The hottest Cloud Computing Substack posts right now

And their main takeaways
Category
Top Technology Topics

IaC wars

Cloud Irregular • 3252 implied HN points • 06 Feb 24
🕹 Technology Cloud Computing
  1. Different cloud providers have different approaches to Infrastructure-as-Code.
  2. There is a need for tools that can migrate configurations to idiomatic Infrastructure-as-Code templates.
  3. New configuration languages like Pkl are emerging to address frustrations with existing options.

Google sees more than other AI companies

Alex's Personal Blog • 98 implied HN points • 05 Dec 25
🕹 Technology Cloud Computing
  1. Google's AI has access to way more internet pages compared to other companies like OpenAI and Microsoft. This gives Google an advantage in providing better answers and improving its technology.
  2. The stock market reactions to layoffs are not always positive, as seen with companies like Meta and Amazon. Investors aren't rewarding these companies with significant stock increases after staff cuts.
  3. Micro1 is doing great by reaching $100 million in annual recurring revenue in a short time, showing that there's strong growth potential in innovative AI startups.

Authorization in microservice architecture, P1: The motivation to change

Hung's Notes • 59 implied HN points • 18 Jul 24
🕹 Technology Cloud Computing
  1. Authorization is a crucial part of managing digital evidence, and it needs to be efficient to handle many users and lots of data. Complex systems can find it hard to keep permissions clear.
  2. Current access control models like Role-Based Access Control (RBAC) and Discretionary Access Control (DAC) can get too complicated when managing many users and permissions. This can lead to messy code and performance issues.
  3. As organizations grow, they must decide how to structure their authorization logic, whether to centralize it in one team or spread it across many. Both choices have their own challenges in consistency and maintenance.

Take on the Kubernetes Resume Challenge

Cloud Irregular • 3104 implied HN points • 14 Feb 24
🕹 Technology Cloud Computing
  1. The Cloud Resume Challenge community is launching a Kubernetes Challenge throughout March to help individuals build their Kubernetes skills by deploying a basic e-commerce website.
  2. The challenge focuses on learning the operations of a K8s cluster such as configuration, scaling, monitoring, and persistence, offering guidance to prevent going off track.
  3. Participants will work through the challenge together over 4 weeks in the CRC Discord server, with special incentives for those who complete it.

Edition 32: BigCo is building in AppSec, but it's too early to get excited

Boring AppSec • 23 implied HN points • 27 Jan 26
🕹 Technology Cloud Computing
  1. Big tech's new AppSec tools are mostly demo-quality right now and aren't yet as capable as mature security products.
  2. This puts pressure on AppSec teams to justify buying dedicated tools or accept platform solutions, shifting the burden of proof onto security teams.
  3. The labs are motivated to build AppSec because LLMs generate lots of code and overwhelm review capacity, so more serious products will likely appear soon while platform and specialist vendors continue to coexist.
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Preparing for Microsoft Security Copilot

Rod’s Blog • 496 implied HN points • 03 Jan 24
🕹 Technology Cloud Computing
  1. Before adopting Microsoft Security Copilot, assess your current security situation by understanding assets, risks, vulnerabilities, and compliance requirements.
  2. Plan your integration strategy by deciding on which features to use, aligning with prerequisites such as licenses, and identifying user roles.
  3. Train your staff and stakeholders on how to use Microsoft Security Copilot, educate them about its benefits and challenges, and equip them with skills to operate and troubleshoot the service.

GroupBy #38: Modernizing Uber’s Batch Data Infrastructure with Google Cloud Platform, Apache Iceberg - What Is It

VuTrinh. • 119 implied HN points • 04 Jun 24
🕹 Technology Cloud Computing
  1. Uber is upgrading its data system by moving from its huge Hadoop setup to Google Cloud Platform for better efficiency and performance.
  2. Apache Iceberg is an important tool for managing data efficiently, and it can help create a more organized data environment.
  3. Building data products requires a strong foundation in data engineering, which includes understanding the tools and processes involved.

