How Scheduled Scale-to-Zero Can Cut AWS ECS Fargate Costs on Non-Production Workloads

·1 views

Running staging or development ECS Fargate services around the clock wastes compute spend, since a 40-hour workweek workload still runs 168 hours if its desired task count never drops to zero. Scheduled scaling actions via Application Auto Scaling can set a service's minimum and maximum capacity to zero overnight and restore them each morning, eliminating Fargate compute charges during idle hours. Good candidates include development and staging environments with predictable usage windows, while shared multi-timezone services or those requiring always-on availability are poor fits. The approach does not automatically reduce costs for load balancers, NAT gateways, databases, or storage, which must be evaluated separately. Engineers are advised to start with a single service, monitor startup reliability and billing changes, and only then expand the pattern across other eligible environments.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Data Analysis Probes Whether Reddit Has a Systematic Astroturfing Problem

A data-driven investigation published on petervijeh.com examines whether Reddit suffers from coordinated inauthentic behavior, commonly known as astroturfing. The analysis attempts to identify patterns in Reddit activity that may indicate organized efforts to manipulate discussions or promote content artificially. The study was shared on Hacker News, though it attracted minimal engagement at the time of posting. Astroturfing on social platforms involves disguising sponsored or coordinated campaigns as genuine grassroots user activity. The findings and methodology are detailed on the author's personal projects page.

0 comments Read more at Hacker News

ProgrammingDEV Community ·

How One Developer Escaped $11/hr Freelance Pricing With a Value-Based Framework

A mid-career freelance developer realized he was effectively earning around $6.50 per hour after accounting for non-billable time, taxes, and overhead — despite charging $11.40 per hour. The turning point came when a logistics client revealed that a reporting automation he had built at a low hourly rate was saving the company roughly $18,000 a year in labor costs. This gap between his effort-based pricing and the client's actual outcome highlighted a core flaw in hourly billing: it rewards slow work and penalizes efficiency. The developer subsequently rebuilt his pricing approach around value to the client rather than time spent, shifting from hourly rates to project-based fees tied to measurable outcomes. He now shares this framework with freelancers stuck in a cycle of undercharging, arguing that small hourly numbers invite easy negotiation while fixed project prices prompt more deliberate consideration from clients.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

structuredClone() vs JSON.stringify(): The Right Tool for Deep Copying in JS

JavaScript developers have long used JSON.parse(JSON.stringify()) as a quick workaround for deep-copying objects, but the technique has notable limitations. It fails to correctly handle data types like Date, Map, Set, and undefined, and throws a TypeError when encountering circular references. The structuredClone() API, built specifically for cloning structured data, handles all these cases more reliably and even preserves circular reference relationships in the cloned output. However, structuredClone() is not without constraints — it cannot clone functions, DOM nodes, or prototype chains. Developers are advised to use structuredClone() for deep copying data and reserve JSON.stringify() for its intended purpose of serialization.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

PackAI Uses AI and Memory Layers to Guide Small Food Businesses on Packaging

PackAI is a prototype AI-powered tool designed to help small food businesses choose appropriate packaging materials without needing an in-house packaging engineer. The system analyzes a food item and recommends materials based on factors such as barrier performance, shelf life, cost, and recyclability. A memory layer built on the Hindsight agent memory system allows PackAI to recall user preferences from past sessions and prioritize relevant options in future recommendations. The current version relies on rule-based logic with manually set profiles rather than trained machine-learning models, so its outputs are meant as guidance rather than validated engineering specifications. Planned improvements include password security upgrades, expert-validated material data, data-driven models, regional recyclability information, and a user-feedback loop to refine recommendations over time.

0 comments Read more at DEV Community