_fat_santa

The fundamental problem with the internet is that hosting sucks and no one wants to do it. It's thankless and it's expensive to maintain, both time and money. Apps are a way to not worry about that.

No it's not. Hosting a web app is one of the most trivial things you can do these days, far more trivial than attempting to get an app into the app store. Hosting API's and Databases is a little more difficult but you still need those things if you're building an app.

There is no world in which getting your app signed, getting it approved, getting every update approved and paying $X/year to Apple or Google is easier than hosting a webapp, even if you host it in the most difficult way possible (on say AWS + Cloudfront). And even that method isn't that difficult, just moreso relative to other ways of hosting a webapp.

When me and my wife were purchasing our first home, we had everyone from mortgage people to realtors to our own friends and family that owned homes tell us to never use our home insurance unless it was something truly catastrophic.

Currently our home insurance deductible is $10k. The logic is that anything less than that we can pay ourselves. Home insurance is only there to cover either catastrophic damage or really, a total loss.

What does the financial compensation need to be for an engineer to actually do this? I'm gonna assume that if you work at Apple and are being recruited by OpenAI, you are not a dummy. Then you probably know that doing something like this runs the risk of you getting sued by a trillion dollar company.

If I had a potential employer ask me to do this, I would reply "oh hell fucking no", withdraw my application, and notify my companies security, legal and HR teams.

But then again it's easy to have the moral high ground when you're not staring down an offer that will completely change your and your families lives. I'm sure most employees probably thought what I'm thinking until they are looking at a 7 figure offer.

I see this case pretty simply. The states want to prove that Meta knew what it was doing to kids and did it anyways to raise engagement. Meanwhile it looks like Meta is trying to sidestep that argument entirely by stating that social media addiction is not a formally recognized diagnosis, essentially saying that while it was slimey, it was not illegal.

Morally I side more with the states but legally you can't ignore the argument that Meta is making. I feel like if social media addiction does become a formal diagnosis in the future then Meta is screwed unless they drastically modify their product. But I also feel like the best time for that to have happened was in the 2010's when all this stuff started to ramp up, if it didn't happen then it's not going to happen now.

I think what everyone underestimated was the absolute bonkers amount of compute it will take and how that compute must scale in order to keep up with larger and larger models.

I still use GPT-5.3-codex-spark which also runs on the Cerebras chips. Spark can run at >1000 tok/s but it's highly limited in it's context window size so it's not suitable many workflows.

Granted this will be a bit slower (relatively speaking) but it will still be awesome.

If anything these models should be compelled to be public since they have been trained off public data

I'm starting to come around to this idea TBH. For a while my position was: "these companies have invested billions into training these models, therefore they should be able to control them and profit off them" but looking deeper at where they got their training data, my view is starting to shift.

IMHO I feel like we need new laws around AI, specifically training data. Something like: "you can train an AI model and ignore copyright laws, BUT you must then make the model open weight", a company can still develop closed weight models but then they must aquire permission to use training data.

But it gets murky because if something like that was on the books then AI labs would just train open weight models and then distill them into their closed weight models.

The US system is built to support entrepreneurship while the EU system broadly is to support the consumer and employee. The US will never be able to match the EU's consumer and employee protections and the EU will never be able to match the US's ease of doing business, because to have one you have to fundamentally give in on the other.

Depending on who you ask, one system is wildly better than the other, but at the end of they day they are just different systems with different tradeoffs.

IMHO I've never found the entire reasoning chain that particularly useful for my work. For me having a summary is honestly better from a context management perspective. I understand why they would encrypt it though, because those reasoning chains are VERY useful if you're distilling the model.

That's just fucking greedy. I've been working on a SaaS and it's honestly hard sometimes to not stoop to those greedy levels because the money is really there.

Our policy is a subscription grants you write access to your account, but read access will always be there even after you subscription expires. We are still working on policies around long term data retention though.

There's a similar project for Codex Desktop: https://github.com/ilysenko/codex-desktop-linux.

After going through this process to get codex installed on Linux I'm honestly baffled why OpenAI doesn't have an official port. Though I haven't tested every part of the app, everything works as intended, even got computer use working without any issues.

At my company we're using Claude Code w/ API Billing and I found that unless you're running ralph loops on Opus with extended thinking, it's very hard to blow through more than $200/mo.

