Oh yeah, the 365 version is terrible. And post of the time, it could have been a Python Gradio interface or similar simple implementation without having to fight so much to make basic things work. Most of what I want Excel to do it just isn’t efficient enough for; particularly with lets and lambdas, it’s gotten quite powerful as a programming paradigm where you can visualize and manipulate your data spatially in a kind of Logo / NetLogo style way which is really interesting, but the second you reference a few thousand cells a few times even a solid CPU starts screaming.

I use Excel for a decent number of tasks and can do some magic with it, but only ever really for work where it’s easier to share a weird Excel sheet than it is to pass around a Python script (which given I teach Python, isn’t actually as often as most people experience).

But what about those of us in R1C1 mode using lambdas to do recursive cell operations across data pulled from multiple sheets? Am I anywhere near the kinda of Eldritch horrors discussed? I’ve also written indirect references based on Sheet name to populate filters from web scraped tables. I just don’t know how deep the pit goes at this point.

C could just be a blank and you have to bit blit the arrow on yourself.

I don’t know why, but I still can’t open a core file without going I’m in. I don’t do QA, though, and so tinkering with final breath of my program frozen in time maintains some novelty.

My two cents, after years of Markdown (and md to PDF solutions) and LaTeX and a full two years of trying to commit to bashing my head against Word for work purposes, I’m really enjoying Typst. It didn’t take long to convert my themes, having docs I can import which are basically just variables to share across documents in a folder has been really helpful. Haven’t gone too deep into it but I’m excited to give it a deeper test run over the next little bit.

Yeah, this is the approach people are trying to take more now, the problem is generally amount of that data needed and verifying it’s high quality in the first place, but these systems are positive feedback loops both in training and in use. If you train on higher quality code, it will write higher quality code, but be less able to handle edge cases or potentially complete code in a salient way that wasn’t at the same quality bar or style as the training code.

On the use side, if you provide higher quality code as input when prompting, it is more likely to predict higher quality code because it’s continuing what was written. Using standard approaches, documenting, just generally following good practice with code before sending it to the LLM will majorly improve results.

The beatings will continue until morale productivity improves.

Edit: Sorry, morale is irrelevant.

When I teach story points (not in an official Agile Scrum capacity, just as part of a larger course) I emphasize that the points are for conversation and consensus more than actual estimates.

Saying this story is bigger than that one, and why, and seeing people in something like planning poker give drastically differing estimates is a great way to signal that people don’t really get the story or some major area wasn’t considered. It’s a great discussion tool. Then it also gives a really rough ballpark to help the PO reprioritize the next two sprints before planning, but I don’t think they should ever be taken too seriously (or else you probably wasted a ton of time trying to be accurate on something you’re not going to be accurate on).

Students usually start by using task-hours as their metric, and naturally get pretty granular with tasks. This is for smaller projects - in larger ones, amortizing to just number of tasks is effectively the same as long as it’s not chewing away way more time in planning.