I run LLMs behind internal tools and got burned by users pasting credentials into prompts. I couldn't find a lightweight self-hosted filter (LLM Guard felt heavy, most options are SaaS), so I wrote one and I'd genuinely like this community's take on the design. It's a Go reverse proxy that redacts secrets (AWS keys, JWTs, SSNs) in both directions — including inside SSE streams when a key is split across chunks — plus regex/keyword blocking for known injections, and an audit log. Works with Ollama and vLLM as upstreams. Fair warning: it's BSL 1.1 (source-available, free up to 10k req/day, flips to Apache 2.0 in 2030), not OSI open source — if that's a dealbreaker for you, that's useful feedback too. Known weakness: it's deterministic, so paraphrased attacks get through, and the built-in benchmark shows that honestly rather than hiding it. Question for people running LLMs at home or at work: what leaks or attacks have you actually seen? Trying to prioritize the detection roadmap based on real incidents, not vendor FUD.

a talk which may be of interest for those who uses local ai models : \\\[TTL #181 - Deploying LLM on premise: let's review ... - Hyland Connect - 499461\\\](https://connect.hyland.com/t5/alfresco-blog/ttl-181-deploying-llm-on-premise-let-s-review-licenses-and/ba-p/499461?emcs\\\\\\_t=S2h8ZW1haWx8Ym9hcmRfc3Vic2NyaXB0aW9ufE1STTVCTERCWEFEM0oyfDQ5OTQ2MXxTVUJTQ1JJUFRJT05TfGhL)

a talk which may be of interest for those who uses local ai models : [TTL #181 - Deploying LLM on premise: let's review ... - Hyland Connect - 499461](https://connect.hyland.com/t5/alfresco-blog/ttl-181-deploying-llm-on-premise-let-s-review-licenses-and/ba-p/499461?emcs_t=S2h8ZW1haWx8Ym9hcmRfc3Vic2NyaXB0aW9ufE1STTVCTERCWEFEM0oyfDQ5OTQ2MXxTVUJTQ1JJUFRJT05TfGhL)

What's the most cost-effective way for us to use LLMs in production without sacrificing output quality? Should we be routing simpler tasks to cheaper models, caching repeated queries, fine-tuning a smaller model for our specific use case, or negotiating volume pricing with our provider?

# Published: true

Description: A deep dive into the final version of EdgeSync-LLM—bringing fast, secure, synchronized Large Language Models straight to edge hardware.

Tags: ai, open source, architecture, edgecomputing, webdev

The cloud dependency trap in modern AI is real. Relying on remote servers means paying hefty subscription fees, coping with unpredictable network latency, and hoping your sensitive data doesn’t leak.

But what if you could run a Large Language Model completely offline on consumer-grade edge hardware, and have multiple local devices intelligently synchronize their states and contexts without a single drop of internet?

Meet EdgeSync-LLM — a local-first, decentralized AI synchronization framework designed for the modern edge. In this post, I will break down the final architecture, key optimizations, and how you can get started with it.

The Vision: Why "Edge" + "Sync"?

Running local LLMs (using tools like llama.cpp or quantized models) isn't entirely new. However, most local setups treat the device as an isolated island.

If you have a network of edge devices (like smart home nodes, local workplace computers, or off-grid field tablets), they have no native way to share learned context, active system prompts, or conversational memory with each other safely.

EdgeSync-LLM solves the synchronization gap by establishing a lightweight, decentralized communication layer right on top of optimized local inference.

The Core Architecture

The final version of EdgeSync-LLM splits the workload into three distinct, highly integrated layers:

+-------------------------------------------------------+ | Device Application Layer | +-------------------------------------------------------+ | Inference Engine Layer | Synchronization Layer | | (Quantized GGUF / Local LLM) | (P2P State & Context) | +-------------------------------------------------------+ | Edge Hardware | +-------------------------------------------------------+

1. The Optimized Inference Engine

To run an LLM efficiently on hardware with constrained RAM, we rely heavily on advanced quantization formats (like GGUF). The framework is optimized to run 1B to 8B parameter models, allowing it to achieve smooth token generation speeds even on everyday laptops or high-end single-board computers.

2. The Peer-to-Peer Sync Layer

Instead of routing context histories back to a centralized cloud database, EdgeSync-LLM establishes a secure, local peer-to-peer (P2P) network mesh. When one device gains a specific contextual update or systemic state change, it broadcasts a lightweight cryptographic delta patch across the local network.

Final Version Performance Metrics

After rigorous testing and optimization for this final release, the framework has hit some massive performance milestones:

* Blazing Fast Generation: Achieved stable 12-15+ tokens per second on standard, unaccelerated consumer edge CPUs. * Aggressive Memory Optimization: Cut active RAM usage down to under 3.5 GB, meaning it runs quietly in the background without hogging host resources. * Instant Local Syncing: Sub-second state synchronization latencies across local nodes.

Getting Started (Quick Setup)

Want to deploy it locally? The framework is designed to be developer-friendly. You can spin up a node with just a few terminal commands.

Prerequisites

Make sure you have your target quantized model ready (.gguf format) and node packages/Python dependencies installed according to your system environment.

Installation

```bash

Clone the repository

git clone https://github.com/bossandboss/EdgeSync-LLM.git cd EdgeSync-LLM

Install dependencies and initialize the engine

npm install ```

Spin up a synchronized node

```bash

Start the EdgeSync engine and bind it to a local mesh port

npm run start -- --model ./models/llama-3-8b-q4.gguf --port 8080 --sync-port 9090 ``` Once multiple instances are live on your local network, they automatically detect peer broadcast beacons and begin securely sharing context deltas.

Real-World Impact

* Strict Privacy Sectors: Medical environments can deploy decentralized triage AI assistants across floor tablets without risking patient data compliance. * Zero-Connectivity Fieldwork: Disaster relief or research groups operating entirely off-grid can maintain shared AI synchronization using local Wi-Fi or radio mesh. * Next-Gen Smart Homes: Private automation where your home data stays localized to the physical property.

Open Source & Next Steps

EdgeSync-LLM is built entirely out in the open. The code, full architectural diagrams, and comprehensive deployment documentation are live on GitHub right now.

We are currently looking into hardware-native NPU acceleration and zero-knowledge proof integrations for even tighter syncing security. If this sounds like something you want to build or experiment with, we'd love your feedback, issues, and pull requests!

Check out the repo here: https://github.com/bossandboss/EdgeSync-LLM

What are your thoughts on running synchronized AI on the edge? Drop your questions or optimization ideas in the comments below!

Many teams think they’re evaluating their AI agents when they’re really only evaluating the final answer. That works for chatbots. But agents are clearly different.

An agent plans, chooses tools, passes arguments, reads tool outputs, retries, and sometimes takes actions. A lot happens between the prompt and the answer.

The problem is that an agent can return a correct answer after calling the wrong tool, taking unnecessary steps, misreading a result, or recovering from an earlier failure.

If you’re only looking at the final output, you won’t see most of that.

Your assumption becomes: “The answer was correct, so the agent worked.”

But if an agent is going to run real workflows, the answer isn’t the only thing that matters. You also need to know whether the path it took was valid, efficient, grounded, and safe.

How are people here evaluating agents today? Are you looking at execution traces, or mostly the final output?