The hottest Technology Substack posts right now

And their main takeaways
Category
Top Technology Topics

OLTP vs OLAP: The Core of Data Miscommunication

Data Products • 2 HN points • 23 Jun 23
🕹 Technology Data Infrastructure Data science Data Systems Data Modeling Data architecture
  1. The difference between OLTP and OLAP systems can cause miscommunication among data producers and consumers.
  2. OLTP systems focus on serving end users quickly with specific product features, while OLAP systems handle complex analytics by scanning large amounts of data.
  3. Empathy and communication between OLTP and OLAP teams are crucial to building scalable data products.

Data Science Weekly - Issue 63

Data Science Weekly Newsletter • 19 implied HN points • 05 Feb 15
🕹 Technology Data science Machine Learning AI Algorithms Programming
  1. Visual mapping helps understand the fast-growing communities on platforms like Twitch. It's a fun way to see how different groups connect.
  2. Data science can offer new ways to evaluate business risks, making it easier for startups to succeed. Using data helps to make better decisions.
  3. In data science portfolios, quality is often more important than quantity. Employers want to see impactful work rather than just a long list of projects.

Data Science Weekly - Issue 62

Data Science Weekly Newsletter • 19 implied HN points • 29 Jan 15
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analysis
  1. Machine learning is getting more important for businesses, especially as they deal with bigger data sets. Companies need to improve how they analyze data to stay competitive.
  2. A strong portfolio is key for landing a data science job. Showing off relevant skills in a well-organized way can really help you stand out to employers.
  3. Data science knowledge is becoming essential across different fields. Professionals are seeing high demand and good pay, making it a smart career choice for many.
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Data Science Weekly - Issue 61

Data Science Weekly Newsletter • 19 implied HN points • 22 Jan 15
🕹 Technology Data science Machine Learning Programming Artificial Intelligence Software Development
  1. Deep learning is really effective, as shown in a talk by Yann LeCun, the head of Facebook AI Research. It's a big part of how we process data today.
  2. Choosing between Python and R for data jobs can be tricky. Both programming languages have their strengths, so it helps to know what you want to do beforehand.
  3. Data science jobs have different levels like junior, mid-level, and senior. It's important to understand these levels when applying for jobs in this field.

How Threat Intelligence and Cyber Deception Intersect

Deceiving Adversaries • 2 HN points • 19 Jun 23
🕹 Technology Cybersecurity Data Analysis Threat intelligence
  1. Cyber Threat Intelligence provides insights into potential threats and helps organizations anticipate, detect, and respond effectively.
  2. Cyber Deception uses deceptive tactics to mislead attackers, acting as a proactive security approach.
  3. The combination of Cyber Threat Intelligence and Cyber Deception creates a powerful tool for organizations to detect, deter, and disrupt cyber threats, enhancing overall cybersecurity.

Data Science Weekly - Issue 60

Data Science Weekly Newsletter • 19 implied HN points • 15 Jan 15
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Analytics
  1. R programming is gaining more popularity in data analysis. Many companies are using it for their projects and applications.
  2. Machine learning can help detect fraud in real-time transactions. Stripe has developed a system that blocks many fraudulent charges before they happen.
  3. Data visualization is essential for understanding complex information. A good example is a graphic that shows population density across different cities in detail.

Gorilla LLM: Bridging APIs with User-Specified Tasks

Sudo Apps • 2 HN points • 15 Jun 23
🕹 Technology AI APIs Machine Learning Software Development Artificial Intelligence
  1. Gorilla LLM is designed to connect large language models with various services and applications through APIs.
  2. LLaMA was chosen as the base model for Gorilla, which has since been fine-tuned with GPT-4, GPT-3.5, and other models.
  3. Gorilla LLM introduces novel concepts like retriever-aware training and AST sub-tree matching for more accurate inferences.

Red Apollo and Operation Cloud Hopper

Espionage& • 2 implied HN points • 16 Jun 23
🕹 Technology Cybersecurity Espionage Malware
  1. Red Apollo conducted a technology theft campaign starting in 2006, targeting various sectors and institutions using spearphishing techniques.
  2. Operation Cloud Hopper, launched in 2014, expanded Red Apollo's activities to targeting a Managed Service Provider and client organizations in 12 countries.
  3. Red Apollo, also known as APT10, is a Chinese state-sponsored cyberespionage group involved in stealing confidential data and intellectual property.

