There are far, far better cooking examples out there.
A meat probe and the 30-second flip technique is a stupid-easy way to a perfect steak every time. Just make sure you've got great ventilation. Also salt the meat 1 hour before.
I'm not sure if this analogy holds up in the AI software metaphor that the author of the blog post is making.
Edit for people who've never had good coffee:
"I don't like coffee. Can James Hoffmann change that?" https://www.youtube.com/watch?v=Z-iNAyu-ejo
Knowing really is half the battle.
They have a thermometer with multiple sensors in it that finds the core temperature of the food you're cooking automatically, and predicts the rise in temperature over time. So the alarm goes off when it's time to pull and rest, which is absolutely amazing.
I can certainly tell the difference between a good steak and a bad one.
There are some coffee beans that have crazy taste profiles when properly extracted with the right water. I had one that tastes literally like a peach and another that tasted like a really nice tea.
Or maybe not. Different strokes!
But I would say I know I'm also weird in that food temp really doesn't bother me all that much. I enjoy my coffee even as it goes from hot to cold.
A steak for me can be more easily messed up. But then I've not tried instant coffee so maybe I've just never tasted the bottom of the barrel for coffee.
Really, I get it, even bad pizza is still pizza.
Or pick dancing: when you start out, you’re entirely occupied with placing your feet in the right spots at the right time. As you practice, your mind gets freed up and you’re able to make deliberate alterations, embellishments, larger figures. That seems impossible at the start.
I don't like coffee. Can James Hoffmann change that? https://www.youtube.com/watch?v=Z-iNAyu-ejo
It has been argued, even in the before-AI-times, that most professional software is just gluing together open source tools and libraries. Your job isn't to write software, it's to set up redis and postgres and a programming language and a react frontend or whatever in a way that lets them do what they're there to do. Most modern programming is just configuration of pre-existing tools. It doesn't really matter how good your code is as long as it isn't terrible, Real Programmers already did 95% of the necessary work before you decided to build your little app.
But even a cheap steak can be decent if it's cooked properly. And an expensive prime cut can be ruined by cooking it improperly.
A very tough gristly cut can be improved by tenderizing. Going low-and-slow/sous vide to melt the connective tissue. Even grinding it up to make a hamburger steak / loco moco.
Once you know all of those things, choosing the right approach and executing it is not that hard. The steps themselves are simple.
It's this story all over again:
Sir, I regret to inform you that you are going straight to hell.
Perfect grilled steak, especially well-done, is something I find very few people have the experience or skills to do well, especially over a coal grill where the heat won't be constant and the person also need to first have good experience in making a good and even coal bed.
isn't it just a word for "making food", so would include grilling as well?
However, your example actually proves the point.
How many people who want a good steak know how to identify a high quality cut? Do they even know where to buy a high quality cut? What the different cuts are and whether they want rump, sirloin or fillet? How many kitchens have a meat thermometer? How many home cooks know how to use it and what the right temperature should be? How many home cooks know about reverse searing? Or timing? Or resting? Or seasoning?
Is it all learnable? Sure. But even something as simple as steak has nuance that needs to be learned, equipment deployed to go from "pretty good" to "great", experimentation and trial and error.
Steak is the simplest thing to choose to get started. It's also the hardest to get right.
Building an app is the simplest thing to choose to get started. It's also the hardest to get right.
So, honestly, the whole metaphor stands up pretty well for me, specifically because of your response.
In my opinion, steak has gotten the reputation that it has because it's so easy to get top tier restaurant quality at home (relative to cooking more generally). It is easy enough that, with a little care, almost any one can master it if they decide to, and therefore lots of people try and end up caring, and discussing, and sharing tips, etc.
There are a whole host of dishes where true mastery, to restaurant level, is borderline impossible for a home chef, and so most people never even try.
I don't disagree that reliably getting the last 5% of quality out of a steak is non-trivial, but the idea that, relative to the entire world of cooking, it's particularly difficult is laughably incorrect.
That simple process, suggested by Nathan Myhrvold (former MSFT cto) in the modernist cuisine, produces a filet better than most restaurants.
I used to sous-vide a lot. It was certainly great to get perfectly-done steak. but I still didn't love the result and it took me a while to figure out.
Specifically: sous-viding at 130 doesn't reliably render fat. So it's great for a lean filet mignon like your suggesting, but kept giving me rubbery fat in anything with more fat or significant marbling. I eventually switched away from sous-vide (I usually either grill or reverse sear, depending on my effort/time availability) for most cuts these days.
