The hottest Software Development Substack posts right now

And their main takeaways
Category
Top Technology Topics

Data Science Weekly - Issue 164

Data Science Weekly Newsletter • 19 implied HN points • 12 Jan 17
🕹 Technology Machine Learning Artificial Intelligence Data science Software Development Programming
  1. TensorFlow is a powerful tool for machine learning, and you can even use it to train AI to play games like MarioKart.
  2. Machine learning can help analyze things like social media behavior, even identifying tweets sent while someone was drinking.
  3. Understanding machine learning trends and best practices can help you in projects, plus there are many resources to guide you in data science.

Data Science Weekly - Issue 160

Data Science Weekly Newsletter • 19 implied HN points • 15 Dec 16
🕹 Technology AI Machine Learning Data science Software Development Algorithms
  1. Neural networks are improving at recognizing drawings, and they will soon be able to analyze them more effectively. This could lead to exciting new developments in how we understand art and creativity.
  2. Deep learning technology is enhancing hearing aids, allowing users to better distinguish voices in noisy environments. This can significantly improve the quality of life for those with hearing difficulties.
  3. AI and machine learning need centralized repositories of information for learning, similar to historical libraries. This is essential for advancing technology and knowledge sharing.

Data Science Weekly - Issue 155

Data Science Weekly Newsletter • 19 implied HN points • 10 Nov 16
🕹 Technology AI Data science Machine Learning Software Development Statistics
  1. AI technology is becoming more accessible, with tools being developed to enhance video communication and creativity directly through mobile apps.
  2. Machine learning is being applied in innovative ways, like LipNet, which helps the hard of hearing by accurately interpreting lip movements.
  3. There's a growing emphasis on the integration of AI in various fields, such as pharmaceutical research, urban transit design, and gaming, showcasing its versatility and impact.
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Data Science Weekly - Issue 146

Data Science Weekly Newsletter • 19 implied HN points • 08 Sep 16
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Web Development
  1. Understanding causality is important in data science. It helps in analyzing data and making better decisions about what affects what.
  2. Machine learning can be applied in many surprising areas, like farming. For instance, a farmer used deep learning to sort cucumbers, showcasing how tech can help everyday tasks.
  3. A/B testing is common in tech companies to improve products, but it can be tricky. If not done carefully, it can lead to biased results, especially in dynamic systems like ride-sharing.

Data Science Weekly - Issue 135

Data Science Weekly Newsletter • 19 implied HN points • 23 Jun 16
🕹 Technology Machine Learning Artificial Intelligence Data science Software Development Programming
  1. Machine learning is becoming crucial for businesses, and understanding its implications can help you stay ahead. It's important to keep learning about new tech like machine learning instead of just focusing on the latest trends.
  2. Companies like Google are adapting to prioritize machine learning in their products. This means they are training their programmers to better integrate AI into their work.
  3. Real-world applications of data science show it isn't just theory. Companies use data science to improve their operations and products, making it more understandable for everyone.

Data Science Weekly - Issue 125

Data Science Weekly Newsletter • 19 implied HN points • 14 Apr 16
🕹 Technology Data science Machine Learning Statistics Software Development Programming
  1. Platforms are important in data science because they help teams work better together and scale their projects. Good organization can make a big difference in data science tasks.
  2. Machine learning can be used to make accurate predictions, such as predicting the outcomes of sports tournaments. This can lead to impressive results in competitions.
  3. Understanding statistics is crucial in software development to assess performance and reliability. Without a solid grasp of statistics, it's hard to know how well software is performing.

Data Science Weekly - Issue 124

Data Science Weekly Newsletter • 19 implied HN points • 07 Apr 16
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. Data science is important for startups and should be integrated early to help in decision-making and culture building.
  2. Machine learning can enhance user experiences, like preventing movie spoilers or predicting bus arrival times.
  3. Learning opportunities, like functional programming and specific data science skills, are available for those looking to enter the field.

