I used to interview mentors for a big EdTech company and met some of the smartest and most humble engineers who were all from Kenya.

If the two are indeed "Linked", I see a case for users-first browsers to show system metrics right along the page.

So the root cause was the model's indisposition to calling the skills. That seems contrary to what we see with function calling. Models call functions quite reliably most of the time. This is more likely because of the instructions not being clear about what skills are, as this snippet, albeit in isolation, seems to suggest:

> Before writing code, first explore the project structure, then invoke the nextjs-doc skill for documentation.

> Before writing code, first explore the project structure, then invoke the nextjs-doc skill for documentation.

Does the model even understand what this line even means?

I get excited every time I see a "Differentiable X" library, but this one had me the most excited! Seeing the methane molecule acquire its geometry is so cool. Can it work with more complex molecules like small amino acids?

The short answer is yes, but either memory (if the electron integrals are cached) or runtime (if they are not cached) currently scales like O(n^4) where n is the number of atoms.

In cached mode, it can currently jit compile the graph for molecules of around 10 atoms in ~5 minutes on one T4 gpu. Once the graph is compiled, the actual geometry optimization only takes a few seconds.

I’m working on optimizations that improve the scaling behavior (such as density fitting) with the goal of achieving similar or even better performance for molecules with ~50 atoms.

You might have mistaken tailwind and tailscale.

I have done so on countless occasions, but this is about the css "framework".

tbh it annoys me when i want to go to my tailscale console but my browser takes me to tailwindcss which I have never used..

You're thinking of Tailscale.

Tailwind is a UI styling and components company. Are you thinking of Tailscale?

I think you mean tailscale

I find kitchenwork provides good case studies for computational thinking. Thinking about stacking dishes by their sizes leads to a tour of sorting algorithms and datastructures. Thinking about predicting the prices of different preparations that use the same ingredients leads to principal component analysis.

There's also the application of computational methods to cooking.

There's a gut feeling that comes from having gotten your hands dirty enough that tells you if the LLM is being smart or spitting out bullshit.

The main issue I have with LLM-generated solutions, is that LLMs never seem to know about “Occam’s Razor.”

Their solution usually benefits from some simplification.

Ability is a combination of aptitude, skills, persistence, and exposure. More importantly, intention matters and it show up in the quality of your work. If the intention is to cut corners, no one can stop you from doing shoddy work. Number of years and titles do not matter much if the other parameters are low.

Aptitude paves the way for exploration: learning languages, paradigms, modeling techniques, discovering gotchas and so on. Skills follow from practice and practice requires a tough mindset where you don't give up easily.

So many software engineers learn to code just to pass exams and interviews. They claim they have strong logical reasoning. However, they have only been exposed to ideas and patterns from competitive programming rut. They have never even seen code more complex than a few hundred lines. They haven't seen problems being modeled in different languages. They haven't had the regret of building complex software without enough design. They have not had the disappointment of overengineering solutions. They have never reverse-engineered legacy code. They have never single-stepped in a debugger. All they have learned is to make random changes until "It works on my machine".

Yes, software is complex, disposable, ever-changing and often ugly but that is no excuse for keeping it that way.

My ex-boss a principal data scientist wiped out his work laptop. He used to impress everyone with his Howitzer-like typing speed and was not a big believer in version control and backups etc.