just now (265+ words) Tired of your AI applications grinding to a halt under heavy load? Imagine trying to serve thousands of hungry customers from a single, overwhelmed food truck. That's what happens when your AI inference can't dynamically scale to meet demand. The core concept is to automatically adjust the computing resources allocated to AI inference based on real-time demand. We leverage a powerful combination: Kubernetes for container orchestration, Slurm for workload management on high-performance computing (HPC) clusters, and an optimized inference engine (like vLLM) to handle large language models (LLMs) with incredible speed. Think of it as having a fleet of food trucks that automatically appear and disappear based on the length of the line. When demand spikes, more trucks (compute resources) are deployed; when things quiet down, the extra trucks are put away, saving valuable resources. Benefits of Dynamic Inference Scaling: One…...

1+ min ago (526+ words) Note: Part 1 of 2 - My journey building a security game with Kiro I personally find security learning boring. You read about SQL injection, mentally note "never hardcode secrets," then immediately forget it all when racing to ship features. Problem: I'd never built anything like this before. I'm not a game designer. I haven't built game applications. But I gave Kiro a try to see how far I could get. Instead of diving into code (my usual "move fast and break things" approach), Kiro walked me through creating a proper spec. Turns out, specs aren't bureaucracy - they're clarity. Kiro introduced me to EARS patterns. Each requirement became crystal clear: "WHEN a user fixes a security vulnerability THEN the system SHALL reduce the corruption level by 33%" Here's where it got interesting. Kiro pushed me to define correctness properties - universal rules that should always…...

5+ min ago (1761+ words) There's a lot of hype around coding agents and the discourse can be annoying. I've always been into new shiny tools though. I'm forever messing with editors, IDEs, CLIs and anything else that seemed useful. Many of which no longer exist. It started with GitHub copilot and fancy autocomplete. Then web chat interfaces, copying and pasting code back and forth like StackOverflow. Now we've got coding agents. Agents are a useful tool because you can give them work that you would otherwise have to do yourself and they'll just go and try to do it, with varying degrees of success. Using techniques I discuss here, I try to increase the frequency of success. Using the term "agents" in this context, I'm thinking about instances of a coding agent in one context window. A single LLM thread, primed with some context…...

14+ min ago (365+ words) This hands-on build guide is designed to complement the main article on securing SSH shell applications and works for a quick planning reference. This guide walks you through how to build a secure, restricted SSH shell application. It complements the full article on Securing SSH Shell Applications, and pairs with the Printable Checklist for quick reference. The goal? To give you a clear, practical pathway to assembling a safe SSH-based terminal application " while leaving the final implementation details up to you. Start by choosing where the application will live. Many administrators use a dedicated directory under /opt, keeping the application isolated from user home folders and system binaries. Create a clean, well-organized folder structure that separates application code, logs, and configuration files. Your application will become the user's entire SSH experience, so you must control how it reacts to input....

22+ min ago (268+ words) Sentiment polarity is one of the most commonly referenced outputs of a sentiment analysis model, yet it is often misunderstood. Polarity measures the direction and intensity of emotional tone within a piece of text. A high positive polarity indicates strong approval, satisfaction, or optimism, while a negative polarity reflects criticism, frustration, or concern. Values close to zero tend to represent neutral or balanced language. Polarity is valuable because it allows analysts and applications to quantify subjective language in a measurable way. This makes it easier to sort user feedback, analyze trends, and classify responses automatically. Even though polarity is a simple metric, it forms the foundation for more advanced sentiment and emotion modeling. Sentiment polarity is one of the most commonly referenced outputs of a sentiment analysis model, yet it is often misunderstood. Polarity measures the direction and intensity of…...

