The hottest Software Development Substack posts right now

And their main takeaways
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Top Technology Topics

Building a Fly.io-like Scheduler Part 2: Resource Requirements and Multi-Region

aspiring.dev • 0 implied HN points • 26 Feb 23
🕹 Technology Software Development Cloud Computing System Architecture Project management Resource Allocation
  1. We can make scheduler systems smarter by adding task requirements like region and resource slots. This means a worker can only take on a task if it has the right resources available.
  2. Workers compare the incoming requests against their available resources. If they can't meet the requirements, they simply ignore the task instead of taking it.
  3. The system can be expanded to include more detailed requirements in the future, such as specific CPU types or GPU support, making it adaptable to different tasks and workloads.

Building a Fly.io-like Scheduler Part 1: Basic Coordinator and Workers

aspiring.dev • 0 implied HN points • 23 Feb 23
🕹 Technology Software Development Systems Architecture Cloud Computing Programming Languages DevOps
  1. Fly.io uses synchronous scheduling, meaning you either get a compute resource when you ask for it or you don't. This makes it simpler to handle workloads like serverless functions.
  2. The scheduler's design allows workers to manage their own availability, removing the need for a separate database. This lets workers freely join or leave the system without issues.
  3. In this system, a coordinator requests and schedules tasks on available worker nodes. The first worker to respond gets the task, making it efficient for various jobs like running Docker containers or AI inference.

AT&T Wireless Service Disruption Affects Customers Due to 'Software Issue'

Vigilainte Newsletter • 0 implied HN points • 28 Aug 24
🕹 Technology Telecommunications Software Development Customer Service Cybersecurity
  1. AT&T is facing a major service disruption due to a software issue, causing many customers to lose their ability to make calls or use data.
  2. People are frustrated with the lack of communication from AT&T's support, which has been overwhelmed and unable to provide clear solutions.
  3. This outage is especially bad timing for AT&T, as they just got fined by the FCC for not notifying 911 about a previous outage.

[in case you missed it] Data Science Weekly - Issue 471

Data Science Weekly Newsletter • 0 implied HN points • 04 Dec 22
🕹 Technology Machine Learning Data science Artificial Intelligence Automation Software Development
  1. MLOps is important for automating machine learning products. It helps researchers and practitioners understand the roles and workflows needed in machine learning.
  2. Companies face challenges when moving to realtime machine learning. They need to balance performance, cost, and complexity in their ML pipelines.
  3. The FDA has outlined guiding principles for using AI in medical devices. These principles aim to ensure safety and effectiveness in tech for healthcare.

[in case you missed it] Data Science Weekly - Issue 469

Data Science Weekly Newsletter • 0 implied HN points • 20 Nov 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Robotics
  1. Learning machine learning can be a challenging but rewarding journey, and it often involves continuous effort to improve skills and practices.
  2. Robotics and AI are making a big impact in industries like fulfillment, but there are still many challenges to overcome as the technology scales.
  3. Emerging AI capabilities, particularly in large language models, are becoming increasingly action-driven, resembling more advanced forms of intelligence.
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[in case you missed it] Data Science Weekly - Issue 467

Data Science Weekly Newsletter • 0 implied HN points • 06 Nov 22
🕹 Technology AI Data science Machine Learning User Experience Software Development
  1. Startups using large language models should focus on improving user experience, as it's currently their biggest hurdle, not the data or algorithms.
  2. Data science notebooks have evolved significantly since they were first created, and there are predictions for how they'll continue to develop in the future.
  3. OpenAI is supporting new AI startups by offering $1 million each and early access to their systems, which could help boost innovation in the field.

[in case you missed it] Data Science Weekly - Issue 466

Data Science Weekly Newsletter • 0 implied HN points • 30 Oct 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Open Source
  1. Teaching science should start with the values and virtues of being a good scientist rather than just tools and techniques. Focusing on qualities like curiosity and creativity is key.
  2. Creating a data dictionary before collection is crucial. It helps guide your data collection and makes interpreting results easier later on.
  3. Open source reinforcement learning is evolving with new organizations to improve standardization and support. This effort aims to enhance the quality and usability of available tools.

