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Qwen 3.6 27B dense is much better than the 35B MoE model for coding, not sure if you've tried that yet.

Have you seen the 8bit quantisation matter a lot? The "consensus" in r/LocalLlama is that up to 4 bits the loss is tolerable.

q6 is fine for that qwen with ctx @ q8, and the dense models of that size are solid at q4 with q8 ctx

Interesting...

Two of the other responses speak about it being abysmal at tool calling.

Overall, I'm pretty impressed a model this small can find/fix ~12% of bugs with crappy context - even if they're about as easy as possible to fix.

I just assumed it would perform better, given all the advancements in the space.

It's possible 1B active parameters is just not enough - even if it has 8B params of knowledge to reason through bugs.

Playing around with the context I fed it, it was able to fix up to ~34% of bugs vs ~46% for Qwen2.5-Coder-3B and ~54% for Qwen2.5-Coder-7B.

I just did the same. Absolutely awful. I assume OpenCode's heavy context is a problem, and it's probably better to use Liquid's own OpenCode alternative for this.

I tested it against Gemma 4 31B and it's expectedly not favorable for world knowledge.

But even against E4B it's shaky, which is surprising given how many tokens they trained on. I guess it was on a lot of synthetic data.

The AI would be an awful husband

Reminder that the human baseline for the car wash question is also pretty bad.

ChatGPT still says walk but adds:

>The main reasons to drive such a short distance would be if you're bringing the car specifically to be washed, carrying something heavy, or the weather or walking conditions make it impractical.

>If your goal is to get your car washed, you'll need the car there—so driving makes sense. If you're just going to talk to someone at the car wash or check it out, walking is probably faster.

Why would a model know that one washes cars at a car wash? We don't clean our bodies at the body wash or clean the kitchen at the kitchen wash.

Yeah, but you are not washing yourself there, I suppose?

The whole twist here is that to wash your car, you need your car, so you cannot go by foot.

These faux questions always have a valid interpretation that the asker doesn't admit (for some reason). The model is then castigated for not making an opinionated choice

maybe unreasoning.

also, naysayers apparently DO have a compelling reason.

good point I have the feeling larger models (20b+) rely too much about their stored knowledge and sometimes fail to use tools because they think they know the answer. smaller specialized tool calling models could be the smart route for the future

Wouldn't the model start overfitting at some point? Degrading generalization for accuracy on the training set.

You’d think so, but I haven’t seen it explicitly discussed in their papers, and nobody else that I know of trains on that many tokens

I use it for triaging my messages and emails and reminding me how all of it ties together. It uses Obsidian to know where to put stuff and how to connect information. It isn't perfect. It's very slow (using a 32GB M2 Max) but fast enough for my needs.

A good example of how it's helpful is that it will make certain things relatively frictionless. Like, I need to pay property taxes. I hate this stuff. I got the email reminder from my municipality and it made an entry in my TODOs which points to page with instructions to pay the taxes, including my folio and access numbers for when I log in. That was taken from the email and a document which contains past property tax information. I have it all there, but it compiles relevant data into dedicated TODO pages.

I'm so bad at doing all of this myself. I really don't enjoy it. Send me to buy a carrot at the store and I'll happily walk 30 minutes there and back to do it. It isn't the effort so to speak; it's how unrewarding, inefficient, and bureaucratic it all is. I'm allergic to it. Why isn't it baked into my income taxes? Why are we still doing this?

Sometimes it does a really bad job of making TODOs. Like my wife messaged me about what our dinner plan was, so Qwen went ahead and made a plan for chicken meatball soup based on messages from a week earlier. It totally fabricated the recipe. Yet, I don't know, it was still helpful to be reminded that I'm in charge of dinner.

It's probably best at scaffolding responses to emails I don't want to send. I will write it, but I appreciate basic information being fleshed out so I can write it without jumping around looking for files or numbers or whatever constantly.

I use it with a custom harness. It could be a lot better. Everything about it could be better. The model is remarkably good for its size and price, though.

Letting Sonnet 4.6 do it instead always yields much better results, much faster, but it's kind of like using a new phone vs a super old one. They can both get you there. The sound quality and camera might be worse, it doesn't look as fancy, but the new one is $1200 and the old one is free on marketplace if you're handy with a screwdriver and a fresh battery. Sounds great to me

Worth noting: this was all vibe-coded using Opus 4.6 and 4.7. It's the only project I've built that is strictly vibe-coded. It's simultaneously exciting and disgusting. I'm not sure if I'll ever 'software engineer' it, or I'll just let it be slop. It works.

its really good at agentic tasks

I find it works well in the browser.