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.
> 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.
I don’t think it’s that simple. I write in a functional language for my day job, yet React hooks style components still give me headaches because they are leaky abstractions where you have to know what they’re doing under the hood anyway.
This argument feels a lot like 15 years ago when C++ developers were saying C++ is bad if you’re bad. Some are probably still saying it, but its pitfalls and crippling complexity are pretty widely acknowledged at this point.
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.
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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.
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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.
To me it boils down to misunderstanding what the technology can do. If you are trying to have model that labels people as "unsuccessful" based on a picture (example from the article), of course you are setting yourself up for failure. If you're looking for a model that tells whether a manufactured part has a defect, you have a good chance at succeeding. The real question I have is why people ever think ML should be used for judgement calls.
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.