I used it for practice Spanish. LLMs have no issues with generating decent sentences in it. Also as I practiced vocabulary building, I didn't need specific grammar constructs or even to be very sensitive to the occasional mistakes in this area (which I don't remember to notice).
It took me quite a bit of iterations to get AI to generate output I found good enough and with acceptable errors rate for my practice but that was the part of fun.
If anybody wants to try the same route, the biggest mistake not to repeat is to trust LLMs to generate words definitions by them itself. It does it quite well, but still gives wrong definitions from time to time (~1% in my case for the OpenAI models 2 years ago). This wasn't acceptable to me and took a lot of effort to review and correct cards I already had. Feed vocabulary article with word definition to LLM instead if you want it to generate word definitions suitable to your level instead.
It doesn't but it also will not create a different sentence to you to practice a word.
I tried this (use sentences instead of words), it sucks: you spend more time per card, but you learn less efficient, as you learn not just a word but all the hints coming from the same sentence.
I wrote my own program 2 years ago, that generates new sentence on each review using AI (at that time I used Anki as scheduler). That was fun and quite effective as far as I can tell.
I would recommend others learning language past A1, maybe A2 level to do the same. This day you don't need Anki for this, just use one of FSRS libraries for scheduling.
Very sad. I never took his courses, for various reasons, but having something like this as an option available was quite inspiring in my never ending quest to become a better software engineer.
I wish David the best with his new teaching direction.
And I hope David will find a practical way to make these courses available for self-study.
I have very similar experience. I vibecoded a foreign language practice app for myself. It works decent from functional perspective and I don’t see too many bugs. But the biggest productivity constraint I see is the time I need to spend using it in order to understand what is working and where the issues are.
You absolutely can. This is one of recommended directions with agentic coding. But you can go farther and ask llm to write tests too. The review/approve them.
There is quite a bit of difference between not making a profit and consistently losing around $100m a year with apparently no path to at least revenue neutrality.
Generally it is a good advise, I found similar things very useful for me.
I think emphasis on paper and one notebook is wrong here and likely will fail quite a bit of people who will try it. Also prescriptions like "They're very detailed" (i.e. notes) are IMO too rigid.
Start from wherever suit you, play, experiment and pay attention what works for you, adjust and iterate. Don't fixate on shiny concepts, i.e. "engineering notebook", and the "need" its records to be dated, etc.
Try something, let it lapse. See if you are worse without it, then adopt it back. If you don't see the difference, so be it.
This question maybe formulated too harsh, but it is valuable. There are quite a few similar applications (I think I tried 2 or 3 of them). Some are around for couple of years.
What is new / unique in your approach?
Well, one can look at this as at least a step in the right direction, compared to the active collaboration with Germany during WW1 by the people who became Irish government by the time of WW2 arrived.
Summary is supposed to give you a taste of what the link destination talks about. If most of the page information can be fitted in one paragraph of summarization, the problem is with webpage, and visiting that webpage would have been a waste of the user time.
Short term - higher stock evaluation.
Long term - depends on what value AI bubble will be able to deliver. But it's not like Amazon is sitting out of this game, they do invest into their AI (Nova models), with little return to show for it.
All analysis there is based on percent of jobs posted.
It says nothing how absolute number of jobs postings has been changing during the time.
Not to mention that number of job posting is the function of employees retention in addition to head count changes, and employees retention will be likely different from region to region.
Same terrible experience for me while I was on FB.
I was spending a lot of time there and I do shop a lot online. They couldn’t come with relevant ad targeting for me.
For my wife they started to show relevant ads AFTER she went to settings and manually selected areas she is interested in.
This is not an advanced technology everyone claim FB has.
In USSR similar adage was: “No matter how much you steal from the State, you can’t get even.” (Сколько у государства не воруй — все равно своего не вернёшь.)
It took me quite a bit of iterations to get AI to generate output I found good enough and with acceptable errors rate for my practice but that was the part of fun.
If anybody wants to try the same route, the biggest mistake not to repeat is to trust LLMs to generate words definitions by them itself. It does it quite well, but still gives wrong definitions from time to time (~1% in my case for the OpenAI models 2 years ago). This wasn't acceptable to me and took a lot of effort to review and correct cards I already had. Feed vocabulary article with word definition to LLM instead if you want it to generate word definitions suitable to your level instead.