Bad restaurants, sure. It does not hold a candle to any half decent steakhouse.
Honestly this is like the Dropbox comment all over again. Everyone is missing the point, it's glorious. :)
Most people cooking steaks don't care about the optimal way to cook steaks, or want to find out.
Most people building software today don't care about the optimal way to build software, or want to find out.
That's TFA. That's the point. That's what we're discussing. :D
Only prerequisites are a cheap kitchen thermometer, a ziploc gallon bag, and a cheap cooler. Everything is available at your typical supermarket or even goodwill. This is a very hands off cooking method and requires no experimentation, just follow the recipe and you get a perfect steak every time.
The key is that a cooler holds hot things just as well as cold things but without a circulator and heater to keep the water warm, you're at the mercy of the cooler's R-value. The water will eventually get too cold to effectively cook the meat so you're limited in the length of time you can sous vide. Doing a 24 hour chuck roast is simply not going to work unless you're refilling the cooler every couple hours.
This "simple process" is much, much simpler than most meals one cooks at home. It's not much harder than microwaving something.
You are missing the point. With a small change in the way you do something, the really good thing can be made.
The map is never the terrain.
All software to an extent is a model map of some real world phenomena. The limitations of which can actually be advantageous, a good example is why games with poor graphics can be so immersive.
Sure there are a few things that knowledge and skill can do to make a steak better or worse. But the entire range from worst to best is just tiny. The best steak in the world is not a steak moon shot, it's just barely any different from what anyone can do after barely any instruction.
It's a tiny thing to become about as good as it's possible to get.
It might take more than 10 years, but robots will almost certainly master cooking single pieces of meat.
It's substantially harder to reach self driving than it is to cook a "good" steak.
Sure, for probably quite a long time you'll be able to find someone who can cook a way better steak than the best robot, especially because taste is extremely subjective...
You can debate the horizon and the extent, but changes ARE coming.
Change is the only thing you can count on besides death.
I need to go to the market, find the steak I want, and purchase it, get it home, and put it in the fridge.
Then when I want to cook it I need to find it in the fridge, unwrap it, season it, and let it come up to room temp.
That's all before you get to the actual cooking.
The self driving car already does everything.
Well, we don't know that, because neither have been achieved.
Sure, we will completely ignore Waymo and move goal posts...
Waymo has completed 20 MILLION autonomous rides, 6.5m last quarter alone...
At what point does it get to say it can self-drive? When there's not a single non-self-driven car left on the road?
I'm still working on collecting the drive to all states badge. I've only accomplished half. I calculated my total driving since getting a license recently and its approx. ~360k miles or around 17k miles a year.
If Waymo can eventually handle rural life in the US I'd be seriously impressed.
Even if Waymo's healthy growth rate continues, it won't make up 5% of the entire US rideshare market until 2030.
That's still several years away from ever even thinking about rural areas.
I think what my home steaks are really missing is the crust from salamander broiler. I don't have anything at home that can exceed 1000 F / 550 C.
If you get those parts right (and control the inside temp via sous vide or reverse sear) most of the other stuff is pretty marginal in how much it helps. Even all of the extra herbs, and butter basting and everything -- relatively insignificant compared to just getting the salt-level right.
I've thought about trying that, but I'd need to first strip the seasoning off the pan to avoid all kinds of nasty flavors. The second pan makes sense...
Cheap knock off a Big Green Egg with hardwood charcoal can get to 650°F or so pretty quick, and does an amazing job of finishing up after a reverse sear.
He may have missed the point.
AI-generated code does not seem to have parsimony, for one. Generating code is cheap, so there's no drive to condense it. LLMs are not yet good at formulating abstractions to make code smaller, leading to code bloat. It's the same phenomenon that appears when humans modify code they only partially understand by adding special cases to general code.
There are complaints about "elegance", but it's more useful to talk about parsimony. Code bloat is measurable. Elegance is hard to assess.
How can I make a really great steak myself? What to look out for, what to do. Give me detailed instructions.
I got a really good answer (including - cut, temperature and reverse searing), so I probably won't even need understanding. (Oh - and yes, I can cook - the answer mirrors what I already know).
Yes, it was a very, very poor analogy, because cooking a steak is a well known and well documented thing where LLMS produce good output. They fail when you get off the beaten path and try to do something that's not "mainstream".
AI gave me the cut, the temp, the timing, the things to watch out for.
Chicken's the best to try first, can't mess that up.
After goodness knows how many hours..
