The hottest Programming Substack posts right now

And their main takeaways
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Top Technology Topics

[in case you missed it] Data Science Weekly - Issue 395

Data Science Weekly Newsletter • 0 implied HN points • 20 Jun 21
🕹 Technology Programming
  1. TinyML is a growing field with many projects and papers exploring its potential. It's basically about running machine learning on small devices.
  2. There are different technologies like Dask and Vaex for processing large datasets in Python. Each has its own strengths, so it's good to know which one fits your needs.
  3. Understanding multi-objective optimization can help you make better decisions in complex situations. It's about looking at several goals at once instead of just one.

[in case you missed it] Data Science Weekly - Issue 394

Data Science Weekly Newsletter • 0 implied HN points • 13 Jun 21
🕹 Technology Programming
  1. The data economy harms our privacy by collecting personal information for profit. It's important to rethink this approach.
  2. New AI methods are improving tasks like chip design, allowing machines to do the work faster and better than humans.
  3. There's a growing interest in data management concepts like data mesh, which focuses on decentralized data ownership and treating data as a product.

[in case you missed it] Data Science Weekly - Issue 383

Data Science Weekly Newsletter • 0 implied HN points • 28 Mar 21
🕹 Technology Programming
  1. AI is making strides in drug discovery by addressing important problems, and there's great research available on the topic.
  2. Jupyter notebooks are loved for data exploration but can be tricky for production use, leading to mixed feelings among data scientists and machine learning engineers.
  3. Detecting names in user messages is a complex challenge that's important for creating better virtual assistants.

[in case you missed it] Data Science Weekly - Issue 379

Data Science Weekly Newsletter • 0 implied HN points • 28 Feb 21
🕹 Technology Programming
  1. Writing a book about data science can be a fun way to share knowledge and inspire others. It's also possible to make money online while doing it.
  2. Understanding Python concurrency is important for data scientists. Learning about topics like async and threads can boost your software engineering skills.
  3. Feature stores are essential for operationalizing machine learning. They help teams manage and deploy machine learning features efficiently.

[in case you missed it] Data Science Weekly - Issue 375

Data Science Weekly Newsletter • 0 implied HN points • 31 Jan 21
🕹 Technology Programming
  1. Building a machine learning (ML) team starts small but can grow significantly. As projects develop, different challenges arise that require specific team structures to tackle them.
  2. Effective machine learning should help systems generalize beyond the data they are trained on. This means creating algorithms that can learn from observations and apply that knowledge to new situations.
  3. AI is starting to influence many fields, like music technology, by learning characteristics of sound and improving products like guitar amplifiers. This shows how machine learning can apply to real-world problems in creative ways.
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[in case you missed it] Data Science Weekly - Issue 374

Data Science Weekly Newsletter • 0 implied HN points • 24 Jan 21
🕹 Technology Programming
  1. Controlled experiments are important in data science to understand how new features perform. They help ensure that changes really make a difference and aren't just random results.
  2. AI is being used in various fields, including drug discovery and medical diagnostics, to improve accuracy and efficiency. Innovations like AI techniques can lead to faster and more accurate results in critical areas like cancer diagnosis.
  3. Understanding the theory behind machine learning can help data scientists create better models. Learning about tools like Support Vector Machines can enhance model performance and application.

[in case you missed it] Data Science Weekly - Issue 371

Data Science Weekly Newsletter • 0 implied HN points • 03 Jan 21
🕹 Technology Programming
  1. Real-time machine learning is becoming important for many companies, with some investing heavily in the necessary infrastructure. This has led to positive financial returns for them.
  2. There is a growing list of tools for machine learning operations, with many new entries improving how developers can manage their ML projects.
  3. Different techniques like Markov models can help in planning and optimizing tasks, like workout routines, by predicting the next steps based on previous actions.

[in case you missed it] Data Science Weekly - Issue 370

Data Science Weekly Newsletter • 0 implied HN points • 27 Dec 20
🕹 Technology Programming
  1. 2020 saw significant advancements in AI, especially with neural volume rendering and models that can learn rules themselves.
  2. Data scientists are in high demand, and platforms like Vettery can help job seekers connect with employers.
  3. Resources are available to help aspiring data scientists improve their skills, build portfolios, and create impactful resumes.

[in case you missed it] Data Science Weekly - Issue 369

Data Science Weekly Newsletter • 0 implied HN points • 20 Dec 20
🕹 Technology Programming
  1. Companies are now changing how they present information because machines and AI read their reports too. They're trying to make it easier for algorithms to understand, sometimes even avoiding negative words that might confuse them.
  2. Monitoring machine learning in production is crucial. It's important to catch any unusual patterns or changes in how models behave to ensure they keep performing well.
  3. Artificial intelligence is being developed to better interact with humans. By using virtual environments, researchers are teaching AI to mimic human behaviors and improve interaction quality.

