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What are the key points from my meeting notes yesterday?

Based on your meeting notes from yesterday, here are the key points:

  • Project deadline extended to next Friday
  • New feature requirements added to sprint
  • Team agreed on weekly progress updates
From: Meeting Notes (Apr 15)

What were the new feature requirements?

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Project Research Notes.md
# Project Research Notes In today's meeting, we discussed the implementation of a new feature that would allow users to automatically organize their notes based on semantic similarity. This approach would leverage natural language processing techniques to identify related concepts across different notes. The key benefits of this feature include: - Reduced manual organization time - Discovery of non-obvious connections - Improved knowledge retrieval

Connected Notes

NLP Techniques Overview

Natural language processing (NLP) is a field of AI that gives computers the ability to understand text and spoken words...

Meeting Notes: Feature Planning

Team agreed on implementing semantic search as the next priority. Timeline estimated at 3 weeks...

Knowledge Management Systems

Modern knowledge management systems leverage AI to reduce information overload and improve retrieval...

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Knowledge Hub
6 notes
Smart View
Categories
Project Kickoff Meeting
Discussion about timeline, resources, and initial requirements...
meeting
2 days ago
Book: Thinking Fast and Slow
Key insights about System 1 and System 2 thinking processes...
book
1 week ago
Weekly Planning
Goals for this week include completing the research phase...
planning
Yesterday
Research on AI Applications
Exploring potential use cases for machine learning in our product...
AI
3 days ago
Product Design Ideas
Sketches and concepts for the new dashboard interface...
design
5 days ago
Learning Resources: React Patterns
Advanced techniques for state management and component design...
react
2 weeks ago
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research_notes.md
# Research Notes: Algorithmic Complexity Analysis
Today I'm exploring the relationship between computational complexity and practical algorithm performance. Below are some key equations and code examples that demonstrate these concepts.
## Mathematical Foundations
The time complexity of an algorithm is commonly expressed using Big O notation:
T(n) = O(f(n))
For recursive algorithms, we can use the Master Theorem to solve recurrence relations:
T(n) = aT(n/b) + f(n)
The expected number of comparisons for quicksort is:
E[X] = 2n\ln(n) - O(n)
## Implementation in Rust
fn quick_sort<T: Ord>(arr: &mut [T]) {
if arr.len() <= 1 {
return;
}
let pivot = arr.len() - 1;
let pivot = partition(arr, pivot);
quick_sort(&mut arr[..pivot]);
quick_sort(&mut arr[pivot + 1..]);
}
fn partition<T: Ord>(arr: &mut [T], pivot: usize) -> usize {
let len = arr.len();
arr.swap(pivot, len - 1);
let mut store_idx = 0;
for i in 0..len - 1 {
if arr[i] <= arr[len - 1] {
arr.swap(i, store_idx);
store_idx += 1;
}
}
arr.swap(store_idx, len - 1);
store_idx
}
## Performance Analysis
- Time Complexity: O(n log n) average case
- Space Complexity: O(log n) for recursion stack
- Not stable, but can be optimized with various pivot selection strategies
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