The hottest Performance analysis Substack posts right now

And their main takeaways
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SemiAnalysis 10708 implied HN points 21 Feb 24
  1. Groq AI hardware showcases impressive speed and cost efficiency, outperforming other inference services while charging less.
  2. While speed is vital, supply chain diversification plays a significant role in evaluating hardware's revolutionary potential.
  3. Understanding the total cost of ownership is crucial in deploying AI software, with significant impacts from chip microarchitecture and system architecture.
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Quant Trading Rules 79 implied HN points 26 May 24
  1. The Turnaround Tuesdays trading strategy is based on stocks rebounding on Tuesdays after a down Monday, with simple entry and exit rules tested over 31 years.
  2. Expanding the strategy to include Tuesdays and Wednesdays improved results, increasing annual returns and win rates while maintaining high simplicity.
  3. Utilizing leverage by applying the strategy to a leveraged ETF like TQQQ can significantly boost annual returns, although it may lead to more frequent and deeper drawdowns.
Low Latency Trading Insights 137 implied HN points 06 Feb 24
  1. Better descriptive statistics are needed for low-latency profiling to accurately capture rare events and spikes.
  2. Descriptive statistics like mean, median, skewness, and kurtosis may be misleading in non-normally distributed data.
  3. Self-adjusting histograms with log-based ranges can provide more accurate data representation and efficient storage.
Klement on Investing 3 implied HN points 04 Dec 24
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Klement on Investing 4 implied HN points 19 Nov 24
  1. ChatGPT can analyze earnings calls and predict how analysts will change their forecasts. This means it can assess important company factors like growth and risk.
  2. The Analyst Insight Score (AIS) created from ChatGPT's analysis is better at predicting analyst actions and stock prices than traditional methods. It's about two to four times more effective.
  3. There's concern that as AI like ChatGPT improves in its analysis, it might replace human jobs in finance. This includes roles like equity analysts and fund managers.
Artificial Fintelligence 4 HN points 16 Mar 23
  1. Large deep learning models like LLaMa can run locally on a variety of hardware with optimizations and weight quantization.
  2. Memory bandwidth is crucial for deep learning GPUs, with memory being the bottleneck for inference performance.
  3. Quantization can significantly reduce memory requirements for models, making them more manageable to serve, especially on GPUs.