Chaos Engineering

Chaos Engineering is a newsletter about software, machine learning, fintech, and startups.

The hottest Substack posts of Chaos Engineering

And their main takeaways
3 implied HN points 19 Jan 25
  1. Kubeflow is an important open-source tool for making AI and machine learning easier and more scalable. It helps developers build and manage their AI projects more effectively.
  2. The Steering Committee aims to increase the use of Kubeflow by collaborating with companies and improving user-friendly features. They want to ensure that more people can use and enjoy the platform.
  3. Open-source AI tools are becoming very important as the technology grows. Focus on building strong communities and good support will help everyone succeed in using AI effectively.
5 implied HN points 04 Dec 24
  1. AI Agents are changing how we think about software. They are smart programs that can do tasks for us, but we still need humans to help out to make sure everything runs smoothly.
  2. Using AI to create software can make things cheaper, but it also makes the software more complex. As we rely on AI, we need to ensure we can trust it to work reliably.
  3. Data is super important for AI to work well. We need to collect good quality data to train these AI Agents so they can do their jobs effectively and produce accurate results.
2 implied HN points 29 Jan 24
  1. Affinity marketing involves targeting specific customer groups based on shared characteristics or interests.
  2. Hispanics in the US represent a large segment of the population, often working in labor-intensive jobs and having lower educational backgrounds and incomes.
  3. The Latino American fintech market presents an opportunity to provide financial services tailored to the needs of the Hispanic and Latino communities.
5 implied HN points 24 Feb 23
  1. ChatGPT can learn some superficial aspects of finance but needs explicit training to become a financial expert.
  2. For ChatGPT to learn fintech, a hybrid architecture combining its pretrained model with a specific ML model optimized for financial tasks is necessary.
  3. Improving ChatGPT's understanding of finance requires training it on structured financial data and updating its architecture to process dense, numeric data.
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