DiaryFolio

If you’re a serious IMDb user like me, you know the built-in IMDb Advanced Search is super powerful — but sometimes it feels a bit clunky to use every time. The cool trick? You can manually tweak IMDb URLs to create custom, detailed searches instantly! Why manually tweak URLs? It’s faster once you know the syntax. You can combine lots of filters that the normal UI doesn’t expose easily. Perfect for bookmarking or sharing specific searches. How do IMDb URLs work? IMDb’s advanced search URLs are basically a list of parameters connected by & . Each parameter filters a part of the search, like: languages=en,hi — English or Hindi language titles num_votes=500, — at least 500 votes release_date=1980-01-01, — from January 1, 1980 onward title_type=feature — only feature films user_rating=7.5, — user rating 7.5 and above Add or combine parameters based on what you want. Examples: Crafting...

Summary Traditional CI/CD tools like GitHub Actions, GitLab CI/CD, and Azure DevOps (ADO) offer powerful pipeline features, but they often tightly couple your automation logic to their platforms. This creates a problem when switching tools or scaling across teams. By using Ansible as the central workflow engine—and treating GitHub, GitLab, or ADO as lightweight orchestrators—you can build a modular, portable CI/CD system. This hybrid approach enables tool-agnostic pipelines and promotes reuse across projects and platforms. Why Use Ansible in CI/CD? Portability: Move your pipeline between GitHub, GitLab, or ADO without rewriting core logic. Modularity: Write Ansible playbooks for each stage (build, test, deploy) and reuse them across environments. Maintainability: Keep your workflow logic in version-controlled, testable, and readable YAML files. Tool Independence: Avoid vendor lock-in by abstracting workflow logic into a standalone orchestration ...

This page provides a clear and concise comparison between the old syslog standard (RFC 3164) and the modern format (RFC 5424). Whether you're working with legacy systems or modern log management tools, understanding the differences in structure, features, and formatting helps ensure better log parsing, monitoring, and compliance across systems. Comparison Table Field RFC3164 (Old Format) RFC5424 (New Format) Example <35>Oct 12 22:14:15 client_machine su: 'su root' failed... <35>1 2013-10-11T22:14:15.003Z client_machine su - - - 'su root' failed... Timestamp BSD-style (no year, no timezone) ISO-8601 with milliseconds and timezone Priority Field Numeric value <Facility * 8 + Severity> Same as RFC3164 Hostname client_machine client_machine Structured Data Not suppo...

n8n is an extendable workflow automation tool that empowers teams to connect APIs, services, and data pipelines with ease. While it's a breeze to get started locally, deploying n8n on Kubernetes unlocks a new level of scalability, resilience, and automation — especially when using Helm to manage the lifecycle. In this guide, we walk through deploying n8n to a Kubernetes cluster using a custom Helm-based setup, backed by official OCI charts and automation scripts that make the experience fast and production-ready. Project Structure n8n/ ├── Chart.yaml # Helm chart metadata ├── values.yaml # Configuration for n8n (DB, persistence, scaling) ├── templates/ # Kubernetes resource templates (Deployment, Service, PVC, Ingress) │ ├── _helpers.tpl │ ├── deployment.yaml │ ├── ingress.yaml │ ├── pvc.yaml │ └── service.yaml ├── scripts/ # Shell scripts for operational tasks │ ├── deploy.sh │ ├── uninstall.sh │ └── clean...

S&P 500 During the Global Financial Crisis 📈 S&P 500 During the Global Financial Crisis A comprehensive timeline of key events and market movements from 2007-2009 🗓️ Key Dates of SPY500 During the GFC Event Approximate Date Details 🟩 Initial Peak 2007-10-09 S&P 500 reached 1,565.15 — all-time high before the GFC. 🟥 Bear Market Begins ...

Property Sale: Leveraging the 15-Year Exemption BobCo, a limited company wholly owned by Bob, originally bought a property for $1 million using $50,000 of its own capital and a $950,000 mortgage. By the time Bob sells BobCo (via a share sale), the mortgage is fully paid off, and the property is now worth $2 million. In this post, we’ll explore how this impacts the disposal—specifically Bob’s sale of BobCo’s shares—and the tax implications, including the potential use of the   small business 15-year exemption   to avoid Capital Gains Tax (CGT). Buckle up—this gets intricate! Key Details Initial Purchase Property Cost:   $1 million BobCo’s Capital Contribution:   $50,000 Mortgage:   $950,000 (fully paid off by the time of sale) Sale Context Property Value at Sale:   $2 million Share Sale:   Bob sells 100% of BobCo’s shares to a buyer for $2 million (reflecting the property’s value, assuming no other assets or liabilities) Mortgage Status:   Paid off...

Pre-training and fine-tuning are integral strategies in the development of deep learning models, particularly in the context of transfer learning. In this section, we'll explore these concepts in detail, elucidating how training works and how models are created through these processes. Strategy & Development Pre-training Definition : Pre-training involves training a neural network model on a large dataset to learn generic representations of data features, typically using unsupervised or self-supervised learning techniques. Training Process : During pre-training, the model learns to capture general patterns and features present in the data without being task-specific. This is achieved by optimizing parameters to minimize a predefined loss function, such as reconstruction loss in autoencoders or language modeling loss in transformers. Model Creation: After pre-training, the model's weights and parameters encode valuable knowledge about the underlying data distribution, formi...