The hottest Cloud Computing Substack posts right now

And their main takeaways
Category
Top Technology Topics

LangSmith, LangGraph Cloud & LangGraph Studio

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 15 Jul 24
🕹 Technology AI Development Software Tools Data Management Generative AI 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.

450: Apple's iPhone Pricing, Hyperscalers, Google, Capital-Light Businesses, Rory Sutherland, Norway, Spotify AI, and Hitchcock

Liberty’s Highlights • 491 implied HN points • 27 Sep 23
🕹 Technology Apple AI Cloud Computing 3D Printing Nuclear power
  1. Adjusted for inflation, the iPhone 15 is the most affordable since the original in 2007.
  2. The White House may require cloud companies to disclose AI customers for security
  3. Google paid Apple a massive estimated $120 billion since 2010 to be the default search on iOS.

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 AI Cloud Computing Tech investment Tech industry
  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.

GTC 2024 Keynote Irrational Recap

Irrational Analysis • 159 implied HN points • 19 Mar 24
🕹 Technology Semiconductor industry Cloud Computing Innovation
  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 E-commerce Cloud Computing Data Management System Architecture Performance optimization
  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.
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Big Tech x Generative AI Q3 '24 Update (Part 2)

Tanay’s Newsletter • 44 implied HN points • 11 Nov 24
🕹 Technology AI Software Consumer Tech Cloud Computing Open Source
  1. Meta is focusing on open-source AI with the Llama models, claiming they are the most cost-effective and customizable option for developers. They are set to release even better versions soon.
  2. Microsoft’s AI business is booming, especially through their Azure Cloud, with expected revenue surpassing $10 billion. They are integrating AI across many of their products, driving impressive growth.
  3. Both companies are seeing success in using AI to enhance user engagement and advertising effectiveness. Meta has increased user time on their platforms, while Microsoft's AI tools are helping businesses save time and improve efficiency.

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

VuTrinh. • 59 implied HN points • 11 Jun 24
🕹 Technology Data Engineering Software Development Cloud Computing Analytics Data science
  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.

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 Machine Learning DevOps Serverless Infrastructure
  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 Data Engineering Software Development Data processing Cloud Computing Open Source
  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.

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 Cybersecurity Software Development Cloud Computing DevOps Data security
  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.

Gsnowflake

benn.substack • 792 implied HN points • 07 Jul 23
🕹 Technology Data AI Cloud Computing Natural Language Processing
  1. Google is technically a database but differs from traditional databases in its structure and content.
  2. Snowflake is introducing features like Document AI that hint at a shift towards focusing on information retrieval rather than just data analysis.
  3. The market for an information database could potentially be larger and more accessible than traditional data warehouses, offering simpler access to basic facts and connections.

Why did Databricks build the Photon engine?

VuTrinh. • 99 implied HN points • 06 Apr 24
🕹 Technology Data Engineering Software Development Cloud Computing Database Systems Big Data
  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 Data Engineering Data Management Software Tools Cloud Computing Data Analytics
  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 Data science Artificial Intelligence Machine Learning Software Development 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 Software Development Architecture Cloud Computing System Design Agile methodologies
  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 Data Engineering Cloud Computing Database Management Data Analysis Software Development
  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 Data Engineering Software Development Cloud Computing Data architecture Programming
  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 AI Research Cloud Computing Data security Partnerships Innovation
  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.

The Tech Buffet #21: Deploy A Production-Ready Streamlit App with Cloud Run and Cloud Build

The Tech Buffet • 99 implied HN points • 22 Mar 24
🕹 Technology Cloud Computing App Development DevOps CI/CD
  1. Cloud Run lets you deploy containerized applications without worrying about server management. You only pay when your code is actively running, making it a cost-effective option.
  2. Using Pulumi as an Infrastructure as Code tool simplifies the process of setting up and managing cloud resources. It allows you to deploy applications by writing code instead of manually configuring settings.
  3. Automating your deployment with Cloud Build ensures your app updates easily whenever you make code changes. This saves time and effort compared to manually deploying each time.

I spent 6 hours understanding the design principles of BigQuery. Here's what I found

VuTrinh. • 159 implied HN points • 20 Jan 24
🕹 Technology Data Engineering Cloud Computing Architecture Big Data
  1. BigQuery uses SQL again after moving away from it, making data analysis fast and easy. Users can now analyze huge datasets quickly without complex coding.
  2. It separates storage and compute resources, allowing for better performance and flexibility. This means you can scale them independently, which is very efficient.
  3. Dremel's serverless architecture means you don’t need to manage servers. You just use SQL, and everything else is automatically handled for you.

Can BIS Control the Cloud?