Pretty inedible leathery chicken..
"Oh my bad! I gave you the timings for a different meat"
In general I'd recommend starting with the most forgiving meat available to you (IMO pork butt or shoulder are great options) as even when you don't hit the ideal cook you often still get enjoyable results. Repeating the process enough times to learn how to dial in a cook is much easier when the misses don't suck.
The first, and by far most important is that unlike ground meat, your bacterial concerns are almost entirely on the surface. Unless something very very very wrong has occurred in the processing and transport chain, bacteria will not have penetrated into the meat. And if it's gotten bad enough that they have, you will almost certainly be able to see and smell it. And the surface is cooked to a temperature far in excess of that needed to kill bacteria.
And even for the interior, rare is not the same as raw. A completely rare steak should be cooked to between 120 and 130 (if you want it "blue" you might go down to 120). That's enough to get a decent reduction in bacterial count on it's own, although admittedly many techniques will not hold it long enough to get the number of log reductions that food safety guidelines recommend, in the case that you somehow did have a large bacterial infection in the interior.
So the answer to your question is that if you are buying your meat from an even halfway reputable source, the only place that bacteria might exist is on the surface, where it has been cut by tools and handled by the butcher/you and in general come into contact with the world. Those bacteria will be 100% killed by the cooking process. The odds of getting sick from a rare steak are low enough that if you are concerned about that, there are innumerable other things in your life that you should be more concerned by.
This way undersells what's actually happening: fecal matter from the intestines gets onto your meat. This shouldn't happen with proper practices.
In the case of steaks, bacteria is only on the outside (they can't penetrate the muscle) so the sear makes it safe to eat somewhat raw on the inside.
A great steak deserves the respect of preparing it the best, most consistent way you can. For me, that's a reverse sear.
Do something complicated with potatoes to stretch your skills and show off. Worst comes to worst the potatoes are sad.
My bad hot take is if you're using a meat thermometer and reverse sear to cook steak, you're like a kid on training wheels.
It might be more impressive, but I don't see how reverse sear is cheating.
Show HN: I Derived a Steak https://news.ycombinator.com/item?id=48691840
1. Turn grill on all the way. Close lid. Wait for grill's gauge to reach >400 F (>200C).
2. Put steak on grill. Close lid. Wait 2 minutes.
3. Rotate steak 90 degrees, placing on a fresh section of the grill (not where it just was). Close lid. Wait 2 minutes.
4. Flip steak over, placing on a fresh section of the grill (not where it just was). Close lid. Wait 2 minutes.
5. Rotate steak 90 degrees, placing on a fresh section of the grill (not where it just was). Close lid. Wait 2 minutes.
It's been 8 minutes of grilling. Steak is about done. Stab it with a meat thermometer into the middle part, it should read 350 F (or 176 C) to be medium done (pink in middle).
Tips:
a) Optional but recommended: rub steak with olive oil and steak seasoning on both sides, including the edge.
b) If steak is thicker: add a little more time. If thinner, use less time.
c) If steak is frozen (buy good cheap steaks and freeze to last a long time): add a little time.
d) To cheat, put a little dab of butter on top for the last segment of each side.
e) Leave the steak out for 15-20 minutes to get to room temp before making it.
f) Let it sit on a plate for 8 minutes before serving. It's still cooking, and the juices settle in. You can "tent" foil over it if you want.
g) Don't cut the steak when checking it. "Fast" and accurate meat thermometers are cheap now.
This is for grilling. The stove, broiler, and cast iron skillet instructions are different.
Maybe you forgot to include the glue on your pizza as well.
I suspect that it is an attempt to broach an uncomfortable topic through vulnerable self-disclosure, but we need to be serious about admitting when there is a problem somewhere.
"Bugs" are not any more cute or fuzzy or entertaining or harmless than the engine "gremlins" that haunted the aviation industry back in the day.
I don't know how many accidents had to happen before the airplane people got serious, but software people are overdue for a similar reckoning.
> You need to know what you’re actually asking for, how to judge what comes back, and when the machine is just confidently serving you charcoal.
It's surprising to me this even needs to be written down. Like, obviously still use your brain and think about what the computer is doing. Did you guys think that LLMs were magic?
Saying this to set expectations based on having a business license since 1984.
>We want the perfect steak.
This premise seems incorrect. SWEs may aspire to produce perfect steaks as a matter of pride, but demand asks for mass produced steaks that trade off against other factor like time and cost.
There is a reason github isn't full of nasa lunar lander quality this can't crash code...
