The hottest Software Development Substack posts right now

And their main takeaways
Category
Top Technology Topics

[in case you missed it] Data Science Weekly - Issue 335

Data Science Weekly Newsletter • 0 implied HN points • 26 Apr 20
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Algorithms
  1. Specification gaming can happen in AI when systems find shortcuts to achieve goals without actually completing the intended task. This is a problem we need to address in AI design.
  2. There’s a lot of gender bias in machine translation, which reflects societal issues. Companies like Google are trying to reduce these biases in their systems.
  3. Training large NLP models is expensive and requires careful budgeting. Understanding these costs can help developers plan better for their projects.

[in case you missed it] Data Science Weekly - Issue 332

Data Science Weekly Newsletter • 0 implied HN points • 04 Apr 20
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Deep Learning
  1. Agent57 is a new AI that can play all 57 Atari games better than humans, showing how deep reinforcement learning can achieve high performance in gaming tasks.
  2. During the COVID-19 crisis, it's important for everyone to approach discussions about data and health with curiosity and honesty, especially if they aren't experts.
  3. ACM is offering free access to their digital library for three months to support researchers and learners during the pandemic, promoting resource sharing in the computing community.

[in case you missed it] Data Science Weekly - Issue 329

Data Science Weekly Newsletter • 0 implied HN points • 14 Mar 20
🕹 Technology Data science Machine Learning AI Wearables Deep Learning Software Development
  1. Human-in-the-Loop Machine Learning helps reduce bias and improve accuracy by involving people in the decision-making process.
  2. Google’s wearable technology analyzes sports performance in real-time, showing how AI can enhance athletic training.
  3. Reinforcement learning can be applied to complex tasks like trading, learning strategies to maximize rewards in dynamic environments.

[in case you missed it] Data Science Weekly - Issue 327

Data Science Weekly Newsletter • 0 implied HN points • 01 Mar 20
🕹 Technology Data science Artificial Intelligence Machine Learning Deep Learning Software Development
  1. Deep learning can help discover new antibiotics, which is really important as antibiotic resistance grows. By using neural networks, scientists found a new molecule that fights a variety of pathogens.
  2. Ethics in AI is becoming essential, especially for technologies that can operate without human intervention. It's important to think about the potential consequences and applications of AI, like in self-driving cars.
  3. Data science skills are in high demand, and there are growing opportunities in various sectors, especially in eCommerce and healthcare. Companies are looking for talent to drive innovations and improve operations.

[in case you missed it] Data Science Weekly - Issue 326

Data Science Weekly Newsletter • 0 implied HN points • 22 Feb 20
🕹 Technology AI Data science Machine Learning Software Development Engineering
  1. AI businesses are different from traditional software companies. They often have different costs and profit structures, resembling more of a service model.
  2. Spotify's Wrapped campaign is a major marketing success that reflects user listening habits over the decade. It was challenging for the engineering team to accomplish this unique data display.
  3. Algorithmic bias is being actively addressed through explainable AI, aiming to make AI decisions fairer and less prejudiced against certain groups.
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[in case you missed it] Data Science Weekly - Issue 325

Data Science Weekly Newsletter • 0 implied HN points • 15 Feb 20
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Research Trends
  1. AI is changing many industries, showing potential in areas like healthcare and self-driving cars. Economists are studying how this technology will affect jobs and the economy.
  2. There are guides available for anyone looking to get into data science. These resources can help you decide what skills you need, build a strong portfolio, and create a standout resume.
  3. Research in machine learning is advancing rapidly, with new models for tasks like seeing transparent objects and improving supply chains. These innovations could lead to smarter, more flexible systems in our daily lives.

[in case you missed it] Data Science Weekly - Issue 322

Data Science Weekly Newsletter • 0 implied HN points • 25 Jan 20
🕹 Technology AI Data science Machine Learning Software Development Programming
  1. The Smulemates project suggests a feature for the karaoke app Smule to help users find singing partners that match their style. This could make karaoke more social and enjoyable.
  2. Facebook AI introduced a new method for teaching machines to navigate in real environments without maps. This could lead to better robots that understand complex spaces, helping them perform tasks with ease.
  3. A tool called Manifold was released as open source to help find problems in machine learning models. It allows users to visually debug and improve their models more efficiently.

[in case you missed it] Data Science Weekly - Issue 311

Data Science Weekly Newsletter • 0 implied HN points • 09 Nov 19
🕹 Technology Artificial Intelligence Machine Learning Data science Healthcare Tech Software Development
  1. Neural networks are becoming smarter by mimicking biological strategies. This could be key to creating truly intelligent machines.
  2. When regulating AI, we should focus on its real-world effects rather than just on fears surrounding the technology.
  3. There are significant challenges in applying AI to healthcare, and we need more examples of successful technology implementation in clinical practice.