Procella - The query engine at YouTube

VuTrinh. • 79 implied HN points • 29 Jun 24
🕹 Technology Cloud Computing
  1. YouTube built Procella to combine different data processing needs into one powerful SQL query engine. This means they can handle many tasks, like analytics and reporting, without needing separate systems for each task.
  2. Procella is designed for high performance and scalability by keeping computing and storage separate. This makes it faster and more efficient, allowing for quick data access and analysis.
  3. The engine uses clever techniques to reduce delays and improve response times, even when many users are querying at once. It constantly optimizes and adapts, making sure users get their data as quickly as possible.

GroupBy #36: Agoda- How We Solve Load Balancing Challenges in Apache Kafka, How to reduce your Snowflake cost

VuTrinh. • 139 implied HN points • 21 May 24
🕹 Technology Cloud Computing
  1. Working on pet projects is fun, but it's important to have clear learning goals to actually gain knowledge from them.
  2. When using tools like Spark or Airflow, always ask what problem they solve to understand their value better.
  3. To make your projects more effective, think like a user and check if they get what they need from your data systems.

The Sequence Radar #787: Rubin, Raises, and Returns: 2026 Starts Fast

TheSequence • 42 implied HN points • 11 Jan 26
🕹 Technology Cloud Computing
  1. AI hardware is moving to rack-scale "AI factories," with companies like NVIDIA and AMD designing integrated systems where chips and CPUs work as a single supercomputing unit. This shifts the unit of compute from individual GPUs to whole racks optimized for large-scale inference and training.
  2. Massive capital rounds are reshaping who can compete in frontier models, as multibillion-dollar raises make training and infrastructure effectively affordable only to hyper-scalers and well-funded entities. That level of spending is turning top labs into utility-like, enterprise infrastructure players.
  3. China’s AI firms proved public markets can reward consumer-facing model strategies, with IPOs like MiniMax and z.AI showing rapid monetization and liquidity. This underscores a growing bifurcation: the West doubling down on heavy infrastructure for AGI, while the East pushes fast consumer exits and application-led growth.

Developing with Kiro: Amazon's New Agentic IDE

Rain Clouds • 311 implied HN points • 14 Jul 25
🕹 Technology Cloud Computing
  1. Kiro is a new IDE that can improve productivity by letting you focus on high-level planning instead of writing code. You describe what you want, and Kiro helps execute the project.
  2. Using Kiro requires creating clear specifications and being detailed in your instructions for it to work effectively. The better you articulate your needs, the better the results you'll get.
  3. Kiro is not perfect and has its limitations. It's key to know when to let it run on its own and when to step in and help it with specific problems or decisions.

Speed Without Understanding - One of the Biggest Risks in Data Engineering

SeattleDataGuy’s Newsletter • 329 implied HN points • 30 Jun 25
🕹 Technology Cloud Computing
  1. Speed in data engineering can be risky. Acting fast without fully understanding the consequences can lead to mistakes, like accidentally deleting important data.
  2. Every new tool or change can add complexity. If something breaks, it may cause confusion for others, so it’s important to think carefully about what you build.
  3. Having a mix of experienced and new team members is really helpful. It encourages sharing knowledge and can prevent big errors when someone leaves the team.

Postgres in a box

benn.substack • 920 implied HN points • 06 Dec 24
🕹 Technology Cloud Computing
  1. Software has changed from being sold in boxes in stores to being bought as subscriptions online. This makes it easier and cheaper for businesses to manage.
  2. The new trend is separating storage from computing in databases. This lets companies save money by only paying for the data they actually use and the calculations they perform.
  3. There's a push towards making data from different sources easily accessible, so you can use various tools without being trapped in one system. This could streamline how businesses work with their data.

What I Learned This Week #6

Eventually Consistent • 59 implied HN points • 01 Jul 24
🕹 Technology Cloud Computing
  1. Data partitioning helps manage query loads by distributing large datasets across multiple disks and processors. Considerations include rebalancing for even distribution, distributed query execution, and dealing with hot spots.
  2. Partitioning secondary indexes can be done locally or globally, with tradeoffs between keeping related data together versus faster lookups for certain queries. Routing queries in distributed systems may use coordination services or gossip protocols for efficiency.
  3. Transactions provide a way to manage concurrency and software failures by ensuring operations either fully succeed or fully fail. AWS Lambda uses worker models for task execution and Rust Atomics for memory ordering control across threads.

