Developer builds private AI home assistant using MQTT to avoid open ports or VPNs

·62 views

A developer wanted to query a home GPU-powered Ollama instance from their phone without exposing their network to the internet. Instead of the usual Docker, reverse proxy, and VPN setup, they used MQTT — a publish/subscribe messaging protocol — as a neutral meeting point between their phone and home PC. Neither the home machine nor the browser client accepts incoming internet connections; all communication is routed through a rented Linux server running a Mosquitto MQTT broker. The entire system spans roughly 1,000 lines of code across a static browser page, a lightweight PC service, and a Python plugin handling tokens and Ollama calls. The developer noted alternatives like Tailscale and Cloudflare Tunnel but preferred MQTT for its simplicity, device-agnostic access via URL, and reliance on broker-level authentication rather than a custom auth layer.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Developer builds Firecracker-based agent sandbox and benchmarks it against Google's design

A developer published a write-up describing their own implementation of an isolated sandbox for AI agents, built on Firecracker microVMs. The project was a response to a sandbox architecture that Google had described publicly. The author measured the performance of their build rather than relying on theoretical claims. The post was shared on Hacker News, where it gathered 8 points and no comments at the time of capture. The full write-up is available on the author's personal blog.

0 comments Read more at Hacker News

ProgrammingDEV Community ·

OpenAI's always-on 'Dots' agents raise privacy and compute cost concerns

At its DevDay event on September 29, OpenAI introduced Dots, agents that run continuously in the cloud, each with its own computer and browser, and connect to many apps through plugins. Users can control when the agent acts alone or asks first, and sensitive actions such as changing passwords remain with the user. Critics such as Signal president Meredith Whittaker argue that deep access to personal data creates privacy and security risks, including exposure of third parties who never consented to an AI reading their messages. Because always-on agents are computationally expensive, OpenAI is also routing some work to a smaller, cheaper model, while companies report AI spending well above their budgets. Experts suggest granting access gradually and treating agent permissions as an architecture decision involving least privilege and cost controls.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Rate limiting Minimal APIs in .NET with the built-in middleware

Public APIs are exposed to clients that may flood an endpoint with requests, whether from a bug, a faulty script or deliberate abuse. Since .NET 7, the framework includes rate limiting middleware, so no third-party package is needed. The article demonstrates a per-IP fixed-window policy that rejects excess requests immediately with a 429 status and a Retry-After header. It also covers applying the policy to specific endpoints, keeping health checks unlimited, and writing integration tests with WebApplicationFactory.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

DejaVu: AI incident agent uses long-term memory to recall past outage fixes

An engineer describes building DejaVu, an AI on-call agent designed to remember every outage a team has resolved. The author argues that most incidents are recurring failure patterns that appear under different service names and error messages, and that general chatbots lack the context to recognize them. DejaVu works through a recall, reason, and retain loop, using the Hindsight open-source memory system to retrieve semantically similar past incidents and feed them to an LLM. In a side-by-side test, the same model reported 20% confidence without memory versus 98% with memory for a simulated cache bug. The author notes that a keyword search would miss many of the 6 cache-related incidents in a 20-incident dataset, which semantic recall can surface.

0 comments Read more at DEV Community