[in case you missed it] Data Science Weekly - Issue 308

Data Science Weekly Newsletter • 0 implied HN points • 20 Oct 19
🕹 Technology Machine Learning Data science AI Robotics Software Development
  1. Neural networks can solve real-world problems like a robot hand solving a Rubik's Cube. This shows they can learn and adapt in unpredictable situations.
  2. There's a shift happening in machine learning tools, with more researchers choosing PyTorch over TensorFlow. While TensorFlow is still popular in the industry, this could change soon.
  3. Companies can use a smart model to find the best regions for hiring offshore talent. This helps them build stronger remote teams by targeting specific skills.

[in case you missed it] Data Science Weekly - Issue 307

Data Science Weekly Newsletter • 0 implied HN points • 12 Oct 19
🕹 Technology Data science Artificial Intelligence Machine Learning Computer Vision Software Development
  1. AI needs to learn how to explain its decisions. A leading expert believes understanding the reasons behind AI's choices is important.
  2. Data science is increasingly used in different fields, even fashion. Scientists are applying their skills to help with style choices and personal recommendations.
  3. Small AI models can make everyday technology, like autocorrect and voice assistants, faster and more efficient.

[in case you missed it] Data Science Weekly - Issue 306

Data Science Weekly Newsletter • 0 implied HN points • 06 Oct 19
🕹 Technology AI Data science Machine Learning Software Development Algorithms
  1. Data scientists have many job opportunities, and the demand for their skills is increasing in various industries.
  2. AI is being used in innovative ways, like helping people choose outfits or teach machines to plan actions using natural language.
  3. Stabilizing techniques for training Generative Adversarial Networks (GANs) are important because they help prevent issues that can arise during the training process.

[in case you missed it] Data Science Weekly - Issue 301

Data Science Weekly Newsletter • 0 implied HN points • 01 Sep 19
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Research
  1. Effective management of data science teams requires specific skills and knowledge. Leaders should know how to build and sustain their teams well.
  2. Research takes time and effort, as shown by the history of neural networks. It's important to have patience and persistence in this field.
  3. Estimating the time for software projects can be difficult. This is often due to the unpredictable nature of problem-solving involved in the work.

[in case you missed it] Data Science Weekly - Issue 300

Data Science Weekly Newsletter • 0 implied HN points • 25 Aug 19
🕹 Technology Data science AI Machine Learning Deep Learning Software Development
  1. There's a new AI optimizer called RAdam that can help improve the accuracy of AI models. It automatically adjusts the learning rate based on training conditions.
  2. Deep learning is an area of study that's compared to classical methods and explores various neural network models. Understanding these models can help grasp the foundations of modern AI.
  3. Data scientists are in high demand, and there are resources available to help newcomers prepare for training programs. This can lead to job opportunities in the field.

[in case you missed it] Data Science Weekly - Issue 299

Data Science Weekly Newsletter • 0 implied HN points • 17 Aug 19
🕹 Technology AI Data science Machine Learning Algorithms Software Development
  1. AI is now being used to improve video gaming, like training in soccer using a new football simulator. This shows how far technology has come in understanding games.
  2. Nvidia is making big strides in AI language models, making them faster and more efficient. This means we could see better and more responsive AI conversations soon.
  3. For those wanting to become data scientists, it's smarter to get a related job first and learn on the job. Skills can be built up as you go instead of trying to learn everything at once.

[in case you missed it] Data Science Weekly - Issue 298

Data Science Weekly Newsletter • 0 implied HN points • 11 Aug 19
🕹 Technology AI Data science Machine Learning Natural Language Software Development
  1. AI is being used in new ways, like apps that can help match people on dates using algorithms.
  2. Natural Language Processing (NLP) is a growing field, and there are new trends and insights coming from conferences around the world.
  3. Data pipelines are crucial for machine learning projects, as they help with data collection and cleaning.

[in case you missed it] Data Science Weekly - Issue 294

Data Science Weekly Newsletter • 0 implied HN points • 13 Jul 19
🕹 Technology Artificial Intelligence Machine Learning Data science Data Analytics Software Development
  1. A new poker bot has learned strategies to beat skilled players, showing the advancements in AI technology. This could change how games are played and studied.
  2. Generative adversarial networks, which are known for creating deepfakes, may have positive uses in medical fields like cancer diagnosis. Before, they were mainly seen in a negative light.
  3. San Francisco is trying to use AI to reduce bias in the prosecution process, aiming to make the legal system fairer. This could help in addressing racial discrimination in the courtroom.

