10 AI Ideas Every Developer Should Actually Understand (2026 Edition)

Every chatbot reply runs the same ten ideas, from neural networks to tokens, attention, transformers and scaling laws. This video explains each one a level deeper than the usual cartoon, with real numbers from the original papers and official docs as of October 2026. It also fixes a few popular examples that break when you actually run the code. In this video: • Neural networks as a function with knobs: why the activation function is what makes depth worth anything. • Training vs inference: loss → backprop → gradient descent. GPT-3 read 300B tokens, but its weights were updated only about 100,000 times, not billions. Prefill vs decode, and "thinking" tokens. • Tokens, from our own tokenizer run: "unbelievable" is un|bel|ievable, not un|believe|able, and with a leading space it's one token. Hindi "नमस्ते दुनिया" is 13 tokens on GPT-4's tokenizer and 5 on GPT-4o's. Why models struggle to count the r's in strawberry. • Embeddings: token IDs become vectors (GPT-3 used 12,288 dimensions), semantic search, and the catch behind "king − man + woman = queen". • Attention as a soft dictionary lookup: query, key, value, softmax. Multi-head attention, and the 2014 → 2017 history. • Transformers: one block (attention + feed-forward + skip connections) stacked; GPT-3 used 96. RoPE positions and the causal mask. • Context windows in 2026: ~1M tokens at the frontier. The three costs: quadratic compute, KV-cache memory, and lost-in-the-middle quality. • Scaling laws in three acts: Kaplan 2020, Chinchilla 2022 (≈20 tokens per parameter), over-training (Llama 3: 15.6T tokens), then test-time compute. • Pre-training vs post-training: base model → SFT → RLHF / DPO → RL on verifiable tasks. A 1.3B tuned model was preferred over the 175B GPT-3. • LLMs: the sampling loop, temperature, total vs active parameters (mixture of experts), and model vs product. 0:00 Ten ideas, one chatbot 0:41 Neural networks: a function with knobs 2:14 Training and inference: learning versus answering 4:08 Tokens: the example everyone gets wrong 5:59 Embeddings: turning IDs into meaning 7:36 Attention: a soft dictionary lookup 9:17 Transformers: the same block, stacked 10:48 Context windows: how much the model can see 12:34 Scaling laws: three acts 14:25 Pre-training and post-training: from autocomplete to assistant 15:55 Large language models: putting it together 17:06 The ten ideas, in one breath SOURCES Attention Is All You Need (2017): https://arxiv.org/abs/1706.03762 Bahdanau et al. (2014): https://arxiv.org/abs/1409.0473 GPT-3: https://arxiv.org/abs/2005.14165 Scaling laws (Kaplan et al. 2020): https://arxiv.org/abs/2001.08361 Chinchilla (2022): https://arxiv.org/abs/2203.15556 InstructGPT (2022): https://arxiv.org/abs/2203.02155 Mikolov, Yih, Zweig (2013): https://aclanthology.org/N13-1090/ Nissim et al. (2019): https://arxiv.org/abs/1905.09866 BPE for NMT: https://arxiv.org/abs/1508.07909 RoPE: https://arxiv.org/abs/2104.09864 FlashAttention: https://arxiv.org/abs/2205.14135 GQA: https://arxiv.org/abs/2305.13245 Lost in the Middle: https://arxiv.org/abs/2307.03172 Llama 3: https://arxiv.org/abs/2407.21783 Test-time compute (Snell et al. 2024): https://arxiv.org/abs/2408.03314 DeepSeek-R1: https://arxiv.org/abs/2501.12948 Emergent abilities (Wei et al. 2022): https://arxiv.org/abs/2206.07682 Emergence a mirage? (Schaeffer et al. 2023): https://arxiv.org/abs/2304.15004 DPO: https://arxiv.org/abs/2305.18290 Claude models: https://platform.claude.com/docs/en/a... Gemini long context: https://ai.google.dev/gemini-api/docs... Qwen3.8-27B: https://huggingface.co/Qwen/Qwen3.8-27B Llama 4: https://ai.meta.com/blog/llama-4-mult... tiktoken: https://github.com/openai/tiktoken Numbers marked "illustrative" on screen (softmax weights, cosine similarity, cost bars) are worked examples, not measurements. #ai #llm #machinelearning #developers