former-aws

6 ms·

1.
by former-aws 7mo ago
Agree with your pov, especially the idea that you can only reject hypotheses, not confirm them. What I keep seeing operationally is that teams are forced to act before hypotheses can be tested or falsified. For example, inspections complete
2.
by former-aws 7mo ago
That’s a great way to put it. What I keep running into is that teams know the work is informal at the edge, but systems are designed as if formality can be enforced at capture time. In practice, that just pushes the work elsewhere. In your
3.
by former-aws 7mo ago
This asymmetry framing is really helpful. I’m curious in the cases where that 1% mattered, what made the reconstruction painful even when the raw artifacts existed? Was it ordering events, understanding intent/decisions, or just findin
4.
by former-aws 8mo ago
@scott-iii That’s exactly the kind of situation I’m trying to understand better. Out of curiosity, when you were piecing it together from the camera roll, what was hardest: ordering events, understanding why decisions were made, or just fin
5.
Ask HN: Where does operational truth live before it reaches "systems of record"?
4 points by former-aws 8mo ago|8 comments
6.
by former-aws 8mo ago
I know some friends who were put on the RIF list for being critical of AI strategy in a division within a large company, whose CEO is talking about intelligent minds and diffusion.
7.
by former-aws 2y ago
Glioblastoma has entered the chat :-)
8.
by former-aws 2y ago
Am better today, thank you :-). Your suggestions align with how we are thinking about addressing this problem. I like the suggestion around assistant vs admin. It strongly resonates with me and points us in a direction. btw, multiple anecdo
9.
by former-aws 2y ago
sorry a bug took me down, so could not reply earlier. Your comment hits the spot. I agree with you about not just seeing raw numbers or some kind of trend line, but get more analytical insights into what is really happening with the data a
10.
by former-aws 2y ago
sorry a bug took me down, so could not reply earlier. Thank you for your reply and a few follow-up questions and observations. 1. I do agree with you on the challenge between "feeling" vs "what i actually want". Is this
11.
Ask HN: How do you manage delivering dashboards between data teams and others?
3 points by former-aws 2y ago|6 comments
12.
Boeing agrees to buy Spirit aerosystems(reuters.com)
1 points by former-aws 2y ago|1 comments
13.
by former-aws 2y ago
Not just arsenic but also to remove the starch. Most folks don’t cool down the rice before eating so it’s preferable to remove the starch before eating.
14.
by former-aws 3y ago
Humans are the exception. Most animals return home as it gets dark. That is the natural pattern, something we are all primed via evolution. Your personal preference to get more light in the evening because you want to spend more time outdoo
15.
by former-aws 3y ago
DST is an abomination and must go. It doesn't align with natural circadian rhythms.
16.
by former-aws 4y ago
They are taking a charge back of $1.2bn with the layoff. Are you suggesting canceling the private performance would have costed a huge amount on similar lines?
17.
by former-aws 5y ago
This still does not make it equal between Amazon and the sellers. Remember sellers have to pay a listing fee, referral fee etc which Amazon does not have to. So with everything else being equal, sellers are basically paying a validation fee
18.
by former-aws 6y ago
My point is every cloud vendor needs to break up revenue by IaaS, SaaS, and PaaS. Each one looks strong and bad across segments and hence such a breakup will never happen. But saying AWS or MSFT is #1 misses the dominant segment where they
19.
by former-aws 6y ago
So does AWS. They include revenue from AWS Workmail and other SaaS offerings. Too bad MSFT or GOOG make more money than AWS with their SaaS or "Productive" offerings.
20.
by former-aws 6y ago
Cannot up vote this enough. During my time both at Retail and AWS it was perfectly normal to trawl production customer data and come up with ideas to launch competing products. Prices were always set lower or free offering justified as data