LLMs vs reinforcement learning in coding
LLMs lack the accuracy and deterministic behavior of reinforcement learning while offering more generality, which makes them weak at problem solving in coding.
LLM is not the only game in town
LLMs may not be the final word in AI coding at least. Reinforcement Learning (RL) models score on many fronts. RL models possess greater predictive accuracy and superior understanding of code syntax and semantics. They adapt and learn from errors, honing their problem-solving skills and enhancing their autonomy over time. Their approach reduces the risk of overfitting, providing a better generalized solution. LLMs are the talk of the town but their lack of context understanding and deterministic behavior might limit their applicability in large-scale autonomous coding. This, and other deep dives, this week in NPW Insights.
Insights
Another serverless pitfall in AWS: Corey Quinn
Citing the example of his newsletter, which uses serverless AWS services like Lambda, DynamoDB, and API Gateway, Corey Quinn says that the “heavy-lifting” that these serverless offerings abstract from the users is what is actually needed to keep them current with the upstream changes. Because most users leave their serverless deployments untouched for years, multiple changes in underlying infrastructure make it difficult to point to the cause of a break, because as they grow “out-of-touch” owing to the serverless abstraction. The only way to address this is to schedule regular redeployments, ideally between minor version changes, and always starting projects with a staging and production environment – all of which adds to the overheads which serverless claims to eliminate. BLOG
