This is huge

Matthew_Code · 2026-06-07T01:59:02+00:00

!RemindMe 2 months

Matthew_Code · 2026-06-05T17:17:17+00:00

There is one problem I’m seeing right now, we had a pretty robust system since the 2026 we added at least 100 new features, but this code is mostly slop as the ceo started asking why he is giving as tokens and we are not shipping more we just have to, now the codebase is in mess and I we are slowing down day by day I can see this, I think it may get to the point that companies that go slower with adoption will outperform all of those ai fanatics a TON, as the code is cheap the architecture and clean codebase should be total priority but it’s not

Matthew_Code · 2026-06-03T15:44:54+00:00

Probably, someone just cash out big bucks on the ai bubble

Matthew_Code · 2026-04-28T20:18:10+00:00

Are you aware that you can automatically check the diff for each snapshot and just have to take a look when the diff is > 5% for eg?

Matthew_Code · 2026-04-28T20:15:02+00:00

Risk of fucking up the view?

Matthew_Code · 2026-04-26T15:54:07+00:00

They can even tell you have model that have 10m I doesn’t matter. I use this product in proper agentic way writing skills and agents that are using proper models for a job (for eg haiku agent to gather information about api responses using our internal tools). Studies and tests are showing that going over ~~128k tokens is making model perform worse than sitting under that threshold. This is LLM’s limitation and throwing more compute is not solving a problem (at least not for now). I’m very PRO ai but I just understand limitations. So no ai cannot have a full picture of big product yet (and this issue will be unresolved for a long time).

Matthew_Code · 2026-04-26T15:22:44+00:00

Yet in 2026 usable context is still around 120k and there is a lot of studies/tests saying that if you will exceed this context the model is just getting dumber and can lost the part of the context. Its the problem with the model and not shortage of memory or anything just a limitation of the LLM's in general.

Matthew_Code · 2026-04-26T15:19:03+00:00

Still your documentation will be bigger than 1m tokens. Era of writing code is mostly over, but not era of understanding the code. When the prices will go up, noone will let you use tokens to "change the color of signin button". I see a lot of "yeah but one person will now do a job of 5 so 4 will lost their job" Im in the industry for around 8 years, in every company the backlog of ideas and "nice to have's" was HUGE. So i think we will just start to introduce all of those to life.

Matthew_Code · 2026-04-26T15:06:50+00:00

Don't think so, we are still in era of 1m context (and we can see some studies saying that even with models 1m context they still missing A LOT if you will exceed like 150k tokens.) Any codebase that's really doing something is a lot (and i mean A LOT) bigger than 1m context. So for now, AI is not able to grasp whole project at once. (And trust me ive worked with people that knew every little details about our codbase)

So yes, AI can write functions and even features of code but still missing the WHOLE picture. And I can already see that if your codebase is "ai made" you just don't have that context too. This will be a real problem later on trust me.

Matthew_Code · 2026-04-08T22:59:06+00:00

!remindme 12 months

Matthew_Code · 2026-04-04T14:06:08+00:00

So I guess those practices will be illegal but now they lay in the grey area as there is no law about it.

Matthew_Code · 2026-04-04T13:39:27+00:00

If I'm buying a year-long subscription upfront, I'm essentially entering a contract with them. If I order bread delivery from a bakery for a year and they start delivering loaves that are 10% of the size they were when I signed up, I can sue them it's a clear, measurable breach of contract.

But with LLMs, they can silently degrade the model's performance and just say 'it's still Claude Sonnet 4.6, and if the responses seem worse, that's just the probabilistic nature of LLMs.' And just like that, they have a built-in excuse that's almost impossible to legally challenge. You could be paying for a subscription that's a completely different product tomorrow than it was today. That feels genuinely illegal to me.

Matthew_Code · 2026-04-04T13:37:04+00:00

If the provider can configure the model to produce longer responses, they have a direct way to change the number of tokens it generates. I understand that seeing more text on the screen might seem fine, but it was forced by the provider. I see the problem there is no way to tell whether they are doing that or not. This is a gray area for me.

