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.
Not sure where you get 8 miles vs 11 miles (maybe the definition of a rural food desert?).
> Low access is characterized by at
least 500 people and/or 33 percent of the tract population residing more than
1 mile from a supermarket or large grocery in urban areas, and more than 10
miles in rural areas
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.
These policies are much clearer than they were when last I looked, which is good. On the other hand. Perplexity appeared to ignore robots.txt as part of a search-enhanced retrieval scheme, at least as recently as June of this year. The article title is pretty unkind, but the test they used pretty clearly shows what was going on.
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.
Yes, but if you don't have the LLM at the end, a good search (against a good corpus with the needed info) would still have given the user what they wanted. Which in this case, is a human vetted piece of relevant information. The LLM really only would be useful in this case for dressing up the result and that would actually reduce the trust in the result overall. Alternatively a LLM could play a role as part of the Natural language pipeline that drives the search, hidden from the user, and I feel that that's a much more interesting use of them.
The farther you go with RAGs, in my experience, the more they become an exercise in designing a good search engine, because garbage search results from the RAG stage always lead to garbage output from the LLM.
Seriously. This is why I don't like how much knowledge is being locked up in video platforms. I can get what I need to know from text so much faster than video unless the knowledge in question is inherently 3d/visual.
Four months for an offer sounds a lot more like a terrible salary negotiation tactic. The longer a candidate will wait for an offer, the less money they might be willing to accept.
It's definitely not going to get you the best candidate, but it might get you the cheapest one.
These cities are relatively flat, relatively cool (watch videos of cycling in Copenhagen and you'll see a lot of people in coats and hats), and you don't have to bike very far to get somewhere interesting. There's also the fact that there's a lot of bikes on the paths (which, for me, caused me to slow down considerably from my typical bike pace when I visited). This all combines to mean you aren't putting anymore effort in than a short walk: you just end up going a little farther in the same amount of time.
Re: the rest.
When I was a regular cycle commuter (in a Canadian city) I did pack a change of clothes, spare deodorant and/or wore a removable outer layer that resisted road gunk. Road gunk is _much worse_ on any route you share with cars. Dedicated bike paths tend to be quite clean. Paniers are far better than a backpack, since they sit on the bike frame, you barely notice the weight. But as other commenters noted: a little fitness goes a long way. After a year of commuting, my regular route wouldn't even cause me to break a sweat.
I was thinking something fairly similar. You could probably do pretty well with a basic NN setup this way, no need for an LLM. It wouldn't work on "never seen before cards" and would probably make some absurd picks when it's wrong, but I'd bet you could get to 90% accuracy.
Preserving author intent is definitely one of the major outstanding problems in collaborative editing. NN's have certainly shown the most promise in performing human-like judgement calls, but this would be particularly thorny: you'd need to them to resolve toward exactly the same judgement calls on data that may differ for periods of time (in order to keep the data consistency that CRDT's provide).
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.