Funding for https://infinite-food.com/ - seeking $100M - now finalizing four strong patents in the non-military drone space. Had a couple of false start time wasting lawyers, but now it's home run time. We've got what seems to be a few simultaneous nice technical edges over the multibillion dollar investments in civilian aerial delivery of food from major early stage players to date. Can't wait to close, itching to get to market and start generating some proper California lunch money.
Simultaneously, working on some technical demonstration materials, including novel fabrication and supply chain, plus some reduced BOM strategies for greater efficiency in mass manufacturing (once we get cash over the line). Bit of electronics in there, some mechanical. Keeps me interested so it's not 100% admin.
Also getting back in to badminton, super fun, losing weight nicely, feeling better every week.
New ideas? AI government will have its day in our lifetime.
I'm currently going about it the other way, specifying custom magnetics to suppliers. Suffice it to say there are a lot more variables when you get in to manufactured designs, which may involve multiple coils, thermals, various component type options (SMD, THT), disparate wire types per coil, various footprint options, multiple core options, load modulation, etc.
I don't envy you at all. I'd ship a working prototype and say 'like this one' :) My ability to spec this would surely become an exercise in finding all of the ways in which I could mess it up.
Learning at industrial scale is sometimes temporally cheaper because it allows you to conclusively exhaust a design path through efficient and spectacularly memorable failure. ;)
For discrete designs rather than integrated chip design there are some less formal type systems already build in to KiCad, for example using net classes, footprint categories, and pin counts to limit parameter selection. I suspect other design tools are similar.
Strong type systems require zero exception domains, but unfortunately physics isn't a zero exception domain in the way software is: there's always an oddball requirement which has to cater to differences in supply chain, production process or partner, final assembly, testing, operating environment, etc.
In my experience what you tend to see emerge in thornier domains are multiple overlapping systems of relaxed formality that get the job done while retaining a comprehensibility through reduced cognitive load. That is, "useful approximations". In discrete design we can view the standard set of formalisms (symbols, footprints, netlists, etc.) as relaxed type system examples.
Each have issues. Each differ somewhat over time and package. Yet they are still how things are usually designed after decades of evolution, and I don't see that changing for manual discrete designs beyond relative triviality any time soon.
In fact, on zeitgeist I'd wager the problems people are seeing trying to march beyond the status quo in to AI designed schematics, board layouts and firmware are analogous to those issues your prospective type system is going to have problems exhaustively formalizing.
I have no experience designing custom silicon, the apparently intended domain here, but I am skeptical enough to wager it isn't free of the thorny problems we see in discrete electronics: that is, physics doesn't let you select arbitrarily without vengeance, so treating your system as a simple system of lego bricks (albeit 'guaranteed type-compatibile') is never going to yield reliable results. Specific decisions probably require multi-disciplinary insights across thermal models, EMI/radiation, timing and power, not just "the plug fits". Further complicating change is social friction and time friction. I'd wager the stronger designers are deeply invested in current tooling (hell, they probably wrote it) and not time-rich enough to try something new because "academia". Their projects are expensive and often set to difficult schedules.
Good luck changing the world! Perhaps building enhanced type systems specifically targeting the AI design domain would be the best approach, because uptake will be quicker than humans? Maybe consider joining one of the AI-designs-my-board startups.
All other names are generally considered either common or historic. Common names are regarded as too ambiguous for scientific use, they are generally only mentioned in relevance to collections such as "How do the local people in <area x> having <population y> of <latin name z> (who might help identify where it is growing) refer to the organism?". In a small number of cases local names confer ethnobotanical or cultural semantics.
I am well aware that laypeople don't always distinguish between various similar species of plants and animals, and I probably can't in some cases myself, but I am specifically interested in some of those "common or historic names" along with their "ethnobotanical or cultural semantics", to see how they might compare with words elsewhere.
For old Irish names I would have thought Gallic-Druidic cultural associations might have some sort of currency or influence. Maybe try looking for research with those conceptual frames of reference. Here's an example query to place with your favourite LLM: "make a list of the top 30 plants associated with traditional herbal lore in pre-modern ireland. seek gallic/druidic associations through etymology, lore and written record (if feasible). table format."
It's not perfect but it's pretty damn good these days. By EE standards it's positively awesome. Single platform vendor toolchain hell is why people leave commercial software and move to KiCad, which runs everywhere, is open source, and has a plugin architecture plus mostly every feature you could ever need except high end simulation (which just needs time).
Has anyone tried EasyEDA? I hear people raving about it, but I don't know if it's because of the tight integration with LCSC, the availability of symbols and footprints, or if it's really that good.
EasyEDA is basically a single-supply-chain web-SaaS alternative to open source multi-platform KiCad. Not a smart thing to invest time in. Furthermore, its design library is of worse than dubious quality. The vast majority of shared designs are probably non-functional to dangerous.
Worst case, you can draw custom footprints pretty easily.
AI is good but it tends to fall down when it comes to multiple conflicting design constraints, supply chain conditions, thermal considerations, mechanical considerations, and other concerns which are not same-domain as an electronic connectivity topology problem. Let's not get started on environmental factors, firmware hacks, board house physics, or assembly labor optimization.
Back when I ran the Shenzhen Gastronomic Society I did most of the alphabet in Bangkok one trip, beginning with Afghan. I'd wager it's better food than London on average, purely based on freshness and tropical inputs. I think if you were to pick the best city on earth for food it would have to have resident international populations, a tropical climate, and at least enclaves of wealth with a relatively free visa policy. Bangkok fits the bill. For variety I much prefer it to Singapore, KL, HK, Jakarta, Taipei, etc. Best for drinks has to be HCMC. Paris and New York are up there too, but you have to be 0.1% to enjoy them exhaustively. I spent 10 years designing food robots mostly because so much damn awesome food is ~unavailable outside its origin. Now raising for GTM/growth: https://infinite-food.com
Bangkok has the best food of anywhere I’ve been. The country is obsessed with food in general, while not being snobby about it. It is an absolute highlight of expat life there. The bar/cocktail scene can also hold its own against literally anywhere.
While you can often get bad versions of things if you go out of your way, many foods are hamstrung by supply chain, which is to say without a critical mass the labor intensive elements do not make sense or the specific crops are unavailable. Ask any migrant (eg. your next Uber driver) which foods they miss from their hometown or childhood and whether locally available versions are up to par. You'd be surprised at the details that emerge.
This is incredibly true even of things you wouldn't expect - Mexican food in Southern California is just different than Ohio even if you cook it yourself - because the ingredients may be labelled the same, but they're not.
Ahh, the naïvety of the scientific mind! The security theater is intended to prevent government beaurocrats' mates from having to get real jobs and keep them happily sponging off public money. Also, set themselves up for post-career high paid gigs with those same private sector beneficiaries, so they can't be done for corruption during their career. Yes, really. Ask an AI about mid to late career public sector transitions to private sector and cross-examine 100 top examples across markets perceived as 'low corruption index'.
Simultaneously, working on some technical demonstration materials, including novel fabrication and supply chain, plus some reduced BOM strategies for greater efficiency in mass manufacturing (once we get cash over the line). Bit of electronics in there, some mechanical. Keeps me interested so it's not 100% admin.
Also getting back in to badminton, super fun, losing weight nicely, feeling better every week.
New ideas? AI government will have its day in our lifetime.
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