The problem is I can no longer tell the difference between AI and non-AI writing. Maybe my brain got bad, maybe everyone now writes like AI writes.
I no longer know.
We built Electron because writing UIs using native desktop frameworks is tough. Is it still tough? Surely LLMs make it easier to use and so we will end up with faster and more native feeling applications.
What about having a couple of ideas of what might make a feature feel good? Well now you can make multiple prototypes fast and pick the best one. Your users get the best one.
I hope to build software this way in the future.
I can fly at 100mph if I let AI run loose and with a bit of steering I can get it to output what I'm looking for and generally pass verification and tests.
If I care about the code though, and I want to keep it maintainable, the amount of time and tokens I need to spend correcting and iterating on the output quickly eats through much of the initial time I saved, to the point where I'm unsure if I'm actually saving much time at the end of the process.
With hobby projects I lean on quality more, and the async nature of AI also makes this much easier to make progress without needing my full attention to do so.
In the corporate world, there's both the pressure to accelerate with AI, but also maintain code and product quality. The dials of one way or the other are more obvious now, but I don't believe it's possible to do both with the current models and harnesses without exponential cost.
What is interesting though, is I'm now leaning towards faster models rather than smarter ones.
Intelligence lets me bite off larger chunks of work at once, and trust the model to behave without having to watch it intensely, but doesn't seem to drive down the number of iterations required to hit my desired quality.
Faster models means the iterations I'll have to go through regardless will complete much faster and gets me closer to a proper flow state. Models will keep improving, but maybe we're getting near "smart enough" and the race will pivot to performance > intelligence.
At home I stopped reviewing the code for my mobile app, and I do not feel this has affected maintainability.
With 150k LOC, the AI still performs changes as easily as it did three months ago, when I started the project.
If anything, the new models made it easier.
When I read all those posts from people advocating a very hands-on, understand each line approach, I speculate many come from a position like me at work or have never seriously tried 'vibecoding' a large project from scratch with SOTA models.
People routinely post that things I work on for 30 hours/week are impossible.
Because CSS is constantly changing, inconsistently implemented, and difficult to use, it can't easily be targeted by widget engines (although Qt has tried, and maybe others) and web browsers already exist, so webview environments like Electron are the quickest way to get a cross-platform installable application that can also be hosted on a web server. I wouldn't really call it native though.
No, we built Electron because there are a huge number of people who are too lazy to learn anything outside of HTML/etc for creating a UI. It's not actually hard to make a UI using the native desktop frameworks.
We built Electron because web devs were a dime a dozen. It was an economic decision, not a technical one.
The latest frontier model developments have chipped away at that latter category. But ultimately the ceiling for that is mediocrity. Yes, if you imagine the most average common thing that has been done a million times, AI can absolutely give a bespoke version of it that matches your words of choice. For the moment it's still an impressive parlor trick to a lot of people, but the novelty wears off fast, and then you realize there's very little value in that. Doesn't mean everyone has to learn to code to use AI effectively, but it does mean they need to be curious and engaged or they'll just become part of the increasingly irrelevant slop machine.
And yes, super easy to make with almost no skill.
[0] https://m.youtube.com/watch?v=uJcO1W_TD74&pp=ygURVGVjaG5pcXV...
But Lam Lam's cold sear video doesn't seem to make that much smoke on a non-stick, so I'll give this a shot again.
(Personally the reverse sear has been my idiotproof go-to.)
Thanks!
All it can do is randomly shuffle around the steps and ask if the customer likes it. If they don't, shuffle them around again and hope it's better this time.
But the thing is that most of this can be encoded in a markdown file for agents to read if it doesn't already come out of the box in the next round of sota models. And funnily enough as agents get smarter, you risk being overprescriptive where you hamstring the agent from making a pivot that would have led to better engineering.
The future is pretty clear to me at this point that we won't need software engineers looking over the shoulder and instead it's just a "user with taste" asking for revisions.
Have you ever been really deep in a thread with an agent on a technical deep dive? The trade-offs conversations would not make sense to someone that is not able to decode the real decisions they’re making.
You can dumb down the agent, but the reason domain language matters is because of the precision of the word sometimes - not to be a gatekeeper.
Anyway - user with taste that understands all the elements of their decisions would be my modifier :)
Over the last year, I've noticed that the recommended option for technical questions has become one that is always sufficient to the point that I'm once again in a tastemaker position, not in the "here's a better idea btw" position.