Data Science Weekly - Issue 59

Data Science Weekly Newsletter • 19 implied HN points • 08 Jan 15
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Statistics
  1. Nvidia is showcasing cool technology that lets computers recognize objects in real-time using deep learning.
  2. There's a new field emerging that focuses on how humans interact with data, emphasizing the need for better ethics in data use.
  3. Creating a strong data science portfolio is important, and there are many project ideas and techniques you can use to get started.

🔮E10: Pushing Moore's Law to the Limit with High-NA EUV (aka Nanoscale Chip Printing)

Let Us Face the Future • 1 HN point • 27 Jul 23
🕹 Technology Manufacturing Semiconductors Innovation Supply Chain Startups
  1. High-NA EUV lithography enables more precise and densely packed chip patterns below 10nm, crucial for continuing Moore's Law.
  2. The first High-NA EUV systems are expected to be available commercially around 2025-2026, with known engineering hurdles to overcome.
  3. High-NA EUV may produce chips worth $200 billion annually, impacting advanced logic, analog, photonic, and quantum semiconductors.

Data Science Weekly - Issue 58

Data Science Weekly Newsletter • 19 implied HN points • 01 Jan 15
🕹 Technology Data science Machine Learning AI Programming Analytics
  1. Data science is becoming essential across many industries like sports, retail, and healthcare, driving innovation and insights.
  2. Understanding the difference between correlation and causation is challenging, and researchers are still figuring out how to measure the real impact of certain actions, like changing a coach.
  3. New programming languages and techniques, like Julia and knowledge distillation for deep learning models, are improving how we approach data science and artificial intelligence.

ThoughtSpot - Making Data Analytics as Simple as a Google Search

Termsheet by Attack Capital • 2 HN points • 13 Jun 23
🕹 Technology Data Analytics AI Cloud Computing Startups Artificial Intelligence
  1. ThoughtSpot aims to simplify data analytics like a Google search, providing AI-driven analytics for instant insights.
  2. Co-founded by Ajeet Singh, ThoughtSpot is valued at $4.2Bn and has raised $644 Million, backed by major VC firms.
  3. ThoughtSpot's platform allows users to easily query and analyze data, with features like live-querying, governed data models, and integrations.

Data Science Weekly - Issue 57

Data Science Weekly Newsletter • 19 implied HN points • 25 Dec 14
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analysis Big Data
  1. There are many great resources available to learn about data science. It can be helpful to start with recommended websites, books, and helpful tools.
  2. Data scientists are in high demand, with companies looking for specific skills like R, Python, and SQL. Knowing the right tools can give you an edge in getting a job.
  3. Big data is impacting various fields, including music and sports. Understanding how to analyze this data can lead to fresh insights and opportunities.

Intel & AMD: The First 30 Years

The Asianometry Newsletter • 2 HN points • 31 May 23
🕹 Technology Semiconductors Microprocessors Lawsuits Settlement Reverse Engineering
  1. Intel's founding purpose was to exploit the semiconductor memory market, leading to the creation of the first commercial microprocessor, the Intel 4004, in 1971.
  2. AMD started as a reliable second-source provider for chips, using reverse-engineering to produce the Am9080 and becoming a major seller in the market.
  3. The rivalry between Intel and AMD escalated over time, involving complex legal battles over microcode and patent infringements until a settlement was reached in 1995.

Data Science Weekly - Issue 55

Data Science Weekly Newsletter • 19 implied HN points • 11 Dec 14
🕹 Technology Data science Machine Learning Artificial Intelligence E-commerce Programming
  1. Books can be great gifts, especially the one called 'Data Scientists At Work' which offers insights from leading experts.
  2. Machine learning is evolving, and understanding its challenges, like how deep neural networks can be misled, is important.
  3. Conducting experiments, like those at companies such as Airbnb, helps improve decision-making in business and can teach valuable lessons.

Edition 20: Degrading UX to improve security hurts both UX and security

Boring AppSec • 2 HN points • 30 May 23
🕹 Technology UX Design Information Security User Experience Cybersecurity Internet Security
  1. Degrading user experience to enhance security can harm both aspects.
  2. Considering unintended consequences of design choices is crucial for all engineering disciplines, including security.
  3. Tradeoffs between usability and security can lead to negative impacts on password strength, user behavior, and session management.