Data Science Weekly - Issue 538

Data Science Weekly Newsletter • 199 implied HN points • 14 Mar 24
🕹 Technology Cloud Computing
  1. Serverless computing can handle big tasks without limits, but it also brings challenges like managing large uploads effectively.
  2. Art careers can be influenced by the reputation of institutions, with established artists facing less access to elite spaces early on compared to newcomers.
  3. Learning about LLM evaluation metrics can help improve understanding and performance when working with large language models.

LangSmith, LangGraph Cloud & LangGraph Studio

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 15 Jul 24
🕹 Technology Cloud Computing
  1. There's a shift in generative AI, moving away from just powerful models to more practical user applications. This includes a focus on using data better with tools that help manage these models.
  2. New tools like LangSmith and LangGraph are designed to help developers visualize and manage their AI applications easily. They allow users to see how their AI works and make changes without needing to code everything from scratch.
  3. We are now seeing a trend towards no-code solutions that make it easier for anyone to create and manage AI applications. This approach is making technology more accessible to people, regardless of their coding skills.

Apache Iceberg Isn't Coming To Save You

SeattleDataGuy’s Newsletter • 341 implied HN points • 27 May 25
🕹 Technology Cloud Computing
  1. Apache Iceberg might seem appealing, but it won't automatically solve your data problems. It's important to really understand what issues you're trying to address before jumping in.
  2. Switching to new tools like Iceberg won't fix a broken data strategy. The focus should be on delivering real business value, not just adopting the latest technology.
  3. If your data team is already doing well and looking to improve, Iceberg could be useful. But make sure it's the right fit for your specific challenges instead of following trends.

Technically Monthly (February 2026)

Technically • 14 implied HN points • 05 Feb 26
🕹 Technology Cloud Computing
  1. Modern generative models mirror pathways in the human brain, and many researchers believe leveraging that similarity could be key to much stronger AI.
  2. Real cloud-spend data shows the fastest-growing AI use cases are coding agents, low-latency LLM inference, and computational biology, while AI art and video generation have plateaued as the market professionalizes.
  3. Models overuse em dashes mainly because of their training data and tokenization quirks—older texts and auto-converted punctuation make the em dash common—and this highlights how dataset quality and representativeness drive model behavior.

449: Amazon + Anthropic, TSMC AZ, Costco, Xero Shoes, Microsoft + Activision, Bloomberg for Sale, AI Copyright, and The Bear

Liberty’s Highlights • 491 implied HN points • 25 Sep 23
🕹 Technology Cloud Computing
  1. The author is incorporating strength training into their routine by researching equipment like dumbbells and a bench for a home gym setup.
  2. Obsessing over equipment can distract from the main goal of strength training, but having a convenient home setup may lead to more consistent workouts.
  3. Soap is a relatively recent invention in human history, dating back around five thousand years, significantly changing personal hygiene practices.

The Sequence Radar #767: Last Week in AI: Google Logic, Amazon Utility, and Mistral Efficiency

TheSequence • 56 implied HN points • 07 Dec 25
🕹 Technology Cloud Computing
  1. AI model development is changing focus from just making models bigger to making them smarter and more specialized. It's now about using different tools for specific tasks instead of one model for everything.
  2. Google's Gemini 3 Deep Think is a significant release that uses a new way of thinking to solve problems. It focuses on careful reasoning rather than quick responses, leading to much better problem-solving skills.
  3. Amazon's Nova 2 and Mistral's Large 3 provide new options for businesses by focusing on efficiency and privacy. These models allow companies to create tailored solutions without relying on large, generic AI models.

GTC 2024 Keynote Irrational Recap

Irrational Analysis • 159 implied HN points • 19 Mar 24
🕹 Technology Cloud Computing
  1. The newsletter focuses heavily on the semiconductor industry and provides analysis based on public information and independent research.
  2. The author reflects on biases and encourages readers to form their opinions after reviewing the presented information.
  3. Jensen Huang from GTC 2024 Keynote introduces impressive innovations in the semiconductor field, like the RAS technology monitoring system and advancements in hardware design.

How VTEX improved the shopper experience with Amazon DynamoDB

VTEX’s Tech Blog • 119 implied HN points • 16 Apr 24
🕹 Technology Cloud Computing
  1. VTEX improved their shopping cart system by switching from Amazon S3 to Amazon DynamoDB. This change was made to enhance speed and make the shopping experience better for users.
  2. They faced challenges because some shopping cart items were too large for DynamoDB's limits. To fix this, they reduced the data size and created a process to store bigger items separately in S3.
  3. After gradually migrating to DynamoDB, VTEX achieved a 30% reduction in shopping cart API latency. This helped their overall efficiency and improved customer satisfaction.