[in case you missed it] Data Science Weekly - Issue 365

Data Science Weekly Newsletter • 0 implied HN points • 22 Nov 20
🕹 Technology Programming
  1. There's a new newsletter called The Batch that shares important AI events and insights. It's easy to read and aimed at both engineers and business leaders.
  2. Dynamic data testing is different from software testing. It requires tests that can adapt to how data changes over time.
  3. Isolation Forest is currently a top choice for detecting anomalies in big data, thanks to its simplicity and effectiveness.

[in case you missed it] Data Science Weekly - Issue 356

Data Science Weekly Newsletter • 0 implied HN points • 20 Sep 20
🕹 Technology Programming
  1. The ICML conference is a big deal for machine learning professionals, bringing together people from different backgrounds to share ideas.
  2. Apache Arrow is an essential library for data processing that aims to improve how we handle and share data efficiently.
  3. Transformers, a popular type of neural network, are closely related to Graph Neural Networks and have made significant contributions to natural language processing.

[in case you missed it] Data Science Weekly - Issue 352

Data Science Weekly Newsletter • 0 implied HN points • 23 Aug 20
🕹 Technology Programming
  1. minGPT is a simple way to understand and train GPT models with only 300 lines of code. It's designed to be clean and educational.
  2. Bias in datasets like CoNLL-2003 can affect how well AI models recognize names. If a model only learns from biased data, it may perform poorly on names that aren't represented.
  3. Real-world challenges in reinforcement learning can hinder its effectiveness. Researchers are working on solutions to make RL more applicable in practical situations.

[in case you missed it] Data Science Weekly - Issue 346

Data Science Weekly Newsletter • 0 implied HN points • 12 Jul 20
🕹 Technology Programming
  1. A workshop at the Santa Fe Institute explored the meaning and understanding in AI, involving participants from different fields to discuss how machines might understand like living beings.
  2. The cost of training AI is dropping much faster than expected, making it easier for companies to adopt AI technology in the coming years.
  3. Training Generative Adversarial Networks (GANs) presents challenges, but new algorithms are being developed to improve stability and performance in machine learning.

[in case you missed it] Data Science Weekly - Issue 345

Data Science Weekly Newsletter • 0 implied HN points • 05 Jul 20
🕹 Technology Programming
  1. Machine learning is becoming more practical and useful in real-world applications. It's important to focus on making this technology work effectively for various industries.
  2. AI is a fast-evolving field with many developments happening globally, and discussions about its future are crucial for guiding its ethical use and advancements.
  3. Transparency in machine learning models is essential. Providing clear documentation about how they work helps ensure they are used correctly and responsibly.

[in case you missed it] Data Science Weekly - Issue 333

Data Science Weekly Newsletter • 0 implied HN points • 12 Apr 20
🕹 Technology Programming
  1. Data science often doesn't meet expectations in the workplace due to misunderstandings about its role and challenges like lack of leadership and unclear impact.
  2. Monitoring machine learning models in production is complex but important, and there are practical ways to start effectively tracking their performance.
  3. Building effective data science platforms requires understanding the needs of data scientists to enhance collaboration and address the limits of local development.

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

Data Science Weekly Newsletter • 0 implied HN points • 25 Jan 20
🕹 Technology 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 321

Data Science Weekly Newsletter • 0 implied HN points • 18 Jan 20
🕹 Technology Programming
  1. Hiring smart people can be tricky because many recruiters rely on strict rules and fancy degrees. There’s a chance to find great talent if you look beyond the typical criteria.
  2. As machine learning gets better, it can sometimes cause mistakes in human decision systems, known as the 'Uncanny Valley.' It's important to design these systems carefully to avoid problems.
  3. TinyML is an exciting area of machine learning that lets small devices analyze data using minimal power. This means everyday items like printers and cars can now perform complex tasks with smarter tech.

[in case you missed it] Data Science Weekly - Issue 319

Data Science Weekly Newsletter • 0 implied HN points • 04 Jan 20
🕹 Technology Programming
  1. Becoming an independent researcher can be tough, but it may open up new paths for publishing. There's a balance to consider between freedom and potential challenges.
  2. AI is making strides in reading medical images like mammograms. This tech might help doctors find signs of cancer earlier and more accurately than before.
  3. Working on data projects is not just good for learning; it's super useful for impressing future employers. Showing what you've done can set you apart in job applications.