[in case you missed it] Data Science Weekly - Issue 464

Data Science Weekly Newsletter • 0 implied HN points • 16 Oct 22
🕹 Technology Data science Artificial Intelligence Machine Learning Data Analysis Software Development
  1. Building a community of R users can greatly enhance collaboration and knowledge sharing, especially in specialized fields like pharmaceuticals.
  2. Generating research ideas often starts with identifying gaps in existing literature, which can be guided by specific frameworks to improve the quality of ideas.
  3. Data cleaning is crucial for model accuracy, and its success relies on effective ETL processes and organizational commitment to maintaining high-quality data.

[in case you missed it] Data Science Weekly - Issue 463

Data Science Weekly Newsletter • 0 implied HN points • 09 Oct 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analysis
  1. To explore a large CSV file, you should use handy tools and methods to quickly understand the data without getting overwhelmed.
  2. AI can help convert messy unstructured text into organized data, speeding up tasks that would usually take a long time manually.
  3. Building a career in data science involves learning not just the technical skills but also how to navigate job opportunities and project management.

[in case you missed It] Data Science Weekly - Issue 459

Data Science Weekly Newsletter • 0 implied HN points • 11 Sep 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analytics
  1. Organizations should work on improving their data quality because it directly impacts their success and competitive edge. Creating better data can lead to better decisions and outcomes.
  2. The modern data stack's activation layer is crucial for turning data into actionable insights. This allows companies to go beyond just looking at data and actually use it to improve their products and services.
  3. Using the right tools, like ONNX for model deployment, can help make machine learning models more portable and less tied to specific programming environments. This makes it easier to run models across different programming languages.

[in case you missed it] Data Science Weekly - Issue 457

Data Science Weekly Newsletter • 0 implied HN points • 28 Aug 22
🕹 Technology Data science Machine Learning AI Software Development Analytics
  1. AI has limits when it comes to understanding human language. It can't fully replicate how humans think because language itself is restrictive.
  2. Observable now offers Free Teams, making it easier for data people to collaborate publicly. You can create teams quickly and share notebooks without complicated setups.
  3. The backpropagation algorithm in machine learning is often misunderstood. It is more complex than just applying the chain rule repeatedly, and oversimplifying it can lead to problems.

[in case you missed it] Data Science Weekly - Issue 456

Data Science Weekly Newsletter • 0 implied HN points • 21 Aug 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Research
  1. Machine learning models need regular maintenance. Even after they're deployed, the changing world means they require constant updates to stay effective.
  2. Specialized skills in data science can lead to better job opportunities. Understanding different roles can help you maximize your impact in the field.
  3. Learning resources for machine learning and data science are widely available. Whether through courses, videos, or discussions, there's plenty of help to get started in this exciting area.

[in case you missed it] Data Science Weekly - Issue 446

Data Science Weekly Newsletter • 0 implied HN points • 12 Jun 22
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Software Development
  1. The connection between literature and AI has a long history. There are many examples of how machines have been used to create and assist in writing over the years.
  2. Jupyter Notebooks are versatile tools for data science. They can be used in surprising ways beyond just coding, mixing visualizations and markdown effectively.
  3. Understanding how to use AI responsibly is important. As AI increasingly relies on crowdworkers for data, it raises ethical questions about oversight and compliance.

[in case you missed it] Data Science Weekly - Issue 444

Data Science Weekly Newsletter • 0 implied HN points • 29 May 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Statistical Analysis
  1. Good ML systems need careful design and planning. It's important to know the difference between research and real-world applications.
  2. Data isn't always the best way to make decisions. Sometimes relying too much on data can lead to worse outcomes.
  3. New AI technologies are changing how we think about intellectual property. We might need new laws to keep up with inventions created by machines.

[in case you missed it] Data Science Weekly - Issue 443

Data Science Weekly Newsletter • 0 implied HN points • 22 May 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Visualization
  1. There's a new initiative where you can share what you're up to, and they might include your story in the newsletter. It's a nice way to connect with others in the data science community.
  2. There's a focus on improving software development skills for data scientists by following best practices like version control and automatic testing. This can help teams work better together.
  3. AI-generated art is being debated, with some arguing it's just imitation and not true art. It raises questions about the value of creativity and human experience in art.

[in case you missed it] Data Science Weekly - Issue 440

Data Science Weekly Newsletter • 0 implied HN points • 08 May 22
🕹 Technology Data science Machine Learning AI Software Development Natural Language Processing
  1. Using advanced tools like JAX and Julia can improve machine learning and scientific computing.
  2. Feedback from people can help train language models to produce better results, avoiding offensive or incorrect content.
  3. Data visualizations can provide deep insights, but they should encourage thoughtful reflection, not just clarity.

