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🔥 SandeepVashishtha / Eventra - Eventra is a comprehensive event management system that empo

GitHub热门项目 | Eventra is a comprehensive event management system that empowers organizers to create, manage, and track events seamlessly. Built with a modern tech stack featuring React frontend and Spring Boot backend, Eventra provides everything needed to run successful events from creation to post-event analytics. | Stars: 106 | 3 stars today | 语言: JavaScript

2026-05-31 原文 →
AI 资讯

🚀 Building an open-source email blast tool — free, self-hosted, no Mailchimp needed. Looking for contributors to help add: 📊 Open & click tracking 🐳 Docker support All issues are open. Jump in 👇 https://github.com/nikhilt101/email-blast-tool

GitHub - nikhilt101/email-blast-tool: Open source HTML email sender tool using CSV/XLSX + Gmail SMTP · GitHub Open source HTML email sender tool using CSV/XLSX + Gmail SMTP - nikhilt101/email-blast-tool github.com

2026-05-31 原文 →
AI 资讯

Hermes Agent's Brain: How Its Skills & Memory System Actually Works

This is a submission for the Hermes Agent Challenge : Write About Hermes Agent Most AI agents have a dirty secret: they forget everything the moment the session ends. You explain your project once. Then again next time. And again. The agent never gets better at your workflow — it just stays a general-purpose tool that happens to be smart. Hermes Agent is built differently. It ships with two systems that together form something closer to a genuine long-term memory: a Skills System and a Persistent Memory layer. This post digs into how they actually work — not the marketing summary, but the mechanics. The Problem With Stateless Agents Before getting into Hermes, it's worth understanding what problem this solves. Standard LLM-based agents operate inside a context window. Everything the agent knows during a session lives in that window. When the session ends, it's gone. The next time you open a conversation, you're talking to an agent with no memory of you, your codebase, your preferences, or the workflows you've developed together. Some tools patch this with naive "memory" — they dump a text blob of past conversations into the system prompt. This works up to a point, but it's not selective, it gets expensive as context grows, and it doesn't help the agent get better at tasks — just recall facts. Hermes takes a different approach with two distinct systems serving different purposes. System 1: The Skills System (Procedural Memory) Skills in Hermes aren't plugins you install. They're on-demand knowledge documents — markdown files the agent loads when it needs them, and more importantly, creates on its own when it discovers something worth remembering. The SKILL.md Format Every skill is a structured markdown file with a YAML frontmatter header: --- name : deploy-runbook description : Our deployment runbook — services, rollback, Slack channels version : 1.0.0 metadata : hermes : tags : [ deployment , runbook , internal ] requires_toolsets : [ terminal ] --- # Deploy Runbook

2026-05-31 原文 →
AI 资讯

How RAGScope Knows Which Chunks Your LLM Actually Used

How RAGScope Knows Which Chunks Your LLM Actually Used Your retriever fetched 10 chunks. Your LLM only used 3. RAGScope shows a precision score of 30 out of 100. The question every new user asks: how does it know? There is no OpenTelemetry attribute that says "this chunk was in the context window." RAGScope infers it — and the way it does this is the most consequential piece of engineering in the whole tool. There Is No "In Context" Attribute in OTel The OpenTelemetry semantic conventions for generative AI ( gen_ai.* ) define attributes for model, input/output tokens, and retrieved documents. They do not define anything like gen_ai.chunk.reached_llm or gen_ai.retrieval.used_document_ids . When your RETRIEVER span fires, you get a list of documents. When your LLM span fires, you get a prompt and a completion. The two spans are connected by a parent-child trace relationship — but there is no attribute that maps which retrieved documents appear in which prompt. This gap matters. A reranker might drop 7 of your 10 chunks. Your application code might apply a token budget and truncate 4 more. From the trace alone, you cannot tell. RAGScope needs this information to compute the precision sub-score — the highest-weighted metric at 40% of the overall score. Getting it wrong would make precision meaningless. The Substring Match — How assembleContext Works RAGScope's answer is in src/enrichment/pipeline.ts , in a function called assembleContext : function assembleContext ( chunks : RagChunk [], llmSpans : ParsedSpan []): RagChunk [] { const llmPrompts = llmSpans . map (( s ) => s . prompt ). filter (( p ): p is string => !! p ); if ( llmPrompts . length === 0 ) return chunks ; let position = 0 ; return chunks . map (( chunk ) => { if ( ! chunk . content ) return chunk ; const inContext = llmPrompts . some (( p ) => p . includes ( chunk . content ! )); if ( inContext ) { return { ... chunk , inContext : true , contextPosition : position ++ }; } return { ... chunk , inContext :

2026-05-31 原文 →
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