It's also driving itself crazy with deadpool & deadpool-r2d2 that it chose during planning phase.

That said, it does seem to be doing a very good job in general, the code it has created is mostly sane other than this fuss over the database layer, which I suspect I'll have to intervene on. It's certainly doing a better job than other models I'm able to self-host so far.

I think this is part of the model’s success. It’s cheap enough that we’re all willing to let it run for extremely long times. It takes advantage of that by being tenacious. In my experience it will just keep trying things relentlessly until eventually something works.

The downside is that it’s more likely to arrive at a solution that solves the problem I asked but does it in a terribly hacky way. It reminds me of some of the junior devs I’ve worked with who trial and error their way into tests passing.

I frequently have to reset it and start it over with extra guidance. It’s not going to be touching any of my serious projects for these reasons but it’s fun to play with on the side.

I can live with this on my own hardware. Where Opus4.6 has developed this tendency to where it will happily chew through the entire 5-hour allowance on the first instruction going in endless circles. I’ve stopped using it for anything except the extreme planning now.

Qwen3.5-35B-A3B means that the model itself consists of 35 billion floating point numbers - very roughly 35GB of data - which are all loaded into memory at once.

But... on any given pass through the model weights only 3 billion of those parameters are "active" aka have matrix arithmetic applied against them.

This speeds up inference considerably because the computer has to do less operations for each token that is processed. It still needs the full amount of memory though as the 3B active it uses are likely different on every iteration.

This terminology is still very much undefined though, so my version may not be the winning definition.

It's really easy to setup with any OpenAI compatible API and I self host Qwen Coder 3 Next on my personal MBP using LM Studio and just dial in from my work laptop with Zed and tailscale so i can connect from wherever i might be. It's able to do all sorts of things like run linting checks and tests and look for issues and refactor code and create files and things like this. I'm definitely still learning, but it's a pretty exciting jump from just talking to a chat bot and copying and pasting things manually.

I'm aligning on Agent for the combination of harness + model + context history (so after you fork an agent you now have two distinct agents)

And orchestrator means the system to run multiple agents together.

[0] https://www.reddit.com/r/LocalLLaMA/comments/1rivckt/visuali...

I’m trying to use local models whenever possible. Still need to lean on the frontier models sometimes.

> Do you feel you could replace the frontier models with it for everyday coding? Would/will you?

Probably not yet, but it's really good at composing shell commands. For scripting or one-liner generation, the A3B is really good. The web development skills are markedly better than Qwen's prior models in this parameter range, too.

What quant are you using? How much ram does it have?

The main quirk I've found is that it has a tendency to decide halfway through following my detailed instructions that it would be "simpler" to just... not do what I asked, and I find it has stripped all the preliminary support infrastructure for the new feature out of the code.

That sounds too close to what I feel on some days xD

> Blah blah blah (second guesses its own reasoning half a dozen times then goes). Actually, it would be a simpler to just ...

Specifically on Antigravity, I've noticed it doing that trying to "save time" to stay within some artificial deadline.

It might have something to do with the system messages and the reinforcement/realignment messages that are interwoven into the context (but never displayed to end-users) to keep the agents on task.

But how do you see the current thinking level and how do you change it? I’ve been clicking around and searching and adding “effortLevel”:”high” to .claude/settings.json but no idea if this actually has any effect etc.

echo 'export ANTHROPIC_EFFORT="high"' >> ~/.zshrc source ~/.zshrc

echo 'export ANTHROPIC_EFFORT="high"' >> ~/.bashrc source ~/.bashrc

Doh.

If you ask it to do something laborious like review a bunch of websites for specific content it will constantly give up, providing you information on how you can continue the process yourself to save time. Its maddening.

Don't get me wrong, it's very good autocomplete and if you run it in a loop with good tooling around it, you can get interesting, even useful results. But by its nature it is still autocomplete and it always just predicts text. Specifically, text which is usually about humans and/or by humans.

Just think about how many thousands of times you've heard "good morning" after noon both with and without the subsequent "or I guess I should say good afternoon" auto-correct.

So it's not surprising that eventually autocomplete can reach up from those circuits and take on some tasks that have already been made simple enough.

I think what's so interesting is how uneven that reach is. Some tasks it is better than at least 90% of devs and maybe even superhuman (which, in this case, I mean better than any single human. I've never seen an LLM do something that a small team couldn't do better if given a reasonable amount of time). Other cases actual old school autocomplete might do a better job, the extra capabilities added up to negative value and its presence was a distraction.

Sometimes there is an obvious reason why (solving a problem with lots of example solution online vs working with poorly documented proprietary technologies), but other times there isn't. They certainly have raised the floor somewhat, but the peaks and valleys remain enormous which is interesting.

To me that implies there is both lots of untapped potential and challenges the LLM developers have not even begun to face.

I tend to work on things where there is a massive amount of code to write but once the architecture is laid down, it's just mechanical work, so this behavior is particularly frustrating.

Used to have the same thing happening when using Sonnet or Opus via Windsurf.

After switching to Claude Code directly though (and using "/plan" mode), this isn't a thing any more.

So, I reckon the problem is in some of these UI/things, and probably isn't in the models they're sending the data to. Windsurf for example, which we no longer use due to the inferior results.

It's amazing how much foundational prompting and harness matters.

It just decided halfway that, nah, removing the field altogether means you don't have to fix the fallout from making that thing nullable.

Lmao.

That's likely coming from the 3:1 ratio of linear to quadratic attention usage. The latest DeepSeek also suffers from it which the original R1 never exhibited.

That would seem logical, as the results are then completely deterministic, but it turns out that a suboptimal token may result in a better answer in the long run. Also, allowing for a little bit of noise gives the model room to talk itself out of a suboptimal path.

I wonder if determinism will be less harmful to diffusion models because they perform multiple iterations over the response rather than having only a single shot at each position that lacks lookahead. I'm looking forward to finding out and have been playing with a diffusion model locally for a few days.

For creative things or exploratory reasoning, a temperature of 0.8 lends us to all sorts of excursions down the rabbit hole. However, when coding and needing something precise, a temperature of 0.2 is what I use. If I don’t like the output, I’ll rephrase or add context.

This is my experience with the Qwen3-Next and Qwen3.5 models, too.

I can prompt with strict instructions saying "** DO NOT..." and it follows them for a few iterations. Then it has a realization that it would be simpler to just do the thing I told it not to do, which leads it to the dead end I was trying to avoid.

I wasn't aware of that, which page mentions that?