Fable 5 is a model you instantly FEEL how smart and superior it is. GPT 5.6 Sol is a HUGE incremental improvement in multiple directions and dimensions.

DeepSeek v4 Flash 0731 is a huge improvement over the preview version, using exactly the same architecture.

Kimi K3 gets open weight models very, very close to the frontier.

No my friend, we are not done yet.

I have done AI coding of huge features in the range of 50-60k LOC before, which were massive already, we managed to ship them and actually deliver value.

I know this sounds borderline ridiculous, but this time I don't know what to do.

What are the next steps here? It is obviously impossible to review, but it is almost as impossible to actually test. I had put A LOT of work into planning and the workflows and features were spec'ed very precisely and with lots of care and analysis. So far I have only found cosmetic issues, the entire core of the application seems to be working just fine.

I have thought about starting with a user manual / knowledge base for it, to capture the features and have plenty of documentation. But then what? Launch it to production?

Have I passed the threshold of what is humanly possible to do with AI coding, or is there any hope?

Comments URL: https://news.ycombinator.com/item?id=49160339

Points: 1

# Comments: 2

Interestingly, it would be the perfect system for payments using AI agents - the agent tries to pay, asks user to provide 6-digit code, submits payment request. User confirms via banking app. Done.

I just purchased a Mac Mini M4 Pro 64GB for $3k - 2nd hand of course.

I am not a hater of Nvidia and I am planning on building a workstation based on RTX cards. You clearly do not seem to understand how convenient the MacMini actually IS - the form factor, how quiet it is, how durable it is, how well it integrates with other Macs, how well it works as a bridge to a personal agent like Hermes (integration with iMessage, Calendar, Reminders, iCloud, etc).

I am pretty sure I know a thing or two about computing, I have been in the trenches for many, many years and I have had machines of all kinds, shapes and colors. It just so happens that Macs are very capable, very convenient machines that happen to work great in the era of LLMs, too.

But you do you.

Soon it is going to be good even for coding using local LLMs. Until then, just run API models on it for coding, local LLMs for "knowledge" work or daily driver agent like Hermes.

qwen3.6 35B A3B MLX 8bit -> 85-90 tok / sec! It is impressively fast and roughly 90% as good as 27B (in my opinion).

But man, I have never purchased a computer which is more expensive than a decent family car.

BUT DO NOT buy this MacBook if you plan on doing serious coding using local LLMs with it. The reason is simple: your fingers will burn and your head will explode from the noise.

Running any kind of sophisticated job on the very laptop you are using is just not viable. Sure you can use it in clamshell mode, but forget touching it while working with AI coding or agents.

If you want to run Qwen3.6 27B / 35B at its best, get a MacMini M4 with 64GB of RAM and put it in the basement - or at least a few meters from your desk. Connect to it over LAN or Tailscale. The MacMini will also cost you almost 1/3 of the MacBook Pro.

Thank me later.