gnatolf

Yea, I know. Their volume may look impressive, the actual amount of material is quite small and 'burying' that absolutely non-toxic stuff isn't any problem.

'recyclable' is such a vague term. E.g. radiation-affected typically easily recycled materials are very hard to deal with (think e.g. pipe steel from power plants) and are effectively non-recyclable, instead of close to 100% recyclable, as their non-contaminated counterparts.

Opposed to that, battery recycling is mostly hard to deal with in terms of economics, and admittedly the chemistry involved is complex, but at least from a technical point of view, plenty of solutions are available - and the tech is coming in relatively quickly now that the demand is there (remember, first generation EVs are just now getting closer to EOL).

It's slightly amusing that recycling of a wind turbine is treated as if it was a big deal - yes the laminated rotor parts can't be part of circular economies, but the total material amount of this laughably small. All the metal components are very easily recycled.

Curious what's the argument for/against that here. I agree it's at least unusual.

Which inexplicably isn't know for soft landings

I am not as optimistic that locally run models will be able to compete anytime soon. And even if, the price to run them means you have to buy the compute/gear for a price that is likely equivalent to a lot of 'remote' tokens

Some shoes (Meindl Boots) actually go up in price for larger sizes (>46 EUR, 13 US I think) due to the additional cost of material.

Not a hint of adaptation to mobile (chrome/android).

My reaction was just the same, but look at gruber:

https://daringfireball.net/linked/2025/05/21/sam-and-jony-io

and gruber is stirring up drama about why his links don't do well on hn.

As disrespectful as it is wrong and overly generalized.

A lot of the comments on HN lately are rightfully focused on this formative brain exercise that leads to intuition and conceptual understanding that is chiselled away by the shortcuts that GenAI provides. I wonder where the gain of productivity from GenAI and the drop off in 'our brain'-quality intersects.

The churn of staying on top of this means to me that we'll also chew through experts of specific times much faster. Gone are the day of established, trusted top performers, as every other week somebody creates a newer, better way of doing things. Everybody is going to drop off the hot tech at some point. Very exhausting.

I couldn't agree more, this 'polished' style the finished comment comes in is super boring to read. It's hard to put the finger on it, but overall flow is just too... Samesame? I guess it's perfectly _expected_ to be predictable to read ;)

Mostly just SNR issues.

Not to take away from that fact, but the share of train-moved freight (over time) is more interesting. It probably highlights the complications of scaling train networks easily.

I did the same. In short examples like the ones used in the article, it's easy to reason about the states and transitions. But in a much larger codebase, it gets so much harder to even discover available transitions if one is leaning too much on the from/into implementations. Nice descriptive function names go a long way in terms of ergonomic coding.

Plenty of Europeans have cargo bikes and make do with 2-3 supermarket trips per week for families of 4-5 peeps.

Only bulk drinks (crates of beer/soda/...) are challenging. But for those, very often delivery systems are in place that surely are more efficient than individual trips anyways.

Mostly it's to cover up that the catalogue isn't as great anymore, isn't it? Since almost every big label took back the rights and started their own streaming service, Netflix simply doesn't have as much content (that anyone would want to see) anymore.

I quit all those platforms recently and I'm not missing the frustration of having to 'switch channels' through their incomprehensible categories and views anymore.

Given the rate of improvement wrt to llms, this may not hold true for long

And for a brief moment I stopped to think about whether we're looking at a horrible middle earth hallmark movie or just some 'clever' parody.

Any way you can back up that Copilot is a flop?

Could you elaborate on your issues with LangChain?

We're kinda headed towards using it as it seems to be a flexible enough abstraction that is relatively stable to work with, so I'd like to know if I'm overlooking something..?

As a material scientist, the roller coaster to figure out what 'diffusion' we're talking about here was surprisingly funny.

Means: maybe edit that title a bit :)

Interestingly enough, e.g. Elsevier accepts latex but has their own typesetting backend. Which typically means that the last editing steps are quite annoying, because even if one is using the provided latex templates, what actually happens for the final typesetting is done by some mechanical turk editor on a slightly different publishing system.

Most important to me was something not mentioned here: to make i/j les ambiguous, I but effort into explicitly add the right swoop at the bottom of the i, which allowed for the j to be a straight longish line dotted at the top.

I haven't tried cursor yet, but how is this different from the copilot plugin in vscode? Sounds pretty similar.

At least the AI-ness of the article is very clear. It's a horribly repetitive read with very little content.

This article makes it sound more complicated than it is, collecting all those edge cases. Many of them can be simply ignored, as in being no-op. Which is also what most cheap calculators do...

This is such an odd take

Thank you.

How is this related to https://docs.divio.com/documentation-system/ ? Looks incredibly identical.