The hottest Cloud Computing Substack posts right now

And their main takeaways
Category
Top Technology Topics

The Observability Pipeline

realkinetic • 0 implied HN points • 12 Sep 18
🕹 Technology Cloud Computing Monitoring Data Collection
  1. Systems are now more distributed and dynamic due to the rise of cloud and containers, requiring new tools and practices to support them
  2. Observability in modern cloud-native environments involves gathering data for granular insights and empowered debugging through structured logging, metrics, traces, and events
  3. Building an observability pipeline helps decouple data collection from ingestion into various systems and allows flexibility to add or replace tools without major disruptions

Why Google App Engine

realkinetic • 0 implied HN points • 23 Jul 18
🕹 Technology Cloud Computing Serverless Data Modeling Services
  1. Google App Engine provides automated operations that manage scalability, fault-tolerance, and traffic splitting, freeing you to focus on your application and business logic.
  2. Designing applications on Google App Engine requires embracing statelessness, optimizing data models, and minimizing request latency to ensure efficient scaling and performance.
  3. Utilize App Engine's features like task queues and services, understand the limitations of Memcache, and plan for modular design to maximize the platform's capabilities and scalability.

There and Back Again: Why PaaS Is Passé (And Why It’s Not)

realkinetic • 0 implied HN points • 06 Feb 18
🕹 Technology Cloud Computing Serverless Containers Application development
  1. In the world of cloud computing, PaaS (Platform as a Service) has lost some of its appeal due to concerns like vendor lock-in and limitations.
  2. The cloud landscape is complex and evolving rapidly, with tools like Kubernetes and serverless reshaping how applications are developed and managed.
  3. Major cloud providers are moving towards unbundling and rebundling PaaS components to offer the benefits of accelerated development while retaining flexibility.

India is Finally Making AI Happen

Sector 6 | The Newsletter of AIM • 0 implied HN points • 05 May 24
🕹 Technology Artificial Intelligence Cloud Computing Software Development Tech industry Digital innovation
  1. India's AI ecosystem is rapidly growing with new announcements and updates.
  2. Ola Krutrim is launching its own AI Cloud to compete with big tech companies like AWS and Google Cloud.
  3. The new cloud service aims to provide affordable AI solutions along with developer tools and an Android app.

AGI Factories Coming Soon 🤖🏭🔜

Sector 6 | The Newsletter of AIM • 0 implied HN points • 02 Apr 24
🕹 Technology Artificial Intelligence Data Centers Cloud Computing Semiconductors IT Infrastructure
  1. Microsoft and OpenAI are launching a massive $115 billion supercomputer project called Stargate by 2028. This shows a huge investment in AI technology.
  2. AWS plans to spend $150 billion on new data centers over the next 15 years to meet the rising demand for AI tools. This indicates that many companies are getting ready for a future filled with AI.
  3. NVIDIA is making advancements in AI with its 'AI factories' and next-gen chips. They are pushing boundaries in technology and aim to help develop artificial general intelligence.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Google’s Q*?

Sector 6 | The Newsletter of AIM • 0 implied HN points • 14 Dec 23
🕹 Technology AI Machine Learning Data science Cloud Computing Software Engineering
  1. Google's AlphaCode 2 has improved significantly, performing better than the earlier version by solving many coding challenges. It shows that Google's advancements in AI are making big leaps.
  2. AlphaCode 2 ranks in the 85th percentile among competitors, meaning it outperforms most human participants in coding competitions. This suggests that AI is becoming very capable in technical problem-solving.
  3. Many people are focused on Google's Gemini project, but AlphaCode 2 might be a game-changer in competitive coding, indicating a shift in how powerful AI tools can be for programmers.

Big Tech "GenAI" Quarterly Report

Sector 6 | The Newsletter of AIM • 0 implied HN points • 29 Oct 23
🕹 Technology AI Cloud Computing Big Tech Earnings Reports Innovation
  1. Big tech companies are heavily focused on generative AI, with Google, Microsoft, Meta, and Amazon mentioning it a lot during their earnings calls. In contrast, Apple seems to be staying quiet about AI.
  2. Microsoft is performing really well in the cloud and generative AI space, especially through its partnerships with companies like OpenAI and Meta. This has helped them achieve a significant revenue increase.
  3. Compared to Microsoft, AWS and Google also saw revenue growth in their cloud services, but Microsoft outpaced them with higher growth numbers.

Amazon Crashes the GAI Party with a Bang!

