The hottest Cloud Computing Substack posts right now

And their main takeaways
Category
Top Technology Topics

To Build or to Reuse? A CDK Question

Making It Up • 1 HN point • 11 Apr 24
🕹 Technology Infrastructure Code Cloud Computing Development Architecture
  1. CDK offers the flexibility to reuse existing resources or create new ones in your architecture for different environments.
  2. By incorporating conditionals and the ability to import resources via ARN, CDK allows code deployment into both fresh and existing environments from the same codebase.
  3. Using CDK, it's feasible to create custom constructs with logic for reusing or building infrastructure, making it simpler to manage and deploy resources across different states of environments.

Masquerading Digitalization

Amirul’s Substack • 1 HN point • 11 Apr 24
🕹 Technology Digitalization Emerging Tech Cloud Computing Digital Transformation
  1. Transitioning to digital should involve more than just adopting new technologies; it should also focus on reimagining business operations and ways of working.
  2. Simply replacing physical processes with digital ones does not equate to true digital transformation; the focus should be on enhancing user experiences and efficiency.
  3. Mixing emerging technologies with traditional IT culture can hinder digital transformation; organizations need to address organizational silos and adapt their operating models for successful digitalization.

Building LLM application using RAG

Mindful Matrix • 1 HN point • 07 Apr 24
🕹 Technology AI Data processing Cloud Computing Machine Learning Applications
  1. LLMs have limitations like not being able to update with new information and struggling with domain-specific queries.
  2. RAG (Retrieval Augmented Generation) architecture helps ground LLMs by using custom knowledge bases for generating responses to queries.
  3. Building a simple LLM application using RAG involves steps like loading documents, splitting data, embedding/indexing, defining LLM models, and retrieval/augmentation/generation.
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OSS vs OpenAI

Gradient Ascendant • 5 implied HN points • 17 Nov 23
🕹 Technology AI Software Hardware Cloud Computing API
  1. There is an ongoing debate between proprietary LLMs like OpenAI and open-source models like Llama-2 and Mistral in the field of artificial intelligence applications.
  2. OpenAI is making significant advancements with their Assistants API, aiming to become both the hardware and software giant of modern AI.
  3. While open-source LLMs have their place for certain tasks, OpenAI's focus on flagship applications and serious pattern recognition makes it difficult for OSS to compete.

How Google Profits from AI-Powered Search, While Killing SEO

Enterprise AI Trends • 2 HN points • 16 May 24
🕹 Technology AI Trends Search Engines Google SEO Cloud Computing
  1. Google's AI-powered search, known as SGE, may hurt small publishers but boost Google's own profits. It reduces the number of visible links, pushing advertisers to pay more for visibility.
  2. By integrating generative AI into search, Google can use its large user base to enhance its own cloud services and chip sales, gaining an edge over competitors.
  3. Google needs to carefully deploy AI features to avoid overwhelming users, especially for complex queries, while also being mindful of its most profitable keywords.

Data Science Weekly - Issue 419

Data Science Weekly Newsletter • 19 implied HN points • 02 Dec 21
🕹 Technology Data science Machine Learning AI Cloud Computing Programming
  1. FluxML is teaming up with NumFOCUS to enhance open science and improve machine learning tools. This partnership will support new applications in areas like scientific machine learning and differentiable programming.
  2. There’s a fun 30-day challenge involving mapping that highlights the importance of community in data science. It celebrates collaboration and innovation in creating visual representations of data.
  3. AI is making strides in pure mathematics by helping uncover new patterns and insights. This collaboration between AI and mathematicians could lead to significant advancements in understanding complex mathematical concepts.

Gradient Flow #41: What’s New in Data Engineering; MLOps Anti-Patterns

Gradient Flow • 19 implied HN points • 12 Aug 21
🕹 Technology Data Engineering MLOps Cloud Computing Open Source Machine Learning
  1. The podcast discusses changes in the data science role and tools, along with insights on new data engineering trends.
  2. An overview of new developments in tools and infrastructure, including a chatbot, recommendation system, and MLOps anti-patterns to avoid mistakes.
  3. Recommendations cover topics like the evolution of PyTorch, guidelines for open datasets stewardship, and insights into the analytical application stack.

Deep-Tech Newsletter #02 | June 2021

Deep-Tech Newsletter • 19 implied HN points • 14 Jun 21
🕹 Technology AI Cloud Computing Startups Deep Tech
  1. Homology.ai is a new platform by Zaiku Group backed by InnovateUK, aiming to help organizations train and deploy privacy-preserving AI models to the cloud.
  2. Zaiku Group plans to launch a deep-tech accelerator in Q1 2022 for technical founders who have completed their advanced mathematical courses, focusing on AI/Geometric Deep Learning solutions.
  3. Zaiku Group is evolving into a deep-tech private equity firm, aiming to invest in and acquire early-stage deep-tech companies aligned with their mission & values.