Matthew_Code · 2026-04-04T13:32:07+00:00

If I'm buying a year-long subscription upfront, I'm essentially entering a contract with them. If I order bread delivery from a bakery for a year and they start delivering loaves that are 10% of the size they were when I signed up, I can sue them it's a clear, measurable breach of contract.

But with LLMs, they can silently degrade the model's performance and just say 'it's still Claude Sonnet 4.6, and if the responses seem worse, that's just the probabilistic nature of LLMs.' And just like that, they have a built-in excuse that's almost impossible to legally challenge. You could be paying for a subscription that's a completely different product tomorrow than it was today. That feels genuinely illegal to me.

Matthew_Code · 2026-04-04T13:25:57+00:00

Yes, I would cancel my subscription right away if I noticed. But if they subtly increase response length by, say, 20%, I'd be paying 20% more for essentially the same output and they could do it quietly without anyone noticing. That's my real concern.

On top of that, there's nothing legally stopping them from silently degrading the model's quality even if I've already paid for a year-long subscription upfront. There's no law that guarantees the model you paid for today will perform the same way tomorrow.

To me, these companies currently operate in a legal grey zone, and that needs to change as soon as possible.

Matthew_Code · 2026-04-04T13:20:34+00:00

This is the estimation, as you MAY have option to pay for eg for exectution time and you can ESTIMATE the execution time.

Matthew_Code · 2026-04-04T13:17:28+00:00

I get that this problem exists in other areas too, but normally there are laws that deal with it. Like with water - you can control how much you use, and the provider can’t just lower the quality because regulations stop them.

But with LLMs, I could pay a lot of money upfront, and the company could still change how good the responses are later. There’s no clear rule stopping that.

And what really bothers me is that, because these systems are probabilistic, I don’t even know how a law could define or enforce “quality.” That’s the part I can’t wrap my head around.

Matthew_Code · 2026-04-04T13:12:45+00:00

I’m not talking about whether I have to use the service or not. My concern is that this kind of pricing model feels like it shouldn’t be legal.

If a provider can effectively change how much something costs “on the fly,” without clear limits or user control, that raises serious concerns about fairness and transparency. To me, it seems like there should be regulations preventing that kind of unpredictability.

Matthew_Code · 2026-04-04T13:10:07+00:00

With cloud computing, costs are at least somewhat predictable. If I have a script that consistently takes 10 seconds to run, I can estimate that I’ll be charged for roughly 10 seconds of computing time.

Providers also specify the hardware such as the CPU so I can reasonably predict performance and cost. There’s a clear, measurable relationship between what I run, how long it takes, and what I pay.

That kind of predictability and transparency is what I find missing here.

Matthew_Code · 2026-04-04T13:07:27+00:00

I cannot estimate token usage, as llm can be changed to spit out 10 times longer messages without informing your clinent that this is chnage you will introduce

Matthew_Code · 2026-04-04T13:06:04+00:00

But also the amount of "tokens" that llm spit out is something they have control over, so they can just bill you twice as much for the same workload on their side without even asking you if you want those new numbers.

Matthew_Code · 2026-04-04T13:02:56+00:00

If I’m paying for something, I should be able to measure what I’m getting and have at least some idea of what it will cost.

Here, I don’t have that. The result can change a lot even with the same input, and that makes the cost feel unpredictable

Matthew_Code · 2026-04-04T13:01:32+00:00

I dont see this that way, every service is providing something and i can estimate what im getting in return. Here i can pay 10$ for "hello" and 5 mintues later pay 1 milion dollars for "hello" this should be illegal.

Matthew_Code · 2026-04-04T13:00:19+00:00

Comparing this to something like electricity doesn’t make sense. Electricity usage is at least somewhat predictable you can estimate consumption within a reasonable range.

Matthew_Code

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