On 03/11/2026 12:18, Rémy Maucherat via dev wrote:
On Tue, Nov 3, 2026 at 11:37 AM Mark Thomas via dev
<[email protected]> wrote:
<snip/>
My reviews have speed up considerably this morning. There is a clear
correlation between code complexity and speed of review / further fixes
being required. That suggests to me that we need to be using more
powerful models for the more complex code. Maybe look at using the free
AI credits for those models that have been offered to ASF committers?
Obviously if the issue is simple, it will work out better ! (also
applies to me if I try to fix an issue !)
Same here :).
To be honest, I think the models I have on hand are more than good
enough and are able to do complex tasks.
However, there are some issues and I need to use them better in some
cases. The main limitation I have to work around when I'm using a
"small" "fast" model is that the context is limited to 256K, and for
some models the quality degrades a lot after compaction. Also my
review step was very suboptimal. And of course my manual review step
after the changes is far from perfect (= it's the worst).
Yes. I am finding the models are picking up a lot more things than I would.
Although the HTTP/3 experiment was written with the small models, the
review steps were done by "big" models (with 1M context; technically
only 512K was ever reached). The main reason is that the components
are large and would lead to non stop compaction and useless results on
small models.
I expect the small open models will show another capability leap
simply by increasing the context. I know I can use some stuff like
rope with the current models, but it seems accuracy goes down.
Can you run your smaller models with a larger context?
Mark
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