GroupBy #39: 2000+ DBT models in airflow; Serverless Jupyter Notebooks at Meta

VuTrinh. • 59 implied HN points • 11 Jun 24
🕹 Technology Cloud Computing
  1. Meta has developed a serverless Jupyter Notebook platform that runs directly in web browsers, making data analysis more accessible.
  2. Airflow is being used to manage over 2000 DBT models, which helps teams create and maintain their own data models effectively.
  3. Building a data platform from scratch can be a valuable learning experience, revealing important lessons about data structure and management.

Data security should be making a comeback

Frankly Speaking • 254 implied HN points • 10 Jun 25
🕹 Technology Cloud Computing
  1. Data security needs a fresh look because the way we use and manage data has changed a lot. With new technologies, protecting data is more complicated now.
  2. Current tools often struggle with identifying what data is sensitive and how to handle it properly. We need better solutions that help organizations use their data wisely while keeping it safe.
  3. Companies must rethink how they approach data risk. Creating clear guidelines on how data can be used could help in managing security while still allowing businesses to benefit from their data.

Arm at Amazon

The Chip Letter • 2184 implied HN points • 18 Jul 23
🕹 Technology Cloud Computing
  1. Arm has found a place in the biggest cloud at Amazon.
  2. The importance of power efficiency in datacenters favors Arm designs due to lower power consumption.
  3. Arm has faced challenges in entering the server market, with various attempts by partners falling short over the past decade.

The Tech Buffet #20: How To deploy a Cloud Function That Summarizes Youtube Videos

The Tech Buffet • 139 implied HN points • 11 Mar 24
🕹 Technology Cloud Computing
  1. Cloud Functions are a serverless way to run your code on Google Cloud without managing servers. You pay only for what you use, making it cost-effective.
  2. You can build a Cloud Function to summarize YouTube videos by extracting their transcripts and using AI to create concise summaries. This is done using Python libraries like youtube-transcript-api and langchain.
  3. Testing your Cloud Function locally is a great way to ensure it works before deploying it. You can use tools like Postman to check the API responses easily.

GroupBy #37: Composable data management at Meta, How Uber Accomplishes Job Counting At Scale

VuTrinh. • 59 implied HN points • 28 May 24
🕹 Technology Cloud Computing
  1. When learning something new, it's good to start by asking yourself why you want to learn it. This helps set clear goals and expectations.
  2. Focusing on one topic at a time can make learning easier. Instead of spreading your time thin, dive deep into one subject.
  3. It's okay to feel stuck sometimes while learning. Just keep pushing through, relax, and remember that learning is a journey that takes time.

Microsoft's worst week in years; Google also stumbles

Achee Alpha • 6 implied HN points • 08 Feb 26
💰 Finance Cloud Computing
  1. Microsoft's stock plunged despite solid revenue because investors doubt its AI strategy and fear AI will compress software profit margins.
  2. Microsoft's consumer AI products have fallen behind competitors and only a small share of Office users have adopted Copilot, suggesting businesses don't yet see enough value.
  3. Big cloud players are pouring money into AI infrastructure and investors are demanding clear paths to profit, which has put pressure on companies like Microsoft and Google amid heavy capex and uncertain monetization.

NIST's "Strategies for Integration of Software Supply Chain Security in DevSecOps CI/CD Pipelines"

Resilient Cyber • 159 implied HN points • 13 Feb 24
🕹 Technology Cloud Computing
  1. Software supply chain attacks are on the rise, so companies need to protect their processes from potential risks. Understanding these threats is key for organizations that rely on software.
  2. NIST provides guidelines to help organizations improve their software security in DevSecOps environments. By following their advice, companies can ensure that their software development processes are safe from compromise.
  3. Implementing zero-trust principles and automating security checks during software development can greatly reduce the risk of attacks. This means controlling access and regularly checking for vulnerabilities throughout the development cycle.

Why did Databricks build the Photon engine?

