RAG infrastructure built for production. Reproducible metrics, operational stability, adversarial robustness.

Reference Material

Technical talks and implementation patterns from the last 90 days.

#OpenAI #ChatGPT #LLM #RAG #GenerativeAI #AIResearch #PromptEngineering #LangChain #VectorDB #AITrends

OpenAI

Coding and Design with GPT‑5

OpenAI

Surprising developers with GPT‑5

OpenAI

Empowering a Medical Researcher with GPT‑5

Bloomberg Technology

OpenAI COO Brad Lightcap on GPT‑5 (Bloomberg Technology)

Meta Developers

LlamaCon 2025 Keynote — Chris Cox

Meta Developers

Conversation: Mark Zuckerberg & Ali Ghodsi (LlamaCon 2025)

LangChain

AI Summer 2025: Building Production RAG Systems (LangChain)

LangChain

Multimodal RAG: Chat with PDFs (Images & Tables)

LangChain

Building a RAG Agent with Multi‑Retrieval (LangChain)

LlamaIndex

Multi‑strategy RAG with Reflection (LlamaIndex Workflows)

LlamaIndex

Using RAG with LlamaIndex for Large Codebases

LlamaIndex

Implementing RAG with LlamaIndex, Elasticsearch & Mistral

My Channel: Noesis Noema App Guide

Technical walkthroughs of infrastructure setup and integration patterns.

Project Setup & Repo Structure

Retrieval Pipeline Wiring

Agent Orchestration & Benchmark Hooks

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Metrics

Retrieval Quality

mAP@10 +8–15% vs BM25-only

Latency (p95)

−32% with hybrid caching

Cost/1K queries

$0.0X – $0.1X (model/runtime dependent)

Baseline measurements. Reproducible benchmarks in repo.

Our Genesis

Human operators use generative tools to design and iterate infrastructure. Taste and constraints remain human. AI accelerates drafting, refactoring, and validation cycles.

Goal: operational stability. Shorter feedback loops, reproducible builds, measurable system gains.

Workflow Loop

Ideate → Generate → Critique → Integrate. Iteration speed without stability loss.

IdeateGenerateCritiqueIntegrate

Toolchain

Hybrid retrieval, stable runtimes, production-grade orchestration.

vLLMllama.cppLangChainLlamaIndexpgvector

↑ Velocity

Shorter idea‑to‑deploy cycles

↓ Cycle Time

Faster iteration loops

→ Signal

More experiments, clearer feedback

Gateway

Code, issues, and discussion. Infrastructure decisions start here.

DiscordAsk & share

GitHubStar & issues

More...

RAG.Fish Hacker Group

OpenFire Exploit Suite — Debut

Offensive research tooling released for hardening infrastructure before adversarial deployment.

View on GitHub Join Discord

Features

Long-lived infrastructure. Designed for scale, variance, and adversarial inputs.

Focus: retrieval stability, multi-tenant isolation, per-user grounding, production observability.

Interactive Roadmap

Live plan tracking milestones and technical decisions.

Explore

Personalized Retrieval

User/org context boundaries reduce cross-tenant leakage.

Coming soon

Developer Toolkit

Opinionated patterns for RAG agent infrastructure.

Open

Benchmarks

Measurement beyond averages: variance, degradation, recovery.

Open

Playground

Constrained environment with sample data.

Coming soon

Security Research

Offensive tooling for controlled testing. Security as design premise, not afterthought.

Openfire Exploit Suite

Offensive suite targeting Openfire CVEs. Includes Burp plugin, Metasploit modules, Python PoC, and Bash recon tools.

SecurityOffensive ResearchCVE-2023-32315MetasploitBurpPythonBashOSS

View repository

Changelog

2025-09-05
Add Proof metrics section & feature expansion
View release notes →
2025-09-04
Introduce teaser filter tabs & hashtag search chips
View release notes →
2025-09-03
Hero copy refresh + Discord CTA
View release notes →