Maybe try https://www.weatherzone.com.au/news/sudden-stratospheric-war...
2,095 karma · joined August 26, 2008
http://paulcollier.ca/
Maybe try https://www.weatherzone.com.au/news/sudden-stratospheric-war...
I also enable keychain sync on my Mac so I can create passwords there too.
Hope your game doesn’t have a big texture budget.
> mlx_example/clip: ... an example to convert CoreNet's CLIP model implementation to MLX's CLIP example with some customized modification.
- FP16 Base variant: 60% speedup over PyTorch
- FP16 Huge variant: 12% speedup
> mlx_example/open_elm: ... an MLX port of OpenELM model trained with CoreNet. MLX is an Apple deep learning framework similar in spirit to PyTorch, which is optimized for Apple Silicon based hardware.Seems like an advantage is extra speedups thanks to specialization for Apple Silicon. This might be the most power-efficient DNN training framework (for small models) out there. But we won't really know until someone benchmarks it.
> During the latter 1990s, when most computers games were still made by and for a fairly homogeneous cohort of young men, too much ludic humor tried to get by on transgression rather than wit; this was a time of in-groups punching — usually punching down — on out-groups. I’m happy to say that The Curse of Monkey Island‘s humor is nothing like that.
Well said. Game has aged beautifully.
The solution to this is to build trains. Heavy trucks destroy asphalt, which is expensive and carbon-intensive to replace. EV trucks emit pollution in the form of tire dust—microplastics that pollute the air and water.
I agree that suboptimal query planning would be a database-layer bug, a defect which could easily be missed by the bug-testing framework.
At the same time, thank goodness Dart doesn't have all the warts of Javascript and a standard sane build system. Dart doesn't have to be the best Javascript to be good at what it does, especially now with algebraic data types and pattern matching.
I'm hopeful they'll make Wasm production-ready soon to move away from JS transpilation.
> In February 2021, a WorkSafe Inspector attended the workplace and observed an employee using a powered abrasive polishing tool to abrasively polish a slab of white coloured stone which was from the brand Stone Ambassador. The tool was being used without the required control measures in place. Instead, the employee was applying water to the stone from a bottle with a small hole in the lid when the tool was in use.
I'm wary of what happens when AIs can interface directly with the real world. LLMs only read about the world second-hand. An embodied neural net that could observe cause and effect, push pokers into fires and witness the metal glowing red, then learn from the experience. Spooky.
An AGI should be able to solve any creative problem a human could, with drive and knowing purpose and coherent vision. The LLMs are still narrowly focused and require human supervision.
We might well get there with chained AIs automatically training new reward models for each new problem, or by some other paradigm, but I don't feel like we're past the threshold yet.