This code creates an HTTP session, sets a cookie within that session, makes another request that automatically includes the cookie, and then prints the response showing the cookies that were sent.
I may miss the error, but could you elaborate where it is?
Thank you! And correct, I didn't modify the outputs. For small changes, you can just feed the commit history and ask an LLM to modify the docs. If there are lots of architecture-level changes, it would be easier to just feed the old docs and rewrite - it usually takes <10 minutes.
The Linux repository has ~50M tokens, which goes beyond the 1M token limit for Gemini 2.5 Pro.
I think there are two paths forward: (1) decompose the repository into smaller parts (e.g., kernel, shell, file system, etc.), or (2) wait for larger-context models with a 50M+ input limit.
Yeah, I'd recommend trying Gemini 2.5 Pro. I know early Gemini weren't great, but the recent one is really impressive in terms of coding ability. This project is kind of designed around the recent breakthrough.
One thing to note is that the tutorial generation depends largely on Gemini 2.5 Pro. Its code understanding ability is very good, combined with its large 1M context window for a holistic understanding of the code. This leads to very satisfactory tutorial results.
However, Gemini 2.5 Pro was released just late last month. Since Komment.ai launched earlier this year, I don't think models at that time could generate results of that quality.
Yes! This is an initial prototype. Good to see the interest, and I'm considering digging deeper by creating more tailored tutorials for different types of projects. E.g., if we know it's web dev, we could generate tutorials based more on request flows, API endpoints, database interactions, etc. If we know it's a more long-term maintained projects, we can focus on identifying refactoring patterns.
Let me clarify: we are discussing how the Agent is internally implemented, given LLM calls and tools. It can be built using a graph, where one node makes decisions that branch out to tools and can loop back.
The workflow can vary. For example, it can involve multiple LLM calls chained together without branching or looping. It can also be built using a graph.
I know the terms "graph" and "workflow" can be a bit confusing. It’s like we have a low-level 'cache' at the CPU level and then a high-level 'cache' in software.