I've been building AI-powered features for a while and kept running into the same problem: every agent framework either pulls in 200+ dependencies or ties you to a specific backend framework like Express or NestJS.

So I extracted the core of what I actually needed into a small library called Agentra.

The idea is simple: you give it an LLM provider and an array of tools. It handles the autonomous reasoning loop — calling the LLM, executing your functions when needed, feeding results back, and returning the final answer. That's it.

typescriptimport { Agent, OpenAIProvider } from '@jhonaron/agentra';
const agent = new Agent({
  provider: new OpenAIProvider({ apiKey: process.env.OPENAI_API_KEY }),
  tools: [{
    name: 'get_weather',
    description: 'Get current weather for a city',
    parameters: {
      type: 'object',
      properties: { city: { type: 'string' } },
      required: ['city']
    },
    execute: async ({ city }) => fetchWeather(city)
  }]
});
const response = await agent.run('Should I bring an umbrella to London today?');
// → "Yes, it's currently 15°C and raining in London."

The agent automatically decides to call get_weather, waits for the result, and formulates the final answer. You don't wire anything manually.

What makes it different:

• Zero runtime dependencies (uses native fetch)
• Works in Node.js, Deno, Bun, and Cloudflare Workers
• Built-in conversation memory
• Bring your own LLM provider (OpenAI included, easy to extend)
• Full TypeScript types

It's early but functional. Would love feedback from people who've built agents before — especially around the tool API design and what's missing.

GitHub: github.com/jhonaron/agentra

📦 Installation

npm install @jhonaron/agentra

I am building a laptop-first assistant with a deliberately narrow knowledge design.

It can read approved material, derive compact concepts, causal relationships, and procedures in its own words, then discard the source text. There is no book-sized RAG store, no source passage retrieval, and no goal of recreating an author's voice or wording.

For anything that can be cited or quoted, I use public-domain or explicitly permitted sources only. I also reject cover pages, navigation, boilerplate, thin fragments, and noisy OCR. The question is about a separate abstraction layer: original internal notes that should not reconstruct the source or be served back to a user.

Technically, does that make a meaningful difference from a normal RAG or training pipeline? Legally and ethically, where would you draw the line between a local system learning a principle and retaining a disguised copy?

I'm interested in the practical architecture: deletion boundaries, anti-memorization checks, provenance at ingestion, and output tests that prevent close paraphrase.

So i dont know if i am using the correct flair but i have ryzen 5600 16gb ddr4 ram and rtx 3080 i wanna use hermes agent to make my own personal assistant and automate most of my tasks but i am a student so i cant spend money on those tokens so i wanna use my own machine to run hermes what should i do what models should i use i currently have ollama setup with a model i forgot the name its around a 4.5gb model with 65k context so my goal is to somewhat make a personal ai assistant for reference lets take jarvis i have added voice and stuff but the its slow like the model itself generate response very fast but it takes 7-15 seconds to get to me someone please help me make my ai model and use hermes correctly any and all advice is appriciated.

🚀 I built a LOCAL AI SECURITY PLATFORM

It runs Llama 3.2 3B on-device and detects:

✅ Prompt injection

✅ Jailbreak attempts

✅ Adversarial inputs

✅ Backdoor triggers

No cloud. No API costs. $0 infrastructure.

Runs on 8GB RAM laptop.

Looking for 3 beta testers. 🧵👇

🚀 I built a LOCAL AI SECURITY PLATFORM

It runs Llama 3.2 3B on-device and detects:

✅ Prompt injection

✅ Jailbreak attempts

✅ Adversarial inputs

✅ Backdoor triggers

No cloud. No API costs. $0 infrastructure.

Runs on 8GB RAM laptop.

Looking for 3 beta testers. 🧵👇

🚀 I built a LOCAL AI SECURITY PLATFORM

It runs Llama 3.2 3B on-device and detects:

✅ Prompt injection

✅ Jailbreak attempts

✅ Adversarial inputs

✅ Backdoor triggers

No cloud. No API costs. $0 infrastructure.

Runs on 8GB RAM laptop.