ChinaTalk • 311 implied HN points • 31 Jan 24
🕹 Technology Cloud Computing AI Regulation Data Reporting Privacy Concerns
  1. New proposed rules by Commerce focus on regulating US cloud providers to identify customers and monitor large AI training with potential risks.
  2. The regulations aim to prevent misuse of cloud services for cyber attacks and dangerous AI systems, using 'Know Your Customer' schemes.
  3. Enforcement measures include restrictions on customers or jurisdictions engaging in malicious cyber activities, with a focus on setting up reporting processes.

A Closer Look Into Databricks's Photon Engine

VuTrinh. • 79 implied HN points • 13 Apr 24
🕹 Technology Software Data Engineering Database Systems Cloud Computing Artificial Intelligence
  1. Photon engine uses columnar data layout to manage memory efficiently, allowing it to process data in batches. This helps in speeding up data operations.
  2. It supports adaptive execution, which means the engine can change how it processes data based on the input. This can significantly improve performance, especially when data has many NULLs or inactive rows.
  3. Photon integrates with Databricks runtime and Spark SQL, allowing it to enhance existing workloads without completely replacing the old system, making transitions smoother.

GroupBy #34: Hybrid Transactional/Analytical Storage, From Predictive to Generative – How Michelangelo Accelerates Uber’s AI Journey

VuTrinh. • 59 implied HN points • 07 May 24
🕹 Technology Data Engineering Artificial Intelligence Machine Learning Data Management Cloud Computing
  1. Hybrid transactional/analytical storage combines different types of data processing. This helps companies like Uber manage their data more efficiently.
  2. The shift from predictive to generative AI is changing how companies use machine learning. Uber's Michelangelo platform shows how this new approach can improve AI applications.
  3. Data reliability and observability are important for businesses as their data grows. Companies need tools to quickly find and fix data issues to keep their operations running smoothly.

Issue #4 - The Five Minute History of Data

The Data Ecosystem • 59 implied HN points • 05 May 24
🕹 Technology Data science AI Analytics Data processing Cloud Computing
  1. Data is generated and used everywhere now, thanks to smart devices and cheaper storage. This means businesses can use data for many purposes, but not all those uses are helpful.
  2. Processing data has become much easier over the years. Small companies can now use tools to analyze data without needing a team of experts, although some guidance is still necessary.
  3. Analytics has shifted from just looking at past data to predicting future trends. This helps companies make better decisions, and AI is starting to take over some of these tasks.

From Dynamo to DynamoDB : Unveiling the Titans of Tech

Mindful Matrix • 119 implied HN points • 18 Feb 24
🕹 Technology Database Management Cloud Computing
  1. Dynamo and DynamoDB are two names often seen in databases, but they have significant differences. Dynamo set the foundation, and DynamoDB evolved into a practical, scalable, and reliable service.
  2. Key differences between Dynamo and DynamoDB include their Genesis, Consistency Model, Data Modeling, Operational Model, and Conflict Resolution approaches.
  3. Dynamo focuses on eventual consistency, while DynamoDB offers both eventual and strong consistency. Dynamo is a simple key-value store, while DynamoDB supports key-value and document data models.

VTEX scales to 150 million metrics using Amazon Managed Service for Prometheus

VTEX’s Tech Blog • 99 implied HN points • 10 Mar 24
🕹 Technology Cloud Computing Software Engineering Data Analytics DevOps
  1. VTEX successfully scaled its monitoring system to handle 150 million metrics using Amazon's Managed Service for Prometheus. This helped them keep track of their numerous services efficiently.
  2. By adopting this system, VTEX cut its observability expenses by about 41%. This shows that smart choices in technology can save money.
  3. The new architecture allows VTEX to respond to problems faster and reduces the chances of system failures. It increased the reliability of their metrics, making everyday operations smoother.

Software Supply Chain Security in DevSecOps & CI/CD

Resilient Cyber • 259 implied HN points • 27 Sep 23
🕹 Technology Cybersecurity Software Development DevOps Cloud Computing Information Security
  1. Software supply chain attacks are increasing, making it essential for organizations to protect their software development processes. Companies are looking for ways to secure their software from these attacks.
  2. NIST has issued guidance to help organizations improve software supply chain security, especially in DevSecOps and CI/CD environments. Following NIST's recommendations can help mitigate risks and ensure safer software delivery.
  3. The complexity of modern software environments makes security challenging. It's important for organizations to implement strict security measures throughout the development lifecycle to prevent attacks and ensure the integrity of their software.

