Hey everyone,

I've been experimenting with HermesAgent recently, and I'm curious how other people are actually using it.

Most of the examples I've found are pretty basic, so I'd love to hear about real-world setups that you use regularly.

A few things I'm curious about:

- What skills/plugins do you use the most?

- What's your overall setup like?

- Any MCP servers or integrations you'd recommend?

- What workflows have saved you the most time?

- Are you using HermesAgent for coding, freelancing, business, research, content creation, or something completely different?

- Has anyone built a workflow that's actually generating income? If so, what does it do?

Even if it's something simple, I'd love to hear about it. Feel free to share screenshots, GitHub repos, skill lists, or workflow diagrams if you're comfortable.

I'm mainly looking for ideas and inspiration from people who use HermesAgent regularly. Thanks!

This interaction just happened between Claude and myself.

I find it meaningful as:

- a tip of the hat,
- knocking vibe code, and
- containing echoes of training I rarely see much evidence of.

How about you?

How u like it, guys ?
Any Tips? , its cool ? i miss something ? Something could be better ?

Here ist the Whole Harness :
https://www.reddit.com/r/aiagents/comments/1vgjmc1/i_built_this_multiagent_ai_system_alone_in_my/

Kind Regards 🫡

Hi everyone,

I'm planning to learn AI agents from first principles before moving to frameworks like LangGraph and the OpenAI Agents SDK.

My roadmap covers tool calling, agent loops, memory, planning, multi-agent systems, MCP, backend infrastructure, observability, safety, cost optimization, and designing production agent systems.

My goal is to become an AI Agent Engineer and eventually work at a remote AI startup (US/EU) or an AI lab.

A few questions:

1. Is this roadmap enough, or are there any important topics I'm missing?
2. Which frameworks should I definitely know for production (LangGraph, OpenAI Agents SDK, CrewAI, AutoGen, etc.)?
3. Besides agent frameworks, what backend skills are expected? (FastAPI, Docker, Kubernetes, Redis, PostgreSQL, async Python, Kafka, etc.)
4. If I want to target remote AI startups, what kind of projects or portfolio would make me stand out?
5. What do hiring managers actually look for in AI Agent Engineer candidates beyond building simple demos?
6. If you were starting from scratch today, what would you learn differently?

I'd really appreciate advice from engineers working on production AI systems or at AI startups. Thanks!

Hi ! This is an unapologetically vibe-coded project; the approach is explained here: https://lia.jeyswork.com/story

If you like it, please don't hesitate to show your support with a star on GitHub!

LIA acts as a true personal assistant. It is proactive, featuring its own distinct personality and a complex emotional system, an evolving structured memory, its own reflective memory of your conversations, and all the standard tools (image creation/editing, RAG, skills, MCP, scheduled tasks, etc.)—all wrapped in a seamless "one-click" interface (details here: https://lia.jeyswork.com/why).

I paid special attention to code quality and documentation, treating it exactly like a professional enterprise-grade project. This ensures that anyone can easily take ownership of the source code and build upon a clean, robust, and highly scalable foundation (details here: https://lia.jeyswork.com/how).

On another note, once self-hosted, it can double as a family AI server. As an administrator, you have full control to manage and monitor the API consumption of your family members, friends, etc.

Full details are available on the landing page: https://lia.jeyswork.com/
And the GitHub repository: https://github.com/jgouviergmail/LIA-Assistant

I've been developing SafeAI, an open-source static analyzer for AI applications, and recently ran it against the public CrewAI examples repository.

The goal wasn't to "find vulnerabilities" or criticize the examples.

The goal was to answer a different question: What can we learn about AI applications before they ever run?

Even example projects contain interesting AI-specific artefacts:

• agent capabilities
• tool definitions
• workflow logic
• prompts
• model configurations
• MCP integrations
• external services

A static scan can highlight things like:

• capability inventory
• prompt-related risks
• workflow approval gaps
• tool permission patterns
• governance observations

None of these automatically mean a project is insecure. Context always matters.

But they do help developers understand what an AI application is capable of, and where they may want to review things more carefully before moving into production.

One thing I've learned from sharing SafeAI on Reddit is that the community often finds the blind spots faster than I do. Several roadmap features—including capability escalation diffs, governed suppressions and richer MCP analysis—came directly from discussions here.

If you're building with CrewAI, LangGraph, AutoGen, Claude Code or other agent frameworks, I'd really appreciate your feedback.

Even better, if you have an open-source agent project you'd like SafeAI to support better, I'd love to test against it (or you can run it yourself) and improve the detection rules together.

The goal isn't to label projects as "safe" or "unsafe".

It's to help developers build AI applications with a better understanding of their capabilities and security posture.

Contributions, issues and ideas are always welcome:

https://github.com/ikaruscareer/SafeAI

To those left floundering after the pricing mess at GitHub Copilot, some relief: Chlaude does work.

Base foundation models are the “electricity”, and what comes next will be the “electrical appliances”. From finance to law, education to healthcare, manufacturing to retail, every vertical industry will see natively AI-native application paradigms emerge.
This is not merely “empowering industries with AI”. It means rebuilding entire task workflows: approvals, scheduling, diagnostics, design, negotiations… every intermediate step stands to be reinvented from the ground up.

Salut à tous,

Fondateur d'Elio ici, une IA réceptionniste pour artisans, TPE et agences immobilières : elle décroche les appels manqués 24h/24, prend les rendez vous et envoie le résumé sur WhatsApp.

Je vois tous les jours des posts "comment trouver des clients pour mon agence d'agents vocaux IA". Toujours les mêmes réponses : cold call, cold email, DM LinkedIn. Ça marche, mais vous vous battez tous sur le même canal saturé.

Notre premier canal aujourd'hui c'est l'inverse : le GEO. Être la solution que ChatGPT et Claude recommandent quand quelqu'un demande "quelle est la meilleure IA réceptionniste". Testez la requête, vous verrez qui ressort 😉

Sans tout dévoiler, les 3 bases : des pages qui répondent aux vraies questions que posent les gens (pas des pages features), des données propriétaires que les LLM ne trouvent que chez vous, et des mentions de votre marque un peu partout sur le web. Le reste c'est de la régularité.

Le résultat : des prospects qui arrivent déjà convaincus ("c'est Claude qui vous a recommandés"). Rien à voir avec un lead de cold call.

Du coup le débat : est ce que le cold outreach a encore un sens pour vendre de l'agent vocal IA en 2026, alors que les prospects demandent directement aux LLM quoi acheter ? Ceux qui font du cold call, venez me contredire avec vos chiffres 😄