The Second Half of Cloud Computing

CyberSecurityMew • 1 HN point • 01 Mar 24
🕹 Technology Cloud Computing Cybersecurity
  1. Cloud computing has reshaped enterprise IT, offering flexibility, scalability, and speed in deploying new businesses.
  2. Issues with Personal Computers include costly hardware maintenance, software bloat, security concerns, and challenging updates.
  3. Cloud-native instant workspace is the future of cloud computing on the client side, providing secure, seamless, and device-independent work environments.

Data Science Weekly - Issue 396

Data Science Weekly Newsletter • 19 implied HN points • 24 Jun 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Cloud Computing
  1. Multi-task learning helps models make several predictions at once, making them smarter. It's better than sticking to just one task.
  2. Deep reinforcement learning is changing how industries like manufacturing work by teaching machines to take actions to achieve specific goals. This can really improve efficiency.
  3. The Netflix Prize taught Netflix valuable lessons, even if the main winning entry wasn't directly useful. It's a good reminder that competitions can offer more benefits than just the final prize.

DevOps Concepts #2: Autoscaling

Cloud Weekly • 8 implied HN points • 03 Jun 23
🕹 Technology DevOps Cloud Computing AWS Infrastructure
  1. Autoscaling allows you to adjust resources automatically based on traffic, ensuring your application stays performant and resilient.
  2. Auto Scaling Groups in AWS help manage resources by defining minimum, desired, and maximum instances, allowing for easy scaling up and down.
  3. AWS Auto Scaling Groups provide strategies like simple scaling, target scaling, and step scaling to optimize costs and react to traffic based on predefined metrics.

Gradient Flow #27: 2021 Trends Report, the Edge, and ML in the Sciences

Gradient Flow • 19 implied HN points • 28 Jan 21
🕹 Technology AI Machine Learning Data Management Cloud Computing Edge Computing
  1. The 2021 Trends Report covers topics like tools for Machine Learning and AI, Data Management, Cloud Computing, and Emerging AI Trends.
  2. Edge computing is becoming more important for bringing AI and computing closer to data sources, as discussed with experts in the field.
  3. In the realm of Machine Learning, there are new tools like GPT-Neo, analysis of popular data science technologies, and the concept of the lakehouse in data management.

Data Science Weekly - Issue 375

Data Science Weekly Newsletter • 19 implied HN points • 28 Jan 21
🕹 Technology Machine Learning Data science AI Ethics Programming Cloud Computing
  1. When building a machine learning team, it's important to adapt the team's structure as projects grow. Start small, but be ready to scale up as your needs change.
  2. Creating machine learning systems that can generalize well requires us to use observations to make inferences. This process, known as induction, helps build smarter algorithms.
  3. Machine learning is now being applied to modeling audio equipment, which could change the way we think about sound and effects in music production.

Newsletter - Adaptable MLOps Architecture for Research Labs

ScaleDown • 5 implied HN points • 03 Jun 23
🕹 Technology Machine Learning Cloud Computing GitHub Automation
  1. Adaptable MLOps architecture can solve challenges in research labs by blending collaboration tools, cloud computing platforms, and automation.
  2. The proposed MLOps architecture can adapt to diverse research scenarios, such as collaborative projects, GPU-less labs, and overburdened ML researchers.
  3. MLOps in research is evolving, with concerns like LLM hallucinations, watermarking LLM outputs, and the impact of using generated content for training models.

Khải Trần: tư duy ngắn hạn - tư duy dài hạn qua câu chuyện xây kho dữ liệu trên mây

Thái | Hacker | Kỹ sư tin tặc • 39 implied HN points • 21 Oct 18
🕹 Technology Cloud Computing Data Warehousing Startup
  1. Different business strategies: Amazon focused on being the first with Redshift, while Snowflake prioritized a new architecture separating computation and storage for a more scalable system.
  2. Cloud computing advantages: Cloud services like Redshift and Snowflake offer flexibility, cost-effectiveness, and scalability compared to traditional on-premise data warehouses.
  3. Market competition: Amazon leads the cloud market with Microsoft following closely, while Google is catching up with strong computing infrastructure despite starting later.

Secure Enclaves

Why Now • 5 implied HN points • 03 Apr 23
🕹 Technology Cybersecurity Encryption Cloud Computing Data security Hardware
  1. Security is a key area for innovation with a focus on problem-solving and wedging opportunities against incumbents
  2. Encrypting data in-use is a challenge in cybersecurity, with solutions like homomorphic encryption and secure enclaves emerging
  3. Secure Enclaves are highly-controlled environments that validate code execution cryptographically, offering a way to protect data in-use

Coding Cells - Computational tools for Protein Design

Kesav’s Lab • 1 HN point • 20 May 24
🕹 Technology AI Biotechnology Cloud Computing Software Development Machine Learning
  1. Artificial intelligence and synthetic biology are changing how we interact with biology. They can help us design new food, medicine, and materials more effectively.
  2. AlphaFold is a powerful tool that predicts protein structures, which is crucial for understanding how proteins work. This insight can lead to breakthroughs in drug discovery and other medical applications.
  3. The author is building a user-friendly tool for protein design using AlphaFold on Google Cloud to help protein engineers. The goal is to create a platform where they can easily make predictions and visualize protein structures.

How Real-Time is Amazon Cloudwatch Logs Live Tail?