I made this argument earlier and I'll make it again, I think a major contributing factor to AI budgets exploding is the token leaderboards, culture of "tokenmaxxing" and the the constant narrative that if you're not burning X tokens a month, you're not a good engineer.

I've toyed around with worktrees but haven't found them very useful beyond that. I generally find it much easier to carefully prompt an agent so $TASK1 does not interfere with $TASK2

I'm on OpenAI's Pro (200/mo) plan.

I wonder how much of Uber blowing their AI budget and MSFT pulling their claude code licenses can be attributed to "tokenmaxxing".

When Meta announced token leaderboards and other followed, I could see this being the logical conclusion. That whole trend is so dumb because it leads to this.

Company announces they will measure developer performance by how many tokens they burn and constantly talks about how the best developers burn the most tokens. Developers see the message and start burning tokens. And then the company acts surprised when their bills go through the roof.

I personally use my OpenAI subscription pretty heavily, 2-3 agents running practically all day on various tasks but I never even get close to running into limits while I hear about others blowing through limits on multiple accounts in the same time period. I'm convinced that most of those folks and their elaborate workflows aren't really for productivity but for bragging rights about how much they use AI.

Their open weight on device models are really impressive. Partly because I think they are the only ones out of all the frontier labs even working on local models.

The biggest issue I see with discourse around AI is you have two voices: one is of the tech CEO's and other elites that talk about it largely in the abstract and how it's going to take everyone's jobs, and then you have folks on Twitter/X that talk about things that they are actually using it for.

Generally what I found listening to both sides is the latter group is very optimistic about AI and what it can do while the former group tries to be optimistic but just ends up coming off as doomery about it. And the problem that the AI space has right now is the doomery group is just more visible to the average person and thus the average person gets their opinion informed by that group.

I really wish there was a way to better surface the sentiment that I see on X about AI, the folks there aren't talking about how AI will replace you at work and make you obsolete, they use AI every day and they know that's just not realistic, not now and probably not ever. Rather they talk about all the cool things that it can help you do now, and how it can be a force multiplier in the best sense.

The problem with the elites talking about AI is everything they say is just so detached and abstract. And their giant egos prevent them from seeing the damage they are doing to the field.

I feel like the way many companies implement AI right now is very very wasteful. For context I'm looking into adding some AI elements to my SaaS app and I'm looking at running on-device TinyBERT intent classifiers then have my API take it from there (still experimenting with this).

I feel like this is a pretty sustainable way to implement AI in an application, meanwhile I see most companies just implement with OpenAI API + some custom prompts on top.

Granted I've had to do this for some of my clients and it's a pretty easy way to implement AI, though I always have the sinking feeling that we could achieve the same thing in a way more efficent manner and a bit more effort.

One distinction the author didn't make was personal sites vs product / services sites. My personal site is for me, but the site for my SaaS app? That's for my customers.

Besides the fact that the founders are in China and are barred from leaving, is there anything that prevents Manus/Meta from just telling the CCP to kick rocks?

Sure they can object to it or claim they are "blocking" the sale, but is there really anything they can do considering that Manus is no longer within their jurisdiction?

This,

Had a recruiter reach out to me the other day from a sports gambling website (one of the major ones, as reputable as you can get in this industry). I heard them out, thinking they would offer above market rate but in actuality, they offered significantly below market rate.

A question I've been asking alot lately (really since the release of GPT-5.3) is "do I really need the more powerful model"?

I think a big issue with the industry right now is it's constantly chasing higher performing models and that comes at the cost of everything else. What I would love to see in the next few years is all these frontier AI labs go from just trying to create the most powerful model at any cost to actually making the whole thing sustainable and focusing on efficiency.

The GPT-3 era was a taste of what the future could hold but those models were toys compare to what we have today. We saw real gains during the GPT-4 / Claude 3 era where they could start being used as tools but required quite a bit of oversight. Now in the GPT-5 / Claude 4 era I don't really think we need to go much further and start focusing on efficiency and sustainability.

What I would love the industry to start focusing on in the next few years is not on the high end but the low end. Focus on making the 0.5B - 1B parameter models better for specific tasks. I'm currently experimenting with fine-tuning 0.5B models for very specific tasks and long term I think that's the future of AI.