[in case you missed it] Data Science Weekly - Issue 280

Data Science Weekly Newsletter • 0 implied HN points • 06 Apr 19
🕹 Technology Artificial Intelligence Data science Machine Learning Big Data Software Development
  1. DeepMind is a big player in AI, and there's tension now that Google owns it. The question of who really controls AI is important.
  2. Warby Parker used an algorithm to help people try on glasses virtually, making shopping easier and more fun.
  3. MIT is experimenting with AI to create new types of food, showing that technology can change the way we think about flavors.

[in case you missed it] Data Science Weekly - Issue 279

Data Science Weekly Newsletter • 0 implied HN points • 30 Mar 19
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Robotics
  1. Three scientists won the Turing Award for their work on neural networks. This award is a big deal in computing, kind of like a Nobel Prize.
  2. Machine learning is being used to create tools that can help doctors focus more on patients instead of taking notes. This could improve healthcare significantly.
  3. There's a new doodling app that uses AI to turn simple sketches into realistic images. This technology could be useful for video games and movies.

[in case you missed it] Data Science Weekly - Issue 269

Data Science Weekly Newsletter • 0 implied HN points • 20 Jan 19
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Education
  1. Neural networks can be hard to understand. Researchers are trying to figure out what these models actually learn during training.
  2. Reinforcement learning is helping robots, like a robot dog, learn to move more like real animals without specific instructions.
  3. Using tools like Flask, you can quickly set up an API for machine learning models. This makes it easier to send data and get predictions.

[in case you missed it] Data Science Weekly - Issue 260

Data Science Weekly Newsletter • 0 implied HN points • 16 Nov 18
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. There are many resources available for learning machine learning, so it's helpful to gather them in one place for quick access.
  2. Lyft has developed tools to handle seasonal market changes, which could help predict when driver incentives are needed.
  3. Getting a data science job can be tough, but reflecting on the journey can show how previous challenges helped lead to success.

[in case you missed it] Data Science Weekly - Issue 257

Data Science Weekly Newsletter • 0 implied HN points • 27 Oct 18
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Software Development
  1. Neural networks can help create new ideas, like unique Halloween costumes. This shows how AI can spark creativity in fun ways.
  2. Uber has built a massive data platform that handles over 100 petabytes of data quickly. This helps them manage and analyze huge amounts of information efficiently.
  3. There are new ways to learn data science, such as hands-on courses with mentoring and payment plans that let you pay after getting a job. This makes it easier for more people to get into the field.

[in case you missed it] Data Science Weekly - Issue 253

Data Science Weekly Newsletter • 0 implied HN points • 29 Sep 18
🕹 Technology AI Data science Software Development Machine Learning Statistics
  1. Uber uses machine learning and deep learning to make better forecasts for their products and services. They focus on combining traditional statistical methods with advanced techniques for accurate predictions.
  2. There's a shift in software development where deep learning is automating much of the coding process. Developers now create a basic outline, allowing the computer to generate the code from past examples.
  3. Tiny computers are increasingly replacing larger controllers in technology. This trend highlights the importance of smaller, more efficient computing solutions in the embedded world.

[in case you missed it] Data Science Weekly - Issue 251

Data Science Weekly Newsletter • 0 implied HN points • 15 Sep 18
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Research
  1. AI systems, like Amazon's Echo, rely on a mix of resources including technology, labor, and nature to work effectively.
  2. Fake news can influence people's voting choices, and there's a mathematical way to show how it happens.
  3. Machine learning tools are becoming easier to use, allowing people to explore and understand models without needing to write code.

[in case you missed it] Data Science Weekly - Issue 246

Data Science Weekly Newsletter • 0 implied HN points • 11 Aug 18
🕹 Technology Data science Machine Learning Artificial Intelligence Statistical Analysis Software Development
  1. Data science can balance fast experimentation with careful research. It's important for teams to adapt quickly while also planning for the long term.
  2. Understanding how land in the U.S. is used can highlight ways to create wealth. Different areas have various productive uses that affect the economy.
  3. Automated machine learning tools like Auto-Keras can help people without a data science background to easily access deep learning models.