Bit by Bit • 3 implied HN points • 29 Jun 23
🕹 Technology Cloud Computing AWS Logging Websockets
  1. Amazon CloudWatch Logs Live Tail helps developers follow cloudwatch logs in real-time with features like dynamic filters and highlights.
  2. Live Tail can stream logs in around 2 seconds from when they are ingested in CloudWatch, while using CLI can vary between 2 to 9 seconds.
  3. Live Tail's pricing model is per-second and includes a free tier of 1,800 minutes per month, making it a useful addition to AWS console tool set for real-time log monitoring.

The S3 Balancing Act: Security vs Simplicity

Bit by Bit • 3 implied HN points • 08 Jun 23
🕹 Technology Cloud Computing Security Infrastructure as Code Developer Experience AWS
  1. AWS made changes to S3 default settings for improved security by blocking public access and custom ACL rules for new buckets.
  2. While enhancing security, the process of creating public buckets has become more complex and requires explicit steps to disable block policies.
  3. The complexities of managing storage like S3 in the cloud call for solutions that balance simplicity, security, and extensibility.

Data Science Weekly - Issue 304

Data Science Weekly Newsletter • 19 implied HN points • 19 Sep 19
🕹 Technology Data science Machine Learning Artificial Intelligence Cloud Computing Software Engineering
  1. Backpropagation is key to how neural networks learn and work. It's important to understand how it makes AI smarter.
  2. There's a lot of interest in AI startups right now, like those that clean and prepare data for analysis. They are getting significant funding due to the AI boom.
  3. If you want a job in data science, gaining real-world experience is crucial. Many people feel discouraged, but projects and hands-on training can help bridge that gap.

Google's First Tensor Processing Unit : Origins

The Chip Letter • 1 HN point • 25 Feb 24
🕹 Technology AI Hardware Cloud Computing Machine Learning Data Centers
  1. Google developed the first Tensor Processing Unit (TPU) to accelerate machine learning tasks, marking a shift towards specialized hardware in the computing landscape.
  2. The TPU project at Google displayed the ability to rapidly innovate and deploy custom hardware at scale, showcasing a nimble approach towards development.
  3. Tensor Processing Units (TPUs) showcased significant cost and performance advantages in machine learning tasks, leading to widespread adoption within Google and demonstrating the importance of dedicated hardware in the field.

An Interview with Renee James, CEO Ampere Computing

More Than Moore • 1 HN point • 28 Feb 24
🕹 Technology Semiconductors Cloud Computing AI Software Innovation
  1. Efficiency is crucial for the future of AI, requiring high-performance CPUs that operate in tight power envelopes.
  2. Ampere Computing has succeeded by tackling challenges such as power constraints and building a full platform that includes software optimization.
  3. The company aims to be an at-scale semiconductor company, emphasizing the importance of diversity in suppliers and the need for merchant market silicon vendors for innovation and problem-solving.

Securing Cloud-Based CI Systems: Best Practices

Iceberg • 1 HN point • 30 Sep 23
🕹 Technology Cloud Computing Cybersecurity Software Development DevOps
  1. Limit who or what can invoke processes in CI systems to reduce the blast radius.
  2. Utilize separate cloud and saas accounts for different environments to enhance security and avoid errors.
  3. Regularly monitor dependency security, distinguish between CI and deployment contexts, and minimize reliance on third-party systems for supply chain risk mitigation.

What is Google's Sec-PaLM?

Machine Economy Press • 3 implied HN points • 25 Apr 23
🕹 Technology AI Cybersecurity Cloud Computing Generative AI Quantum Computing
  1. Google's Sec-PaLM is a specialized AI language model fine-tuned for cybersecurity use cases.
  2. Generative AI in cybersecurity is being utilized by cloud giants like Google to enhance security measures.
  3. Sec-PaLM assists in threat intelligence analysis, incident prevention, and enhances the capabilities of Google's cloud cybersecurity services.

Data Science Weekly - Issue 288

Data Science Weekly Newsletter • 19 implied HN points • 30 May 19
🕹 Technology AI Machine Learning Data science Deep Learning Cloud Computing Computer Vision
  1. Creating general artificial intelligence might be possible through AI-generating algorithms, which could be a better approach than manually piecing together intelligence components.
  2. Generative adversarial networks (GANs) could greatly change the fashion industry by allowing realistic digital models to replace human models in online shopping.
  3. Recent advances in AI technology are enabling more efficient processing on devices, reducing the need for powerful cloud machines and making AI applications more accessible.

Snowflake Summit 2023

Magis • 2 HN points • 02 Jul 23
🕹 Technology Cloud Computing Software Development Cybersecurity Data Governance
  1. Snowflake Summit 2023 introduced key features including a partnership with Nvidia, Snowpark Container Services for machine learning, and updates to the Native Application Framework.
  2. Snowflake announced new options for paying Marketplace Listings using Snowflake capacity contracts, custom billing events for native applications, and data governance features like Aggregation Constraints.
  3. Additional announcements at Snowflake Summit 2023 included updates in Snowflake SQL, a new Snowflake Performance Index, and the ability to set spending alerts and calculate cost run-rates.