[in case you missed it] Data Science Weekly - Issue 318

Data Science Weekly Newsletter • 0 implied HN points • 28 Dec 19
🕹 Technology Programming
  1. Data visualization tools help us understand complex data better. New projects like VisualizeMnist and butterfly datasets show exciting ways to use these tools.
  2. AI is becoming powerful in games, as seen with Pluribus, an AI that beats professional poker players. This success highlights the advancements in AI competition.
  3. Learning the math behind neural networks is important. Resources are available to help demystify the concepts, making it easier for beginners to grasp.

[in case you missed it] Data Science Weekly - Issue 316

Data Science Weekly Newsletter • 0 implied HN points • 14 Dec 19
🕹 Technology Programming
  1. NeurIPS 2019 saw a huge increase in submissions, with an acceptance rate of 21.6%. This highlights the growing interest and importance of data science research.
  2. Many data science teams use both R and Python, which can create challenges. Finding ways to combine these languages is key to enhancing team collaboration.
  3. Training projects like predicting dog adoption outcomes and understanding game strategies show the real-world applications of data science in improving lives and decision-making.

[in case you missed it] Data Science Weekly - Issue 313

Data Science Weekly Newsletter • 0 implied HN points • 23 Nov 19
🕹 Technology Programming
  1. Google Cloud is improving AI transparency by explaining how data influences machine learning decisions. This helps companies understand AI outputs better.
  2. Sony is launching a new AI division to compete with big players like Google and Facebook for talent and projects. This shows that the AI race is heating up.
  3. It's important to differentiate between real AI and fake claims. Many products marketed as AI may not actually work as promised, so being cautious is key.

[in case you missed it] Data Science Weekly - Issue 293

Data Science Weekly Newsletter • 0 implied HN points • 06 Jul 19
🕹 Technology Programming
  1. The State of AI Report 2019 highlights how fast AI is growing and important developments from the past year.
  2. Machine learning is now being used to translate languages that were previously lost, opening up new ways to understand history.
  3. There are many resources and guides available for those wanting to get started in data science, covering everything from building a portfolio to writing a great resume.

[in case you missed it] Data Science Weekly - Issue 252

Data Science Weekly Newsletter • 0 implied HN points • 22 Sep 18
🕹 Technology Programming
  1. Researchers found a pattern in prime numbers that resembles certain crystal patterns, which could change how we understand them. It's exciting because primes are usually seen as random and mysterious.
  2. DeepMind's AI is being used to improve Android battery life, showing how tech can help make our devices work better. It's important to see if these changes truly benefit users.
  3. Transfer learning allows using knowledge from similar problems to tackle new tasks more easily. This can save time and resources in machine learning projects.

[in case you missed it] Data Science Weekly - Issue 247

Data Science Weekly Newsletter • 0 implied HN points • 18 Aug 18
🕹 Technology Programming
  1. Coursera has a new AI tool that helps companies see what skills their employees are learning. This way, workers can find out what skills they might still need.
  2. A small team of student AI coders has achieved success that rivals Google's machine-learning code. This shows that innovative work in AI can come from anywhere, not just big companies.
  3. Facebook's chief AI scientist believes that better collaboration between industry and academia is essential for AI innovation. This partnership can lead to exciting developments in the field.

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

VuTrinh. • 0 implied HN points • 26 Dec 23
🕹 Technology 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.

30 Days of Creative Programming: Day 10 Update

polymathematics • 0 implied HN points • 21 Jun 23
🕹 Technology Programming
  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.

Geometry-Aware Hashing of GeoJSON objects

Altay's Blog • 0 implied HN points • 26 May 24
🕹 Technology Programming
  1. Hashing can make comparing large lists of GeoJSON objects faster and easier. It lets you quickly find duplicates without much effort.
  2. GeoJSON shapes, like polygons and lines, can be represented by different sets of coordinates. This means we need a special way to hash them to recognize their shape, not just the order of points.
  3. To achieve this, we can create a consistent way to choose starting points or reorder coordinates. This way, the hash will stay the same, even if the coordinates are ordered differently.

Git Stash Like a Pro: Tips for Every Developer

Curious Devs Corner • 0 implied HN points • 09 Sep 24
🕹 Technology Programming
  1. The `git stash` command lets you temporarily save your changes without committing them. This is useful when you need to switch branches but want to keep your work safe.
  2. You can list, apply, or pop your stashes to manage them easily. When you pop a stash, it removes it from your stash list for good.
  3. To avoid losing track of changes, you can also show changes or create a new branch from your stash. This makes it easier to keep your work organized.

