- jacobr1Honestly it feels like what I, or many of my colleagues would do if given the assignment. Take the current front page, or a summary of the top tropes or recurring topics, revise them for 1 or 2 steps of technical progress and call it a day. It isn't assignment to predict the future, it is an assignment to predict HN, which is a narrower thing.
- Is anything objectively amazing? Seems like an inherently subjective quality to evaluate.
- The cycle repeats frequently in industry. New waves of startups address a problem with better UX, and maybe some other details like increased automated and speed using more modern architectures. But feature-creep eventually makes the UX cumbersome, the complexity makes it hard to migrate to new paradigms or at least doing so without a ton of baggage, so they in turn are displaced by new startups.
- Right - the quality of your locks matter a lot less if your average 5-year-old tee-baller can through brick through the wind and climb in. One always needs to consider their threat model when considering what security to invest in getting.
- It is a focus, data, and benchmarking problem. If someone comes up with good benchmarks, which means having a good dataset, and gets some publicility around, they can attract the frontier labs attention to focus training and optimization effort on making the models better for that benchmark. This is how most the capabilities we have today have become useful. Maybe there is some emergent initial detection of utility, but the refinement comes from labs beating others on the benchmarks. So we need a slideshow benchmark and I think we'd see rapid improvement. LLMs are actually ok at a building html decks, not great, but ok. Enough so that if we there was some good objective criteria to tune things toward I think the last-mile kinks would get worked out (formats, object/text overlaps). the raw content is mainly a function of the core intelligence of model, so that wouldn't be impacted (if you get get it to build a good bullet-point markdown of you presentation today it would be just a good as a prezo, but maybe not as visually compelling as you like. Also this might need to be an agentic benchmark to allow for both text and image creation and other considerations like data sourcing. Which is why everyone doing this ends up building their own mini framework.
A ton of the reinforcement type training work really just aligning the vague commands a user would give to the same capability a model would produce with a much more flushed out prompt.
- We've reach a point of price stabilization and longevity for smartphones now that didn't exist for the first 10 year ramp. When every new model added fundamental capability, you always want to upgrade, with the sweet spot often being every other year. But now, with better build quality, batteries, and stabilization of features people will keep their phones for much longer. Or buy "new" models that are of older versions since the price/features have been acceptable to run most of the apps they care about for years now. Plenty of people still want the top end for similar reasons to why people buy design clothing, but we've reached a feature plateau. We hopefully are getting close to that with EVs. Seems like around 300 mile range standard was the key thing. Though improved AI driving could change that again.
- I think they meant the reverse. The majority of US EV sales are of Tesla's: https://cleantechnica.com/2025/04/15/auto-brands-leading-the...
- > If so, why aren't used dealers just including a battery swap in the price?
I think that is the main thing that needs to be figured out. I suspect the problem is that you need to get OEM battery replacements for older model cars and those aren't yet readily available or cheap. We are going to need aftermarket batteries to drive price competition in the market. The current car manufacturers aren't incentivised to support a secondary market when they are still focused on primary sales. Also not in the ICE market there is much more ability to scale capacity. The supply chain constraints for EVs, and batteries are much tighter, though that keeps getting better.
- That is the technicality here. Bullshit is getting spewed, but in most cases, direct falsehoods aren't gett reported. If you quote someone saying something untrue, the paper didn't present a falsehood, same with bias, omission, emphasis and misleading narratives or framings. If you avoid stating facts and just cite sources, you can maintain, that the media outlet didn't lie. But only in the limited technical sense of direct commission.
- Transportation crime fear is compounded by another issue: "scary people." I've personally never witnessed a crime. But I've seen plenty of people that raised my hackles, usually they seem intoxicated or are exhibiting some kind behavior that may indicate mental illness. Are they going to get up and stab me? Probably not, but it sure seems like it could happen, and it sometimes (though rarely in terms of transite miles) does happen. I can intellectually dismiss other low prevalence issues in a way that it is hard to do with public transit, viscerally.
- There difference isn't renting or selling a hammer. The difference is providing a hammer (rent/sell) VS providing a handyman that will use the hammer.
In the first case the manufacturer is only liable for defects, for normal use of the tool. So the manufacturer is NOT liable for misuse.
In the second case, the service provider IS liable for misuse of the tool. If they say, break down a whole wall for some odd reasons when making a repair, they would be liable.
In both cases there is a separation between user/manufacturer liability - but the question relevant to AI and SaaS is just that. Are you providing the tool, or delivering the service in question? In many cases, the fact the product provided is SaaS doesn't help - what you are getting is "tool as a service."
- Well, that depends on what we are selling. Are you selling the service, black-box, to accomplish the outcome? Or are you selling a tool. If you sell a hammer you aren't liable as the manufacturer if the purchaser murders someone with it. You might be liable if when swinging back it falls apart and maims someone - due to the unexpected defect - but also only for a reasonable timeframe and under reasonable usage conditions.
- >It's hard to be positive about the idea of your skills getting devalued and getting kicked to the curb.
I think it depends where people build their own identities in the value stream. Do you see yourself as a product/hacker type person and writing code just is a blocker on delivering your vision? Building greenfield prototypes is now 100x easier! Do you see yourself as a craftsperson that brings years of experience to hard technical challenges? Some folks see AI as an attack, and some see it as a way to remove some drudgery while they focus on harder problems. It is about mindset.
What skills do you value?
- And need to acquire land
- The availability angle changes things quite a bit. Having a single source of truth online sheet is much different than a file that is passed around.
- This is why in microservice architectures you try to have data stores encapsulated via each service. So the APIs can support some kind backward compatibility path as you roll out changes to clients that interact with the service (presuming the db migration has public API implications).
- I think this is part of the reason why I've had a bit more success with AI Coding than some of my colleagues. My pre-llm workflow was to rapidly build a crappy version of something so that I could better understand it, then rework it (even throw away to the prototype) to build something I now know how I want to handle. I've found even as plenty of thought leaders talk about this general approach (rapid prototyping, continuous refactoring, etc ...) that many engineers are resistant and want to think through the approach and then build it "right." Or alternatively just whip something out and don't throw it away, but rather toil on fixes to the their crappy first pass.
With AI this loop is much easier. It is cheap to even build 3 parallel implementations of something and maybe another where you let the system add whatever capability it thinks would be interesting. You can compare and use that to build much stronger "theory of the program" with requirements, where the separation of concerns are, how to integrate with the larger system. Then having AI build that, with close review of the output (which takes much less time if you know roughly what should be being built) works really well.
- Why would someone buy services from a prison vs an established company. Presumably the quality would be worse and there is a potential risk to reputation. The answer would be because the prison is substantially cheaper due to not needing to abide by labor laws. There are plenty of services where I'd be willing for forgo (some) quality for significant costs decreases.
- The prison could, for grift reasons. They can undercut competition because their costs are lower. If a union, or even a market-rate shop needs to pay, say, $20-hour for labor, and the prison can pay $1-hour (or day) they can charge much less, and then pocket the difference. Their advantage isn't a higher quality product just a cheaper one.
- There seems to be a presumption that private prisons are widespread. And while not rare, they are only 8% of prisons. There is widespread use of profit-seeking vendors like food suppliers or phone companies though.
I only bring this up because it seems like the mental model most people have is that 50--90% of prisons are private - mainly because it gets discussed so much. But the problems with prisons by-and-large involve government administration, not for-profit companies running the amok (despite that also happening in a much smaller number of cases).