Sector 6 | The Newsletter of AIM • 0 implied HN points • 16 Apr 23
🕹 Technology AI Machine Learning Data science Cloud Computing Software Development
  1. Amazon was focusing on transfer learning to improve their AI, like making Alexa learn new languages. However, they recently stopped this project because it was losing a lot of money.
  2. The company has experienced several failures in the past, showing that they are not unfamiliar with setbacks. This suggests they are trying to learn and adapt from their mistakes.
  3. Despite their challenges, Amazon's efforts in AI and technology continue to impact the industry, making them a major player in the field.

Microsoft OpenAI’ffair

Sector 6 | The Newsletter of AIM • 0 implied HN points • 12 Mar 23
🕹 Technology AI Software Innovation Cloud Computing Data
  1. Microsoft's turnaround began when Satya Nadella became CEO in 2014, bringing fresh ideas and energy to the company.
  2. The company is making waves in the tech world with its AI-powered products, like the new Dynamics 365 Copilot, which helps streamline tasks.
  3. With its innovations, Microsoft is competing strongly in various markets, especially in search engines and business software.

Empires of the Cloud: Azure's Ascension

Sector 6 | The Newsletter of AIM • 0 implied HN points • 12 Jan 23
🕹 Technology Cloud Computing Data Storage Artificial Intelligence Networking Startups
  1. Microsoft is making big moves in the cloud space, especially with the recent acquisition of Fungible, a company that makes advanced data processing units.
  2. This move shows Microsoft is focusing on improving Azure's performance and efficiency, moving away from traditional data centers.
  3. They also plan to incorporate OpenAI's technology into their services, which could enhance their offerings in the market.

Clash of Tech Civilisations

Sector 6 | The Newsletter of AIM • 0 implied HN points • 05 Jan 23
🕹 Technology Cloud Computing Database Systems Market Trends Data Management Tech industry
  1. Cloud database providers like Redis and MongoDB are facing major challenges from big companies like AWS, Microsoft, and Google.
  2. These cloud giants have recently grabbed a larger share of the database market, taking 6% from traditional leaders like IBM and Oracle.
  3. In the past, the top companies controlled almost all of the market, but now their dominance is slipping due to the rise of cloud solutions.

Struggle Continues: Is the Cloud Giant Losing Ground?

Sector 6 | The Newsletter of AIM • 0 implied HN points • 03 Jan 23
🕹 Technology Cloud Computing Software Data science AI Enterprise
  1. Salesforce is facing tough times with declining demand for its software. It's struggling to keep up with changes in the market.
  2. The company's leadership is under pressure, which raises questions about its future and stability.
  3. Investors are worried about Salesforce's valuation as it experiences a dip in performance compared to competitors.

Don't think. Just Do

Sector 6 | The Newsletter of AIM • 0 implied HN points • 24 Dec 22
🕹 Technology AI Data Innovation Cloud Computing Gaming
  1. Generative AI is becoming popular with tools like DALL.E 2 and ChatGPT, but some companies are focusing on real breakthroughs instead.
  2. AI is being developed for games, like AlphaGo and AlphaStar, which shows its potential in complex problem-solving.
  3. DeepMind is working on innovative AI applications rather than just creative ones, aiming for significant advancements.

50 Most Influential AI Leaders In India – 2021

Sector 6 | The Newsletter of AIM • 0 implied HN points • 03 Oct 21
🕹 Technology AI Leadership Innovation Cloud Computing Data science
  1. The AI landscape in India is led by influential figures who shape the industry. These leaders play a crucial role in driving technology and innovation in AI.
  2. Collaborations, like the one with AWS for a conference, highlight the importance of sharing knowledge and strategies in the data and analytics field.
  3. Events like the Data and Analytics Conclave bring together experts and business leaders to discuss how to use AI and machine learning effectively for innovation.

Responsible AI in India, GCP vs AWS & Azure and more...

Sector 6 | The Newsletter of AIM • 0 implied HN points • 06 Jun 21
🕹 Technology AI Cloud Computing Data science Ethics Analytics
  1. Responsible AI is important in India, focusing on ethical use and fairness in technology.
  2. Google Cloud Platform (GCP), Amazon Web Services (AWS), and Azure all offer unique features for AI development, so choosing the right one can depend on specific needs.
  3. There are events and workshops available for those looking to improve their data science skills and learn more about AI tools.

The Future of Cloud Computing

Splattern • 0 implied HN points • 23 Dec 23
🕹 Technology Cloud Computing Innovation Startups Distributed Systems Business strategy
  1. Big tech cloud companies like AWS, Azure, and Google Cloud don't really foster innovation. They were built on existing technology, and their focus is more on business strategies than improving their tech.
  2. These companies have lost many of their original experienced employees. This means current workers might not have the skills needed to innovate in a fast-moving tech world.
  3. Startups are emerging with new models that can offer better pricing and solutions for cloud computing. This could threaten the big tech clouds and change the landscape of cloud services.

