The hottest Cloud Computing Substack posts right now

And their main takeaways
Category
Top Technology Topics

Cloud without Kubernetes

realkinetic • 0 implied HN points • 12 Feb 24
🕹 Technology Cloud Computing
  1. The industry has mainly focused on Kubernetes as the go-to cloud solution, but serverless options like Cloud Run can be effective for certain use cases and offer cost-efficiency.
  2. Cloud Run offers a simplified platform for businesses with cyclical traffic patterns and minimal need for Kubernetes-level complexity, allowing developers to focus on delivering value rather than managing infrastructure.
  3. Adopting Cloud Run can provide a flexible, cost-effective cloud solution that maintains the option to evolve to a more complex platform as needed, catering well to 'normal' businesses outside of internet-scale operations.

Data Engineering: A Software Engineer’s Approach

realkinetic • 0 implied HN points • 25 Jan 24
🕹 Technology Cloud Computing
  1. The tech industry varies in its expectations of data engineers, leading to challenges in team performance and hiring.
  2. Companies today need to be data-driven, utilizing modern data stack tools, which necessitates a blend of data engineering and software engineering skills.
  3. Data engineering benefits from adopting software engineering principles like treating systems as products, clear communication, and implementing CI/CD pipelines.

Setting Up Google Cloud Composer in a Shared VPC Network

realkinetic • 0 implied HN points • 15 Jan 24
🕹 Technology Cloud Computing
  1. Plenty of resources are available for setting up a Cloud Composer environment in a single GCP project, but integrating it into a professional enterprise environment with a Shared VPC network can pose challenges with communication and permissions.
  2. Setting up two GCP projects, a service project, and a host project is essential. Understanding how to create and configure a Shared VPC network and subnet for the Cloud Composer environment is crucial for data and infrastructure engineers.
  3. Permissions preparation is key, including roles like Compute Shared VPC Admin and Project IAM Admin, and setting up the necessary permissions for Google APIs service accounts, GKE service accounts, and Composer Agent Service Accounts at both project and subnet levels.

What do we mean by ‘High Availability’ in the Cloud?

realkinetic • 0 implied HN points • 25 May 23
🕹 Technology Cloud Computing
  1. Availability is expressed as a percentage of uptime; higher percentages require substantial investment and multi-team efforts
  2. Achieving high availability in the cloud involves significant costs and considerations like multi-master databases, multi-zonal deployments, and failover testing
  3. Five nines (99.999%) availability is considered the gold standard, but it requires extensive resources, multi-region support, and rigorous infrastructure and data replication
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Six practices for implementing Kubernetes on GCP

realkinetic • 0 implied HN points • 27 Feb 23
🕹 Technology Cloud Computing
  1. Use Minikube for local Kubernetes development to ensure consistency with production version.
  2. Build containers with caution, favoring restricted base images to reduce vulnerabilities and improve security.
  3. Ensure automation in deployments, design for rollbacks, and use immutable infrastructure principles for managing Kubernetes applications.

Getting big wins with small teams on tight deadlines

realkinetic • 0 implied HN points • 02 Nov 20
🕹 Technology Cloud Computing
  1. Using serverless and managed services is critical for achieving big wins with small teams on tight deadlines in the cloud.
  2. Choosing a cloud platform and fully embracing its capabilities is key to success, even though leveraging multiple platforms for different strengths can also be beneficial.
  3. Serverless computing allows teams to focus on business outcomes, accelerating product launches, reducing team sizes, and shifting the focus to more differentiated work.

Implementing ETL on GCP

realkinetic • 0 implied HN points • 15 Jul 20
🕹 Technology Cloud Computing
  1. ETL processes are vital for data analytics, involving extracting, transforming, and loading data for storage in a warehouse.
  2. GCP offers options like Data Fusion and Cloud Dataprep for implementing ETL pipelines, catering to varying technical skill levels and preferences.
  3. Alternative approaches on GCP for ETL include using services like Cloud Dataflow for more code-heavy processes or leveraging BigQuery for ELT if your team is SQL-focused.

Using Google-Managed Certificates and Identity-Aware Proxy With GKE

realkinetic • 0 implied HN points • 24 Jun 20
🕹 Technology Cloud Computing
  1. Google-Managed Certificates in GKE are provisioned, renewed, and managed by Google, simplifying HTTPS setup for your domain.
  2. Identity-Aware Proxy (IAP) in GKE provides zero-trust security, allowing secure access to applications without a VPN based on user identity and context.
  3. Combining GCLB, GCP-managed certificates, and IAP offers a robust solution for serving and securing internal applications in the cloud.

Zero-Trust Security on GCP With Context-Aware Access

realkinetic • 0 implied HN points • 22 Jun 20
🕹 Technology Cloud Computing
  1. Serverless architecture on GCP allows for quick application development with minimal operational overhead, setting Google Cloud apart from other providers.
  2. Implementing a zero-trust security model on GCP, especially with context-aware access, enhances security for applications and services.
  3. Transitioning from perimeter-based security to a zero-trust model with tools like IAP and IAM Conditions Framework provides a more flexible and secure approach, even beyond GCP.

