📦 METS Standards

Structural Metadata, Digital Library Architecture, Package Formats, Content Management

Charting Time – An Interview with Robert Morris
blogs.loc.gov·21h
📚MARC Records
Exploring Common Governance for Open Knowledge Infrastructures and Outputs
openfuture.eu·2d
📇Dublin Core
Pure Storage: New systems, new software and new markets
heise.de·1d
💿WORM Storage
What is OpenTelemetry? [not in a nutshell] :)
signoz.io·18h·
Discuss: Hacker News, r/programming
👁️System Observability
BBC and Heise boldly exploring the Fediverse frontier
stylestitches.substack.com·13h·
Discuss: Substack
📡RSS Readers
Adaptive, simplified design system colors
gfor.rest·4h·
Discuss: Lobsters, Hacker News
📖Nordic Typography
Markdown (2004)
daringfireball.net·4d·
Discuss: Hacker News
🏺ZIP Archaeology
Intelligent Document Creation: Generate PDFs with AI in pdfAssistant.ai
pdfa.org·21h
📄Document Digitization
SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
arxiv.org·1d
🔍BitFunnel
Lessons Learned Shipping .NET Apps with Docker, Alpine, and Kubernetes
dev.to·1h·
Discuss: DEV
📦Container Security
TITAN: Query-Token based Domain Adaptive Adversarial Learning
arxiv.org·7h
📊Learned Metrics
Azure Fundamentals: Microsoft.VMwareCloudSimple
dev.to·8h·
Discuss: DEV
🏠Homelab Orchestration
HarmonyOS5-NewsAPP-NewsDetailPage
dev.to·1d·
Discuss: DEV
📡RSS Extensions
Show HN: Pocket2Linkding – Migrate from Mozilla Pocket to Linkding
github.com·18h·
Discuss: Hacker News
🏺ZIP Archaeology
Enterprise Large Language Model Evaluation Benchmark
arxiv.org·1d
⚙️Compression Benchmarking
Single Core Hundred Thousand Concurrency（1750947668443300）
dev.to·21h·
Discuss: DEV
🌊Stream Processing
Machine Learning Fundamentals: accuracy with python
dev.to·2d·
Discuss: DEV
👁️Observatory Systems
DigitalOcean Fundamentals: API
dev.to·2d·
Discuss: DEV
🔌Interface Evolution
Advanced Path of Network Programming Deep Exploration from TCP to Application Layer Protocols（1750830996145900）
dev.to·2d·
Discuss: DEV
🌐Network Protocols
Define-ML: An Approach to Ideate Machine Learning-Enabled Systems
arxiv.org·1d
🧠Machine Learning