Seeing through the black box, a wave of LLM observability and safety tooling is moving from theory into production. A tool from Nilenso lets you observe your LLM’s context window in real time, sparking a broader push toward context-window observability [1]. Meanwhile, discussions are crystallizing practical observability fields for prompts to standardize what to log and why [2].
Context-window observability is gaining traction as teams chase clarity on latency and bottlenecks [1]. In parallel, people propose practical observability fields for prompts to standardize what to log and why, spanning three layers of data [2].
• AI Review - universal AI code review for any LLM or CI/CD. It runs entirely in your infrastructure, supports any LLM provider — OpenAI, Claude, Gemini, Ollama, or OpenRouter — and integrates with GitHub, GitLab, or Gitea. Setup takes 15–30 minutes and it posts inline comments, summaries, and AI-powered replies in PRs/MRs [3].
• latentcontroladapters - a lightweight Python library that injects latent vectors into hidden states for multi-vector steering of local LLMs. It extracts direction vectors from activation space, applies them during inference, and supports 4‑bit quantization via bitsandbytes; tested on some Qwen models [4].
• Agent Aegis - an autonomous system that stress-tests LLM apps with a team of AI agents, profiling your AI to understand function, generating attacks, scoring vulnerabilities, and suggesting fixes. It’s powered by the Gemini API and invites feedback from early users [5].
Closing thought: observability, safety tooling, and automated red-teaming are converging into an ecosystem that actually helps deploy safer, more reliable LLMs.
References
Show HN: A tool to properly observe your LLM's context window
Show HN post introduces a tool for observing LLM context windows to mitigate context rot via observability
View sourceWhat parameters you actually needed in LLM observability
Discusses required fields, telemetry, and metadata for observing LLMs; mentions Keywords AI; questions automated rating of LLM prompts and experiments
View sourceShow HN: AI Review – Universal AI Code Review for Any LLM or CI/CD
Open-source AI Review turns CI/CD into AI-powered code reviewer, runs locally, supports OpenAI, Claude, Gemini, Ollama, OpenRouter, etc.
View sourceLatent Control Adapters: Multi-vector steering for local LLMs (open Python library for AI safety research, jailbreaking, or whatever)
Open-source Python library injecting latent vectors to steer LLMs; discusses safety, jailbreaking potential, and multi-vector composition tested on Qwen models
View sourceI built an autonomous agent to find and fix security vulnerabilities in LLM apps
Autonomous multi agent system to stress test LLM apps, generate attacks, score vulnerabilities, and guide fixes using Gemini API.
View source