LLMs

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How LLMs work | Practical Leaders

 🤖LLM

How to Build an Agentic RAG with RubyLLM and Rails

 ✍️Prompt Engineering  Content type: Blog
panasiti.me··Hacker News

LLM are universal simulators

 🤖LLM

manavgup/context-analyzer: Context window usage analyzer for Claude Code — MCP server + interactive dashboard

 🔌MCP  Content type: Code
github.com··Hacker News

Prompt Injection in RAG Agentic Systems

 🛡️AI Security
ulad.net··Hacker News

Unlocking dependable responses with Gemini Enterprise Agent Platform’s Agentic RAG

 🤖Agent Architecture  Content type: Blog
research.google·
Less-relevant results

our workplace LLM mass delusion

 ✍️Prompt Engineering  Content type: Blog

Initial impressions of Claude Fable 5

 🤖LLM
simonwillison.net··Hacker News

Melanie Mitchell: What We Get Wrong About AI

 🤖LLM

Research Proposal: Decoupled RISC-LLM Architectures via Circadian Synaptic Consolidation

 🤖LLM
aermia.com··Hacker News

ashp15205/guardian-runtime: A zero-latency, local-first runtime firewall for LLMs. Intercept every prompt and response locally to stop data leaks and runaway token costs.

 🤖LLM  Content type: Code
github.com··Hacker News

Larger context windows and configurable reasoning levels for GitHub Copilot - GitHub Changelog

 🪟Context Windows  Content type: Blog
github.blog··Hacker News

Tokenminning: Because Tokenmaxxing Is a Bad Idea

 ✍️Prompt Engineering

Show HN: Lore – LLM proxy for coding agent context and memory management

 🤖LLM
withlore.ai··Hacker News

Meet Hades: The malware that lies to AI security agents

 ✍️Prompt Engineering  Content type: News

Dense Contexts Are Hard Contexts: Lexical Density Limits Effective Context in LLMs

 🤖LLM  Content type: Academic
arxiv.org··Hacker News

hashwnath/KMCP: Open-source MCP server for your docs. Zero LLM at query time. docker compose up and go.

 🔌MCP  Content type: Code
github.com··Hacker News

How we fight GPU scarcity without compromise

 🤖LLM  Content type: Blog
equixly.com··Hacker News

Agentic search - retrieval, harness, or model?

 ✍️Prompt Engineering  Content type: Blog

Show HN: LLM memory without context bleed; 100% precision vs. <10% vector search

 ✍️Prompt Engineering

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