One previous company I was CTO of got acquired by Amazon and they spent 60 days going through everything, including every line of code. I doubt a fraud of this caliber would have gone unnoticed with that kind of due diligence.
Sometimes I wonder if there is a lot of scrutiny in small things but when things get large and complex they basically give up and wave it through.
I see a similar thing at my work in medical devices. In theory we have to validate all libraries we are using. So if you want to share some code you have to create a ton of documents. But when we use something like nodejs with hundreds of dependencies the whole process basically gets handwaved away because validating everything would be too much work.
I wouldn’t be surprised if they waved it through because “who would be dumb enough to provide us a fraudulent list of customers?” She was always going to be discovered once they tried to market to the list. So I could see them speedrunning due diligence under the assumption that, if it’s totally fraudulent, it will be obvious eventually and then we’ll sue her. The deal is not large enough to affect our bottom line, and the obvious risk of defrauding us makes it unlikely she’s defrauding us.
It's not that complex, there was nothing technical here. You could say this was 'social engineering' at some level.
She pushed back against access to the customer list claiming privacy laws as a shield. JPMorgan was overly eager and didn't want to blow up the deal by challenging her.
In programming, this is called bikeshedding. You present plans for some massively complicated industrial plant, and people will mostly skim it. Then you want to build a small bike shed for construction workers to use during the project, and now that they're presented with something understandable, everyone involved has to have input and the whole process drags out.
Sure but AI innovation is moving very fast, so three years is a long long time. Curious if he has some massive innovative work that’s going to change everything or it’s all just going to be a bust.
Great. The only thing missing is the infinite scrolling that surreptitiously loads another article to keep you reading, thinking you're still with the original article.
I made the move from CA to TX and don't regret it.
1. No state income tax.
2. Lower cost of living.
3. Less government interference in private affairs.
4. Less out-of-touch wokeness in public policy.
5. Shoplifting and car break-ins are still crime.
It seems to me that the right move here is to buy not one, but two Fisker Oceans from the used market: one for daily use and another for spare parts. This will cost only $42k, which is still 40% cheaper than one new.
This is the future. I think that in 3-4 years we'll all be able to generate our own TV shows, share them with friends, and collaborate on getting new seasons done, maybe even sell them. Personally, my entire digital shelf of show would be bleak dystopian sci-fi.
Yeah - I think you don't necessarily (or only the best) go to a Netflix or MGM, but you could see success like a lot of smaller podcast content creators do.
10,000 screaming fans can take you a long long way.
This is a weird article. I felt baited by its content, which has little to do with the title. Also, lots of marketing-y buzzy words glossing up a food forum.
There are very few pieces of software I have most respect for than Excel. It powers entire industries, from Finance to Science to Big Tech, is stable as hell, backward compatible, and won't stop innovating. And now you can run Python on it - I'd say it would dominate the world now, but it's already there.
> You can't underscale part of your app with a monolith. Your pipe has to be big enough for all of it.
Not necessarily. I owned one of Amazon’s most traffic-heavy services (3 million TPS). It was a monolith with roughly 20 APIs, all extremely high volume. To keep the monolith simple and be able to scale it up or down independently, the thousands of EC2 instances running it were separated in fleets, which serving a specific use case. Then we could for example scale the website fleet while keeping the payment fleet stable. The only catch is that teams needed to be allowed to call only the APIs representing the use case of that fleet (no calling payment-related APIs on the website fleet). Given the low number of APIs and basic ACL control, it was not a challenge.