VuTrinh. • 99 implied HN points • 06 Apr 24
🕹 Technology Cloud Computing
  1. Databricks created the Photon engine to simplify data management by combining the benefits of data lakes and data warehouses into one system called the Lakehouse. This makes it easier and cheaper for companies to manage their data all in one place.
  2. Photon is designed to handle various types of raw data and is built with a vectorized approach instead of the traditional Java methods. This means it can work faster and better with different kinds of data without getting bogged down.
  3. To ensure that existing customers using Apache Spark can easily switch to Photon, the new engine is integrated with Spark’s system. This allows users to continue running their current processes while benefiting from the speed of Photon.

Lean Data Engineering with Dagster and DuckDB

The Data Jargon Newsletter • 158 implied HN points • 05 Mar 24
🕹 Technology Cloud Computing
  1. Data lakes can be convenient but often lead to problems when trying to manage the data effectively. Keeping things simple with familiar tools can help make the data more useful.
  2. Using Dagster and DuckDB allows you to process data efficiently without complicated setups. You can do key tasks like aggregation and data cleaning right in your data flow.
  3. It's important to consider memory limits and choose the right file formats, like Parquet, for better processing. This way, you can keep your data pipeline running smoothly and avoid needless costs.

Data Science Weekly - Issue 522

Data Science Weekly Newsletter • 259 implied HN points • 23 Nov 23
🕹 Technology Cloud Computing
  1. This newsletter shares weekly interesting links and updates in data science, AI, and machine learning. It's a great way to stay informed about new developments in these fields.
  2. There's a focus on practical tools and techniques for improving data science work, like using cloud processing for large datasets and methods for fine-tuning AI models effectively.
  3. The newsletter also highlights job opportunities and resources for those looking to enter or advance in the data science industry. It's beneficial for anyone looking to grow their career in this area.

Monolithic vs MicroServices.

Software Ninja Handbook • 3 HN points • 12 Sep 24
🕹 Technology Cloud Computing
  1. Monolithic applications have a single codebase, which makes them easier to manage for smaller projects, but harder to debug as they grow. Everything is tightly connected, so a problem in one part can affect the whole system.
  2. Microservices break down applications into smaller, independent services that can be developed and deployed separately. This allows teams to work faster and use different technologies for different parts of the application.
  3. Choosing between monolithic and microservices depends on factors like project size and team structure. Monoliths are good for small projects while microservices are better for larger, complex systems that need flexibility and scalability.

I spent 3 hours figuring out how BigQuery inserts, deletes and updates data internally. Here's what I found.

VuTrinh. • 139 implied HN points • 17 Feb 24
🕹 Technology Cloud Computing
  1. BigQuery manages data using immutable files, meaning once data is written, it cannot be changed. This helps in processing data efficiently and maintains data consistency.
  2. When you perform actions like insert, delete, or update, BigQuery creates new files instead of changing existing ones. This approach helps in features like time travel, which lets you view past states of data.
  3. BigQuery uses a system called storage sets to handle operations. These sets help ensure processes are performed atomically and consistently, maintaining data integrity during changes.

GroupBy #35: The Netflix Data Engineering Stack, Atlassian - Evolve the data platform with a Deployment Capability

VuTrinh. • 59 implied HN points • 14 May 24
🕹 Technology Cloud Computing
  1. Netflix has a strong data engineering stack that supports both batch and streaming data pipelines. It focuses on building flexible and efficient data architectures.
  2. Atlassian has revamped its data platform to include a new deployment capability inspired by technologies like Kubernetes. This helps streamline their data management processes.
  3. Migrating from dbt Cloud can teach valuable lessons about data development. Companies should explore different options and learn from their migration journeys.

Import AI 319: Sovereign AI; Facebook's weights leak on torrent networks; Google might have made a better optimizer than Adam!

Import AI • 439 implied HN points • 06 Mar 23
🕹 Technology Cloud Computing
  1. Google researchers achieved promising results by scaling a Vision Transformer to 22B parameters, showcasing improved alignment to human visual perception.
  2. Google introduced a potentially better optimizer called Lion, showing outstanding performance across various models and tasks, including setting a new high score on ImageNet.
  3. A shift toward sovereign AI systems is emerging globally, driven by the need for countries to develop their own AI capabilities to enhance national security and economic competitiveness.