[in case you missed it] Data Science Weekly - Issue 244

Data Science Weekly Newsletter • 0 implied HN points • 28 Jul 18
🕹 Technology Data science Machine Learning AI Analytics Software Development
  1. Companies need to define data science roles clearly, focusing on three areas: Analytics, Inference, and Algorithms. This helps businesses meet their specific needs effectively.
  2. Google's AutoML grabs attention for simplifying machine learning tasks, but understanding concepts like transfer learning is essential to grasp its true potential.
  3. Multi-task learning allows machines to learn multiple tasks at once, making them smarter and better at complex challenges, similar to how humans learn.

[in case you missed it] Data Science Weekly - Issue 238

Data Science Weekly Newsletter • 0 implied HN points • 16 Jun 18
🕹 Technology Machine Learning Artificial Intelligence Data science Energy Efficiency Software Development
  1. Neural networks can struggle with humor if they don't have enough examples to learn from. More data might help them learn to tell better jokes.
  2. Machine learning is expected to become more effective on smaller devices, like smartphones, thanks to energy-efficient technologies. This could solve many current problems.
  3. Data cleaning is a big part of data science, often taking up to 80% of a person's time. Using tools like Python and Pandas can help make this process easier.

Data Science Weekly - Issue 209

Data Science Weekly Newsletter • 0 implied HN points • 23 Nov 17
🕹 Technology AI Machine Learning Data science Algorithms Software Development
  1. Flies have a special way of categorizing smells, and researchers are using that idea to improve how computers find similar images.
  2. AI can detect art forgeries by examining just one brushstroke, making the process cheaper and quicker than traditional methods.
  3. Apple is still working on being more open in AI research despite promising to engage more with the academic community last year.

Data Science Weekly - Issue 208

Data Science Weekly Newsletter • 0 implied HN points • 17 Nov 17
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Conferences
  1. Neural networks are changing how we develop software, not just being another tool in machine learning. They represent a big shift towards 'Software 2.0' which impacts many projects.
  2. Evolution strategies are a method in machine learning that can be explained visually, making it easier for people to understand how they work.
  3. There is a growing interest in how AI can be used in creative ways, such as in cooking or video games, showcasing its potential beyond traditional applications.

S2 E1: Solving Self-Serve Analytics

CAUSL Effect • 0 implied HN points • 02 Oct 23
🕹 Technology Data Analysis User Experience Software Development
  1. Self-serve analytics lets non-analysts access and analyze data without always needing help from an analytics team. This can help speed up decision-making and reduce bottlenecks.
  2. The goal is to create tools and provide education for everyday users so they can do their own analytics easily. Training and tutorials will be essential to help users become comfortable with these tools.
  3. The focus is on keeping users engaged and motivated to use self-serve analytics. Understanding what stops people from doing analytics themselves is key to improving the program.

GroupBy #24: Enabling near real-time data analytics on the data lake at Grab, Aligning Velox and Apache Arrow at Meta.

VuTrinh. • 0 implied HN points • 27 Feb 24
🕹 Technology Data Engineering Cloud Computing Analytics Machine Learning Software Development
  1. Grab is working on letting users analyze data quickly with their new approach to data lakes. This helps businesses get insights much faster.
  2. Meta is aligning Velox and Apache Arrow to improve data management. This should make it easier to handle and analyze large amounts of data.
  3. PayPal is using Spark 3 and NVIDIA's GPUs to cut their cloud costs by up to 70%. This helps them process a lot of data without spending too much money.

GroupBy #22: Data Engineering Landscape in 2024, how I scaled my $1m/year revenue startup's data model

VuTrinh. • 0 implied HN points • 13 Feb 24
🕹 Technology Data Engineering AI Machine Learning Software Development Startups
  1. The data engineering field is evolving, and it's important to understand the upcoming trends that will impact how we work with data.
  2. Creating a simple and efficient data model is key for startups, but as they grow, it's crucial to adapt and scale the data model to meet new demands.
  3. Learning SQL remains essential, as it is still a fundamental tool in data manipulation, making it important for anyone in the data field to master.

GroupBy #19: How Apple built iCloud to store billions of databases, Palette-Uber feature store, Definition of Data Modeling

VuTrinh. • 0 implied HN points • 23 Jan 24
🕹 Technology Data Engineering Machine Learning Cloud Computing Software Development AI Tools
  1. Apple uses special databases like Cassandra and FoundationDB to manage iCloud's huge storage system. This helps them keep track of billions of databases effectively.
  2. Uber created a feature store called Palette that helps in managing data for machine learning projects. It collects and organizes useful features for easy access by developers.
  3. Data modeling is a key concept that defines how data is organized and related in a system. Different experts might have varying definitions, showing the complexity of the topic.

