861–870 of 1001 posts
> I don't know what to do. Ride the wave. You rode it when websites/webapps were the wave. I came into software industry before internet, kept changing my horse. You are never too old to learn new tricks. The new wave create new kind of work and workers. Be one of them. Ride the beast, master the tools. It's the same game again.
I see many comments saying, "AI can't do X with 80-100% accuracy; therefore our professions are in good hands." While I don't want to sound overly pessimistic, the models are improving at a rapid rate. If asked ~3 years ago where the state of the models are today, it would sound like sci-fi if answered, "the models are creating full MVP apps in ~30 minutes with one prompt". The hurdles the models are facing now, like…
Nothing corroborated this. Performance on benchmarks has practically leveled off. The big gains have come from architecture (have a secondary LLM review output) or searching the internet. Also prices are going up. Everything points to the likelihood that we're at the top of the curve.
[There is plenty of data to support the claim that AI continues to improve, even exponentially.](https://epoch.ai/trends)
As for benchmarks I feel compelled to remind you that as soon as a metric becomes a goal, it ceases to be a useful metric. The models optimise for solving the benchmark and we create new benchmarks to assess broader intelligence. As models converge on 100%, progress obviously slows. That doesn't mean intelligence isn't improving fast. It just means that that benchmark is being well served and we need other benchmarks to assess other forms of intelligence.
I would like to take your bet that we're near the top of the curve. I take the side of Geoffrey Hinton, the Nobel Prize laureate scientist known for his work on artificial neural networks. He believes AI is getting better even faster than he predicted. He estimates that every seven months AI becomes able to handle tasks twice as long.
Sounds like the Dunning-Kruger effect to me. "I can do this with LLMs, therefore any Sr. engineer can do this with LLMs"
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> Or just like a 700 or so 1-founder companies in the world and everyone else is without work? This. But instead of 700 it's more likely that everyone will be a founder (more or less). It's already scary how easy it is to launch an MVP or produce prototypes with the latest models.
Then why don’t we see a bunch of amazing software for sale that solves every niche issue?
The app really is just several simple forms with some if/else logic, but claude code allowed them to get the app up and running and deployed on vercel's free tier, and it's Good Enough™ to save them an hour or so each day lost in messaging and chasing up things.
I don't think anyone would ever have targeted an app for sale to them, and it would have been hard to twist some sort of flow management app and integrate it with Zapier or something to handle external api calls. With claude code they could just tell it what they wanted and solve their very niche issue. That's why I think that even though LLM coding has improved so much you might not see more software for sale - it's easier for people to just...make their own software.
Ironically the entire blog title is the "human in the loop", is probably the biggest counter argument for this FUD. The AI will never ever be concious and responsible, and to function and govern properly in the universe you need to be concious. AI I repeat will never ever becomes one. Not even in the popular fictional Star Wars movie franchises where you can have cute robots but they all devoid of the conciousness for the ever powerful force. Heck even the clones cannot control and balance the force, the Star Wars ultimate conscience.
>Of course, this is good for brilliant engineers that never had the chance to get deep into the domain and now have better chances at getting a job, but it's also sad to think that other brilliant engineers that spent their lives collecting domain knowledge are now competing on the same lane.
Actually overall it's definitely a very good thing for humanities. For example, currently it's very difficult to become a medical specialist and most medical students just stop at GP. But globally there are severe shortages of medical specialists for example typical cardiologists to patients ratio in developing countries is about 100,000:1, and for neurologists it's even worst.
Let's say with AI enabled tools and LLM now these GP can upgrade themselves to become cardiologists easier than before. Let's say due to AI/LLM suddenly there's a big jump of the ratio to 10,000:1 or 10x incraese and improvement in the number of cardiologists without degrading much of the quality of services. Imagine if the typical waiting time for important and necessary procedures like angiography now is reduced to merely days or weeks rather than several months or up to a year. Thus naturally the salary of cardiologists will be not be as high as now, but they still will be compensated handsomely. But as humanity do we really care the cardiologists family just live in semi-D landed houses instead of bungalows, not much me think.
I always remember of the infamous Steve Jobs quote "Ideas are cheap". If execution is everything, and frontier LLMs solve execution, then ideas are the gateway to abundance now, but abundance alone does not guarantee "stickiness". What I think is often overlooked is the human "Willingness" and "Care" of staying with the thing for the lack of a better term. What I mean by that is that a lot of people just don't care e…
In this case, it's difficult for me to see what exactly is gained by offloading all of those decisions to a mean regression algorithm. I agree it's a fun toy, but I can't imagine actually listening to or deeply enjoying any song made with these music models.
I could imagine it being used successfully in some targeted part of the process. For example, manipulating drums or changing the timbre of some previously recorded instrument. In that case it's not much different than traditional music creation tools like sampling.
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"Woah, I cajoled a chatbot into building something I don't understand at all, I'm so good at my job!" That ship sailed when people started using compilers and stopped learning assembly language.
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> It's the expertise of engineers on the team that push it back on track. But how are you so sure your colleagues are not more "expert" than you? Prior LLMs there was room for very good engineers and mediocre engineers to work together in 99% of the companies out there. With LLMs, only the "best" engineers will survive, because nobody needs mediocre engineers anymore. This being HN, I imagine every engineer reading t…
The corporations and executives are already winning if you swallowed the concept of 'rockstar' engineer. Sure there are more and less experienced engineers, but even interns can and often do provide good input and spot mistakes made by seniors. The 'rockstar' engineer at most tech companies simply equates to the somewhat autistic guy with a brown nose who's working 15 hour days for a pat on the head from management (…
I love it! And posts on HN about Big Ideas and uses corpspeak to justify driving people to long hours. The engineer who's picked up talking points of his employer because he's well-paid and trapped on the spectrum, making it hard to comprehend a life of Play outside of work.
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Posting this under a burner so I don't dox myself: I work in FinTech on a regulated product. We have access to Mythos. Mythos identified part of our codebase that it confidently asserted was not complaint with a particular regulation and we were at grave risk by allowing it to operate the way it was. Except this was not the case, it had of course hallucinated what the regulation actually required (I know this because…
It was my impression that a whole lot of products are only pretending to be compliant, and that it's much more profitable to operate like that.
Early stage startups, maybe, but most of those are three failsons in a trenchcoat. That sort generate more PR than revenue.