[in case you missed it] Data Science Weekly - Issue 435

Data Science Weekly Newsletter • 0 implied HN points • 27 Mar 22
🕹 Technology Data science Machine Learning Artificial Intelligence Healthcare Technology Software Development
  1. Algorithmic impact assessments are important for using data in healthcare. They help ensure that the technology is safe and beneficial for everyone involved.
  2. Using deep learning on electronic medical records may not work well right now. It might be better to focus on improving IT support and fixing underlying structures instead.
  3. There's a problem with how we explain AI systems. Many explanations offered today don't truly help us understand how these systems work.

[in case you missed it] Data Science Weekly - Issue 413

Data Science Weekly Newsletter • 0 implied HN points • 24 Oct 21
🕹 Technology Data science Machine Learning AI Software Development Education
  1. Understanding your tools is essential for success in computer science. Learning how to use the command line and version control can help you a lot.
  2. Improving language models to reduce harmful content is a complex task. It's important to ensure these models are safe while still being effective.
  3. Getting a job in data science is easier when you know what companies look for. Keep an eye on the key skills and experiences employers value most.

[in case you missed it] Data Science Weekly - Issue 409

Data Science Weekly Newsletter • 0 implied HN points • 26 Sep 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. Trees are becoming a new model for understanding ecology and plant intelligence. They help researchers think more deeply about the environment.
  2. Effective machine learning often starts without actually using machine learning. It’s important to focus on gathering quality data and defining clear processes first.
  3. Business Intelligence (BI) tools are evolving, but they should focus on providing clear and complete answers to data-related questions for users.

[in case you missed it] Data Science Weekly - Issue 400

Data Science Weekly Newsletter • 0 implied HN points • 25 Jul 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Visualization
  1. A new documentary used AI to generate Anthony Bourdain's voice, raising questions about ethics in media. It's important to think about how technology like this affects what we perceive as real.
  2. Deep learning is becoming more effective despite challenges, and understanding its success can help bridge gaps between traditional statistics and modern AI. Bigger and deeper models often yield better results, even with less data.
  3. Combining different AI models, like Transformers and convolutional neural networks, can lead to better performance in tasks like image recognition. This shows that mixing approaches can help overcome the limitations of each technology on its own.

[in case you missed it] Data Science Weekly - Issue 396

Data Science Weekly Newsletter • 0 implied HN points • 27 Jun 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Cloud Computing
  1. Understanding hype cycles can help us see how technology develops over time. It's interesting to look back at these cycles to learn from past trends.
  2. Multi-task learning is beneficial as it allows machines to make multiple predictions. This can lead to more effective and efficient models.
  3. AI struggles with understanding basic concepts like 'same' and 'different.' This limitation raises questions about how truly intelligent AI can become.

[in case you missed it] Data Science Weekly - Issue 390

Data Science Weekly Newsletter • 0 implied HN points • 16 May 21
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Big Data Data Analysis
  1. AI can solve complex puzzles better than humans, but humans still have unique skills. Don't give up on challenging word games just yet!
  2. Defining trees in biology is tricky because many plants don't fit into clear categories. It's surprising how many things that look like trees actually aren't.
  3. New technology makes searching through large image databases easier. With smart algorithms, you can quickly find the pictures you're looking for without remembering file names.

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

Data Science Weekly Newsletter • 0 implied HN points • 28 Mar 21
🕹 Technology AI Machine Learning Data science Programming Software Development
  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 378

Data Science Weekly Newsletter • 0 implied HN points • 21 Feb 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analytics Software Development
  1. Creating robots that can think morally is similar to parenting. Teaching them right from wrong can be approached in the same way we teach children.
  2. Transformers are important in both language and image processing. Understanding how to use them can help with many tasks in data science.
  3. Building systems for data quality and observability is essential. By using tools like SQL, we can keep track of how our data changes and ensure it stays reliable.