LangChain Just Launched LangGraph Cloud

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 01 Jul 24
🕹 Technology Artificial Intelligence Software Development Cloud Computing Data Management NLP
  1. LangGraph Cloud is a new service that helps users build and host their LangGraph applications easily. It's like having a managed platform to run your projects without worrying about servers.
  2. Agents are becoming more common and can handle complicated user questions automatically. They break tasks into smaller steps, making it easier to manage them.
  3. LangGraph Studio lets users visualize how data flows in their applications. This tool helps with debugging and understanding processes, even though you can't change the code directly in it.

Gemini From Google

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 07 Dec 23
🕹 Technology AI Models Machine Learning Natural Language Cloud Computing Data processing
  1. Google's Gemini is a powerful AI that can understand and work with text, images, video, audio, and code all at once. This makes it really versatile and capable of handling different types of information.
  2. Starting December 6, 2023, Google's Bard will use a version of Gemini Pro for better reasoning and understanding. This means Bard will soon be smarter and more helpful in answering questions.
  3. Gemini has shown it can outperform human experts in language tasks. This is a significant achievement, indicating that AI is getting very close to human-like understanding in complex subjects.

Multi-Label Text Classification With Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Mar 23
🕹 Technology Artificial Intelligence Machine Learning Cloud Computing Data science Natural Language Processing
  1. Google Cloud Vertex AI allows for multi-label text classification, which means multiple tags can be assigned to a document. This helps in better organizing and processing text data.
  2. Training a model on Vertex AI can take a long time, especially with large datasets. For example, using nearly 12,000 training items can take over four hours to complete.
  3. The system's interface for managing training data and labels can be complex and a bit confusing. This makes it harder to easily update and manage the training data.

Training & Testing Text Classification Models with Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 28 Mar 23
🕹 Technology Machine Learning Artificial Intelligence Cloud Computing Data science Software Development
  1. Google's AutoML makes it easy to build classification models without needing much technical know-how. It simplifies the process, allowing more people to create models.
  2. Vertex AI can classify text into single or multiple categories, but it doesn't support complex class structures. So, simple classifications work best.
  3. While AutoML speeds up model creation, training times can be long. It's important to plan your data splits and annotation sets for better model performance.

Creating Training Data For Text Classification In Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 27 Mar 23
🕹 Technology AI Data science Machine Learning Cloud Computing Software Development
  1. Creating training data for AI is a crucial first step in making it work well. It involves careful organization and structuring of data to help the AI learn effectively.
  2. A data-centric approach requires ongoing exploration and refinement of the training data. This means continuously checking the data for patterns and making adjustments as needed.
  3. Using human labelers to categorize data can be costly and complex. It's often easier to automate this process with human oversight rather than sending data out for labeling.

Large Language Models, Generative AI & Google Cloud Vertex AI

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 23 Mar 23
🕹 Technology AI Machine Learning Cloud Computing Natural Language Data science
  1. Large Language Models (LLMs) have two sides: Generative and Predictive. Generative AI is popular for its ease of use, while Predictive AI requires specific training data and high accuracy.
  2. Google Cloud has focused on predictive AI before delving into generative AI. They offer tools for developers to create AI applications quickly, like chatbots and digital assistants.
  3. Classification is a key part of Predictive AI. It involves sorting input into predefined classes, which helps the model understand and respond accurately to user input.

Working effectively with AWS Lambda - Logs

Thoughts from the trenches in FAANG + Indie • 0 implied HN points • 09 Jun 23
🕹 Technology Cloud Computing Software Development DevOps Serverless AWS
  1. AWS Lambda allows you to run code without managing servers, making it a great choice for many developers.
  2. Using AWS CLI to stream logs from Lambda to your terminal is much faster and more efficient than using the AWS Console.
  3. You need to know the log group for your Lambda function, but once you do, setting up log streaming is a simple process.

Segregate resources and good hygiene between AWS accounts

Thoughts from the trenches in FAANG + Indie • 0 implied HN points • 06 Jun 23
🕹 Technology Cloud Computing Software Engineering Data security Project management Resource Management
  1. Using different AWS accounts for each project helps keep resources separate and makes billing easier. This way, it's simple to track costs for each project.
  2. Having separate accounts reduces confusion and complexity for engineers. It keeps projects organized, making it easier to find the resources they need and limits mistakes.
  3. Segregated accounts also improve security, as a problem in one account won't affect others. This protects against errors and minimizes potential damage from mismanaged access.

DLD #1 | Data Landscape Digest 🗞️

Practical Data Engineering Substack • 0 implied HN points • 25 Aug 24
🕹 Technology Data Engineering Open Source Cloud Computing Software Development Analytics
  1. Data engineering is evolving rapidly, and staying updated on new tools and technologies is important for success in the field.
  2. Mastering the fundamentals, like SQL and Python, is crucial as they form the foundation for using advanced tools effectively.
  3. Open source solutions, like Apache Hudi and XTable, are gaining popularity and can provide great benefits for managing data efficiently.

