At some point I realized the scope was too large. I had spent the most time with msdfgen and hadn’t yet learned enough about the other libraries to write a proper guide. They all worked differently. I kept getting stuck. So I reduced the scope. In redesign 2 I decided to only use msdfgen, but show the various tradeoffs involved (atlas size, antialias width, shader derivatives, smoothing function).
I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.
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As quoted above, a TEE is a hardware-backed secure area of the main processor (like ARM TrustZone or Intel SGX). Technically speaking, the TEE is just the hardware fortress (exceptions exist like TrustZone) whilst a Content Decryption Module (CDM) like Google’s Widevine, Apple’s FairPlay, and Microsoft’s PlayReady use the TEE to ensure cryptographic keys and decrypted media buffers are never exposed to the host operating system let alone the user’s browser. For the purposes of this article, I may at times refer to them interchangeably but all you need to know is that they work together and in any case, the host OS can’t whiff any of their farts so to speak.，推荐阅读WPS官方版本下载获取更多信息