GroupBy #15: How Meta built the infrastructure for Threads, Notion's data scale journey

VuTrinh. • 0 implied HN points • 26 Dec 23
🕹 Technology Data Engineering Software Development Machine Learning Infrastructure Programming
  1. Meta created a strong infrastructure for Threads to handle massive user growth right after its launch. This enabled over 100 million sign-ups in just five days.
  2. Notion's data infrastructure had to evolve to keep up with its rapid growth and new product uses. This involved significant changes to manage their increasing data scale.
  3. The 'Grokking Concurrency' book is a helpful resource for learning about concurrent programming. It makes complex topics easier to understand with clear examples.

GroupBy #11: Python at Meta, Netflix Incremental Processing with Apache Iceberg, 2023 AI year in brief

VuTrinh. • 0 implied HN points • 28 Nov 23
🕹 Technology Data Engineering Artificial Intelligence Machine Learning Software Development Cloud Computing
  1. Meta is working on improving how developers use Python, making it smoother with better tools like a new linter.
  2. Netflix has built a system for processing data incrementally using Apache Iceberg, which helps manage and update data efficiently.
  3. There are free courses available from Microsoft and Google Cloud that teach the basics of Generative AI, helping anyone to get started in this exciting field.

GroupBy #9: FDAP stack, Iceberg and Hudi ACID Guarantees, Data Driven Management

VuTrinh. • 0 implied HN points • 14 Nov 23
🕹 Technology Data Engineering Data Analytics Machine Learning Software Development Data Management
  1. The FDAP stack is important in building reliable data systems. It helps to manage data more efficiently by using advanced technologies.
  2. Learning about data quality is crucial. It ensures that the information used for decision-making is accurate and trustworthy.
  3. Data-driven management is all about making decisions based on solid data insights. It helps businesses understand what works and what doesn't.

GroupBy #8: Demystifying the Parquet File, the future of the data engineer, intro to data modeling.

VuTrinh. • 0 implied HN points • 06 Nov 23
🕹 Technology Data Engineering Data Modeling Data science Software Development Big Data
  1. The Parquet file format is becoming popular for data storage because it is efficient and works well with big data tools. Understanding how to use it can help data engineers be more effective.
  2. Data engineering is evolving, and new trends like data mesh are changing how data platforms are built. Keeping up with these changes is important for anyone in the field.
  3. Starting a small data engineering project can be a great way to learn new skills. Even a quick project can teach you important techniques, like web scraping and using cloud storage.

GroupBy #3

VuTrinh. • 0 implied HN points • 22 Sep 23
🕹 Technology Data Engineering Big Data Cloud Computing Software Development Data Management
  1. Docker commands can be simplified with a cheat sheet, making it easier for developers to use container technologies effectively.
  2. Apache Spark was created at UC Berkeley to improve cluster computing, focusing on faster interactive computations than previous systems like Hadoop.
  3. There are key differences between HDFS and S3, especially in how they handle data, and many people confuse them even though they serve different purposes.

GroupBy #2

VuTrinh. • 0 implied HN points • 15 Sep 23
🕹 Technology Data Engineering AI Data science Cloud Computing Software Development
  1. The Lakehouse concept combines the best features of data lakes and data warehouses. It's a new way to manage and analyze data effectively.
  2. Good data quality is essential for making AI work. If the data is bad, the results will also be poor.
  3. AI tools might help data teams work more efficiently, but they won't reduce the demand for data professionals. In fact, they might increase it.

30 Days of Creative Programming: Day 10 Update

polymathematics • 0 implied HN points • 21 Jun 23
🕹 Technology Programming Creative Coding Digital Art Software Development Tech Culture
  1. Creative programming can be a fun and imaginative way to code. It's about enjoying the process and exploring new ideas.
  2. Updating your online presence can help reflect what you love doing. A catchy bio can attract like-minded people and build a community.
  3. Sharing your projects regularly helps keep you motivated. It’s great to have a goal to create something new every day.

Fail Safe, Fail Smart… Succeed! (Part 3 of 3)

It Depends / Nimble Autonomy • 0 implied HN points • 30 Jul 24
🕹 Technology Software Development Cloud Computing Microservices Product Management DevOps
  1. It's important to expect failure in technology work. Today, we design systems with the understanding that things can go wrong at any moment.
  2. Building small, separate services helps manage problems better. If one part fails, it doesn't ruin the whole system, making the user experience smoother.
  3. Learning from failures is key to improvement. When mistakes happen, analyzing them without blame leads to better results in the future.