Fix macOS tests (it was a driver bug ¬¬)

This commit is contained in:
Martín Lucas Golini
2026-05-16 14:18:46 -03:00
parent 3dadd58ce1
commit acc27edd8a
3 changed files with 11 additions and 785 deletions
@@ -495,798 +495,13 @@
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&gt; If you charted performance over time,
it'd look like a sawtooth
<p>
People have, though, and it doesn't show
that. I think it's more people getting hit
by the placebo effect, the novelty effect,
followed by the models by-definition
non-determinism leading people to say things
like "the model got worse".
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Is this insider info? The 'charted
performance' caught my eye instantly. Couple
things I find odd tho: why sawtooth? it would
likely be square waves, as I'd imagine they
roll down the cost-saving version quite fast
per cohort. Also, aren't they unprofitable
either way? Why would they do it for
'profitability'?
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It's rumors based on vibes. There are attempts
to track and quantify this with repeated model
evaluations multiple times per day, this but
no sawtooth pattern has emerged as far as I
know.
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I don't want to go too far down the conspiracy
rabbit hole, but the vendors know everyone's
prompts so it would be trivial for them to
track the trackers and spoof the results. We
already know that they substitute different
models as a cost-saving measure, so
substituting models to fool the repeated
evaluations would be trivial.
<p>
We also already know that they actively seek
out viral examples of poor performance on
certain prompts (e.g. counting Rs in
strawberry) and then monkey-patch them out
with targeted training. How can we be sure
they're not trying to spoof researchers who
are tracking model performance? Heck, they
might as well just call it "regression
testing."
</p>
<p>
If their whole gig is an "emperor's new
clothes" bubble situation, then we can
expect them to try to uphold the masquerade
as long as possible.
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It's not insider info, it's common knowledge
in the industry (Google model optimization). I
think they are unprofitable either way, but
unoptimized models burn runway a lot faster
than optimized ones.
<p>
The reason it's not a square wave is because
new optimization techniques are always in
development, so you can't apply everything
immediately after training the new model. I
also think there's a marketing reason: if
the performance of a brand new model
declines rapidly after release then people
are going to notice much more readily than
with a gradual decline. The gradual decline
is thus engineered by applying different
optimizations gradually.
</p>
<p>
It also has the side benefit that the future
next-gen model may be compared favourably
with the current-gen optimized (degraded)
model, setting up a rigged benchmark. If no
one has access to the original pre-optimized
current-gen model, no one can perform the
"proper" comparison to be able to gauge the
actual performance improvement.
</p>
<p>
Lastly, I would point out that vendors like
OpenAI are already known to substitute
previous-gen models if they determine your
prompt is "simple." You should also count
this as a (rather crude) optimization
technique because it's going to degrade
performance any time your prompt is falsely
flagged as simple (false positive).
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You have a point but current LLM architectures
in particular are very fragile to data
poisoning [1,2].
<p>
[1]
<a
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rel="nofollow"
>https://www.anthropic.com/research/small-samples-poison</a
>
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[2]
<a
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rel="nofollow"
>https://arxiv.org/abs/2510.07192</a
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Yes, there are quite a few anti-AI projects.
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@@ -18,6 +18,8 @@
#include <eepp/window/backend/backendhelper.hpp> #include <eepp/window/backend/backendhelper.hpp>
#include <eepp/window/engine.hpp> #include <eepp/window/engine.hpp>
#include <cstdlib>
#if EE_PLATFORM == EE_PLATFORM_WIN #if EE_PLATFORM == EE_PLATFORM_WIN
#include <eepp/window/backend/SDL2/displaymanagersdl2.hpp> #include <eepp/window/backend/SDL2/displaymanagersdl2.hpp>
#endif #endif
@@ -182,6 +184,12 @@ bool WindowSDL::create( WindowSettings Settings, ContextSettings Context ) {
} }
#endif #endif
if ( const char* forceSoftwareRendering = std::getenv( "EEPP_FORCE_SOFTWARE_RENDERING" ) ) {
if ( forceSoftwareRendering[0] != '\0' &&
!( forceSoftwareRendering[0] == '0' && forceSoftwareRendering[1] == '\0' ) )
SDL_GL_SetAttribute( SDL_GL_ACCELERATED_VISUAL, 0 );
}
#ifdef SDL2_THREADED_GLCONTEXT #ifdef SDL2_THREADED_GLCONTEXT
if ( mWindow.ContextConfig.SharedGLContext ) { if ( mWindow.ContextConfig.SharedGLContext ) {
SDL_GL_SetAttribute( SDL_GL_SHARE_WITH_CURRENT_CONTEXT, 1 ); SDL_GL_SetAttribute( SDL_GL_SHARE_WITH_CURRENT_CONTEXT, 1 );
+3
View File
@@ -126,6 +126,9 @@ UTEST( UIHTMLTable, complexLayout2 ) {
sceneNode->setURI( "file://" + Sys::getProcessPath() + "assets/html/" ); sceneNode->setURI( "file://" + Sys::getProcessPath() + "assets/html/" );
std::string html; std::string html;
FileSystem::fileGet( "assets/html/hn_threaded_test.html", html ); FileSystem::fileGet( "assets/html/hn_threaded_test.html", html );
// Keep this fixture close to the captured viewport. Apple Software Renderer drops the
// large offscreen solid-background quad if the original full HN thread is used, even
// though GPU renderers and other software renderers rasterize it correctly.
sceneNode->loadLayoutFromString( HTMLFormatter::HTMLtoXML( html ) ); sceneNode->loadLayoutFromString( HTMLFormatter::HTMLtoXML( html ) );
win->setClearColor( Color::White ); win->setClearColor( Color::White );