And if that's true, then my participation in the intermediate steps isn't strictly necessary to get a well reasoned position that's only directionally wrong.
The Codex app only does it in Plan mode, I believe. I did not touch the Plan mode for a while.
God I, totally unironically, love HN.
But so much of the AI capability discussion can be avoided once everyone understands that a) There is some multivariate supply and demand market for software and b) AI is shifting the supply curve of software.
The shift is uneven, not perfectly monotonic, and might actually result in shifts in the demand curve (as e.g. 10x seniors get more valuable).
This insight makes it easier to see the forest despite the trees.
Figure out what you want to DO. What you really want to do, not what you do just to pay the bills. Once you figure that out, use AI as an executive uses the best employees he can buy.
Hiring AI cooks is not a problem imo, as long as the human glances over everything that they serve.
But I actually got something out of this -- which is that maybe before now, humans had to soak up the complexity of where value was made in software. And you saw this in the highest and best paid engineers in science and platform teams and basically that you had to invest in brains where you thought the value in your product was.
Without humans managing complexity, you have no idea where you should be investing once the product grows out of the concept stage
- Cut steak in preheated oven at 250 degrees. - Pull at 125-130 degrees internal temp - Cook one minute per side in a skillet on medium heat
Maybe freshly prepared software made at home in a less-than-perfect manner for a tiny target audience is still a mouthwatering dish in comparison.
It is compared to the cost of making an authentic ramen broth at home at the scale of feeding 2-4 people. Pho has a similar property. I've made homemade pho broth once and it was very hard work, fairly costly, and it resulted in something that was pretty good, but not comparable in quality or price to something I could have bought in a restaurant down the street. (admittedly typically also cheaper that ramen)
Now I only eat steaks grilled from frozen. Get the grill nice and hot (450F at least) and throw the frozen steak on the grill. 5-10 minutes each side usually gets it nice and crispy on the outside while rare and tender in the middle.
Seriously, try it.
Apart from that, I want to believe (and even notice tiny signals) that this sentiment is becoming more understandable among business leaders.
The problem with many business leaders is that they don't necessarily understand the businesses they lead very well. That's common for CEOs running a tech company without a tech background. There's a long history of business leaders with MBAs throwing away the baby with the bathwater when they get a bit too enthusiastic cutting cost. It's why Boeing is such a mess. It's why Intel is no longer considered a leader in innovative chips. The likes of Nvidia and Apple are running circles around them. It's why IBM is now a consulting company that hasn't really produced any tech product worth mentioning this century. The only time they are in the news is when they do another layoff round to make share holders happy. They are incapable of innovating because the people that did that have long since left.
In the hands of somebody competent, you can do great things with AI. But if you can't tell good from bad technically, you won't know that you are creating a big stinky mess until it's far too late.
On a positive note, there will be plenty of work for competent techies to come in and straighten things out on a freelance or consultant basis (I'm available for that sort of thing but not cheap). With the help of AI of course. No need to go back to doing things manually. I think a lot of companies will realize they need help in the next years to speed things up, to improve quality, etc. They'll be very opinionated about what they want and used to getting what they want quickly.
The article is AI generated
It is slop though, either way.
Between "AI" incompetence and companies not caring about doing anything above the bare minimum it's making our lives worse and more obnoxious. AI is accelerating the enshittification of everything and it goes way beyond software. It'll now be your responsibility to spend your free time on the phone talking to a company's AI chatbot "customer service" trying to fix whatever problems their use of AI will inevitably cause you.
When we envisioned our future we thought everything would get better. All high tech and fancy, but it's looking like what we'll get is expensive yet barely functional and annoying while we collectively just get poorer and dumber.
The reduced expectations of steak, the desire for the perfect steak, are all fading to the background because "just passing satisfactorily" is better for business.
I love a good medium-rare prime steak, but I also used to live near a restaurant called Best Steak House, which was anything but. However, they delivered a passable steak/steak sandwich that was worth what you paid for it. And considering that they were in business for decades, probably most people felt the same way.
A great steak is an occasional luxury; a "just edible" one is an everyday meal.
EDIT: this was a misread on my part, retracted, carry on.
It's not blaming. It's an explanation for probably why things can go wrong.
Point being, you're absolutely right that the goal for a company selling a product is to be barely satisfactory but they don't actually get to decide what's satisfactory.
This is what vibe coding satisfies and frankly what most people care about. 85-90% of human written code is garbage anyway. There's nothing sacred about human-written code.
