update numbering format

Author: rivaquiroga

_posts/2025-12-03-nyasita-third-indaba.md

@@ -18,7 +18,7 @@ The [Deep Learning Indaba](https://deeplearningindaba.com/) (DLI) (or simply the
 
 There are usually three things I look forward to at the Indaba… wait, no, four:
 
-[1] The people. When I interviewed [**Siobhan Mackenzie Hall**](https://we-are-ols.org/people.html#smhall97) (an OLS mentor and one of the OGs of the Indaba) back in 2023 for my podcast *The* *Science in Real Life*, she told me: *“Look forward to the people.”* She was right. The Indaba community is warm, curious, and adventurous. Exactly my kind of people. This year, I reconnected with Siobhan, and I finally met [**Ahmed Unshur**](https://www.linkedin.com/in/ahmedunshur/) and [**Tajuddeen Gwadabe**](https://we-are-ols.org/people.html#tajuddeen1) in person. I’d seen their names in OLS co-workings and other meetings, but we hadn’t crossed paths face-to-face until now. It was refreshing to chat about our work and share highlights from the Indaba. They’re doing amazing things, by the way!
+**1. The people.** When I interviewed [**Siobhan Mackenzie Hall**](https://we-are-ols.org/people.html#smhall97) (an OLS mentor and one of the OGs of the Indaba) back in 2023 for my podcast *The* *Science in Real Life*, she told me: *“Look forward to the people.”* She was right. The Indaba community is warm, curious, and adventurous. Exactly my kind of people. This year, I reconnected with Siobhan, and I finally met [**Ahmed Unshur**](https://www.linkedin.com/in/ahmedunshur/) and [**Tajuddeen Gwadabe**](https://we-are-ols.org/people.html#tajuddeen1) in person. I’d seen their names in OLS co-workings and other meetings, but we hadn’t crossed paths face-to-face until now. It was refreshing to chat about our work and share highlights from the Indaba. They’re doing amazing things, by the way!
 
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-[2] The tutorials. My background isn’t in AI or coding per se, so I find the tutorials incredibly valuable. They’re like friendly lecture-style intros that ease you into the technical depth. My favorite this year was from Luis Serrano, who explained something that completely reframed how I think about ChatGPT. He said, *“The thing that drives me crazy about ChatGPT is that it generates one word at a time.”* At first, I was baffled, because as a user, it feels like it writes whole paragraphs in seconds. But from what I understood, what he meant is that ChatGPT is a predictive model: at every step, it selects the most likely *next word* based on everything it has generated before. What makes it powerful (and better than phone autopredict) is its ability to look far back into the context, sometimes thousands of words, so the sentences come out coherent, not just like WhatsApp or message predict suggestions strung together. That clicked for me.
+**2. The tutorials.** My background isn’t in AI or coding per se, so I find the tutorials incredibly valuable. They’re like friendly lecture-style intros that ease you into the technical depth. My favorite this year was from Luis Serrano, who explained something that completely reframed how I think about ChatGPT. He said, *“The thing that drives me crazy about ChatGPT is that it generates one word at a time.”* At first, I was baffled, because as a user, it feels like it writes whole paragraphs in seconds. But from what I understood, what he meant is that ChatGPT is a predictive model: at every step, it selects the most likely *next word* based on everything it has generated before. What makes it powerful (and better than phone autopredict) is its ability to look far back into the context, sometimes thousands of words, so the sentences come out coherent, not just like WhatsApp or message predict suggestions strung together. That clicked for me.
 
-[3] The practicals. The Indaba wouldn’t be the Indaba without the hands-on sessions in Colab notebooks. This year, I stuck to the LLM track and loved the “behind-the-scenes” look at generative AI. Two stood out:
+**3. The practicals.** The Indaba wouldn’t be the Indaba without the hands-on sessions in Colab notebooks. This year, I stuck to the LLM track and loved the “behind-the-scenes” look at generative AI. Two stood out:
 
    * Exploring the World of Generative AI – We used Stable Diffusion to remix sketches and photos, played “AI or Real?” guessing games, and discussed ethical tensions around bias, deepfakes, environmental cost, ownership, and global equity. AI has become so creative and accurate that it’s hard for us to tell AI-generated images from actual images. Like, look at these two below, are you able to tell which one is real and which one is AI? 
 
@@ -62,7 +62,7 @@ There are usually three things I look forward to at the Indaba… wait, no, four
 
 * Large Language Models under the Hood – We unpacked Transformers, attention, tokenization, and embeddings. Beyond using LLMs like ChatGPT or Gemini, we even trained a small Shakespeare-inspired model ourselves. These are things I didn’t learn in school, and thanks to the Indaba, I am learning them. 
 
-[4] The culture dinner. Nothing screams *ubuntu or umoja* more than this night of African dress, music, food, and celebration. Even after three Indabas, I’m still as excited as I was at my first culture dinner. But unlike that first indaba, I tried to represent Kenya as much as I could, adorning Maasai accessories with my somewhat shein-looking outfit. *(for image reference, see the picture above with Tajuddeen)*
+**4. The culture dinner.** Nothing screams *ubuntu or umoja* more than this night of African dress, music, food, and celebration. Even after three Indabas, I’m still as excited as I was at my first culture dinner. But unlike that first indaba, I tried to represent Kenya as much as I could, adorning Maasai accessories with my somewhat shein-looking outfit. *(for image reference, see the picture above with Tajuddeen)*
 
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