I like the concept!

thanks, do star the repo, help us spread the word and do contribute to the repository if you can!

Oops- typo in the title, should be “How I Maintain My Blog in the age of Agents”

Author here - I agree that to learn the best thing is to implement and fail along the way. My point was I would never professionally opt to write a sorting algorithm instead of using the builtin sort() most languages come equipped with

Author here - wrote this myself but I’ll take that as a complement :)

Nice! I made my own version of this many years ago, with a very basic manim animation

https://www.jerpint.io/blog/2021-03-18-cnn-cheatsheet/

I did a post [0] about this last year, and vanilla LLMs didn’t do nearly as well as I’d expected on advent of code, though I’d be curious to try this again with Claude code and codex

[0] https://www.jerpint.io/blog/2024-12-30-advent-of-code-llms/

LLMs, and especially coding focused models, have come a very long way in the past year.

The difference when working on larger tasks that require reasoning is night and day.

In theory it would be very interesting to go back and retry the 2024 tasks, but those will likely have ended up in the training data by now...

> LLMs, and especially coding focused models, have come a very long way in the past year.

I see people assert this all over the place, but personally I have decreased my usage of LLMs in the last year. During this change I’ve also increasingly developed the reputation of “the guy who can get things shipped” in my company.

I still use LLMs, and likely always will, but I no longer let them do the bulk of the work and have benefited from it.

Last April I asked Claude Sonnet 3.7 to solve AoC 2024 day 3 in x86-64 assembler and it one-shotted solutions for part 1 and 2(!)

It's true this was 4 months after AoC 2024 was out, so it may have been trained on the answer, but I think that's way too soon.

Day 3 in 2024 isn't a Math Olympiad tier problem or anything but it seems novel enough, and my prior experience with LLMs were that they were absolutely atrocious at assembler.

https://adventofcode.com/2024/day/3

Last year, I saw LLMs do well on the first week and accuracy drop off after that.

But as others have said, it’s a night and day difference now, particularly with code execution.

Current frontier agents can one shot solve all 2024 AoC puzzles, just by pasting in the puzzle description and the input data.

From watching them work, they read the spec, write the code, run it on the examples, refine the code until it passes, and so on.

But we can’t tell whether the puzzle solutions are in the training data.

I’m looking forward to seeing how well current agents perform on 2025’s puzzles.

They obviously have the puzzles in the training data, why are you acting like this is uncertain?

Best is very subjective depends what you want it to do and if you want to fine tune and how big you consider small

Let me ask the same with: - runs on a laptop CPU - decide if a long article is relevant to a specified topic. Maybe even a summary of the article or picking the interesting part as specified in prompt instructions. - no fine tuning please.

Thank you for any response!

Runs on a laptop. Good at friendly conversational dialogue.

Hey this is awesome! I built something very similar, context-llemur, it’s a CLI and MCP interface to manage context

https://github.com/jerpint/context-llemur

Major difference is a conversation doesn’t get stored, the LLM (or you) can use the MCP/CLI to update with the relevant context updates

I like the concept and have built my own context management tool for this very purpose!

https://github.com/jerpint/context-llemur

Though instead of being a single file, you and LLMs cater your context to be easily searchable (folders and files). It’s all version controlled too so you can easily update context as projects evolves.

I made a video showing how easy it is to pull in context to whatever IDE/desktop app/CLI tool you use https://m.youtube.com/watch?v=DgqlUpnC3uw

This is great! Gonna try this on my next project