Lambda calculus is from the 1930s and predates computers, its point is that it is bare bones model of computation. It doesn't make much sense to compare it to modern languages in efficacy, as that seems to imagine someone came up with lambda calculus in 2010 along Java, C, Python, etc.

The super cool thing about it is that it is capable of expressing ALL the computation you know today, from the few primitives. An "If" statement for example is λb.λx.λy. b x y

If you need high scale globally distributed persistent data, uniform distribution of hash reads/writes, dont care for schema, and know your query will remain simple yeah its a fine choice.

I just wouldn't consider it outside of enterprise level

1. you have a limited number of global supported indexes, 5 iirc, which means your queries are very limited. If your use case ever expands beyond that you're pretty screwed. 2. You will have race conditions. Strong consistency is 2x the cost, and not supported on global indexes. 3. Data is split into 10GB partitions and all the read/write quotas are split evenly by the number of partitions. 100 reads you're paying for is actually 10 reads per partition if you have 10 partition. Hot sharding becomes a real problem.

Take your document data, stick it in a JSONB and you get the same performance way cheaper + query able/indexable columns. The only time Dynamo wins I think is it scales well globally, but you probably dont need it

Comments URL: https://news.ycombinator.com/item?id=47872628

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Even all the websites, desktop/mobile apps will become obsolete.

Ive also been getting increasingly annoyed with how tedious it is to do the same repetitive actions for simple tasks.

On the other hand technical books can be so overwhelmingly difficult that you need to go outside and do hours of learning to understand one tidbit of it

1. Using AI helps as part of the training process.

2. All the prompts going to openai/claude is a gold mine.