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Research Paper#Software Engineering, Microservices, High Concurrency🔬 ResearchAnalyzed: Jan 3, 2026 06:20

Securing High-Concurrency Ticket Sales with Microservices

Published:Dec 31, 2025 16:05
1 min read
ArXiv

Analysis

This paper addresses a practical problem: handling high concurrency in a railway ticketing system, especially during peak times. It proposes a microservice architecture and security measures to improve stability, data consistency, and response times. The focus on real-world application and the use of established technologies like Spring Cloud makes it relevant.
Reference

“The system design prioritizes security and stability, while also focusing on high performance, and achieves these goals through a carefully designed architecture and the integration of multiple middleware components.”

PermalinkArXiv
Software Development#Privacy & Security👥 CommunityAnalyzed: Jan 3, 2026 16:42

Local Privacy Firewall - Blocks PII and Secrets Before LLMs See Them

Published:Dec 9, 2025 16:10
1 min read
Hacker News

Analysis

This Hacker News article describes a Chrome extension designed to protect user privacy when interacting with large language models (LLMs) like ChatGPT and Claude. The extension acts as a local middleware, scrubbing Personally Identifiable Information (PII) and secrets from prompts before they are sent to the LLM. The solution uses a combination of regex and a local BERT model (via a Python FastAPI backend) for detection. The project is in early stages, with the developer seeking feedback on UX, detection quality, and the local-agent approach. The roadmap includes potentially moving the inference to the browser using WASM for improved performance and reduced friction.
Reference

“The Problem: I need the reasoning capabilities of cloud models (GPT/Claude/Gemini), but I can't trust myself not to accidentally leak PII or secrets.”

PermalinkHacker News
Research#llm📝 BlogAnalyzed: Dec 29, 2025 07:29

Patterns and Middleware for LLM Applications with Kyle Roche - #659

Published:Dec 11, 2023 23:15
1 min read
Practical AI

Analysis

This article from Practical AI discusses emerging patterns and middleware for developing Large Language Model (LLM) applications. It features an interview with Kyle Roche, CEO of Griptape, focusing on concepts like off-prompt data retrieval and pipeline workflows. The article highlights Griptape, an open-source Python middleware, and its features such as drivers, memory management, and rule sets. It also addresses customer concerns regarding privacy, retraining, and data sovereignty, and mentions use cases leveraging role-based retrieval. The content provides a good overview of the current landscape of LLM application development and the tools available.
Reference

“We dive into the emerging patterns for developing LLM applications, such as off prompt data—which allows data retrieval without compromising the chain of thought within language models—and pipelines, which are sequential tasks that are given to LLMs that can involve different models for each task or step in the pipeline.”

PermalinkPractical AI