Since the release of ChatGPT more than 2 years ago, I have felt a strong disconnect with the opinions on HackerNews about AI. It really feels like an Anti-AI (especially anti-OpenAI) echo chamber, neverendingly predicting the advent of AI winters, always repeating the same arguments (usually some flavor of “it’s not intelligent”, “what is AGI?”, “how can you trust it?”). Well, given that the generated code compiles, the tests pass and the program works as expected, I’m not sure what there is to complain about. I feel that HN is about as wrong about AI as it was about cryptocurrencies. Eventually, I mostly stopped reading the comments, I just use it as a link aggregator now. ...

I took a day off on this Christmas day, and I got inspired to revive this blog by reading Simon Willison’s “approach to running a link blog”. I like his blog to keep updated about the new models’ capabilities and developments in the AI world in general. He makes small experiments to put the models into practice, with a good dose of enthusiasm, and I want to follow a similar approach for this blog: sharing interesting articles I read, and some of my daily programming experiments, while “trying to add something extra”. ...

In this post, we’ll discuss various common ways of handling API rate limits, and I’ll introduce a new tool I’ve been working on called MeterFlow. Most APIs have rate limits, usually counted in requests per minute, but other resources can be limited as well (for instance, the number of characters you can translate with the Google Translate API, or the number of tokens you can generate with some LLM API). When you exceed the rate limit, the API will return a 429 status code, telling you to slow down. There are various ways of dealing with API rate limits in your code. ...