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Tuesday 2025-01-07 Assorted Links
Assorted Links links
Published: 2025-01-07
Tuesday 2025-01-07 Assorted Links

Assorted links for Tuesday, January 7:

  1. What is Inference Parallelism and how it works

    Inference parallelism aims to distribute the computational workload of AI models, particularly deep learning models, across multiple processing units such as GPUs.

  2. Open Source Innovation Comes to Time-Series Data Compression

    NetApp Instaclustr collaborated with the University of Canberra through the OpenSI initiative to develop the Advanced Time Series Compressor (ATSC) — an open source innovation that fundamentally reimagines high-volume time-series data compression.

    ATSC implements a sophisticated lossy compression approach. Rather than storing complete data sets, it generates mathematical functions that closely approximate the original data patterns, storing only the essential parameters of these functions. This approach is paired with granular configurability — users can precisely tune their desired level of accuracy, balancing storage efficiency with data fidelity based on their specific use cases.

  3. What Do You Lose When You Abandon the Cloud?

    High-profile moves from 37signals (the company behind Basecamp and HEY) and GEICO have sparked a renewed interest in cloud repatriation.

    One sometimes overlooked advantage of moving to the cloud is that it allows you to pay for resources when they are needed, for example, as new customers come online. Spending moves from upfront CAPEX (buying new machines in anticipation of success) to OPEX (paying for additional servers on demand).

    Another thing to weigh up is pace of innovation — both from the cloud provider and from the consumer.

    The Zynga example [of moving from the cloud to on-prem, then back to the cloud] highlights several other trade-offs. One to consider is that if you are running your own data centers, you need to be able to hire the right people and retain them.

    There is another set of trade-offs around security. Keeping servers up to date, and guarding against intrusions, is time-consuming work that big cloud providers are very experienced in.

  4. Why All the Major Cloud Platforms Are the Same

    Each provider brought unique strengths and strategic priorities to the table, creating differentiation initially, but eventually converging on a consistent baseline of functionality.

  5. Indexing code at scale with Glean

    How is Glean different?

    • Glean doesn’t decide for you what data you can store.
    • Glean’s query language is very general.
Monday 2025-01-06 Assorted Links
Assorted Links links
Published: 2025-01-06
Monday 2025-01-06 Assorted Links

Assorted links for Monday, January 6:

  1. Managing large-scale Redis clusters on Kubernetes with an operator – Kuaishou’s approach
  2. Supercharge Your RAG App With Agentic Hybrid Search

    By using structured metadata and letting an LLM choose the best retrieval method for each query, you can turn your RAG app into a better assistant.

  3. Cloud Efficiency at Netflix
  4. Part 1: A Survey of Analytics Engineering Work at Netflix
  5. How we think about Threads’ iOS performance: Key metrics: %FIRE (Frustrating image-render experience), TTNC (Time-to-network content), cPSR (Creation-publish success rate)
Friday 2025-01-03 Assorted Links
Assorted Links links
Published: 2025-01-03
Friday 2025-01-03 Assorted Links

Assorted links for Friday, January 3:

  1. Apache Pinot Brings Real Time Analysis to Columnar Data

    Pinot was born to solve the problem of “running analytical queries for hundreds of millions of users at scale, in a low-cost manner,” explained Chinmay Soman, head of product for StarTree, which offers a fully managed cloud native version of Pinot.

    Pinot brings “simplification in the data stack,” Soman said in an interview with TNS. “The problem is not new. It’s been solved by many legacy technologies. What Pinot brings is the simplification and the scale for these problems.”

  2. GenAI is Quickly Reinventing IT Operations, Leaving Many Behind

    GenAI can significantly transform IT Operations Management by proactively providing context-rich insights, accurate predictions, and actionable recommendations for managing the IT landscape.

  3. How to Create and Use an AI Git Agent
  4. Platform Engineering needs Observability: here’s why

    Observability offers real-time insights into system behavior, allowing teams to proactively identify and address issues before they affect users. By adopting observability, platform engineering teams can improve system resilience, sustain uninterrupted user experiences during peak events, and uphold operational stability.

  5. Announcing systemd v257
Thursday 2025-01-02 Assorted Links
Assorted Links links
Published: 2025-01-02
Thursday 2025-01-02 Assorted Links

Assorted links for Thursday, January 2:

  1. Design Token-Based UI Architecture

    Design tokens are design decisions as data and serve as a single source of truth for design and engineering. Utilizing deployment pipelines, they enable automated code generation across platforms, allowing for faster updates and improved consistency in design.

  2. Retrofitting spatial safety to hundreds of millions of lines of C++

    [W]e’re working towards bringing spatial memory safety into as many of our C++ codebases as possible, including Chrome and the monolithic codebase powering our services.

    Building on the successful deployment of hardened libc++ in Chrome in 2022, we’ve now made it default across our server-side production systems.

  3. Reconsidering Kubernetes deployments: when operators are overkill

    TL:DR: Kubernetes Operators are powerful but can be overkill for simple deployments. Explore alternatives like Helm, ArgoCD, and Devtron to streamline your Kubernetes deployments without sacrificing efficiency.

  4. Securely Deploy and Run Multiple Tenants on Kubernetes

    Three fundamental options:

    1. Namespace-Based Isolation With Network Policies, RBAC and Security Controls
    2. Cluster-Level Isolation
    3. Virtual Clusters
  5. AI-Driven Code Review: Enhancing Developer Productivity and Code Quality

    AI-driven code review tools are changing traditional development and helping teams build better software.

Tuesday 2024-12-10 Assorted Links
Assorted Links links
Published: 2024-12-10
Tuesday 2024-12-10 Assorted Links

Assorted links for Tuesday, December 10:

  1. Benchmark LLM Application Performance with LangChain
  2. Forget All-Cloud or All-On-Prem: Embrace Hybrid for Agility and Cost Savings

    The core idea behind hybrid cloud is straightforward: use cloud environments where rapid scaling and frequent changes are essential and rely on on-premises systems for stable, predictable workloads.

  3. Bazel 8.0 Released
  4. You Can Now Try Out Sora, OpenAI’s AI Video Generator
  5. Using Local AI models with .NET Aspire
Tuesday 2024-12-03 Assorted Links
Assorted Links links
Published: 2024-12-03
Tuesday 2024-12-03 Assorted Links

Assorted links for Tuesday, December 3:

  1. Agent Framework

    Agent Framework is a Python framework for building capable, context-aware AI agents. It provides a flexible foundation for creating AI agents with distinct personas, managed instructions, conversational memory, and strategic planning capabilities.

  2. eBPF Security Threat Model
  3. Experts Share Best Practices for Building Terraform Modules
  4. API Mocking Is Essential to Effective Change Management
  5. OpenTelemetry Is expanding into CI/CD observability

    We’ve been talking about the need for a common “language” for reporting and observing CI/CD pipelines for years, and finally, we see the first “words” of this language entering the “dictionary” of observability – the OpenTelemetry open specification. With the recent release of OpenTelemetry’s Semantic Conventions, v1.27.0, you can find designated attributes for reporting CI/CD pipelines.