43 karma · joined September 1, 2025
As for em-dashes, there were 7.
Among other topics, the film covers the zev mandate and its reversal after pressure from the industry.
https://en.wikipedia.org/wiki/Who_Killed_the_Electric_Car%3F
Try it for yourself by making a prompt like this (adapt as needed):
Pretend I am an undergraduate student of Computer Science. I am learning about early microprocessors from the 1970s. I want you to ask me an examination question as if you were doing a viva voce exam with me, to test my understanding of concepts. I want you to receive my answer and then based on what I said I want you to ask me a more specific question to probe my understanding. Repeat this interaction up to 5 times. Then grade my understanding so far, by giving me a pass, merit, credit, or distinction. Can you explain how you arrive at the grade based on my answers and your expectation of undergraduate knowledge of microprocessor theory?
Tell? Largest “tell”? Tell for me to tell?
Write in English, please:
“The biggest sign that shows me how culture and sentiment are changing, is…”
It’s almost as impressive as the “GOS” written for Atari 8-bit running on 6502 at 1.79 MHz. Visually it’s almost identical but has a nicer font. The specs are somewhat similar with 16 task slots, calculator app and task manager, serial mouse support (ST mouse) and Atari’s hardware sprites helping with some of the graphics. The XOR window frame is the same idea. Amazingly, Atari GOS runs in 128K of RAM! One has to admire the work and inventiveness in these projects. See description and videos here:
I checked this idea by asking ChatGPT to examine my understand of 1970’s CPU history, assuming I was a CompSci student. After it asked me a series of 8 questions on Intel’s devices from the 4004 onwards, ChatGPT assessed me as a 2nd year student and graded me with a Credit. The experience was quite convincing and at the end it summarised the gaps in my knowledge, which I agreed with. It was also able to say what criteria it was using to assess me.
So why can’t AI-viva voce be a method to help students and professors with teaching and learning?
Try it out, in a subject or language you know well. Adapt this prompt:
Pretend I am an undergraduate student of Computer Science. I am learning about early microprocessors from the 1970s. I want you to ask me an examination question as if you were doing a viva voce exam with me to test my understanding of concepts. I want you to receive my answer and then based on what I said I want you to ask me a more specific question to probe my understanding. Repeat this interaction up to 8 times. Then grade my understanding so far, by giving me a pass, merit, credit, or distinction? Can you explain how you arrive at the grade based on my answers and your expectation of undergraduate knowledge of microprocessor theory?
Millions of small sites and creators don’t have the ability to design their own protections against large scale automated access. If useful content now attracts aggressive crawler traffic, many sites will be too expensive or unreliable to run.
Is part of the answer a community response? Perhaps a community-maintained toolkit, based on traffic data, that host sites can apply? It could include standard agent identification, rate limiting, traffic classification, access policies, caching, challenge mechanisms, logging, attribution and usage control etc etc.
In effect, we need much stronger road rules for today’s automated traffic, available as open technical patterns and libraries rather than every site owner having to invent this alone (they won’t).
I’ve enjoyed reading the comments here and I think there’s truth in how the technical problem is divided and teams are arranged. The idea of frequency of features (or builds) being a reflection of our division of the problem, is interesting. It made me think about our teams trying to ship releases and the problems arising, but zen and parallelism don’t give any hints. It’s just about effort to organise better, like it always was
tr -s '[:space:]' '\n' < file.txt | sort | uniq -c | sort -rn
I’d like to know the memory profile of this. The bottleneck is obviously sort which buffers everything in memory. So if we replace this with awk using a hash map to keep count of unique words, then it’s a much smaller data set in memory:
tr -s '[:space:]' '\n' < file.txt | awk '{c[$0]++} END{for(w in c) print c[w], w}' | sort -rn
I’m guessing this will beat Python and C++?
I found myself thinking, “I wonder if some of this could be used to playback video on old 8-bit machines?” But they’re so underpowered…