SShortSingh.
Back to feed

Developer Builds Free Open-Source Browser Extension for Batch Image Downloading

0
·1 views

A developer and graphic apparel creator has built a free, open-source browser extension called Image Downloader & Collector to streamline visual asset collection for designers. The tool was created out of frustration with slow manual image-saving methods and bloated or paywalled existing solutions. Built with Vanilla JavaScript and Manifest V3, the extension lets users hover over images to quickly download or save them into custom folders. It also supports one-click ZIP batch downloads, high-resolution image detection, and processes everything locally with no user tracking. The extension is designed for designers, creators, and shoppers who frequently gather visual inspiration from sites like Pinterest, Behance, and Unsplash.

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)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Dev Tutorial: Automating Bug Reports from Playwright Test Failures Using Claude Code

A new tutorial in the 'Automating Playwright with Claude Code' series demonstrates how to build a bug-reporter Skill that converts detected test failures into structured, ready-to-file bug reports. The Skill builds on earlier installments, drawing on evidence gathered by a flaky-test-debugger and applying guardrail patterns to prevent fabricated reproduction steps. Each generated report follows a consistent template covering title, numbered repro steps, expected versus actual results, evidence, environment details, and a severity suggestion. Consistency in report structure is highlighted as a key benefit, helping teams triage issues faster regardless of who filed the report. The tutorial uses GitHub Issues as its primary example but notes the same pattern applies to tools like Jira.

0
ProgrammingDEV Community ·

Beginner Documents First Week of Cybersecurity Learning on TryHackMe

A beginner cybersecurity learner shared key takeaways from their first week on TryHackMe, a popular online platform for learning ethical hacking and security skills. Their studies focused on the Networking Fundamentals module, covering core concepts such as IP and MAC addresses, routers, switches, and subnetting. They also learned about protocols including ARP, which maps IP addresses to MAC addresses, and DHCP, which automatically assigns IP addresses to new devices through a four-step process. Additionally, the learner explored the seven-layer OSI model, gaining an understanding of how each layer handles different aspects of network communication. The article is intended to serve as a beginner-friendly recap for others entering the cybersecurity field.

0
ProgrammingDEV Community ·

Most proxy checkers miss IP leaks — here is how to properly verify anonymity

A successful proxy connection does not guarantee that your real IP address is hidden from destination servers, as many proxies silently forward it via headers like X-Forwarded-For. Proxies can be graded into three categories — transparent, anonymous, and elite — based on whether they expose the real IP or reveal that a proxy is in use at all. A developer has released an open-source command-line tool called proxyprobe that checks proxy anonymity, response times, and geolocation for bulk proxy lists with concurrency support. The tool highlights two common implementation pitfalls: greedy password masking to prevent credential leaks in logs, and configurable echo endpoints to avoid routing proxy data through unintended third parties. It is designed to integrate into CI pipelines, returning exit codes based on whether any proxies remain functional.

0
ProgrammingDEV Community ·

Why Meta and Mistral Release Free AI Models — and What They Really Gain

Meta and Mistral are releasing powerful open-source AI models like Llama 3 at no cost, but analysts argue the move is driven by strategic business interests rather than generosity. By making AI models freely available, these companies aim to lock developers into their ecosystems, attract top talent, and gain market share against proprietary rivals like OpenAI. Meta benefits from ecosystem dependency and data advantages, while Mistral uses open-source releases to build brand recognition and competitive positioning. Proprietary AI providers are responding by emphasizing safety, speed, and convenience as premium differentiators. In the long term, open-source models are expected to become the industry default, with the true competitive edge shifting toward companies that control the best data.