[aka-rider]

I run Qwen3.6 on RTX4090, and it does amazing job for the most parts.

For coding task, one needs to break the session among multiple calls I made https://github.com/aka-rider/orqestra but it's possible to do the same in almost any modern harness directly.

The main idea is: - separate session that burns context on reading code and calling tools (context7, etc) -> markdown report "here are relevant patrts of code, docs" "with evidence" to prevent hallucinations

- separate session for planning (architect) - (critic <-> architect) 1-3 times because small model skip over details - worker <-> validator, again, the same reason

Qwen3.6 can run for hours looking for a complex bugs in read-only mode, and usually it gets it. Proposed fix would probably be hacky, but so as Sonnet's

Qwen3.6 can mechanically write code by Opus-made plan. You would have to prompt afterwards:

"Review your own changes. Any bugs? Cross-validate against the original plan - any gaps? Any violations of CLAUDE.md"

But again, I need to do this for Sonnet. But also I use local llms for reindexing knowledge base.

Grooming tickets: I can leave a caveman note "single panel for errors rendering, move all error messages" and come back to 90% ready specs with the end goal and context.

[NBJack]

I'm afraid prompts and clever arrangements of data don't really negate the parent post warnings. It's great if it works for you and your projects. Unfortunately, I can almost guarantee your approach will break down once you get a project large enough or switch to a less popular language.

My favorite example is Godot; most local models just can't get it through their thick AI skull that code alone won't be enough to generate working solutions. They must accept a more complex harness, or you must provide much more info that eats the precious available context on every run.

[aka-rider]

There is no replacement for large models, indeed. And this is not the point I'm trying to make. There are numerous applications for self-hosted models.

As a simplest example, when you ask "explain what this code does" advantage of large models is negligible.

I tried Fable, "look at this repo, find all bugs" — yeah, neither Qwen nor Opus can do this.

> I can almost guarantee your approach will break down once you get a project large enough or switch to a less popular language.

I can guarantee you it is not, I used my Qwen on 10-15 years of PHP — I just know how and where it will break; what to ask for, what not. Orqestra was/is self-hosted, being developed by, well, orchestra of Qwen agents.

Moreover, Opus and GPT-5.5 break similarly, yeah they will withstand much more pressure, but they will hallucinate and loop nevertheless. My Qwen experience translates seamlessly. I learned so much about agentic engineering, harnesses, tooling, building custom MCPs...

[NBJack]

PHP is ancient in the current timeline of programming history. The Venn diagram of your code base and what it digested during training is basically a circle.

[TacticalCoder]

> Qwen3.6 can run for hours looking for a complex bugs in read-only mode, and usually it gets it. Proposed fix would probably be hacky, but so as Sonnet's

I'll go on a tangent but to me that's what we're all seeing. It's the "record number of CVEs found by AIs" thing: these tools are extremely good at searching inside code. And that is a godsend.

We' got people (claiming they're from Anthropic) posting comment saying: "Yes GLM 5.2 found that security bug in library xxx, but we just tried with Fable and it found it too".

More code-searching, more bugs finding. Dick-measuring contests on bug finding abilities.

But the headlines we don't see at all are: "1000 CVEs found by AI, 1000 CVEs fixed by code written by AI". These are nowhere to be found.

We don't see "GLM 5.2 suggested an elegant fix to CVE-2027-xxxxxx" to then have a paid Anthropic shill posting "Fable suggested an eleganter fix than GLM 5.2".

These headlines are, as of 2026, nowhere.

You wrote the result would be "hacky". Here's why I saw from a top, paid for, SOTA model from the top company of the moment: instead of doing two integers comparison (literally one line of code) to verify that a value is between a range, the thing somehow noticed a "pattern" in the hexadecimal representation of the two values and went insane. It started converting the value to its hexadecimal string representation and then started doing substring string matching on that.

"Hacky" is too nice of a word.

This is pure garbage.

Those who go hiking "while their agents ship features" don't realize the level of underperforming, buggy, insecure crap that their LLMs are generating.

I found it very interesting the schism between those who use LLMs to find issues but who verify/modify or even don't use at all the fix they suggest and those who vibe-code while on a yoga retreat.

It's 2026: LLMs do find bugs. But can they fix them?

And do we even care: isn't finding a bug 99% of the job?

[aka-rider]

The best metaphor I heard about LLMs so far - it's a search engine. The bigger the model the bigger the search space. Small models tend to have a "tunnel vision" or fall into "rabbit holes" - they have less visible options to choose from.

> underperforming, buggy, insecure crap that their LLMs are generating

The biggest challenges with AI-generated code are: models actively destroy security features, Opus explained to me once that authorization mechanism is "bad development experience" all while making a backdoor (he made a skeleton key if token=="test" then all permissions granted). Also models actively destroy QA gates. I don't even complain when they delete tests - at least it's visible, they can flip condition to make a test pass, and with vast code changes these are hard to spot.

I myself, and some people I know "vibe-code" professionally though, but then we often assess not the code but it's behaviour. For instance, whether hand-made tests are all pass, p95 is under 50ms, and so on, I may not care about the implementation details.

On the other hand, my friend told me about garage owner he visited, 60 yrs old auto-mechanic, CRM, parts inventory management, payments processing terminal, passwords in txt, people's personal data God knows where, could be unprotected MySQL looking into the Internet bare for all we know.

2026 onwards will be wild.

[aayush0325]

my experience has been similar, qwen is very good at ALMOST getting the job done for large tasks and does fine on smaller/medium tasks.