0x7f3a 0xe891 0x2c4d 0xb7f0 0x51a3 0xd826 0x9f1b 0x4e70 0xa3c9 0x6d52 0xf148 0x8b95 0x3207 0xc6de 0x0a74 0x7f3a 0xe891 0x2c4d 0xb7f0 0x51a3 0xd826 0x9f1b 0x4e70 0xa3c9 0x6d52 0xf148 0x8b95 0x3207 0xc6de 0x0a74
W_q @ x.T → softmax(QK^T/√d) @ V → concat(heads) → W_o → residual += out → layernorm(x + sublayer(x)) W_q @ x.T → softmax(QK^T/√d) @ V → concat(heads) → W_o → residual += out → layernorm(x + sublayer(x))
tensor([0.0234, -0.1892, 0.4521, 0.0078, -0.3341, 0.2190, -0.0562, 0.1847, -0.2903, 0.3678, 0.0445, -0.1236]) tensor([0.0234, -0.1892, 0.4521, 0.0078, -0.3341, 0.2190, -0.0562, 0.1847, -0.2903, 0.3678, 0.0445, -0.1236])
{"input_ids": [101, 7592, 1010, 2088, 102], "attention_mask": [1, 1, 1, 1, 1], "token_type_ids": [0, 0, 0, 0, 0]} {"input_ids": [101, 7592, 1010, 2088, 102], "attention_mask": [1, 1, 1, 1, 1], "token_type_ids": [0, 0, 0, 0, 0]}
▁The ▁model ▁predict ed ▁that ▁the ▁prob ability ▁of ▁token ▁42 ▁is ▁0 . 89 ▁given ▁context ▁window ▁of ▁1M ▁The ▁model ▁predict ed ▁that ▁the ▁prob ability ▁of ▁token ▁42 ▁is ▁0 . 89 ▁given ▁context ▁window ▁of ▁1M
sha256:e3b0c44298fc sha256:1c3bc8a2f1d sha256:9f86d081884 sha256:5e884898da sha256:d7a8fbb307d sha256:2cf24dba5f sha256:e3b0c44298fc sha256:1c3bc8a2f1d sha256:9f86d081884 sha256:5e884898da sha256:d7a8fbb307d sha256:2cf24dba5f
logits[:, -1, :] → top_p(0.9) → temperature(0.7) → sample() → token_id:31842 → detokenize → " the" logits[:, -1, :] → top_p(0.9) → temperature(0.7) → sample() → token_id:31842 → detokenize → " the"
██░░██████░░░███░██░░░░██████░░███████░░░██░░██████░░░░███░░██████░░███░░░░██░████░░░░██████░░░███ ██░░██████░░░███░██░░░░██████░░███████░░░██░░██████░░░░███░░██████░░███░░░░██░████░░░░██████░░░███
0xfa91 0xbb03 0x44d7 0x1ef2 0x73c8 0xa605 0xde3b 0x5910 0x8c4a 0x27fd 0xf0b6 0x6389 0x9d2e 0xc571 0x0847 0xfa91 0xbb03 0x44d7 0x1ef2 0x73c8 0xa605 0xde3b 0x5910 0x8c4a 0x27fd 0xf0b6 0x6389 0x9d2e 0xc571 0x0847
attention_mask · position_ids · kv_cache · rope_theta · sliding_window · flash_attn · paged_attention · speculative_decode attention_mask · position_ids · kv_cache · rope_theta · sliding_window · flash_attn · paged_attention · speculative_decode
transformer · self-attention · cross-entropy · backprop · gradient descent · batch norm · dropout · residual · RoPE · SwiGLU · GQA transformer · self-attention · cross-entropy · backprop · gradient descent · batch norm · dropout · residual · RoPE · SwiGLU · GQA
loss: 0.0234 → 0.0198 → 0.0156 → 0.0121 → 0.0089 · GPQA: 88.4% → 90.8% → 92.0% · MMLU: 92.0 · HumanEval: 99.0 loss: 0.0234 → 0.0198 → 0.0156 → 0.0121 → 0.0089 · GPQA: 88.4% → 90.8% → 92.0% · MMLU: 92.0 · HumanEval: 99.0
RLHF → DPO → KTO → ORPO · reward_model.score() · preference_pairs += 1 · policy.update() · kl_div: 0.034 RLHF → DPO → KTO → ORPO · reward_model.score() · preference_pairs += 1 · policy.update() · kl_div: 0.034
input → embed → encode → attend → decode → project → sample → detokenize · ctx: 1M · params: 1T · Vera Rubin H300 input → embed → encode → attend → decode → project → sample → detokenize · ctx: 1M · params: 1T · Vera Rubin H300