[in case you missed it] Data Science Weekly - Issue 377

Data Science Weekly Newsletter • 0 implied HN points • 14 Feb 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analytics
  1. Using Active Learning can save time and effort in machine learning. It allows models to learn with less labeled data by letting them ask questions about unclear data.
  2. There is a growing shift from Excel to Python in many industries. This change is driven by the need for more advanced data analysis and the capabilities Python offers.
  3. Understanding the importance of machine learning in healthcare is crucial. Innovations like AI systems that can identify smells may lead to new diagnostic tools and enhance medical practices.

[in case you missed it] Data Science Weekly - Issue 376

Data Science Weekly Newsletter • 0 implied HN points • 07 Feb 21
🕹 Technology Data science Machine Learning Artificial Intelligence Statistics Software Development
  1. Data quality is really important in high-stakes AI because it can greatly affect results in areas like health and finance. Many people focus on building models instead of ensuring good data quality.
  2. DanNet was a game-changer in computer vision when it was released ten years ago. It showed that deep learning models could even surpass human performance in certain tasks.
  3. Cohort analysis helps businesses understand their customers better by tracking different groups over time. It's useful for figuring out things like customer engagement and product performance.

[in case you missed it] Data Science Weekly - Issue 368

Data Science Weekly Newsletter • 0 implied HN points • 13 Dec 20
🕹 Technology Data science Machine Learning Artificial Intelligence Neural Networks Software Development
  1. Hyperparameters and latent variables are important in machine learning. We need better methods to create reliable systems that make a real impact.
  2. Understanding how deep neural networks work can help us harness their power effectively. A new method called network dissection can help explain the roles of different units in these networks.
  3. Creating a successful data science team involves building strong collaborations and having the right tools in place. Focus on understanding goals and measuring performance to drive improvements.

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

Data Science Weekly Newsletter • 0 implied HN points • 22 Nov 20
🕹 Technology Data science Machine Learning AI Research Software Development 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 363

Data Science Weekly Newsletter • 0 implied HN points • 08 Nov 20
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Computing
  1. Synthetic biology has advanced significantly in its second decade, showcasing real achievements beyond just hype from the first decade.
  2. Data poisoning attacks can seriously impact machine learning models by manipulating their predictions, so it's important to use trusted data.
  3. Building a strong data science portfolio and tailoring your resume are key steps in landing a data science job.

[in case you missed it] Data Science Weekly - Issue 359

Data Science Weekly Newsletter • 0 implied HN points • 11 Oct 20
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Open Source
  1. Arduino is making it easier for everyone to use machine learning by providing resources to get started quickly. You can learn to set up voice recognition on devices like the Arduino Nano.
  2. TensorSensor is a new tool that helps programmers understand and debug deep learning code easier by visualizing tensor operations. This can be really helpful for those new to coding in this area.
  3. Papers with Code now links machine learning research with relevant code, making it easier to access both studies and their implementations for better understanding and usage.

[in case you missed it] Data Science Weekly - Issue 353

Data Science Weekly Newsletter • 0 implied HN points • 29 Aug 20
🕹 Technology AI Machine Learning Data science Software Development Human-computer interaction
  1. Testing machine learning systems is different from testing traditional software. It's important to do this testing well to ensure the models work as intended.
  2. Fast.ai has released new resources for deep learning, including a complete course and several libraries. These tools can help people learn and apply deep learning more effectively.
  3. AI systems can make decisions that seem efficient but might also cause unfair outcomes. It's vital to consider ethical implications when using algorithms in important areas like hiring or policing.

[in case you missed it] Data Science Weekly - Issue 350

Data Science Weekly Newsletter • 0 implied HN points • 09 Aug 20
🕹 Technology AI NLP Data science Machine Learning Software Development
  1. GPT-3 can create very human-like text and it can even write computer programs with just a few examples. This shows how advanced AI language models are becoming.
  2. Many languages are spoken around the world, but most natural language processing work has focused only on English. It's important to include other languages in research.
  3. Graph technologies are being used to solve complex business problems, such as making recommendations and detecting fraud. They are becoming essential tools in data science.

[in case you missed it] Data Science Weekly - Issue 348

Data Science Weekly Newsletter • 0 implied HN points • 26 Jul 20
🕹 Technology Data science Machine Learning Deep Learning Artificial Intelligence Software Development
  1. Deep learning papers can be overwhelming for beginners, so having a reading roadmap can help newcomers start with the right materials.
  2. Machine learning is creating valuable opportunities in different industries, and knowing where this value will occur can help companies stay competitive.
  3. New techniques in machine learning, like those for detecting earthquakes or improving developer experiences, show how technology is continuously evolving to solve real-world problems.

