NPM Safety Guard offers 23-layer supply chain protection for JS developers

·1 views

NPM Safety Guard is a free, open-source developer tool built by SendWaveHub that provides 23 layers of security scanning for npm projects. It detects threats that standard npm audit misses, including known malicious packages, typosquatting, dependency confusion, exposed secrets, and AI credential theft hidden in node_modules. The tool integrates with both VS Code and JetBrains IDEs and is available on their respective marketplaces. It leverages multiple intelligence sources such as OSSF Scorecard, Socket.dev, and ReversingLabs to assess supply chain risk in real time. Released under the MIT license, the project is also hosted on GitHub where developers can review its source code and contribute.

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)

MacGet uses adaptive connections to outsmart CDN throttling during downloads

MacGet, a free open-source macOS download manager, was built after its developer discovered that opening more parallel download connections often slows transfers down rather than speeding them up. Modern CDNs treat multiple simultaneous connections from a single IP as abusive behavior, responding with resets, throttling, or outright blocks. To counter this, MacGet's engine starts with four connections and dynamically scales up or down based on real throughput gains, learning and storing each host's optimal connection limit for future use. Workers are also staggered 100ms apart to avoid triggering anti-abuse systems, and a dynamic chunk-stealing mechanism prevents slow network paths from bottlenecking the entire download. Additional features include HTTP/3 support, SHA-256 integrity verification, and a workaround for macOS App Nap, which can throttle background processes.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

LLM Inference Optimization Can Cut AI Serving Costs by Up to 10x

Running large language models in production makes inference the dominant AI cost, with a meter running on every request around the clock. The gap between unoptimized and optimized serving typically amounts to a 5–10x difference in cost and a 3–5x difference in latency. Key techniques include continuous batching, which can push GPU utilization from roughly 20–30% up to 80–90%, and KV-cache management methods like PagedAttention, which nearly eliminate memory waste and allow two to three times more concurrent requests. Quantization approaches such as FP8 and INT4 reduce data movement and model footprint, while speculative decoding lowers latency without sacrificing output quality. Together, these well-established methods can determine whether an AI feature is economically viable enough to ship at all.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Audit of 100 LeRobot Datasets Finds 81% Flawed or Unloadable

A developer audited 100 publicly available LeRobotDataset repositories on the Hugging Face Hub and found that 81% either contained data errors or could not be linted at all. Of the datasets that did load successfully, nearly 19% suffered from a known migration bug where episode-to-frame index boundaries were corrupted during a v2.1-to-v3.0 conversion, causing frames to be silently assigned to the wrong episode during training. A separate floating-point timestamp drift issue, which can cause video decoding to fail mid-training run, was found in about 3% of successfully linted datasets. To address the lack of automated quality checks, the developer released an open-source tool called trajlens that runs 16 validation checks across categories including structural integrity, timestamp consistency, and video decodability. The tool is available via pip and is designed to complete a lint pass on a 100-episode dataset in under 30 seconds, with CI-friendly output formats.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Mininglamp Technology Open-Sources Octo, an Agent Collaboration Network for Enterprises

Mininglamp Technology has officially released Octo, an open-source platform designed to enable structured collaboration between humans and AI agents within organizations. The platform supports private deployment, giving enterprises control over their own data and knowledge. Octo addresses the challenge of isolated AI agents by connecting them through shared collaboration structures called Channels and Threads, where tasks and discussions can be coordinated in real time. AI agents participate as Bots with individual profiles and work histories, while complex tasks can be handled through six distinct collaboration modes including parallel, sequential, and competitive workflows. The project repository is publicly available on GitHub under the Mininglamp-OSS organization.

0 comments Read more at DEV Community