I would say this still holds true but not for singular products. Take Costco for example, long term you save money and you get high quality products. But that comes at the cost of having to spend quite a bit up front to buy in bulk.

Not to mention it also assumes you have the space to store those products.

Its EVERYTHING

I would argue not everything, just the things we remember. Those brands got popular, got sold and enshittified.

We remember these brands fondly (personally I had a JanSport bag all through elementary school) and that's why it sucks that they suck now but what we forget is now is there are 1000X more brands to choose from, some from megacorps trying to cut corners at every step. Some from small shops that genuinely want to make a great product.

The problem is visibility. Those good brands you have to go look for, you can't just go to WalMart or Target like in the early 2000's and expect to get a quality product. All the quality products now live on small websites scattered across the internet.

are you sure you want to leave

I would argue there is a place for this in web-apps. For example I have a SaaS app and I employ this on any form pages where the user has already started to enter information in.

I have considered form persistence so in the event a user goes back to a previous page, realizes it's a mistake and goes forward again, their form state from the previous state is persisted.

But I would like to ask, what would users prefer the behavior be on a form page like this?

I feel like CS is just correcting back to what it was.

Even back when I was in college (graduated 2017), I noticed there was this clear bifurcation among the students. Alot of the students at that time did it because you could score a great job after college but the smaller cohort were the students that just loved the game. And even back then we had loads of students wash out or graduate then take other jobs after college from the former group.

It's no different today except that the group that did it for money are washing out before they even get to college because they fear that AI will take their jobs, meanwhile the latter group is still here and were able to do more and more with AI.

It's a truly wild time to be alive in this industry. Half of us are seeing the doom and gloom of AI and the other half are seeing the "next age" happen right before our eyes.

And I'll be honest I kinda feel sad for the folks that take the negative view of AI right now. Cause I'm having more fun than I've ever had before in this industry.

It's not an automated process (at least in my case). I primarily use Codex so I will do the initial pass with 5.3 or 5.4 xhigh and then cleanup with spark on medium or low.

Spark is great for this kind of cleanup work because the feedback loop is so tight compared to just about anything else. It's quite hampered by a very small context window but in the context of cleanup/refactoring that's more of a feature than a bug IMHO.

My suggestion for folks that want to do this is make sure you keep reasoning low. The cleanup should be very much human directed and derived from your "taste", at that point you don't want the model to think at all and just blindly do what you tell it to. You want reasoning to be just high enough so it doesn't eff up the code in the process.

After the CC leak last week I took a look at their codebase and my biggest criticism is they seem to never do refactoring passes.

Personally I write something like 80-90% of my code with agents now but after they finish up, it's critical that you spin up another agent to clean up the code that the first one wrote.

Looking at their code it's clear they do not do this (or do this enough). Like the main file being something like 4000 LOC with 10 different functions all jammed in the same file. And this sort of pattern is all over the place in the code.

I have a tangential theory to this.

Being rich != being famous. There are tons of extremely wealthy people out there that keep a very low profile. Sure they might be well known within their circle but ask the average person and they have no clue who that person is. I would say this is the case for like 90-95% of billionaires.

Musk, Andreessen, Zuck and others were all in this camp 10 years ago but they all decided that simply being rich wasn't enough, they wanted to be famous. These folks have all the resources and connections to become famous so they can get on all the podcasts, write op-eds, and are guaranteed to get the best reach on social media and thus the most eyeballs on their content and the most attention paid to them.

But when you go from making a few media appearances a year to constantly making media appearances in one way or another is that you need more "content" so to speak. Just like a comedian needs more content if they are going to do a 1hr special versus a 10min set at a comedy club.

The problem for all these guys is they have a few genuinely insightful ideas mixed in with a ton of cooky and out of touch ideas. Before they could safely stick to the genuinely insightful ideas but as they've made more and more appearances, they have to reach for some of those other ideas. They don't realize that their cooky ideas sound very different than their few insightful ideas. They think all their ideas are insightful based on the feedback they have been getting for the past decade or so.

I was just reading a X thread that published some of the more notable things and overall it's pretty innocuous. The most "controversial" thing thus far is he took a trip to Cuba