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Pokémon Company announced that it is considering using Japan’s My Number Card for ID checks. This will apply to some online purchases and events: >covering special lotteries and direct sales of popular Pokémon Trading Card Game >products on the Pokémon Center website >signing up for some official tournaments and events in Japan. It would work by scanning your My Number Card with your phone and linking to your Pokémon account via an external service. Pokémon Center says it does not store or access your personal ID details. They want to make things fairer for genuine fans by stopping scalpers. The problem here is that My Number Cards are primarily for residents of Japan. This will make it harder for buyers from other countries to get these items.

Japanese retailers are heavily restricting purchases of the Nintendo Switch 2 ahead of an upcoming price increase. Nintendo is raising the price of the standard model in Japan from ¥49,980 to ¥59,980, with similar increases for bundles and accessories. The announcement has triggered a last-minute buying frenzy, causing rapid sell-outs at major chains such as Yodobashi Camera and Bic Camera. Physical stock has already disappeared from many locations, online availability is extremely limited, and Bic Camera has begun imposing purchase limits. Other large retailers are expected to follow with similar loyalty-based policies, such as requiring membership tiers or a history of past spending. The restrictions want to support regular customers and stop scalpers

Valve has announced that reservations for the new Steam Controller open tomorrow, May 8, at 10:00 AM Pacific Time. The company is using a queue system similar to the original Steam Deck launch to handle high demand and limit bots and scalpers. Important details: • Limit of one controller per Steam account. • Once your turn arrives, you will receive an email and have 72 hours to complete the purchase. • Eligibility requires an account in good standing that made at least one purchase on Steam before April 27, 2026. • Anyone who already bought a controller during the initial May 4 wave is not eligible for this round. Valve will continue adding stock as it becomes available from the manufacturer.

📽️ New 4 hour (lol) video lecture on YouTube: "Let’s reproduce GPT-2 (124M)" The video ended up so long because it is... comprehensive: we start with empty file and end up with a GPT-2 (124M) model: - first we build the GPT-2 network - then we optimize it to train very fast - then we set up the training run optimization and hyperparameters by referencing GPT-2 and GPT-3 papers - then we bring up model evaluation, and - then cross our fingers and go to sleep. In the morning we look through the results and enjoy amusing model generations. Our "overnight" run even gets very close to the GPT-3 (124M) model. This video builds on the Zero To Hero series and at times references previous videos. You could also see this video as building my nanoGPT repo, which by the end is about 90% similar. Github. The associated GitHub repo contains the full commit history so you can step through all of the code changes in the video, step by step. Chapters. On a high level Section 1 is building up the network, a lot of this might be review. Section 2 is making the training fast. Section 3 is setting up the run. Section 4 is the results. In more detail: 00:00:00 intro: Let’s reproduce GPT-2 (124M) 00:03:39 exploring the GPT-2 (124M) OpenAI checkpoint 00:13:47 SECTION 1: implementing the GPT-2 nn.Module 00:28:08 loading the huggingface/GPT-2 parameters 00:31:00 implementing the forward pass to get logits 00:33:31 sampling init, prefix tokens, tokenization 00:37:02 sampling loop 00:41:47 sample, auto-detect the device 00:45:50 let’s train: data batches (B,T) → logits (B,T,C) 00:52:53 cross entropy loss 00:56:42 optimization loop: overfit a single batch 01:02:00 data loader lite 01:06:14 parameter sharing wte and lm_head 01:13:47 model initialization: std 0.02, residual init 01:22:18 SECTION 2: Let’s make it fast. GPUs, mixed precision, 1000ms 01:28:14 Tensor Cores, timing the code, TF32 precision, 333ms 01:39:38 float16, gradient scalers, bfloat16, 300ms 01:48:15 torch.compile, Python overhead, kernel fusion, 130ms 02:00:18 flash attention, 96ms 02:06:54 nice/ugly numbers. vocab size 50257 → 50304, 93ms 02:14:55 SECTION 3: hyperpamaters, AdamW, gradient clipping 02:21:06 learning rate scheduler: warmup + cosine decay 02:26:21 batch size schedule, weight decay, FusedAdamW, 90ms 02:34:09 gradient accumulation 02:46:52 distributed data parallel (DDP) 03:10:21 datasets used in GPT-2, GPT-3, FineWeb (EDU) 03:23:10 validation data split, validation loss, sampling revive 03:28:23 evaluation: HellaSwag, starting the run 03:43:05 SECTION 4: results in the morning! GPT-2, GPT-3 repro 03:56:21 shoutout to llm.c, equivalent but faster code in raw C/CUDA 03:59:39 summary, phew, build-nanogpt github repo