Does anyone remember the art of optimising MSDOS startup to have enough free memory for games? And inspecting gorillas.bas? For me, this probably contributed to an interest to learn more and experiment. In fact, I'd like to encourage my son to a similar creative exploration, but don't how this is going to happen when pulled into the current generation of games and videos.
Yeah, I spent more time than I cared to admit fiddling with DEVICE(HIGH) lines, tweaking FILES= and BUFFERS=, running MEMMAKER.EXE over and over as if that would do something, but it was never the real thing. The real thing is making the machine do something I wanted instead of what the manufacturer wanted. For a kid of this generation, I'd look for games with reasonable modding APIs, perhaps something like Lua, and ideally something where playing multiplayer lets him show his creations off to his friends.
From there, look to packages like LÖVE which still use Lua but give full control over the whole game, and help him explore and wrangle the things he needs to understand to make his programming real. And if the lower levels interest him, help him dig deeper. But I think modding and scripting is probably the best place to start.
Indeed: config.sys autoexec.bat, EMS, HIGHMEM, and all those terrible early sound blaster drivers, mouse drivers, network drivers... I think the one I found hardest to get running was Quarantine. But it was definitely one of the best games. So imaginative for the era. And Australian! With music from some later famous bands! https://www.youtube.com/watch?v=QwO8XWbB1Pk&list=PLA5hK1g6CN...https://www.playdosgames.com/play/quarantine
This is the right mindset. Securing huge piles of heterogeneous data while giving PhD students the freedom to "play" with it are quite conflicting goals.
From a bioinformatics perspective, the general approach is fine. Here is another post from today where someone landed on the similar technologies: https://news.ycombinator.com/item?id=47825381
Maybe a future direction will be the submission of detailed research, specifications and change plans for feature requests. Something that can be assessed by a human and turned into working code by both slides.
I wonder if that is an opportunity to build an Open-Source platform focused on this, replacing GitHub as the collaboration platform of a time where code was valuable.
Yes, they are only mentioned in the last section. Other notable misses would be Ion Torrent and MGI. Still a nice article with a focus on the key technologies.
Agree. I used a color e-ink to display my son's school timetable (previously discussed at https://news.ycombinator.com/item?id=42408546), and it has been running reliably for 18 months, with charging needed every 4-5 months. I note that the author (cool project!) also earlier took the route of displaying websites as PNGs.
Using it for both transactional and marketing emails (but "only" for thousands of recipients) for some years. Could need some polish IMO, but the core offering is solid. Support is helpful, too.
I'd like to hypothesize a little bit about the strategy of OpenAI. Obviously, it is nice for academic users that there is a new option for collaborative LaTeX editing plus LLM integration for free. At the same time, I don't think there is much added revenue expected here, for example, from Pro features or additional LLM usage plans. My theory is that the value lies in the training data received from highly skilled academics in the form of accepted and declined suggestions.
It is nice for academics, but I would ask why? These aren't tasks you can't do yourself. Yes it's all in one place, but it's not like doing the exact same thing previously was ridiculous to setup.
A comparison comes to mind is the n8n workflow type product they put out before. N8n takes setup. Proofreading, asking for more relevant papers, converting pictures to latex code, etc doesn't take any setup. People do this with or without this tool almost identically.
Even that would be quite niche for OpenAI. They raised far too much capital, and now have to deliver on AGI, fast. Or an ultra-high-growth segment, which has not materialized.
The reason? I can give you the full source for Sam Altman:
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