Ultimately, we need to know the true cost of this technology to evaluate how effectively or ineffectively it can displace the workforce that existed before it.
Some people are turn out slop. I was really excited to try and make some impressive shit. My whole life has been dedicated to trying to embody what Apple preached in the early days.
I knew this was coming, but I thought I had a little more time to try and get them over the finish line, ya know?
Maintenance by hand might be achievable, but it’s extremely hard when you’ve built something really big.
I’ve only got so much savings left to live on.
I’m not saying anyone owes me anything, but we all need to pivot and in a lot less sure my pivot is going to work out now
Based on what, exactly?
It's very easy to claim some software would've taken you months to make, but this is ridiculous. Estimating project duration is well known to be impossible in this field. A few years ago you'd get laughed out the room for making such predictions.
> I’ve only got so much savings left to live on.
Respectfully, what are you doing here?
Yeah sure, the Apple dream. But supposing AI did in fact make you this legendary 100x developer, so it would to everyone else including those with significantly more resources. You'd still be run out of the market by those with bigger budgets or more marketing, and end up penniless all the same.
I would strongly recommend you not put all your proverbial eggs in this basket.
That said: competition will soon kick in.
/s
With the hidden reasoning tokens and tool calls, I have no idea how many tokens I typically use per message. I would guess maybe a quarter of that, which would make the new pricing cheaper.
The infrastructure build out just can't keep up with it.
And I just subscribed for a year's worth of Claude... Terrible timing I guess. Do you know if the open models are viable?
Now I'm going to have to find the new best deal.
My next step is going to be evaluating open and local models to see if they are sufficiently close to par with frontier models.
My hope is that the end of seat based pricing comes with this tech cycle. I was looking for document signing provider that doesn't charge a monthly, I only need a few docs a year.
Opencode was able to create the library as well. It just took about 2x longer.
If you have an M processor then I would recommend that you ditch Ollama because it performs slowly. We get double or triple tok/s using omlx or vmlx, respectively, but vmlx doesn't have extensive support for some models like gpt-oss.
The only way I can do serious development with Gemini models is with other tooling (Cline, etc) that requires API based access which isn't available as part of the subscription.
You could probably be charging google literally thousands if all 6 members were spamming video and image generation and antigravity.
There's a few complaints online about the same happening to multiple users.
Otherwise anti-gravity has been great.
Unfortunately gemini as a coding agent is a steaming useless pile. They have no right selling it, cheap open weight Chinese models are better at this point.
It's not stupid it just is incompetent at tool use and makes bad mistakes. It constantly gets itself into weird dysfunctional loops when doing basic things like editing files.
I'm not sure what GOOG employees are using internally, but I hope they're not being saddled with Gemini 3.1. It's miles behind.
It's very variable though recently I'm noticing it's more reliable but there was a patch where it was nearly unusable some days.
I guess I won't complain for the price and YMMV.
If I recall correctly, Ed Zitron noted in a recent article that one of the horsemen of his AI-pocalypse would be price hikes from providers.
I'm still running local LLMs and finding perfectly acceptable code gen.
At any rate, this observation is not unique to Ed, lots of people have made the same conclusion that the math doesn’t add up from a business profitability perspective.
Hot take, but really it's more of an observation than a take: We saw this exact response in Blockchain & crypto circles a few years ago. (Though HN wasn't quite as culturally "central" to those)
Economic Bubbles are subject to the Tinkerbell Effect. They exist so long as people exist in them, and collapse when either 1) They become so financially unsustainable as to collapse, having consumed all the money the economy could possibly give them, or 2) People stop believing in the bubble and stop feeding it money.
In this regard, the statement "NTFs are stupid" was not merely ridiculing those who bought them, but a direct attack on the bubble and those invested in it. And this is something the people involved in the bubble understand instinctively, even if they aren't consciously aware of it. (There's a psychological mechanism to that, but it's not relevant)
So consequently, they react aggressively to dissent. They seek to enforce their narrative, because not doing so is a threat to the bubble and their financial interests.
---
AI's not much different to that. It's clearly a bubble to everyone including the AI execs saying it out loud.
And people react aggressively to dissent like Ed's, because if the wider public stops believing in AI's future, the bubble bursts. They'll stop tolerating datacenter construction, they'll sell their Nvidia shares, they'll demand regulators restrict AI.
(And to those who can feel their aggression rising reading this comment. Hi, yes. I see you. If I were wrong, nothing I said would matter. You'd be wasting your time engaging with it, history would simply prove me wrong. But by all means, type up that reply or click that button.)
> This format replaces average per-message estimates with a direct mapping between token usage and credits.
It's to replace the opaque, per-message calculation, not the subscription plan.
But Gemini's API based usage also has a free tier and if that doesn't work for you (they train on your data) and you've never signed up before you get several hundred dollars in free credits that expire after 90 days. 3 months of free access is a pretty good deal.
But let's not cry for the founders, they managed to get away with tons of money. The problem is for the fools holding the bag.
Gemini burned me too many times but maybe the situation has improved since.
> This format replaces average per-message estimates for your plan with a direct mapping between token usage and credits. It is most useful when you want a clearer view of how input, cached input, and output affect credit consumption.
Well, I know why. I just wanted to be snarky. It's just that trying to hide the actual price is getting a bit old. Just tell me that generating this much code will cost me $10.
And now it feels like the are gamifying the compute we use for work for all the same reasons.
If you have some left over that you can’t spend, it feels like you’ve “wasted” them.
The answer is so that they can charge different prices per credit. If you buy low amounts, they can charge one price. If you buy in bulk, they can offer a discount. The usage is the same, but they can differentiate price per usage to give people more a favorable price if they are better customers.
Is there anything wrong with that?
Even for a single standalone LLM that's the case, and the 'agentic' layers thrown on top just make that problem exponentially worse.
One'd need to entirely switch away from LLMs to fix this problem.
SilverElfin•2h ago
afrisch•1h ago