cjf101

And over a increasingly small slice of time, as, due to the commodification of these things, the frontier businesses will see their customer base walk to the next one that does any better on their task.

This is probably ultimately unsustainable.

Seattle should do restaurants next, the number of places with %fees tacked on that aren't tips and aren't optional, just so they don't end up having $20+ entrees on the menu is growing steadily.

If the model was trained proper to the conversation with the researcher took place, there'd be no question of tainting the results. But if any amount of training on the model took place afterward, then yes, everything is thrown into doubt (a core problem with considering anything "original" from a model because of how >a % of everything ever written has been used a corpus for the training).

I hate to hand anything to Generative AI tools, but

While Great design breaks the mould, Very Good design is about surfacing the most expected outcomes for any action which reduces friction and lets people get work done. And this generation of Generative tools is very good at identifying the most common/most expected response to a prompt.

It's a weird circle with these things. If you _can't_ do the task you are using the LLM for, you probably shouldn't.

But if you can do the task well enough to at least recognize likely-to-be-correct output, then you can get a lot done in less time than you would do it without their assistance.

Is that worth the second order effects we're seeing? I'm not convinced, but it's definitely changed the way we do work.

One possible saving grace for Z is that, due to how expensive it is to keep around, video will probably disappear much more readily than text and photos.

I think that's the right broad question. Though LLMs properties mean that for some number of cases they will either make the results worse, or more confidently present wrong answers. This prompts the question: what do we mean by "quality" of results? Since the way current LLM interfaces tend to present results is quite different from traditional search.

If the current iteration of search engines are producing garbage results (due to an influx of garbage + SEO gaming their ranking systems) and LLMs are producing inaccurate results without any clear method proposed to correct them, why would combining the two systems not also produce garbage?

The problem I see with search is that the input is deeply hostile to what the consumers of search want. If the LLM's are particularly tuned to try and filter out that hostility, maybe I can see this going somewhere, but I suspect that just starts another arms race that the garbage producers are likely to win.

There was a bunch of reporting on how AI companies and researchers were using tools that ignored robots.txt. It's a "polite request" that these companies had a strong incentive to ignore, so they did. That incentive is still there, so it is likely that some of them will continue to do so.

Anecdata, with a few exceptions, the VR games I tried were impressive as an experience, but not really all that fun once the novelty passed. The limitations of the format clash with the kinds of games that are being made so I often felt like the games were limited, or toy-like. I think the argument made by the article does hit on something about why VR isn't really getting accepted. The games are wrong: but they might be wrong because of the limitations vs expectations of the developers and audience.

The comfort issue is real too. Even with the fairly svelte PSVR2, it's annoying to wear those things.

This is, IMO, the better way to approach this problem. Minification applies rules to transform code, if we know the rules, we can reverse the process (but can't recover any lost information directly).

A nice, constrained, way to use a LLM here to enhance this solution is to ask it some variation of "what should this function be named?" and feed the output to a rename refactoring function.

You could do the same for variables, or be more holistic and ask it to rename variables and add comments (but risk the LLM changing what the code does).

Another way I've encountered this is performance vs results. Performance is the things you do that you believe will lead to results. Results aren't always in your control (especially in competitive environments), but performance absolutely is. It's a lot easier to feel you are getting somewhere when you focus on things that you control.

I dig this. Reminds me of column mode in MacOS's finder, which is similarly helpful in "rewinding" an exploration of a file system.

Would be interesting to rabbit other rabbit hole resources like Wikipedia or IMDB in this way too.