The hottest AI & Machine Learning Substack posts right now

And their main takeaways
Category
Top Technology Topics

🧊 The Container Iceberg, New Era of Global Databases, the 70% Dilemma in AI Coding, & More

HackerPulse Dispatch • 5 implied HN points • 10 Dec 24
🕹 Technology Software Development Cloud Computing AI & Machine Learning Database Systems Programming Languages
  1. Companies are moving away from VMware because of high cost increases. Many are finding open-source options like OpenNebula to save money and improve efficiency.
  2. A new coding language called PyGyat has playful syntax, making Python coding more fun. It allows developers to switch between traditional Python and PyGyat easily.
  3. AI tools can help speed up coding, but they have limitations. While they help create initial code quickly, the last touches needed for quality often still require human expertise.

2022 Introspective

Technology Made Simple • 99 implied HN points • 27 Dec 22
🕹 Technology AI & Machine Learning Content creation Community Building Audience Growth Online Platforms
  1. The newsletter is going on break till January 5th to recharge and will have a 'start here' section for newcomers.
  2. More AI/Machine Learning content will be integrated into the newsletter on specific days to cater to a general audience.
  3. The growth of the newsletter was mainly influenced by partnerships, Substack recommendations, and content on other platforms including LinkedIn and Medium.

Tech Talks Weekly #5

Tech Talks Weekly • 19 implied HN points • 06 Mar 24
🕹 Technology Software Development Programming Languages Conferences Web Development AI & Machine Learning
  1. Tech Talks Weekly shares recent tech talks from various conferences, making it easier to find valuable content to watch.
  2. There's a special edition summarizing all Java talks from 2023, which has gained attention on Reddit.
  3. You can share your interests and add missing conferences to improve the content that gets shared.

Structure Response from LLMs

mayt writes • 1 HN point • 02 Aug 23
🕹 Technology AI & Machine Learning Sentiment Analysis APIs Natural Language Processing
  1. Large Language Models (LLMs) can process unstructured text data to find information, summarize, and answer basic questions.
  2. Developers face challenges in handling unstructured data generated by LLMs and desire structured outputs for easier processing.
  3. By using novel features like function calling in LLMs, structured data can be generated for specific tasks like sentiment analysis, making data handling more efficient.
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