Yea. It's hard to tell what's true anymore. I thought Russia would be out of resources in 3 months. It's been 4 years. I thought Rafah would survive. It's completely flattened. Thought global markets would crash after tariffs. It has survived.
I'm convinced we're in some kind of propaganda machine right now.
I used to watch him a lot, but he started talking about AI (I work at a big lab) and it was all wrong, so I'm not sure if I can trust his analysis anymore :(
This guy is crazy to write an opinion article without examples (only the opinions) and without expertise or understanding of the history, the current solutions, and their differences.
React is bad is youre bad. Its much more fundamental than other frameworks. It also paradigms shifts from imperative to (more) functional. So if you are not comfortable with closures and side effects (like the author), you will get lost.
None of the pattern from any framework is new. Theres only so many ways to design systems, you either use callbacks, observers, or events. All have their pros and cons (where imo observers and events are inferior due to their quick branching factor in larger codebases). React gives the option to use any of these.
React is bad in the same way C++ is bad. It's not really, but it does take a while to learn the ins and outs. In the end, the pattern React provides is much more powerful when used right. But it's not a one size fits all, a lot of the time you don't need the flexibility if you're new to it and just want to get something done.
Right? If anyone likes 2 way binding they should have tried Polymer. 2 way binding is disgustingly complex for large projects and that's why everyone eventually drops it.
Scale | Fullstack Engineer, Frontend Engineer, Field Engineer, Product Engineer, UX Designer | San Francisco | Full Time
Scale (YC16) is the leader in the data annotations, ML data management, AI infrastructure service. I'm a senior dev on the Nucleus team, it's been super exciting seeing all big tech innovators signed up to use our services. But, we've fallen significantly behind on our hiring. On my team, we are looking for senior frontend eng. On other teams, we are looking to hire a lot of designers, engineers for our e-commerce product, engineers with security clearance, engineers familiar with synthetic data generation, and engineers for with experience in bottoms up adoption.
Fill out the form and I can provide a referral or just have a chat if you're curious about anything.
Scale | Backend Engineer, Field Engineer, ML Research Engineer, Product Engineer, UX Design | San Francisco | Full Time
Scale (YC16) is the leader in the data annotation and data management for ai and we are looking to significantly increase our engineering team by next year. Above are some of the role titles we are looking for. In addition, we are looking for people with experience in any of: 3d rendering, nlp/language, ml infra, full stack eng.
Fill out the form and I can provide a referral, or if you just want to learn more Scale. I'm a senior dev on the Nucleus team and will be willing to just chat as well if you want to learn more about the company.
Recently, I came across some threads asking why data labeling is difficult. (I have my biases as engineer) In my opinion, it's because labeling it essentially determining truth. But, truth requires context, interpretation, and domain knowledge. Sometimes it's easy (with caveats like dataset bias, labeler bias, taxonomy bias). But, for more complex labels, truth is not easily abstractable nor tractable.
I'm convinced we're in some kind of propaganda machine right now.