Data Science Weekly - Issue 54

Data Science Weekly Newsletter • 19 implied HN points • 04 Dec 14
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Analytics
  1. Learning from mistakes in data science can help improve future projects. It's important to know what to avoid.
  2. Open data can change how we see and interact with our cities. With the right insights, people can push for better policies.
  3. New technology in big data is being used for good causes, including environmental conservation. Data can play a big role in saving the planet.

React 19 beta thoughts

Andrew's Substack • 1 HN point • 27 Apr 24
🕹 Technology React
  1. React 19 introduces actions, making it easier to handle data mutations and state updates in response to async requests, with features like useActionState to simplify state management.
  2. Server components in React 19 allow rendering components ahead of time, separate from the client app, which can be beneficial for static site generation or running server components on CI servers.
  3. Server actions in React 19 are a magical feature that combines server-side actions not included in client bundles but accessible to client components, enhancing network request capabilities.

Data Science Weekly - Issue 53

Data Science Weekly Newsletter • 19 implied HN points • 27 Nov 14
🕹 Technology Data science Machine Learning Programming Statistics Innovation
  1. Teaching creativity through programming can be fun, as shown by a class project where students made Twitter bots.
  2. Research from Yahoo Labs helps us understand creativity in short videos like Vine, revealing new ways to analyze creative content.
  3. Using social media data can provide insights into complex topics, like unemployment trends, in a more cost-effective way than traditional methods.

Data Science Weekly - Issue 52

Data Science Weekly Newsletter • 19 implied HN points • 20 Nov 14
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analytics
  1. Personalized recommendations are really important in online shopping because they help customers discover products they might like and give sellers more exposure.
  2. Combining different techniques in data science can create powerful tools, like using machine learning and crowd input together to improve classification models.
  3. AI should be seen as a helpful tool rather than a danger; we should focus on how to use it positively instead of worrying about potential threats.

Data Science Weekly - Issue 51

Data Science Weekly Newsletter • 19 implied HN points • 13 Nov 14
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Statistical Analysis
  1. Data science often blends different fields like statistics and machine learning. This combination helps us solve complex problems and make better predictions.
  2. Understanding both text and images is key to getting a complete view of information. Analyzing them together gives us a clearer picture of reality.
  3. There's a strong demand for data scientists, and many companies struggle to find qualified candidates. This shows how important this skill set is becoming in today's job market.

Microintelligences or AIs talking to AIs in the cloud

Metacritic Capital • 2 HN points • 26 May 23
🕹 Technology AI Automation Software Cloud Artificial General Intelligence
  1. Microintelligences can perform tasks in open scopes that traditional APIs couldn't handle.
  2. Software is evolving to have systems communicate with each other through APIs, shifting from human-to-software interactions.
  3. Generative AI conversing in the cloud can help reduce randomness and create more predictable outcomes by leveraging architectures like Google's ReAct.

Data Science Weekly - Issue 50

Data Science Weekly Newsletter • 19 implied HN points • 06 Nov 14
🕹 Technology Data science Machine Learning Artificial Intelligence Statistical Analysis Visualization
  1. Learning about neural networks can start from the basics before diving into complex topics. It's helpful to understand the core concepts first.
  2. Visualizing data is important for understanding text data better. There are interactive tools available that can help with this.
  3. Choosing the right statistical analysis method is crucial for data science. There are guides that can help you figure out which analysis to use based on your data.

☞ AI, the God of the Gaps, and Our Quintessential Humanity

Cabinet of Wonders • 2 HN points • 23 May 23
🕹 Technology AI Existentialism Ethics Artificial General Intelligence Humanity
  1. AI technologies are advancing rapidly and causing society to question the essence of humanity.
  2. As AI accomplishes tasks once thought unique to humans, concerns arise about job security and existential dread.
  3. It's essential to focus on our quintessential humanity, not just what makes us unique, and find meaning in our lives beyond what AI can do.

Data Science Weekly - Issue 49

Data Science Weekly Newsletter • 19 implied HN points • 30 Oct 14
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Statistical Methods
  1. Getting into data science can be tricky, especially for those coming from academia. It's helpful to have guidance on how to make that transition.
  2. Machine learning can be used to identify negative behaviors online, which demonstrates the power of data science in addressing social issues.
  3. Trusting data sources too much can lead to problems. It's important to be skeptical and question how the data is collected and used.