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Unsigned & Still Available❗️ Class of 2026 | IOL Juco Transfer Physical. Consistent. Versatile. 6’4 330 | LAVC | 3.3 GPA AA in Hand | Criminal Justice Major Full Qualifier⭐️ 14 Offers⭐️ 0 Sacks Allowed⭐️ 0 Holding Penalties⭐️ 25+ Pancakes⭐️ OL/Team Captain⭐️ Hudl Link: #JUCOPRODUCT# #UNSIGNED26# #OL# @ValleyCollegeFB @coach_aguirreOL @coachnickwalker @CoachJJ_LAVC @CoachTowns_LAVC @EvanNelson05 @CoachJayWagSr @CoachMoose @GregBiggins @LoganTillman @JUCOFFrenzy @JuCoFootballACE

2026 Men's Basketball 🏀 Players: Lamar Sports Academy @LSABBALL is a post-grad located in Jacksonville, FL. Coach White has asked me if there are any interested 2026 Unsigned Players, with 2 openings. Tap this image for more info, or email them at lamarsportsacademy@yahoo.com

CPUs don't have vector search instructions. You borrow from neural nets and video codecs instead. Elasticsearch's simdvec engine reformulates vector math to fit whatever the CPU already runs fast. Four recent examples: - int7 quantization: fit unsigned-only multiply-accumulate by trading 1 bit of precision. ~6x faster. - int8 bias rewrite: algebraic shift + precomputed correction. ~20% bulk gain. - bf16 Euclidean distance as 3 dot products instead of a float32 conversion. Up to 2.4x. - Binary dot product via popcount: AND the bits, count the 1s. ~4x over scalar. A compiler can't make these calls. Each one requires reformulating the problem to fit an instruction the hardware was never designed to use this way.

James Harden and Draymond Green will remain unsigned until LeBron James officially makes his free agent decision. Both opted out to allow the Cavaliers and Warriors additional financial flexibility to aggressively pursue LeBron.

In today's episode of programming horror... In the Python docs of random.seed() def, we're told "If a is an int, it is used directly." [1] But if you seed with 3 or -3, you actually get the exact same rng object, producing the same streams. (TIL). In nanochat I was using the sign as a (what I thought was) clever way to get different rng sequences for train/test splits. Hence gnarly bug because now train=test. I found the CPython code responsible in cpython/Modules/_randommodule.c [2], where on line 321 we see in a comment: "This algorithm relies on the number being unsigned. So: if the arg is a PyLong, use its absolute value." followed by n = PyNumber_Absolute(arg); which explicitly calls abs() on your seed to make it positive, discarding the sign bit. But this comment is actually wrong/misleading too. Under the hood, Python calls the Mersenne Twister MT19937 algorithm, which in the general case has 19937 (non-zero) bits state. Python takes your int (or other objects) and "spreads out" that information across these bits. In principle, the sign bit could have been used to augment the state bits. There is nothing about the algorithm that "relies on the number being unsigned". A decision was made to not incorporate the sign bit (which imo was a mistake). One trivial example could have been to map n -> 2*abs(n) + int(n < 0). Finally this leads us to the contract of Python's random, which is also not fully spelled out in the docs. The contract that is mentioned is that: same seed => same sequence. But no guarantee is made that different seeds produce different sequences. So in principle, Python makes no promises that e.g. seed(5) and seed(6) are different rng streams. (Though this quite commonly implicitly assumed in many applications.) Indeed, we see that seed(5) and seed(-5) are identical streams. And you should probably not use them to separate your train/test behaviors in machine learning. One of the more amusing programming horror footguns I've encountered recently. We'll see you in the next episode. [1] [2]