[in case you missed it] Data Science Weekly - Issue 347

Data Science Weekly Newsletter • 0 implied HN points • 19 Jul 20
🕹 Technology Data science Machine Learning AI Robotics Software Development
  1. Netflix is improving its data efficiency by using a dashboard that helps everyone see costs and usage trends. This way, decision-makers can make better choices based on clear information.
  2. Creating a strong portfolio and resume is really important for landing a data science job. Focus on showcasing your best skills and experiences to attract employers.
  3. There's a shift in building robots to assist humans instead of replacing them. The future should focus on robots that enhance our capabilities rather than take over our jobs.

[in case you missed it] Data Science Weekly - Issue 344

Data Science Weekly Newsletter • 0 implied HN points • 28 Jun 20
🕹 Technology Data science Machine Learning Artificial Intelligence Automation Software Development
  1. As AI and autonomous systems grow, figuring out who is responsible for problems is important. We need to think about who is accountable when things go wrong.
  2. Scientists discovered that a long earthquake swarm was likely caused by natural fluids in the earth. This finding shows how detailed studies can help us understand complex natural events.
  3. The landscape of machine learning tools is extensive but still developing. A recent analysis of over 200 tools revealed both challenges and opportunities for those in the field.

[in case you missed it] Data Science Weekly - Issue 343

Data Science Weekly Newsletter • 0 implied HN points • 21 Jun 20
🕹 Technology AI Data science Machine Learning Algorithms Software Development
  1. Image GPT can create images just like large language models create text. This means we can now generate detailed images by understanding pixel patterns.
  2. MLOps helps data scientists work better together by automating tasks like testing and version control. This makes it easier to manage machine learning projects.
  3. There is no proper regulation for algorithms that affect our daily lives. A group of citizens should help oversee how these algorithms are used to ensure fairness and accountability.

[in case you missed it] Data Science Weekly - Issue 341

Data Science Weekly Newsletter • 0 implied HN points • 07 Jun 20
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Research
  1. Data scientists are in high demand, so it's important to know how to market yourself effectively. Building a strong project portfolio can make you an attractive candidate.
  2. Recent advancements in language models, like GPT-3, show that larger models can perform tasks with fewer examples. This could change how we approach natural language processing.
  3. Managing expectations in data science jobs is crucial, especially for newcomers. Many people feel disappointed because they might not understand the job's realities.

[in case you missed it] Data Science Weekly - Issue 340

Data Science Weekly Newsletter • 0 implied HN points • 31 May 20
🕹 Technology Artificial Intelligence Machine Learning Data science Automation Software Development
  1. AI has some issues that limit its usefulness in businesses. By understanding these problems, businesses can find ways to effectively use AI and even save money.
  2. Human and machine cooperation is essential, and fully automating processes might not be the best approach. We should find ways for machines and people to work better together.
  3. Learning about basic machine learning models is still very important. Many companies don't need advanced techniques, so knowing the basics can help you in real-world jobs.

[in case you missed it] Data Science Weekly - Issue 338

Data Science Weekly Newsletter • 0 implied HN points • 17 May 20
🕹 Technology Machine Learning Data science Artificial Intelligence Software Development Neural Networks
  1. AR and AI can merge to create tools for editing images by cutting and pasting elements from our surroundings. This could revolutionize how we visually manipulate content.
  2. Researchers are working on mapping the human brain's connections to better understand how it functions and what happens when it gets sick. This could lead to major breakthroughs in neuroscience.
  3. Active learning techniques in AI can make label management easier by tracking what data has been labeled and what still needs attention. This saves time and reduces errors during data annotation.

[in case you missed it] Data Science Weekly - Issue 336

Data Science Weekly Newsletter • 0 implied HN points • 02 May 20
🕹 Technology Data science Artificial Intelligence Machine Learning Natural Language Processing Software Development
  1. Tornado plots are a unique way to display time series data, showing how values change over time in a more dynamic way. They help visualize trends in a more engaging format.
  2. An open-source chatbot named Blender, developed by Facebook, is designed to be more human-like in conversations. It is the largest chatbot model available and can be used by other researchers.
  3. The use of machine learning (ML) for optimizing chip design is becoming important as hardware needs to keep up with advancing technology. It could help speed up the design process significantly.