# Things I want to learn

## 2026:
- ✅ Agentic AI / [OpenAI Codex](https://openai.com/codex/), [Claude Code](https://claude.com/product/claude-code), [Gemini CLI](https://github.com/google-gemini/gemini-cli), etc.
- Bike repair: How to change tires, fix breaks, adjust gear system, etc.
- Skating; Ice Skating of just rollerblades

## 2025:
- Answer Engine Optimization (AEO). AEO is like Search Engine Optimization (SEO) but aimed at AI assistants, chatbots, and voice search systems that give direct answers instead of just a list of links.
- Carpentry and woodworking

## 2022:
- ✅ [Apache Spark / PySpark](https://spark.apache.org/docs/latest/api/python/#). At a certain scale, data doesn't fit in memory anymore, and one needs to learn to use Spark to build their machine learning solutions at scale. 
- [Non-fiction Writing](#) I want to learn how to write engaging non-fiction. Some authors have a certain rhythm and their words sound like poetry. Some authors know how to incorporate elements of fiction, a la storytelling, into non-fiction.

## 2021:
- [Ethereum Development](https://ethereum.org/en/developers/). My motivation: There is a chance that crypto is the next big thing after the internet and AI. Maybe it is time to learn about smart contracts and crypto development. I am listing Ethereum here since it is the most popular, think market cap, but I am open to learning other protocols such as [Polkadot](https://polkadot.network/), [Solana](https://solana.com/), [Algorand](https://www.algorand.com/), [Elrond](https://elrond.com/), [Waves](https://waves.tech/), etc. I'd do some research, but pick the one with cleaner documentation to avoid any analysis paralysis.
- [Clustering Ensembles](https://ueaeprints.uea.ac.uk/id/eprint/62679/1/Alqurashi_Tahani_Final.pdf) and [Subspace Clustering](https://www.kdd.org/exploration_files/parsons.pdf). My motivation: When clustering high dimensional data, we have to apply some dimensionality reduction first or else the clustering algorithm will not work. I was thinking, why not iteratively pick some random dimensions, apply clustering algorithms using those feature subsets, and apply another layer of clustering on those clustering outputs. Then I stumbled upon the Clustering Ensembles and the Subspace Clustering concepts.
- [Neural Search](https://jina.ai/). My motivation: Not sure about this one. Just listing it here. Probably will only learn it once I need it; otherwise, I will skip it.

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Tarek Amr - Jan 24, 2026