LangGraph · Semantic Kernel · AutoGen · Atomic Agents · Claude Code · Cursor · Aider · vLLM · llama.cpp · PyTorch LangGraph · Semantic Kernel · AutoGen · Atomic Agents · Claude Code · Cursor · Aider · vLLM · llama.cpp · PyTorch
epoch 3/10 · batch 1247/5000 · lr: 2.4e-4 · grad_norm: 0.82 · throughput: 142k tok/s · ETA: 2h14m · gpu_util: 97% epoch 3/10 · batch 1247/5000 · lr: 2.4e-4 · grad_norm: 0.82 · throughput: 142k tok/s · ETA: 2h14m · gpu_util: 97%
tool_use · computer_use · mcp_server · function_calling · structured_output · json_mode · streaming · vision · audio tool_use · computer_use · mcp_server · function_calling · structured_output · json_mode · streaming · vision · audio
Anthropic: Claude Opus 5.5 · Muse Wonder Full (Max) · OpenAI: GPT-6 Astra · Google: Gemini 4 Argon · Meta: Muse Wonder · SpaceXAI: Grok 4.7 · Xiaomi: MiMo-V2.6-Pro · Qwen: Qwen3.8 Max (0902) · Z.ai: GLM 5.3 · Step 5 Preview Anthropic: Claude Opus 5.5 · Muse Wonder Full (Max) · OpenAI: GPT-6 Astra · Google: Gemini 4 Argon · Meta: Muse Wonder · SpaceXAI: Grok 4.7 · Xiaomi: MiMo-V2.6-Pro · Qwen: Qwen3.8 Max (0902) · Z.ai: GLM 5.3 · Step 5 Preview
context: 1M tokens · latency: 48ms TTFT · 180 tok/s output · vision + audio + tools · $2.50/M input · $15/M output context: 1M tokens · latency: 48ms TTFT · 180 tok/s output · vision + audio + tools · $2.50/M input · $15/M output
OpenAI raises $110B at $730B · Anthropic $30B Series G · xAI acquired by SpaceX · ElevenLabs $500M Series D · Waymo $16B OpenAI raises $110B at $730B · Anthropic $30B Series G · xAI acquired by SpaceX · ElevenLabs $500M Series D · Waymo $16B
pip install transformers · docker pull vllm · from anthropic import · import openai · cursor · claude-code · aider pip install transformers · docker pull vllm · from anthropic import · import openai · cursor · claude-code · aider
AGI benchmark: ARC-AGI-2 77.1% · FrontierMath 43% · SWE-bench Verified 72.7% · GPQA Diamond 82.6% · MMMB 78.9% AGI benchmark: ARC-AGI-2 77.1% · FrontierMath 43% · SWE-bench Verified 72.7% · GPQA Diamond 82.6% · MMMB 78.9%
GPQA 92.0% · MMLU 92.0 · HumanEval 99.0 · AIME 95.7 · ARC-AGI-2 77.1% · 1M context · 1T params · $2.50/M tokens GPQA 92.0% · MMLU 92.0 · HumanEval 99.0 · AIME 95.7 · ARC-AGI-2 77.1% · 1M context · 1T params · $2.50/M tokens
Apple Intelligence · Google Gemini · Microsoft Copilot · Meta AI · Samsung Galaxy AI · Amazon Alexa+ · Tesla FSD v13 Apple Intelligence · Google Gemini · Microsoft Copilot · Meta AI · Samsung Galaxy AI · Amazon Alexa+ · Tesla FSD v13
import · from · async · await · model.generate() · agent.run() · tool_use · streaming · ctx_window · embed · attend import · from · async · await · model.generate() · agent.run() · tool_use · streaming · ctx_window · embed · attend
} · { · ] · [ · => · -> · |> · :: · ... · ** · // · @tool · @agent · @retry · @cache } · { · ] · [ · => · -> · |> · :: · ... · ** · // · @tool · @agent · @retry · @cache