27+ min ago (576+ words) Real-time codebase indexing with CocoIndex lets you turn a messy, evolving repo into a live semantic API that your AI tools, editors, and SRE workflows can query in milliseconds. Once your repo is indexed, you get a universal "code context service" that many tools can plug into. Some examples: CocoIndex is not "yet another Python script around an embedding model." It gives you a flow definition that describes how data moves from raw files to vector storage, and it tracks enough metadata to support incremental recomputation. For a codebase index, the high-level flow looks like this: This flow is declared once in Python with @cocoindex.flow_def, and CocoIndex turns it into a reproducible pipeline that can be updated with cocoindex update main whenever your repo changes. The first step is teaching the flow where your code lives and which files to…...

28+ min ago (143+ words) Processes (or threads) request resources (mutexes, files, devices, memory pages). A deadlock occurs when processes each hold some resources and wait forever for resources others hold. Simple OS example (classic) Prevention & Avoidance (OS) Prevention: disallow one Coffman condition (e.g., enforce ordering of resource acquisition, or disallow hold-and-wait by forcing processes to request all resources at once). Avoidance: Banker's algorithm (process claims max resources; OS grants only if system stays in a safe state). Works well only with fixed, known maximums. What it means in DBMS Transactions acquire locks (row/page/table locks) to ensure isolation. Two (or more) transactions can form a cycle waiting for locks the others hold. DBMSs commonly use lock managers and wait-for graphs to detect deadlocks and resolve them automatically. DB-specific concepts Lock modes: Shared (S) vs Exclusive (X). Prevention & Avoidance (DBMS)...

29+ min ago (309+ words) I wanted to make a working proof of concept that you can login with an oAuth provider directly in the database. Surreal Database should have this built in, and I am hoping they will in the future just like Gel, Firebase and Supabase! Thanks to Zorimyll for pseudocode in Discord. I make a fully working SurrealDB SvelteKit login with GitHub button that automatically merges with existing users and their email! See Repo. We have three main tables: users, auth_accounts, and auth_providers. We must first create a GitHub oAuth App under GitHub Developer Settings. Save the client_secret and client_id. You can't get the secret later. You will also need two different apps for production and development. There are no environment variables in Surreal, but we can save our data in a safe table we just created. This allows it to be available safely to any…...

30+ min ago (28+ words) " " "-" ", " " " " ". " " " " " " " " " " " " " " (, " ", " " "). " " " " " " " " " " " " ", " " " " " Go. " " " " " API, " " "-. " " " " " ", " " " " " " " ": " " Go, " " ", " " " ", " " ". " " " " " " " ", " " " " " " ": " endpoints " " ", " " ". " " " " net/http " " ", encoding/json " " " " " " " " " " ". " " "transfer" " " " " " " " " ", " " " " " ", ", " " " . Webhooks (" callbacks) " " " " " " " " " " " ". " ", " " " " " " " " " " (, " " ", " "). Go-: " " HTTP-, " " ". ": " webhook endpoints " " " " (HTTP 200 OK), " " " " " ". " "- " ", , " " " (Kafka, RabbitMQ) " " Go-" " workers. HMAC " " " ", " ", " ". " " " " " (" " ") " " " " " " ". " " " " ". RSA " " ": " " (" " ") " " " (" " "). " " " " " ", " " " " ". " " " ", " " " " ". " ", " " " " " " " ". " " " " " " " " " " callback, " " " ". " " " " " " " " " ". " " " " ", " " " " " " ". " " " " " ". "', " " " " " ", " "-, " " " " ". " " " Go!...

32+ min ago (1040+ words) When working in an organization, whether it's a startup, multinational corporation (MNC), or unicorn, there are specific standards and processes that are universally followed. These standards are crucial for ensuring the smooth functioning of the organization, especially in the software or IT industry. One of the most important of these standards is the Software Development Life Cycle (SDLC). The Software Development Life Cycle is a structured process used by the software industry to design, develop, and test software. It provides a systematic approach to ensure that high-quality software products are delivered to end users. Whether you're working at companies like Amazon, Flipkart, or Tesla, all organizations follow these core steps'design, develop, and test'as part of their SDLC. While the SDLC might also involve additional phases, these three are at the heart of the process, ensuring that the final product meets…...