Data Science Weekly - Issue 118

Data Science Weekly Newsletter • 19 implied HN points • 25 Feb 16
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. Netflix uses special computer programs to suggest shows to viewers, helping them find stories they love. This helps Netflix connect with more people around the world.
  2. The eating habits in Britain have changed a lot over the last 50 years, with traditional foods being replaced by more modern options. There are tools online that let you see these changes over time.
  3. Airbnb is working to make sure their hiring practices are fair and that they have a more diverse team. They're using research and testing to understand and improve their interview processes.

Cross Development for Fun and Profit, part 1

Dan’s MEGA65 Digest • 1 HN point • 21 Jun 23
🕹 Technology Software Development Programming Languages
  1. Vintage computers like the Commodore offer a unique and focused environment for program development without distractions.
  2. Cross development, where modern computers are used to develop programs for vintage machines, provides advantages like faster work, better collaboration, and code reusability.
  3. Tools like XC=BASIC, a cross-compiled language, enable powerful programming with the simplicity of BASIC and speed of machine code for the MEGA65.

Data Science Weekly - Issue 113

Data Science Weekly Newsletter • 19 implied HN points • 21 Jan 16
🕹 Technology Data science Artificial Intelligence Machine Learning Deep Learning Software Development
  1. Analyzing different State of the Union addresses can reveal changes in language and topics over time. It's interesting to see how leaders communicate their ideas.
  2. Video games can be very useful for developing artificial intelligence. They provide specific challenges that help researchers create better AI solutions.
  3. There's a growing interest in Bayesian methods among R users, thanks to new tools that make these techniques easier to adopt. This could change how many people approach data analysis.

Data Science Weekly - Issue 96

Data Science Weekly Newsletter • 19 implied HN points • 24 Sep 15
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analysis
  1. Job hunting in data science can be really stressful, even for the most confident candidates. It's important to talk about it and share experiences to help each other.
  2. Learning to find patterns in how data scientists work can make the job easier. This means using tools to enhance our own decision-making processes.
  3. When interviewing for data science roles, showcasing business knowledge is just as crucial as proving your technical skills. Understanding how data impacts businesses can set you apart.

Data Science Weekly - Issue 86

Data Science Weekly Newsletter • 19 implied HN points • 16 Jul 15
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Visualization
  1. A simple neural network can be built in just 11 lines of Python code, showcasing how backpropagation works in machine learning.
  2. There's interesting data visualization in sports that shows how team performance changes over time, affecting how we view their success.
  3. Data science can be used for social good, and there are many ways to get involved in projects that make a positive impact on the world.

Data Science Weekly - Issue 78

Data Science Weekly Newsletter • 19 implied HN points • 21 May 15
🕹 Technology Data science Machine Learning Algorithms Analytics Software Development
  1. Machine learning can create interesting comparisons in sports, like calculating fair distances for athletes with different strengths.
  2. Using data creatively can lead to fun projects, such as making beer recipes reflect local demographics or generating rap lyrics with algorithms.
  3. There's a shift in how we think about recommendation systems; they should focus more on user experience than just maximizing success metrics.

Data Science Weekly - Issue 77

Data Science Weekly Newsletter • 19 implied HN points • 14 May 15
🕹 Technology Data science Machine Learning Software Development Statistics Artificial Intelligence
  1. Data scientists often come from different backgrounds, not just math or computer science. Learning some software development skills can be very helpful for data scientists.
  2. Machine learning has advanced to a point where algorithms can outperform experts in certain fields, like art history. This shows how powerful technology can be in analyzing complex data.
  3. Understanding statistical methods, like p-values, is important for good science. It's crucial to scrutinize every step of data analysis, not just the final results.

Data Science Weekly - Issue 65

Data Science Weekly Newsletter • 19 implied HN points • 19 Feb 15
🕹 Technology Data science AI Machine Learning Analytics Software Development
  1. Researchers are using neural networks based on monkey brains to help recognize human faces better. This approach shows how similar our brain processes can be to those of monkeys.
  2. Automating data analysis might make things easier for companies. New software can find patterns in data and create reports, which can save time and improve decision-making.
  3. Robo-advisers are changing how people invest their money. They are becoming popular for managing wealth and could change the financial industry significantly.