Data Science Weekly - Issue 510

Data Science Weekly Newsletter • 279 implied HN points • 31 Aug 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Cloud Computing
  1. Autonomous drones can now race at human champion levels using deep reinforcement learning. This shows how advanced technology can mimic skilled human behavior in competitive sports.
  2. Google is rapidly developing its AI capabilities and plans to surpass GPT-4 by a significant margin soon. This could lead to more powerful AI tools for various applications.
  3. Reinforced Self-Training (ReST) is a new method for improving language models by aligning their outputs with human preferences. It offers better translation quality and can be done efficiently with less data.

Brief: Google received cloud contract supporting U.S. Special Operations Forces

All-Source Intelligence Fusion • 223 implied HN points • 18 Mar 24
🇺🇸 U.S. Politics Military Technology Contracts Cloud Computing
  1. Google received a cloud contract to support U.S. Special Operations Forces as part of a $9 billion program run by the Pentagon, along with Microsoft, Oracle, and Amazon.
  2. The U.S. Special Operations Command (SOCOM) with roughly 60,000 personnel works closely with elite units for direct action missions and core activities like counter-insurgency operations.
  3. The cloud computing contracts are seen as linking the CIA's cloud contract and the Pentagon's JWCC initiative, suggesting a bridge between intelligence and military operations.

Who cares about AuthZ? We went to KubeCon!

Permit.io’s Substack • 79 implied HN points • 28 Mar 24
🕹 Technology Cloud Computing Software Development Cybersecurity Data Management Open Source
  1. Fine-grained authorization is becoming really important as more developers talk about it. People see that better security can happen with smooth developer experiences.
  2. The rise of cloud-native architecture and big data means we need better ways to manage authorization decisions. It helps reduce decision fatigue and improves security.
  3. Tools like Policy as Code and various authorization engines are helping different teams work together better. This can lead to faster and more efficient development processes.

A detailed guide to running dbt Core in Production in AWS on ECS

The Orchestra Data Leadership Newsletter • 79 implied HN points • 28 Mar 24
🕹 Technology Cloud Computing Data Engineering AWS CI/CD Orchestration
  1. A detailed guide to running dbt Core in production in AWS on ECS is outlined, focusing on achieving cost-effective and reliable execution.
  2. Running dbt in production is not highly compute-intensive, as it primarily serves as an orchestrator, making it more cost-efficient compared to running Python code that utilizes compute resources.
  3. By setting up dbt Core on ECS in AWS and using Orchestra, you can achieve a scalable, cost-effective solution for self-hosting dbt Core with full visibility and control.

In search for ideal CDP for B2B SaaS

nonamevc • 24 implied HN points • 10 Nov 24
🕹 Technology SaaS Data Management Cloud Computing Digital Infrastructure
  1. Customer Data Platforms (CDPs) are becoming important for B2B SaaS companies by helping them unify data from different sources. This makes it easier for teams to work together and drive better marketing and sales efforts.
  2. There are two main types of CDPs: packaged and composable. Packaged CDPs are more like ready-made solutions, while composable CDPs allow for customization to better fit a company's specific needs.
  3. B2B companies might not need a standalone CDP as many existing tools are starting to include features traditionally offered by CDPs. This means businesses can often get what they need from tools they are already using.

What's a data migration?

Technically • 29 implied HN points • 12 Nov 24
🕹 Technology Data Management Software Development Cloud Computing Database Systems IT Infrastructure
  1. Data migration is the process of moving information from one place to another, like relocating files when changing devices. It involves transferring various types of data, such as documents and databases, to ensure everything is in the right spot.
  2. Migrations can be complex and risky, often causing errors or service disruptions if not done carefully. This makes it crucial for companies to have good planning and oversight to avoid losing important data or negatively affecting users.
  3. There are many reasons to migrate data, such as upgrading technology or meeting new security regulations. Companies often need to adapt to growth or changes in the market, which can lead to costly and lengthy migration projects.

The Next Wave Of AI Computing

Startup Pirate by Alex Alexakis • 235 implied HN points • 10 Mar 23
🕹 Technology Edge Computing AI Chips Cloud Computing Machine Learning
  1. Artificial intelligence has come a long way since Alan Turing, with AI chips being a key component for advanced computations.
  2. Edge computing moves computing power closer to where data is generated, enabling faster responses for AI applications like self-driving cars.
  3. Axelera AI is focusing on AI chips for edge computing and advancing technology for applications like computer vision in the physical world.

If It Fits, Gemma Sits 💎

Sector 6 | The Newsletter of AIM • 99 implied HN points • 23 Feb 24
🕹 Technology AI Software Open Source Cloud Computing Innovation
  1. Google has integrated its new model, Gemini, into Google Workspace, showing its focus on developing AI tools for users.
  2. While Google has released a model called Gemma, it is not truly open-source, which raises questions about its commitment to the open-source community.
  3. This year, Google is heavily promoting its Gemini brand, including recent updates and changes to its existing AI products like Bard.