run LLM’s locally. I got an M2 with 96G RAM. Makes a huge difference to your thinking about LLM’s when you can run your own and integrated it with little tasks here and there for experimenting.

Else I find most people think only of centralized closed LLM’s when they think of what’s possible. Severely limiting.

/r/LocalLlama on Reddit is a great community. Other than that check out llama.cpp and ggml.cpp and the whole ecosystem around that.

Cheers and good luck. Ping me on DM if you want more pointers.

Slightly unconnected but I’ve found a rather disturbing “dark pattern” in Amazon sales of food items in CA.

Background: CA requires a warning on food that has some threshold level of carcinogenic or harmful substance - esp lead and arsenic. It’s called CA 65 Warning or a Proposition 65 warning.

I make sure to look for these on the images of labels and ingredients when I buy food especially ground spices off Amazon.

The dark pattern: Label images show no CA 65 warning but when the food turns up it has such a warning! I bought bulk powdered cinnamon with this issue.

Recently I noticed that while Amazon does not show the warning on the label it has an innocuous small print link in your shopping cart that leads to the full text of the warning should you notice it and click on it

This is now in the vicinity of actually increasing the probability of physical harm. Because of not having the warning in the label AND having an almost ignorable warning at checkout when you are more focused on getting things done and move on to the next thing, as opposed to when you are in a more deliberative state while browsing.

Just want to put that out for folks in CA who might care about such things. I’m sure they wouldn’t try shtuff like this in EU.

Programmers Introduction to Mathematics by Jeremy Kun should get you started. Look on Amazon. Then you can decide if you want to do a formal course and which one. I would also strongly suggest you buy yourself a personal copy of Mathematica which will give you a very powerful portal into Mathematics via a notebook interface and an elegant functional programming language. The documentation is probably the best I have ever seen for a software product.

Book overview for Jeremy Kun book from Amazon A Programmer's Introduction to Mathematics uses your familiarity with ideas from programming and software to teach mathematics. You'll learn about the central objects and theorems of mathematics, including graphs, calculus, linear algebra, eigenvalues, optimization, and more. You'll also be immersed in the often unspoken cultural attitudes of mathematics, learning both how to read and write proofs while understanding why mathematics is the way it is. Between each technical chapter is an essay describing a different aspect of mathematical culture, and discussions of the insights and meta-insights that constitute mathematical intuition. As you learn, we'll use new mathematical ideas to create wondrous programs, from cryptographic schemes to neural networks to hyperbolic tessellations. Each chapter also contains a set of exercises that have you actively explore mathematical topics on your own. In short, this book will teach you to engage with mathematics. A Programmer's Introduction to Mathematics is written by Jeremy Kun, who has been writing about math and programming for 10 years on his blog "Math Intersect Programming." As of 2020, he works in datacenter optimization at Google.The second edition includes revisions to most chapters, some reorganized content and rewritten proofs, and the addition of three appendices.

That’s not quite correct.

Private data especially in the enterprise cannot use public LLM’s like GPT-4 or 5 or N. Use cases needing data privacy have to use an internally implemented LLM application. In Currently, RAG is a concrete and pragmatic enterprise use of LLM’s aside from summarization, which is not amenable to using GPT-4.

GPT-5 may very well be amazing. But unless it runs on-prem it can’t be used in many scenarios because of data privacy.

To the OP - learning how to run LLM’s locally via say Ollama (see ollama.ai) will get you started in a hands on manner. See the /r/LocaLlama subreddit for a very active community around running LLM’s locally.