I've started self-hosting email in ~2021. It took about 6 months (of mostly waiting) to clean up reputation of IP I've gotten (was in a few spam lists). After that - it was incredibly easy to maintain. It's literally just running mailcow update every now and then and that's it. So, if you are willing to fight for a bit in that initial stretch - no, it's not hard; yes, you can self-host your email.
It told me my ~10 year old js project was 50% AI generated. Yeah, this is more or less the same as "AI text detector" stuff that won't work reliably (but people who don't understand LLMs will still use it to blame others)
Those games are usually brilliant - but those are very rare. Like "once in a few years" kind of rare IMO. While that is a valid approach, I play way more than that haha!
What I found interesting with Vaporlens is that it surfaces things that people think about the game - and if you find games where you like all the positives and don't mind largest negatives (because those are very often very subjective) - you're in a for a pretty good time.
It's also quite amusing to me that using fairly basic vector similarity on points text resulted in a pretty decent "similar games" section :D
Built vaporlens.app in my free time using LLMs (specifically gemini, first 2.0-flash, recently moved to 2.5-flash).
It processes Steam game reviews and provides one page summary of what people thing about the game. Have been gradually improving it and adding some features from community feedback. Has been good fun.
I've started self-hosting mailcow (dockerized) on a Hetzner VM ~6 years ago or so. It took about 6 month to clean the rep of my IP address as it unfortunately been in spam lists previously - but after that, it's been a pretty smooth sailing. And all for a measly ~10 euro/mo including daily backups.
Having a research background - for me, doing this in research was always easier. Because you generally have very straightforward ways to validate your hypotheses.
Validating things with customers - in my experience - can be extremely tricky as they might not even know what they want
To finally figure out how to effectively design and test MVPs / prototypes without overdoing it. I think this has been a bane of my startup life for pretty much whole ~14 years I've been doing it.
We're using top level wikidata classes (you can see specific classes in JSON response). Full list is not published yet, but will be available in the near future.
So, er, how exactly would I prove it? Do you want me to share our code / db / etc we've worked on for past ~two years? :)
For one - how many wikidata classes exactly do you get from Wikineural? If I remember correctly, it can do four (person, location, organization, other). Our models do several thousands.
It'll likely annotate similar things in text since our model is also transformers-based (which is basically current state of art) - can't really do anything about that.
Yep, that'd be the pitch! In the beginning we'll just provide API for people who know what they need / want. Later on the plan is to have "all-in-one" products for end users directly (but that'll take time).
On KGs and industry - as far as we are aware, they are quite widespread. Most of fortune 500 companies use KGs in some form. QA is definitely one of the applications. There's also been quite a bit of work done on e.g. explainable AI using KGs lately (one of the areas we're working on as well).