The approach that works for me is using Justin's Skycak methods he mentions in his books:

https://www.justinmath.com/books/

Check the shorter "Advice on upskilling" or "The Math Academy way" for well researched approach.

Basically, using the books as the source for the LLM to limit itself by RAG-ing the text and then generate the required tests, questions and explanation of the concepts.

The biggest problem wasn't the workload itself, but how inefficient learning was back then. It was the early days of the Internet, no coursera, no Google indexed uni sites, no LLMs, no online tutors. I only had a mediocre textbook and some notes I borrowed from friends when I couldn't attend lectures or labs. It took ages. I’d burn hours trying to parse two paragraphs in the textbook, only to ask mates who didn't understand it either.

Now, we have an incredible amount of information & tech at our fingertips: LLMs, Google NotebookLM, online tutors, dozens of textbooks, and sites like Anna's Archive packed with high-quality "free" material. With all this technology, the time it takes to understand, prepare, and master a topic is a fraction of what it used to be. It still takes work and discipline, but man... you can feed textbooks into an LLM and use targeted prompts to debug your exact knowledge gaps on the practice test you get from the LLM.

So I reckon it's easier now than before, simply because learning can be done so much more efficiently. After all these years, I still remember the hardest classes and the most stringent professors with great affection - even though I hated those bastards back then. My Group Theory prof once invalidated a definition I wrote because of a single missing word. At the time, I thought it was an injustice, but later I realized that word was crucial.

University is where young people must face hard work and reality, because it teaches them what true meritocracy is. It should be hard, it's the same way a gym workout should be hard or it’s ineffective.

The approach that works for me is using Justin's skycak methods he mentions in his books:

https://www.justinmath.com/books/

Check the shorter "Advice on upskilling" or "The Math Academy way" for well researched approach.

So what works for me

- open a project in ChatGPT/Notebook LM - dump all the relevant and highly cited materials (textbooks, papers) - dump the advice on upskilling text or a short summary I've written for the LLM

- create "Learning Goals", that contain what I want to learn, and how to estimate is my level good enough

1) Ask it to create a learning path from the materials, following the approach. Give that to an adversarial LLM for cross check. (just for sanity check)

2) Ask it to create an "entry test" to check what I do know and what I don't

3) Iterate step by step on each module/submodule from the learning path that intermingles the approach of: small theory step + small practical task + small test. Log what's missing/wrong in my dept log. Give the dept log at the end of the session to the LLM to incorporate/create another test/task.

What I have found useful in this approach is that it will generate a lot of practical tests/tasks for me and it will explain a concept in many ways until I understand it. Also it finds some prerequisites I might miss, but based on my tests and debt log unexpected things I thought I understood surface.

So with the limits of LLM and while building a mental map of the relevant parts it's usually enough to spot the hallucinations, but if you apply structured approaches these are minimal. And it's super good, because the number of practice tests and explanations is endless.

The interfaces are a bit clunky, but current multimodal LLMs are ok with images or even hand writing.

I will recommend that structured approach.

The modern societies run via those devices and the enforcement will move to the mostly free Internet that was "a long time ago, when it didn't matter as much".

The policy shift came around 2017 when the government understood that subsidies and closed market created lazy automakers (e.g. check the US market for the same dynamic in trucks). In 2018 they removed subsidies for cars less than 150km of range and changed the requirements to receive them tying them to battery density.

After Tesla came and subsidies were reduced, most of the inefficient companies went bankrupt. It was the free-market competition that killed them. The reason why it changes was the competition not the subsidy. The subsidy was somewhat important, but as many of us knows, nothing government run produces good consumer products, you need market and free choice to get productivity. So it was the free-market and subsidy cutting that created the BYD, Li Auto, Xpeng and Nio. That's the proof that without free market it's impossible to have strong companies. And without subsidies you can have strong companies, even if it takes more time.

In EU and USA subsidies didn't create a lot (Northvolt?), because there were not enough free-market driven demand and too much regulation to jumpstart the free-market forces that will start the engine of innovation.

So the answer is not "capitalism and free-market doesn't work and governmental industrial policy is the answer", but it's the market that creates the strong companies. You can speed it up with venture/government capital, but without the brutal competition in the marketplace where people vote with their wallets you won't make it work.

After Tesla came with special laws allowing it to work without Chinese co-ownership, but with a mandate to source from Chinese suppliers, this created the ecosystem needed for the next iteration, by the capitalist urge of Elon to get into the market and by free-market economy of suppliers to Tesla that are up to the standards to supply it.

The government set up the battlefield, and let the free-maket filter the strongest to survive.

In the Western countries the government and regulations are frequently the bane of the car companies. Good luck opening a mine or a battery factory in Germany, regulation, unions, green mandates, 36 hr. week. You can't fight with 1 leg tied.

https://www.youtube.com/watch?v=HUEvRyemKSg

As the internet become the place where people do a lot of things, no government (and especially no security services) will be able to keep themselves from trying to control it or at least monitor it. And with the new LLM features they can automatically do much more than before.

Human nature is a constant and when the government sees an easy way to enforce something, many more bureaucrats will try to do it.

I have a measure for all content I consume, quality/hr of reading/listening. If it's just a long video that has 2-3 questions that has caught my attention I'd be listening only those. If it's a long text that I might find something interesting I'll ask the LLM to summarize the main ideas as a filter before I decide to dive in.

Books, and their audiobooks version have on average much more bang per hour than random podcasts, because they're structured, authors had spend more time on them and you can cherry pick from a structure.

I also have caught myself using sloppy content as excuse not working on planned tasks with excuses like "this might be useful", or watching "productivity porn" videos. I think LLMs are good as a pre-filter for that.

But when it goes over the safe limit it should be enough to decide to ventilate.

https://www.youtube.com/watch?v=HUEvRyemKSg

As the internet become the place where people do a lot of things, no government (and especially no security services) will be able to keep themselves from trying to control it or at least monitor it. And with the new LLM features they can automatically do much more than before.

Human nature is a constant and when the government sees an easy way to enforce something, many more bureaucrats will try to do it.

There is a whole class of tricky documents. A decent (if you ignore the marketing bias) post about this problem can be found here:

https://getomni.ai/blog/ocr-benchmark

Even though the house is really old, it has been taken care of. There is no mold, the doors are still the same original ones, the fireplaces with decorative tiles are still there, and the wooden fireplace parts are still in good condition. I don't know how they did it, but it was built rather well.

Interesting tidbit: on the ceiling there was something like a Star of David. After asking LLMs what it was, one of them said that when Australia became a country, the Federation Star had only six points, denoting the newly incorporated states, but later a seventh point was added. Gemini told me that the frieze details were typical for the 1901-1910 period, and this helped me date the house.

This took about 10 minutes. Before, it would probably have taken me at least several hours of Googling.

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