Personally, I'm using Qwen, Phi, etc. to squeeze as much performance out of constrained hardware as possible. They're pretty good at searching my personal files which you mentioned in a previous comment.
edit: typos
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Personally, I'm using Qwen, Phi, etc. to squeeze as much performance out of constrained hardware as possible. They're pretty good at searching my personal files which you mentioned in a previous comment.
edit: typos
I'd counter forming a rebuttal against a search result would be fairly trivial. I.e an AI summary for "are oranges poisonous" would say "Moms Against Big Citrus says oranges are poisonous, but the Association of Orange Groves says they are not." When used for search, LLMs are a source for sources instead of the source of truth itself. The user decides which source to believe instead of deciding to believe the LLM itself.
> you might be fortunate to be in a position where nobody is relying on your velocity to ship products at competitive speed.
Admittedly the biggest drawback. AI is pretty terrible at my profession (electrical engineering, regardless of recent demos that are horrendous when examined under a microscope), but the software engineers I know haven't hand written industry code in months.
2. I don't know the laws of your jurisdiction, but banning a whole group of people from a public place would, by definition, not make it a public place.
3. The overwhelming majority of librarians view libraries as a service for everyone, including the homeless[0].
[0]: https://www.ala.org/sites/default/files/aboutala/content/olo...
Edit:clarification
That's the point. The solution should avoid information not being available. Requiring login will incentivize bots to create spam accounts and move the battle to a new frontier, hurting real people in the process.
Software moves so fast that its actually an anti feature. Libraries and SDKs measure lifetime support in years, but industrial hardware support *at minimum* is a decade. A lot of these new startups dont understand that established industry wont use them unless they known this shiny new system wont ve abandoned in a few years.
On the other hand, softwate devs trying to quickly make a product have something to get them going.
This is becoming more true as a larger percentage of hardware infrastructure is occupied by the datacenter economy.
A lot of datasheets are inaccessible without signing an NDA. Even if you're a VC-backed enterprise, some unnamed chip vendors will tell you to pound sand if you're less than 8 figures of annual revenue.
Worse, many recommendations in datasheets were written in the 80s, overkill for many applications, or completely wrong and won't be updated until an errata is published 6 months later. Given current workflows, I don't see how AI would help designing a PCB with an alpha chip that's doing anything remotely novel.
More to the articles point, AI designing ASICs or other Verilog/HDL defined components could be very interesting use case
It doesnt hurt to have multiple projects, im genuinely curious.