Choosing the Right GCP Compute Platform

realkinetic • 0 implied HN points • 20 Aug 19
🕹 Technology Cloud Computing
  1. When choosing the right GCP compute platform, consider the level of abstraction that fits your application, team, and investment allocation.
  2. Google's compute product continuum offers options from raw VMs in Compute Engine to highly abstracted options like Firebase and Cloud Functions.
  3. Different GCP compute platforms have good and bad fit characteristics based on considerations like complexity of server-side logic, statefulness, and architectural maturity.

Serverless on GCP

realkinetic • 0 implied HN points • 20 Aug 19
🕹 Technology Cloud Computing
  1. Serverless computing means cloud providers fully manage server infrastructure, allowing focus on application code and business logic.
  2. Benefits of serverless model include automatic scaling, fault-tolerance, and paying only for the resources used.
  3. GCP offers various serverless compute options like Firebase, Cloud Functions, App Engine, and Cloud Run, each with specific characteristics and use cases.

GCP vs. AWS: A Manager’s Guide

realkinetic • 0 implied HN points • 30 Jul 19
🕹 Technology Cloud Computing
  1. AWS is considered more of an "ops engineer's cloud" while GCP is seen as a "software engineer's cloud."
  2. Deploying on AWS Fargate involves lower-level tasks like networking and IAM roles, providing fine-tuned control but requiring more effort.
  3. Google's App Engine Flex streamlines deployment, handling networking, scaling, and fault tolerance, allowing developers to focus more on application code and architecture.

Authenticating Stackdriver Uptime Checks for Identity-Aware Proxy

realkinetic • 0 implied HN points • 29 Jan 19
🕹 Technology Cloud Computing
  1. Google Stackdriver provides free uptime checks for monitoring service availability across regions and response latencies.
  2. Implementing Stackdriver uptime checks with Cloud Identity-Aware Proxy can be challenging due to authentication requirements.
  3. A workaround solution involves using Google Cloud Functions as a proxy to authenticate Stackdriver uptime checks for IAP-protected resources.

API Authentication with GCP Identity-Aware Proxy

realkinetic • 0 implied HN points • 25 Jan 19
🕹 Technology Cloud Computing
  1. Cloud Identity-Aware Proxy (Cloud IAP) enables authentication and authorization for applications in Google Cloud Platform (GCP) by requiring users to login with their Google account and have appropriate access roles.
  2. Configuring Identity-Aware Proxy involves associating it with an App Engine application or HTTPS Load Balancer and adding service accounts for programmatic authentication.
  3. Authenticating API consumers with Cloud IAP involves generating a JWT signed with service account credentials, exchanging it for a Google-signed OIDC token, and making authenticated requests by setting the bearer token in the Authorization header.

Multi-Cloud Is a Trap

realkinetic • 0 implied HN points • 14 Sep 18
🕹 Technology Cloud Computing
  1. Multi-cloud can create unnecessary constraints and distractions, costing more than it's worth.
  2. Disaster recovery, vendor lock-in, and pricing are main reasons why multi-cloud is considered, but they may not always justify the strategy.
  3. For some large enterprises or specific use cases like leveraging the strengths of different clouds, multi-cloud may make sense, but it shouldn't be the primary focus for most companies entering the cloud space.

The Observability Pipeline

realkinetic • 0 implied HN points • 12 Sep 18
🕹 Technology Cloud Computing
  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
  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
  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.

AGI Factories Coming Soon 🤖🏭🔜

Sector 6 | The Newsletter of AIM • 0 implied HN points • 02 Apr 24
🕹 Technology Cloud Computing
  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.

Google’s Q*?

Sector 6 | The Newsletter of AIM • 0 implied HN points • 14 Dec 23
🕹 Technology Cloud Computing
  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 Cloud Computing
  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 Cloud Computing
  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 Cloud Computing
  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
  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
  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.

Don't think. Just Do

Sector 6 | The Newsletter of AIM • 0 implied HN points • 24 Dec 22
🕹 Technology Cloud Computing
  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 Cloud Computing
  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 Cloud Computing
  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.

Coming soon

Beekey’s Substack • 0 implied HN points • 22 Jul 24
🕹 Technology Cloud Computing
  1. The writer is launching a new project soon. Stay tuned for updates!
  2. They have a selection of previous articles that cover various software development topics. Check them out if you're interested!
  3. The posts focus on practical insights and issues in software development, which could be helpful for developers.

The Future of Cloud Computing

Splattern • 0 implied HN points • 23 Dec 23
🕹 Technology Cloud Computing
  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 Cloud Computing
  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 Cloud Computing
  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 Cloud Computing
  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 Cloud Computing
  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 Cloud Computing
  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 Cloud Computing
  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
  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.