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Practical writing on software architecture, SaaS products, AI automation, legacy modernisation, and the business of building reliable systems.
Curated links from external sources — not 360Softy original articles.
Article URL: https://www.scientificamerican.com/article/white-house-proposes-new-rules-giving-political-appointees-final-say-on-research-grants/ Comments URL: https://news.ycombinator.com/item?id=48331511 Points: 278 # Comments: 587
A dependency confusion campaign leveraged 33 malicious npm packages to collect reconnaissance data from developer and build environments. This report details the attack chain, observed tradecraft, and detection opportunities to help organizations identify and disrupt related activity. The post Malicious npm packages abuse dependency confusion to profile developer environments appeared first on Microsoft Security Blog.
Article URL: https://daringfireball.net/2026/05/what_is_a_dickover Comments URL: https://news.ycombinator.com/item?id=48330882 Points: 489 # Comments: 187
TL;DR: I built and deployed 7 specialized AI agents to handle different parts of my development workflow. After 30 days of continuous operation, here's exactly what worked, what failed, and the real numbers behind AI-powered development automation. Thirty days ago, I made a decision that would either save me hundreds of hours or waste a significant amount of time: I would delegate as much of my development workflow as possible to specialized AI agents. Not just code completion. Not just chatbot
Lambda has always been one request at a time per execution environment. Your function starts, processes a single invocation, and sits idle until the next one arrives. If you need to handle a thousand concurrent requests, Lambda spins up a thousand execution environments - each with its own memory, its own cold start, and its own per-GB-second bill. Lambda Managed Instances changes that model. Announced at re:Invent 2025 and expanded with 32 GB memory / 16 vCPU support in March 2026, LMI runs you
I wanted to see live Formula 1 data on my desktop while watching the race — not buried in an app or a browser tab, but always visible in the corner of my screen. So I built a widget that pulls from two free public APIs and renders everything directly on the macOS desktop. Here's what went into it and what I learned along the way. During a live session the widget shows: Live standings — position, driver code, gap to leader, last lap time, tyre compound and age, pit stop count Race Control banner
This post demonstrates a comprehensive observability solution using Amazon Managed Grafana dashboards that provides a holistic view of both quality and quantity for LLMs served on Amazon SageMaker AI endpoints with inference components.
An honest look at what happens when you hand your GitHub account to an autonomous AI agent and let it loose on open source bounties. Spoiler: it's not what you think. On May 28, 2026, I did something most developers would consider insane: I gave an AI agent full access to my GitHub account and told it to hunt open source bounties autonomously. No supervision. No approval gates. Just "go find bounties, write code, and submit PRs." Why? Because I wanted to answer a question that's been bugging me
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