David Gasquez
@davidgasquez.com
5.1K followers 1.1K following 960 posts
Data @ Protocol Labs. Open Data, Open Source, Open Protocols. Walks taker. Progressive Metal enjoyer. davidgasquez.com
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I've been curating some "Agentic Coding" tips and tricks for a while. Check them out if you're into that!

davidgasquez.com/handbook/art...
David Gasquez
David Gasquez personal website
davidgasquez.com
This pattern works like a charm with LLMs! Start with a simple and dumb question, get details, iterate it!
Las Lightning Talks suelen ser de mis momentos favoritos en las PyCon. Una pena que no haya este año. ¡El programa, por otro lado, se ve genial!
Love teaching teaching agents about CLIs!

davidgasquez.com/cursor-agent...

It's a bit dated now (write it on January 2025!) but showing them `uvx` and `npx` is an amazing way to give tons of CLIs without worring what is installed or not.
David Gasquez
David Gasquez personal website
davidgasquez.com
Reposted by David Gasquez
When you try to predict about the impact of AI tools on software engineering - a great analogy I heard from @simonwillison.net is to look at the impact of smartphones (+ the amazing cameras they have) on filmmaking.

Noticed that those able to shoot pro films using a smartphone are... pros?
Reposted by David Gasquez
I personally see collapse as an inevitability for every individual institution. Nothing lasts forever. The smart way to build systems is to enable reforms so that they can adjust to reality without destroying what works in the process.

This is pretty baked into the philosophy of Bluesky
Reposted by David Gasquez
if only there were a decentralized, immutable, and versioned way to fix this!
LINK ROT: 38% webpages that existed in 2013 were no longer available 10 years later.

Even among pages that existed in 2021, 22% no longer accessible just two years later. This is often because individual page was deleted or removed on otherwise functional website.

Many implications for knowledge 🧪
A line chart showing that 38% of webpages from 2013 were no longer accessible one decade later.
Reposted by David Gasquez
Oh, love this. Thanks for sharing Shawn!
I used to get to the same state ("feeling" how I want it to be done) by researching and looking at similar problems. Now, I go directly to implementation and learn by watching agents do it.
Is there a name for the pattern where you try building the same thing (feature, project, ...) a bunch of times with different agents to get a sense of the implementation design space?

I usually run things at least 2 or 3 times until I "feel" what is the way I want it to be built.
Been following the new notebook approach and really like it! Awesome work, Mike!
Both Evidence and Observable Framework seem to be slowing down in development.

- github.com/observablehq...
- github.com/evidence-dev...

Not sure why. Anyone has any context or ideas?
Setting up @tangled.sh as a mirror for a GitHub repository is simple:

$ git remote set-url --add --push origin [email protected]:TANGLED_USERNAME/REPOSITORY

$ git remote set-url --add --push origin [email protected]:GH_USERNAME/REPOSITORY.git
My dotfiles repository is now mirrored on @tangled.sh too!

tangled.sh/@davidgasque...
Spent some time refining the approach and benchmarking it. Just published a blog post about the core idea.

Can a bunch of LLM Agents be used to rank an arbitrary set of items in a consistent way? 🤖

davidgasquez.com/ranking-with...
I can't compare against MacOS since I've never had one but I haven't had any major issues in my development laptop in years.🤞

I've had a couple of interesting debugging sessions on my desktop computer though! Too much tinkering without reading the consequences.
Thanks for sharing the experience! Linux is not perfect but is fun*! 😅

* If you are into this type of fun.
From hallucination to reality! Cool example! 😅
I am! The times I've used it (low risk one off tasks), it has worked pretty well.

In this case, I manually reviewed the summaries and they all seemed correct. I already had some context on the projects so it only took a quick check to verify.
In the old times, I would have written an ad hoc Python script using a random library to do the processing/cleaning.

These days, inlining Claude Code (or `llm`) is just so much easier and faster!
Reposted by David Gasquez
usually the baseline is “nothing” so the right heuristic is usually not “is it ideal” but rather “is it better than nothing?”
Are there any benchmarks/evals that track model performance over time?

I've heard things like Claude/Cursor getting dummier over time (haven't noticed much myself) and am wondering if there is some data to back this.