woke up at 3 a.m. so disorientated and confused.

had 10 hours of sleep and i felt so well-rested. i had 3 hours of deep focus and flow listening to worship music.

Wrote a prompt for summarizing research papers.

You're an AI and ML expert in a paper reading group. Your task is to carefully summarize this research paper into 4 main sections: Task, Data, Model, Evaluation and Analysis. For each section:

## Task:

- State the primary objective of the research
- Describe any secondary goals or sub-tasks
- Explain the motivation behind the research
- Mention any unique approaches or perspectives

## Data:

- Describe the dataset(s) used, including size, source, and time period covered
- List all types of data collected
- Detail any preprocessing steps or feature engineering
- Mention any data cleaning or handling of missing values
- Note any assumptions made about the data

## Model:

- List all machine learning models evaluated
- For each model:
  - Describe its architecture or key components
  - Specify hyperparameters and how they were chosen
  - Detail the training process (e.g., loss function, optimization algorithm)
- Describe how the training data was split (e.g., cross-validation)
- Mention any ensemble methods or novel modeling approaches

## Evaluation and Analysis:

- List all evaluation metrics used and justify their selection
- Present the main results for each model and dataset combination
- Discuss how the models perform across different prediction horizons
- Discuss any limitations of the approach
- Summarize the key findings and their implications
- Discuss further work

For each section, quote relevant passages from the paper to support your summary. If you're unsure about any detail, state that explicitly rather than making assumptions. Highlight any novel contributions or unexpected findings. Return in markdown format.

printed out the papers and reading them line by line is so different.

this paper: Making Disk Failure Predictions SMARTer! | USENIX is one of the most approachable papers i've read. it even explains how the models work. reading it was actually fun and insightful. i wish all papers were written like this.

this interview has completely enveloped my life. every waking minute has been spent thinking and studying and practicing and learning. what will be the winning question or answer? what will make my interviewer go "oh i should hire this guy?". what important fact or information should i come prepared with? how do i make sure i don't say anything dumb? the thought of those 20 minutes are holding me in chains. in the process of preparing, it's like a breadth-first search of information, and you risk going to deep in the tree, you can't spend too much time on it, it might not even matter in the interview. stay surface level, but deep enough to show intuition. you have to explain complex topics in simple words, but not too simple because you're dealing with someone technical. its a hard problem to optimize on.

just think of it as a conversation. a friendly chat. a coffee chat with a researcher working at meta. except every thing you say will be held against you. bu don't think about that. just be grateful for this opportunity to chat with them. it'll be okay. after tuesday 10:21 i will be free again. and i can go back to my normal life. just one more day to go.