I can provide evidence for your claim. The technical debt can easily snowball if the review process is not stringent enough to keep out unnecessary functions.
It’s going to be extremely difficult if PR and code reviews do not prune unnecessary functions. From what I’m experiencing now, there’s a lot of additional code that gets generated.
This is exactly the issue I’m facing especially when working with AI-generated codebases.
Coding is significantly faster but my understanding of the system takes a lot longer because I’m having to merge my mental model with what was produced.
There could be many factors at play here so it’s not clear what the main issue is. However, from experience, US VC funds typically come from other US institutions and so it’s an easier sell when the corporation is US-based. Rules and regulations are more well understood and less complex for funds. The article states the requirement is to flip the structure to have the parent company based in one of the 3 countries mentioned. Presumably, better business/returns/policies
What’s missing from the writer’s analysis is also the desire from the population to create such businesses. Having lived in Asia for a bit, most of these small businesses are not wildly profitable and not everyone is willing to put in the hard work and effort to running these affordable restaurants.
Have you tried using non-LLM based methods? Like starting with something rules-based and working through a layered multi-model setup?
That’s what we’ve been using for document extraction where accuracy needs precision (capital markets documents, medical assessments). We had a go at pure LLM with medical documents but the output was poor and felt like it would take substantial investment to create something more robust.
Yes - 100%. That's why I have a Slack where I invite friends and other people hacking away. Planning on setting up more remote social stuff to beat the 'loneliness' factor. Happy to send you an invite if you want. We're friendly.
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