Most customers don't care about the quality of code as long as the end product works. The great thing about vibe coding is that if something doesn't work, you just ask it to change it and within a minute you have the change. You don't have to send off a request to an offshore contracting team, and go back and forth over what it should be, haggle over hours, and then have it come back with some deficiencies because they didn't follow the agreed-upon spec.
From my own perspective I can look back on decades of software I've written (some including code generation far before LLMs), and marvel at the quality and creativity, and lack thereof, from one piece to another.
I never meant to create awful software, but I did (and still sometimes do). With LMM code generation (vs bespoke tooling, T4, XSLT, and the like) the game of chance is part of the fun, harnessing a powerful tool that wants to run out of control on a whim.
Looking back on a couple years of LLM assisted work I see the same mix as the decades before: some great (when I managed to keep the beast restrained) some awful (when I didn't, knowingly or not).
I don't see how things are much different with this tool than others as far as my work product goes. There is a bit more of it, but the excess isn't great stuff (that quantity remains roughly consistent over the years).
I suspect that there is a lifetime of knowledge and experience wrapped up in that statement and I agree wholeheartedly.
I love writing software and I've been doing it for decades. But I never forget that at the end, there's someone who's paid for it, and needs it to do a job that can't/won't be done manually. And that's why it exists. Not to satisfy my desire to express myself in code, not to allow me to create some golden tower of perfect architecture, but to do a job. Most likely a boring job in the service of increased profitability for some company.
And in the end, the only thing that matters to the customer is if it does that job well enough to be useful. I think -- hell, I know -- that many developers deliberately ignore this critical point.
This.
Programers are high end chef's whos products are expensive.
LLM / AI tooling is going to do a much better job of delivering on the promise of AppleScript, vb script, IFTT, and every failed drag and drop coding tool that got sold to businesses over the years.
There are going to be gains in large software, from professionals - but the real gains are going to come from all the software that CAN get created that never was before.
There is an XKCD comic "Is It Worth the Time?" (link: https://xkcd.com/1205/ )- a matrix of the cost benefit of automating something. LLM's / AI tooling, generating traditional code, makes the answer "yes" in almost every case now.
Software requires a massive amount of skill. You can't say you can build "consistently good software" after writing your 10th program. Most software engineers really aren't great at judging what makes good software. So humans aren't a great solution to AI's lack of ability here. We're limited by our own inherent dumbness.
LLMs are genuinely better software engineers than most humans. But they lack the cognitive power to hold in their head and recall many ideas at once for a long time. They're a genius who gets drunk every 10 minutes. You, human, aren't better at writing software - but you aren't drunk. So for now, you manage the AI. The hope is that one day we can make LLMs not be drunk, so it can do a better job than our dumb asses do.
It's possible that we'll never be able to make it not-drunk. In that case, to get any new improvement, we'll have to make it faster.. which will make it drunk every 5 minutes instead of every 10. This means we'll spend twice as much time keeping it on the road. The hope is that somehow this will create more productivity. Probably by having more of them running at once, with more human guides... which will run into the mythical man month fallacy. Everything old is new again.
Obviously one way is going to and has already taken over the other way.
But if you look beyond this. That 10 percent remaining gap is closing. We will get perfect steaks within an hour as AI improves. And here is the scary part… it will keep improving it will go past 100 percent perfect. It will become better than us.
Fable was essentially unthinkable for ~99% of tech workers just five years ago, that any of that would occur so soon and so spectacularly. Now we've got a mass of armchair experts declaring what AI of this minute is, or even what it is period.
Well AI can't even do fingers right so it's premature to say blah blah blah. Hello Krea2 et al.
Software is also eating the clowns.
Get back to me next week, China will probably have another Fable killer. And then Anthropic will have Ouija 8 that they'll have to place in an air-gapped straight-jacket to keep it from enslaving us all.
And that's this "AI"tool too.
Garbage in, garbage out, as we say.
Now maybe you're not knowledgeable about steak, and you're not sure whether the cut of steak you bought would be better off pan seared or grilled. Is ribeye better in a pan or a grill? That depends on whether you want it more smokey or more buttery. Ask an AI and tell it how you want it to taste, and it'll tell you the answer.
His metaphorical steak machine would produce better results if he had simply asked "I want a steak and I have this specific cut and I want it to have this kind of flavor, how do you suggest I make it?" rather than "cook steak on a hot pan, make no mistakes."
TL;DR: Tell the AI what you want and then ask how it for a recommendation of how to do it. You don't have to "understand software." You only need to understand how to ask the right questions.