Something that's been amusing me about Cursor is that I feel a lot of the excitement about it isn't so much about Cursor, it's people realizing that LLMs have got REALLY good at writing code now. GPT-4 was good for the past year, but the latest models (especially Claude 3.5 Sonnet) are spectacular. Those of us who've been copying and pasting LLM-generated code back and forth from ChatGPT and Claude.ai for the past ye…
I don’t share the sentiment that it’s “very good”. It’s strongly mediocre at basic tasks and regularly fails at anything complex.
I usually try to actually provide AWS resource IDs / VPC id's, bucket names and everything and at least with AWS / Python stuff it is good (yes, some corners still but the trend is very good).
My prompts aren't short but it spell out very clearly what I want. To process 2 million XML files let's say stored in S3, using an SQS queue ZZZ that will pass in S3 object ID's in this format, store results back to RDS Postgresql here. Use Loguru or whatever for logging. I provide sample XML files, sample results, schema for database (DDL) etc etc. Then you can iterate on this - deploy using docker to ECS, use fargate, then do some scale out when queue depth is deep, go to asyncio or multi-process or multi-threading etc etc and just build. It goes quick.