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I went in trying to earn why Pi was such a good harness and did not learn anything. I’m really curious to learn why someone might consider it to be 10x better than Claude Code.

> Pi is Lindy ↗. It’s been around for a year and has arguably the biggest developer community across all harnesses. It’s here to stay.

BTW this isn’t what Lindy really means

What’s the context window?

Many people who enjoyed building and coding things are going through this right now, myself included.

In my case, I felt that I was pretty good at rapid product prototyping and had made that part of my identity. Then AI agents made it trivial. It took me some time to process and accept that reality. But I’m okay with it now. I simply had to move on.

After a while, I found two things that have helped:

1. Leaning into other areas where AI can boost my skills rather than just replace them, such as writing (for example, my last two posts made it to the front page of HN) and sales (leads, proposals, etc)

2. Taking on more ambitious projects. I used to think about side projects I could complete in days, and now I think more about projects I can complete in months. With AI, days have become hours and months have become days, so I simply try to think of projects that would have taken me a long time before.

What’s the context size?

That's not a fair read of what I said

I also found that interesting but our evaluation methodology is quite different, so I didn’t go too deep into it

It does, tbh I was also surprised by the results!

Thank you! Will take a look at your results.

I couldn't find much when I first looked into this, which is why I ended up writing the article.

In case anyone is curious about it:

Submission: https://devpost.com/software/netra-empowering-the-visually-i...

Discussion: https://gemini3.devpost.com/forum_topics/43663-winners-rant

Same for me. I actively ask the LLM to write as few tests as possible. Otherwise you end up redundant and low value ttests.

Reminder to use a cooldown period: https://dylancastillo.co/til/securing-package-managers.html

Just Pyodide for now!

I have the same experience with local models. I really want to use them, but right now, they're not on par with propietary models on capabilities nor speed (at least if you're using a Mac).

I’ve worked with great engineers from India/Pakistan. I didn’t hire them, so don’t know too much about the process of how to find them but they were definitely as good as anyone I’ve seen in Europe.

I live in Spain. I’ve been in the industry for the last 10 years.

I’ve seen from a very close distance several European companies move a big part of their operations to India. Have had close friends laid off recently and seen them struggle for months to find a new jobs. Plus, I see tighter freelance market these days.

This was unthinkable not long ago.

Which is why fewer and fewer companies are hiring in Europe.

Except we didn’t and there’s already an ongoing refugee crisis.

[0] https://terrytao.wordpress.com/2024/08/02/what-are-the-odds-...

[1] https://en.wikipedia.org/wiki/Venezuelan_refugee_crisis

While I agree that you must be careful when using structured outputs, the article doesn't provide good arguments:

1. In the examples provided, the author compares freeform CoT + JSON output vs. non-CoT structured output. This is unfair and biases the results towards what they wanted to show. These days, you don't need to include a "reasoning" field in the schema as mentioned in the article; you can just use thinking tokens (e.g., reasoning_effort for OpenAI models). You get the best of both worlds: freeform reasoning and structured output. I tested this, and the results were very similar for both.

2. Let Me Speak Freely? had several methodological issues. I address some of them (and .txt's rebuttal) here: https://dylancastillo.co/posts/say-what-you-mean-sometimes.h...

3. There's no silver bullet. Structured outputs might improve or worsen your results depending on the use case. What you really need to do is run your evals and make a decision based on the data.

Makes sense! I like the slider idea, but not sure if it’d introduce some bias to the results.