> flagged it as “no” because they had too many position changes
This is wrong and misleading in many ways, you should ask people why they left, you should give a chance to those who have good skillset, not based on number of jobs they had. I know a lot of people who worked on contracts, so they had to change employers frequently, and I know a lot of people who were looking for a place, as juniors, to learn from good mentors. It is really hard to find mentors in Startups, and not everybody can make it to big companies. If companies don't invest on their employees, they leave.
I think the author didn't cover a lot of fundamental research behind NoSQL. If you are reading this comment, and want to dive into SQL/NoSQL DB fundamentals, I highly recommend checking CMU DB (Andy Pavlo) lectures [1] [2].
One book that helped me understand these challenges was "Hard things about hard things". I recommend/gift the book to any (engineer) friend who is burning to start his own business. It's a different game. Even Sr. Engineer is a different game than coding, we spend some time on System design, mentoring, presenting technical outcomes of our work/PoCs, writing new product requirements, .. etc. I think these tasks require other skillsets as well.
Disinformation is a problem for all social media platforms, for all languages, FB is one of those platforms. I have the feeling that media focuses more on FB & English, but we should rather focus on the problem as whole and its harm for all communities around the globe.
I work in a large organization, in an industry that is naturally slow. The work (in the industry) is slow because it's critical to deliver stable and reliable solution because large part of population and economy rely on this industry SW/HW. I have heard a lot of complains on how the process is slow, but in many cases, building on top of existing work takes a long time. It takes long time to understand legacy work, to go through older studies and projects, and to find spots to add contributions. Not all of us work on green field projects, but many of us maintain code/work that doesn't look "cool".
Is there any reference to delve in the details of GIL, multithreading, multiprocessing, high performance computing on top of Python eco-system?. There are some interesting projects such nimba, Dask, etc, but the resources are a bit scattered.
Social platforms amplifies all of our human qualities, and our interaction habits. Since old ages, people were striving to seek attention and show their work [1] [2]. After reading this book [3] indistractable, I started to reflect on how our educational systems are not designed to prepare students to live in this digital age, yesterday it's FB, today it's TikTk and tomorrow there will be something else. FB is just one Pawn in this game.
I know that siding with FB is one of these topics that are very controversial in HN, but I am not finding excuses for the practices of these companies, my point is that our kids will live in a different age than the one we lived in, educational systems should keep up with these challenges and find innovative way to prepare people to efficiently manage that.
I recently read "Software Engineering at Google" https://www.amazon.com/Software-Engineering-Google-Lessons-P..., and my take away was that engineering culture matters a lot to drive any success and innovation. Those who work there have the freedom to challenge anything, to steer the product, and as a result, find opportunities as described in the previous comment and fix it. The management works on shaping that innovation.
There is a graveyard in most Telecom company. ATT bought ad tech business AppNexus and media giant WarnerMedia back in 2018 (stone age of the internet) and didn't manage to operate it well, it's not the only one, Verizon has heavily in ad tech and media companies such as Yahoo!, but none of these companies brought the same internet culture shifting in the market as Google/FB/.. etc. We all love to hate social media companies, but these companies managed to tap into the opportunities presented and -arguably- didn't stop innovating.
I think the author was focusing more on general applications (given his research & industrial background). In computer vision & NLP, the field is a bit advanced and it's harder to come up with rules. The promise of Auto-ML is bigger in these two fields.
ML can help reduce technical debt at logic layer, but it increases the technical debt at the infrastructure layer. It's a challenge for any company to deploy, manage and monitor models in production. If you can get away with a simple rule, that's a bigger win for the product (I'm not talking about research here).
In the community, there is a trend that "complicated == better". imho, more is less in industrial ML. You need to deal with model management, worry about inference & latency when the model gets bigger. The author has another article where he argues that data scientists need to be full stack ninja. While I don't fully agree with that statement, I think it benefits the company in many many ways. Data scientists need to meet engineers in the middle, and all these challenges need to be considered from day 1. Another trend I see is that some data scientists are not driven by the question "Can we solve this problem for the company?", but rather "Can we solve this problem using ML/DL?". This will lead data scientists to use the shiny and trendy models, even if it is not suitable for the job. I would blame management here, in some environments, data scientists are evaluated based on "fancy" models they build, not solutions that they provide. Solutions can be simple (but not simpler) rules.
Oh Boy! Life is a marathon, not a sprint. You are still young (revisit these words when you reach 32, 42, and 52). So, hear me out. You need to work on developing habits, habits of winning, winning every time, develop a process of learning and improving and trust the process. Ignore failures, you might look boring and unsuccessful to some people, but once your habits start to work, it can change your orbit.
You’re not failing, boy. You are growing up and this is how life look like. If you read a lot of books/biographies, you will realize how some very successful people have had setbacks. Ignore these negative depressing whispers, and learn, read more. Most of us were in that spot before, do yourself a favor, and take some time to watch this talk
"Last Lecture: Achieving Your Childhood Dreams", Randy Pausch https://www.youtube.com/watch?v=ji5_MqicxSo&vl=en
> .. I still believe that ranking/recommendation models should be open.
Today, we don't know how social media recommend posts to us, we don't know if it is biased, or good for some sub communities. There are [1] some engineering blogs where we can see that is based on some research ideas (e.g. embeddings) are prune to bias [3] and "rich gets richer" phenomena. There should be an open marketplace where institutes/researchers submit open & explainable ranking/recommender systems. This is how you democratize access to such social platforms, but the business model opposes such idea to make it reality. There is a large body of research in the area of explainable recommendation systems/Explainable AI. There are regulations today to use Explainable AI systems in healthcare field, but few of them in areas that impact our mental health (e.g. the use of Social Media).
It is disappointing to read some comments saying that "It is not FB/Insta's fault?". Those who don't see the challenges that these teens go through is clearly delusional. While there are some good things in the tech, it clearly has a negative side that impact children's mental health through unrealistic beauty standards, and trap them into dopamine loops.
I still believe that ranking/recommendation models should be open, parents/individuals should have -at least- the choice to make these algorithms less harmful.
This is wrong and misleading in many ways, you should ask people why they left, you should give a chance to those who have good skillset, not based on number of jobs they had. I know a lot of people who worked on contracts, so they had to change employers frequently, and I know a lot of people who were looking for a place, as juniors, to learn from good mentors. It is really hard to find mentors in Startups, and not everybody can make it to big companies. If companies don't invest on their employees, they leave.