Gemini Omni 1.2 Flash explained: 15-second AI video, conversational edits and a 30% lower price

This page is the free part.

The course goes deeper on building products on top of paid model APIs

₹1,333 in India/$35 everywhere elseonce, for the whole course

The Generative AI and Agentic AI course covers building products on top of paid model APIs across a run of lessons, not one page. These 4 alone are about 258 minutes of step-by-step reading, every one with code you run in the browser, all with a quiz.

1. Cost per Prediction: What Do 1,000 Answers Really Cost?

   I rented five small computers from Amazon, one at a time, each with one core. On each, I found the most questions a second it could answer while 99 in 100 answers came back within 25 thousandths of a second. I tried answering one question at a time, and in small groups. Then I divided each computer's price per hour by the answers it gave in an hour. The cheapest computer per hour, the a1.medium, was the dearest per answer: $0.0385 per million answers one at a time. The newest Graviton chip, in the c8g.medium, was the cheapest: $0.0078 per million one at a time and $0.0025 in groups. Groups gave every computer 3.0 to 4.6 times the answers. The test program shared each computer's one core, 4 to 9% of it one at a time and 13 to 19% in groups, so all these rates are a little low. A real shop is quiet much of the day, so it pays for idle hours. All five computers gave identical scores. Every AWS step of the first computer is recorded; the other four ran the same steps from a script, shown.

   84 min · diagrams · code you run · quiz

2. What a Token Is: How a Language Model Reads Text

   A language model never sees letters or words. It sees tokens: small pieces of text, each with a number. This lesson shows how real tokenizers cut text, measured with OpenAI's tiktoken. This course's 1.27 million words come to 1.29 tokens per word. Numbers are cut into pieces of up to three digits. One sentence written in eight languages took 11 tokens in English but up to 100 in other languages with GPT-2's tokenizer, and 12 to 19 with GPT-4o's. That difference is paid in money and in context space.

   51 min · diagrams · code you run · quiz

3. What a Long Prompt Really Costs: Wait, Writing Speed and Memory

   Measured on a laptop with qwen2.5:3b over 30 shuffled calls: the first word came back after a median 1.0 s for a 256-token prompt and 36.6 s for about 7,900 tokens. With the laptop's own slowing taken out, writing got about 1.1 tokens a second slower for every 1,000 prompt tokens. A bigger context window made Ollama report more memory: 2.05 GB at 2,048 tokens, 2.79 GB at 32,768.

   47 min · diagrams · code you run · quiz

4. Training a Small Model on 500 Tickets: What LoRA Changes, and How It Compares With a Prompt

   I trained three small Qwen2.5 models (0.5B, 1.5B and 3B) with LoRA on the 500 training tickets, gave them a one-line prompt instead of the house rules, and scored them field by field against four prompted models on the same 140 test messages. Under 1% of each model's weights was trained, into an add-on of 12 to 27 MB. The fine-tuned 0.5B got 93 whole tickets right of 131, the same as the prompted qwen2.5:14b, but on different messages: 22 right only in one, 22 right only in the other, sign test p = 1. On the 13 comments that ask for nothing, which the prompted models almost always got wrong, the fine-tunes got 11 or 12 right; they also made mistakes the prompts did not.

   76 min · diagrams · code you run · quiz

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