Managing Dependencies Between Data Pipelines

Practical Data Engineering Substack • 0 implied HN points • 26 Aug 23
🕹 Technology Data Engineering Data Pipelines Workflow management Analytics Cloud Computing
  1. Managing dependencies between data pipelines is crucial for ensuring that upstream tasks are completed before downstream tasks start. This avoids issues with incomplete or faulty data.
  2. There are different techniques to manage these dependencies, ranging from simple time-based scheduling to more complex orchestrations that adjust based on the successful completion of previous tasks.
  3. Choosing the right method for managing pipeline dependencies depends on the complexity of the data workflows and the need for independence between different teams and tasks.

Internal Storage Design of Modern Key-value Database Engines [Part 2]

Practical Data Engineering Substack • 0 implied HN points • 09 Aug 23
🕹 Technology Database Systems Data Structures Software Engineering Cloud Computing
  1. Sorted Segment files, or SSTables, help databases manage data more efficiently by keeping key-value records in order. This sorting makes searching and accessing data faster.
  2. In-memory storage, called Memtables, acts like a buffer that groups new data before it's saved to disk. This keeps data organized and speeds up how quickly new information can be written.
  3. Using a structure called the LSM-Tree helps optimize how databases write and read data. It focuses on reducing the time and effort it takes to handle a lot of updates and inserts, which is common in many apps.

Range Partitioning: Zero to One

aspiring.dev • 0 implied HN points • 17 Mar 24
🕹 Technology Databases Distributed Systems Data Structures Software Engineering Cloud Computing
  1. Range partitioning splits data into key ranges to improve performance and scalability. This method helps databases manage heavy loads by distributing data efficiently.
  2. Unlike hash partitioning, range partitioning allows for easier scaling. You can adjust the number of ranges as needed without the hassle of rewriting data.
  3. While range partitioning is powerful, it can be tricky to implement and may struggle with very sequential workloads. Planning is necessary to avoid creating performance hotspots.

How To Build AWS-Compatible APIs: AWS Sigv4

aspiring.dev • 0 implied HN points • 01 Mar 24
🕹 Technology Software Development Cloud Computing APIs Web Development Programming
  1. AWS Sigv4 is a way to authenticate requests when using AWS services. It works by signing requests with your Access Key ID and Secret Access Key, similar to RSA keys.
  2. You can create your own AWS-compatible APIs by implementing signature verification in middleware. This allows your API to mimic AWS services like S3 or DynamoDB.
  3. Building these APIs can be a good idea for startups. You can create custom services that interact with AWS or even replace AWS services entirely while maintaining compatibility.

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.

Cybersecurity Insights: July Bulletin

Vigilainte Newsletter • 0 implied HN points • 08 Aug 24
🕹 Technology Cybersecurity Cloud Computing Data Protection
  1. DDoS attacks are getting stronger, as shown by a major one that took down Microsoft's Azure cloud. This means companies need better protections to keep their services running.
  2. Many companies are facing vulnerabilities, like a default password issue from Acronis that attackers can exploit. It's really important for everyone to manage their passwords securely.
  3. Cybercriminals are using sophisticated methods like fake ads and Generative AI to spread malware and steal data. We all need to be careful when clicking online and keep our software updated.

[In case you missed it] Data Science Weekly - Issue 454

Data Science Weekly Newsletter • 0 implied HN points • 07 Aug 22
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Cloud Computing
  1. NASA is using AI to categorize millions of astronaut photos of Earth, making it easier for scientists to find specific images.
  2. Data-driven companies can have a competitive edge, especially in industries where expertise and speed matter.
  3. Understanding and explaining complex models is important for making ethical and business decisions before automating processes.

[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 313

Data Science Weekly Newsletter • 0 implied HN points • 23 Nov 19
🕹 Technology AI Machine Learning Data science Cloud Computing 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.

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 #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 #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 #4: Polars and Pandas, 1.8 trillion events, data quality

VuTrinh. • 0 implied HN points • 10 Oct 23
🕹 Technology Data Engineering Data Quality Big Data Machine Learning Cloud Computing
  1. Polars and Pandas are tools for data processing, but they have different performance levels. Understanding when to use each can help manage large datasets better.
  2. Data quality is crucial for successful data engineering. Companies like Google and Uber have strategies in place to ensure their data is accurate and reliable.
  3. Learning SQL execution order can really help in data tasks. It outlines the steps SQL takes to process a query, which is key for optimizing database interactions.