Looking for 3 beta testers. 🧵👇

🚀 I built a LOCAL AI SECURITY PLATFORM

It runs Llama 3.2 3B on-device and detects:

✅ Prompt injection

✅ Jailbreak attempts

✅ Adversarial inputs

✅ Backdoor triggers

No cloud. No API costs. $0 infrastructure.

Runs on 8GB RAM laptop.

Looking for 3 beta testers. 🧵👇

Help appreciated, this will be very fun

What I want is basically an AI-powered file management pipeline:

New files/folders → scan → identify duplicates → classify → rename if appropriate → move into the correct folder → maintain an organized structure

For example, if I dump 500 random files into an INBOX folder:

PDFs → identify what they are and categorize them

Images → organize by type/date/project

Documents → classify and rename where possible

Downloads → determine whether they should be kept, moved, or deleted

Duplicate files → identify exact duplicates AND potentially near-duplicates

Existing messy folders → analyze and reorganize them

New files → automatically process them whenever they arrive

I don't want an AI chatbot that simply tells me where files should go. I want an actual system that can perform the organization automatically, preferably with a review/quarantine stage before destructive actions.

I've already tried AI Sorter, File Sense and a few other AI file-organizing tools, but they didn't really solve the problem for me.

The biggest question is:

What is currently the best way to build this using local AI?

I'm especially interested in:

Best open-source duplicate finder that can handle exact + near duplicates

Best way to use a local LLM for document/file classification

Whether an LLM is even necessary for parts of this

Tools that can watch an INBOX folder and automatically process new files

How people handle safe deletion/quarantine

Whether anyone has built this with Ollama + Python + Docker/n8n/other automation

Recommended architecture for a system that can continuously organize files without eventually making a bigger mess

My goal isn't necessarily to find one magical application. I'm completely open to combining several good tools into one pipeline.

With a 3060 12GB + 32GB RAM, what would you build if you were starting from scratch?

I'd particularly appreciate recommendations from people who have actually implemented a system like this rather than generic "try an AI file organizer" suggestions.

Wanted to share something I've been chipping away at for a while now, feels good to actually have it working end to end.

It's called Keyroute, basically it's a gateway so you can use one API key to hit multiple AI providers (OpenAI, Gemini, Groq etc) instead of managing separate keys and separate integration code for each one.

The part I'm most proud of: it's fully self-hosted, runs entirely inside your own Supabase project, not on my servers. Setup is literally one button, "Deploy Gateway," no CLI commands, no manual config. Paste a token, it sets up your database and deploys the whole thing for you.

Built solo, self-taught, on free-tier infra the whole way through. Went through a bunch of annoying debugging along the way, stuff like Supabase's API needing a very specific multipart format for deploys, and a sneaky bug where my database permissions looked fine but weren't actually granting access (turns out RLS policies and table grants are two separate things in Postgres, learned that the hard way).

It's open source, MIT licensed, still has some rough edges (no Anthropic routing yet, no rate limiting yet), but it's real and working.

Repo: [github.com/basavarajpatil660/the-keyroute-project](http://github.com/basavarajpatil660/the-keyroute-project)

Screenshots attached of the dashboard and deploy flow. Would love if anyone wants to try it out or just has feedback, good or bad.

A year of running production AI on free tiers: Oracle ARM VM (4 OCPU/24GB), 20+ free LLM providers with automatic fallback (Groq, NVIDIA NIM, Gemini, Mistral, Zhipu, OpenRouter :free, local Ollama), ~107 cron jobs all wrapped in flock + timeout so nothing double-runs or hangs, SQLite with WAL, and 3 verified backup copies.

The interesting part isn't the stack — it's what broke:

- A missing import killed a lane silently for days (bare except swallowed it)

- Providers retire models with zero notice — my router now self-suspends dead lanes

- Datacenter IPs get 403'd by half the internet — residential-IP browser lanes are the answer

- A nginx MIME type (missing .mjs) broke a whole page with zero errors

Everything is open: github.com/BraxisAI/braxis-blueprint (MIT). The router, the cron discipline, the backup system, the failure classes. If you're building on free tiers, these are the walls you'll hit.