Craft Powerful Bash Pipelines with the Unix Xargs Command

Curious Devs Corner • 0 implied HN points • 28 Aug 24
🕹 Technology Programming
  1. The `xargs` command helps to build and run new commands by passing input from one command to another. It's particularly useful when you want to handle lots of files at once.
  2. You can use `xargs` with commands like `find` to perform specific actions on multiple files, making tasks like deleting or renaming files easier.
  3. By using options like `-p` and `-n`, you can interactively confirm actions and control how many arguments are processed at a time, allowing for safer execution of commands.

Streamlining Notification Processing with Kafka, MinIO, and Python

Curious Devs Corner • 0 implied HN points • 16 Jul 24
🕹 Technology Programming
  1. You can streamline your application's notification processing by using Kafka and MinIO together. This combination helps in managing event-driven communications effectively.
  2. Setting up a local development environment with Docker is a great way to get started. You can easily configure MinIO to send notifications through Kafka with just a few settings.
  3. Kafka acts as the central hub by consuming event data from MinIO, while Zookeeper helps track everything in the Kafka cluster. This setup keeps your notifications organized and properly managed.

Master Dynamic Queries in Spring JPA

Curious Devs Corner • 0 implied HN points • 13 Jul 24
🕹 Technology Programming
  1. You can create fully dynamic queries in Spring JPA based on user input. This allows users to choose which columns to select and how to group them.
  2. When using 'group by', all non-aggregated columns from the select statement must be included in the group clause. Otherwise, you'll get an error.
  3. Using the Java Persistence Criteria API can help effectively manage these dynamic queries and avoid common issues.

How to Calculate Image Similarity Using Python

Curious Devs Corner • 0 implied HN points • 12 Jul 24
🕹 Technology Programming
  1. You can easily compare images using Python with the image-similarity-measures library. It has different ways to measure how similar two images are.
  2. The library supports eight different methods to evaluate image similarity, like RMSE and SSIM. You just need to pick one and pass your images to it.
  3. You can run comparisons quickly from your terminal or create a Python script. It's a straightforward way to find out how similar different images are.

Boost Unix Script Efficiency with Heredoc

Curious Devs Corner • 0 implied HN points • 10 Jul 24
🕹 Technology Programming
  1. Heredoc is a way to write multiple lines of code in a clean format for Unix scripts. It makes your scripts easier to read and manage.
  2. You can use heredoc with commands like ssh, sftp, and cat to run multiple instructions at once. This saves time and reduces the complexity of your scripts.
  3. With heredoc, you can also add comments and organize your code better. Plus, it allows for things like parameter substitution to make your scripts even more powerful.

Hidden Gems: Powerful Unix Commands to Boost Your Tech Skills

Curious Devs Corner • 0 implied HN points • 09 Jul 24
🕹 Technology Programming
  1. The 'disown' command helps keep a running process alive even after you close your terminal session. It allows you to remove jobs from the job table so they won’t get stopped when the shell closes.
  2. The 'at' command is used to schedule a job to run just once at a specific time. It's great for when you need to execute something later without using a cron job.
  3. The 'batch' command runs jobs when the system's load is low. It’s useful for scheduling tasks without overloading the system, ensuring smoother operation.

Harnessing the Power of Expect: Automating Interactions with Ease

Curious Devs Corner • 0 implied HN points • 08 Jul 24
🕹 Technology Programming
  1. Expect is a tool that helps automate tasks in the terminal by handling inputs automatically. This means you don't have to type everything manually when running programs or scripts.
  2. You can use Expect for common tasks like logging into remote servers or transferring files easily. It saves time by doing these repetitive tasks for you.
  3. Setting up Expect is straightforward; you just need to install it on your Unix-based system and write a simple script to get started automating your commands.

Integrating OpenAI with a Spring Boot Application Using Spring AI

Curious Devs Corner • 0 implied HN points • 08 Jul 24
🕹 Technology Programming
  1. Spring AI makes it easier to add AI features to your applications. It provides tools and support for using AI models in your software.
  2. You can create an AI language assistant to help students practice a foreign language. The AI can generate fun scenarios and stories to keep the practice interesting.
  3. To get started, you need to set up your project with specific dependencies and an OpenAI API key. This will allow your application to interact with the OpenAI services.

Naming best practices in PHP

Weekly PHP • 0 implied HN points • 29 Oct 24
🕹 Technology Programming
  1. Naming practices in PHP are important for clear and maintainable code. Using meaningful names helps others understand your code better.
  2. PHP 8.4 will introduce Property Hooks, which let you customize behavior for specific properties. This feature can enhance how your code functions without breaking existing parts.
  3. Understanding core PHP concepts is crucial for being proficient. Key topics include syntax, error handling, and data types, which all help in writing better PHP code.