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.

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.

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 40

Data Science Weekly Newsletter • 19 implied HN points • 28 Aug 14
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Software Development
  1. Building an online resource like RoboBrain can help robots access important information and AI tools easily. This could make robots smarter and more capable.
  2. Data scientists are using vast amounts of data from major tech companies to improve fields like healthcare. This work shows how valuable data can be in solving real-world problems.
  3. Amazon's shopping data gives it a unique advantage for advertising. By knowing what people buy, Amazon can target ads more effectively than competitors like Google.

Data Science Weekly - Issue 37

Data Science Weekly Newsletter • 19 implied HN points • 07 Aug 14
🕹 Technology Data science Machine Learning Algorithms Software Development Statistics
  1. Deep learning can enhance music recommendations, like the approach used by Spotify to suggest songs based on content.
  2. Algorithms can be very accurate in predicting outcomes, such as Supreme Court rulings, by analyzing historical data.
  3. New technology can even extract audio from video by examining tiny vibrations, showcasing how advanced data analysis can be.

Data Science Weekly - Issue 28

Data Science Weekly Newsletter • 19 implied HN points • 05 Jun 14
🕹 Technology Data science Machine Learning Predictive Analytics Natural Language Software Development
  1. Machine Learning can be used to analyze emotions in real-time. Tools like NLTK and ZMQ make it easier to develop services for this purpose.
  2. Apache Spark is gaining popularity as more companies see its benefits for processing large datasets. This trend is fueled by improvements in its components and an expanding community.
  3. Text analysis can significantly improve stock price prediction accuracy. It has been shown that including text data can enhance predictions by over 10% compared to traditional methods.

Data Science Weekly - Issue 14

Data Science Weekly Newsletter • 19 implied HN points • 27 Feb 14
🕹 Technology Machine Learning Data science Artificial Intelligence Software Development Big Data
  1. Andrej Karpathy developed a tool called ConvNetJS, making it possible to train deep learning models directly in a web browser. This means that you can experiment with machine learning without needing powerful local hardware.
  2. LinkedIn uses machine learning to classify jobs, which helps improve job search and matches candidates better with roles. This shows how machine learning can tackle real-world problems effectively.
  3. There's a lot of discussion around the ethics of using machine learning in areas like crime prediction, as it can sometimes lead to unfair biases. It's important to approach these technologies carefully to avoid negative impacts.

Linux sẽ thất bại nếu bắt chước Windows

Thái | Hacker | Kỹ sư tin tặc • 19 implied HN points • 01 Mar 08
🕹 Technology Operating Systems Software Development
  1. Linux should embrace its differences and innovations instead of trying to mimic Windows to succeed.
  2. Linux and Windows have different strengths and weaknesses, like driving a car vs. a motorcycle.
  3. Promoting open-source software is crucial for enjoying the freedoms it brings and contributing to the development of the country.

Little Tasks, Little Trust

Rethinking Software • 1 HN point • 11 Oct 20
💼 Business Management Work Culture Productivity Software Development Project management
  1. Breaking work into small tasks can make workers feel like they have no creative control. When given bigger responsibilities, they feel trusted and more motivated to contribute ideas.
  2. Focusing too much on metrics and small tasks can take away from the bigger picture of a project. It can lead to a lack of respect for individual skills and creativity.
  3. Estimating small tasks accurately is tough. It's easy for simple tasks to become complicated, leading to time wastage and misunderstandings in the project.

The World Of Helm

Luminotes • 0 implied HN points • 09 Apr 23
🕹 Technology Engineering Kubernetes Programming DevOps Software Development
  1. Helm is like a package manager for Kubernetes, providing templating capabilities and the ability to manage resources and infrastructure.
  2. A Helm chart defines Kubernetes resources in a folder with templates in YAML format, allowing for the creation of releases and packages.
  3. Using Helmfile in combination with Helm simplifies managing multiple charts and deploying different sets of features or values in Kubernetes environments.