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Comment by blfr | Hacker News Reader

There are some good points, and I ask the question of where is the ground breaking stuff myself, but severely weakened by

* stretching the timeline: the actual real programming ability appeared in LLMs in the last 6-8 months, not 3-4 years,

* using the weakest possible tool: and I bought $10 worth of DeepSeek credits that is a far cry from Claude with Fable.

Also, I know nothing about marathons but for most uses putting the app, database, and background processes on the same server is very much the right starting point. With the next steps being employing Cloudflare or similar solutions long before managing a fleet of servers.

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I remember being told in early 2025 that only now has "real programming ability" appeared in LLMs.

And since then there's been 3 more "now real programming ability has been made available and previous stuff was just toy examples" cycles (summer 2025, winter 2025 and spring 2026)

Looking forward to the next "everything before this was trivial and bad, here's the good stuff" moment

Yeah, it's interesting how the current thing is always the end of programming, but then when a new thing comes it turns into absolute trash that can't be used for anything.

If you haven't been using the latest NanoQwen27B-5.2.10-Turbo-KimiGLM-Pro-10.4 in the past 13 hours, your information on what LLMs can do is outdated and you're falling behind. Check your priors.

It's a bit Goomba Fallacy. For different people the threshold was crossed at different times. That you're getting told different things by different people doesn't make those statements self-contradictory.

Even as someone using AI on the regular I'm starting to hate the "You didn't actually use this exact most expensive model so your point is invalid" argument.

This is fair to say if someones last experience with AI was copy-pasting code into GPT3 chat windows years ago, but Deepseek is a more than capabale model and enough for someone to get an informed opinion about the technology.

If people have actual counter argument, use those. And if some of those counter argument are "What you say isn't possible, the neweste model can do and here are examples of that", that is fine.

But a blanket "Nuh-uh, it wasn't Model X" is not only a poor argument but also automatically invalidates any criticism when a new, better model comes out - and that can't be the basis of a good argument.

In this case, the argument is valid though. Only recently have some of the models become powerful enough (for some) to actually be useful in day to day programming, without too much hand-holding. For most, this change occurred with the introduction of Claude Opus, and OpenAI and Google have caught up. Unfortunately, none of the "open" models is at this level yet.

The field is moving fast, and asking for scientific arguments is not realistic. It takes an extreme amount of effort to show what exactly is different.

We were in a similar position with static vs dynamic typing for decades. There is still no scientific proof that one is better than the other, but it is quite obvious to professionals which flavor works better in a given situation.

So, even though the argument might be sloppy, I subscribe to it. Using DeepSeek to dismiss better models is the bad argument here.

Edit: added "(for some)" as a disclaimer that you still need to be a fairly decent programmer to actually benefit.

It’s always static just FYI.

> Only recently have some of the models become powerful enough (for some) to actually be useful in day to day programming, without too much hand-holding.

That still is very much not the case. Every model, yes, including $most_recent_model, needs supervision or you will get burned.

The models got good starting with 2026 and that isn't some attempt at excusing it. Companies like OpenAI started building dedicated models around their coding harness called Codex, there was gpt-5.3-codex and it was both cheaper and better at using the harness than the regular models. Then they started merging the two model types into their main release models. All of this happened like 6 months ago.

You don't have to pay money to use Codex, there is a very generous free tier that costs you nothing, you just have to accept being told you're out of tokens every day. Because your token limits are low, you need to make sure that you accept or reject everything manually and when it tells you that it wants to run a command you have to paste in the command into your terminal and only paste the relevant output back otherwise it floods the context window.

> The models got good starting with 2026 and that isn't some attempt at excusing it.

No, they still aren't good.

> Even as someone using AI on the regular I'm starting to hate the "You didn't actually use this exact most expensive model so your point is invalid" argument.

What the author of the article is doing is dismissing a technology so disruptive that it's basically all everyone's talking about in the "tech space" at the moment (I mean look at HN frontpage for the past few months), by trying a relatively mediocre (but still quite good) model for about 10 seconds.

The reality is that frontier models suddenly got very good in the past 3-6 months. It has it's problems, and you need to learn how to use this new tool (as with any tool).

But models can and do generate good code. They also can and do generate absolute garbage (even Fable).

You need a good harness, tools to help the model check it's own output, good context, and a good idea of what you actually want. If you have those 4 things, the chances of generating absolute garbage are pretty slim (but yeah, still there).

Is the issue that nobody has come up with the idea for the great ground breaking ideas?

Or B that the ideas are there but are not get released as the code is ai slop?

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