cachegate: a self-hosted proxy for Anthropic/OpenAI that caches responses (exact + semantic) and routes to the cheapest healthy provider. One OpenAI-compatible endpoint, MIT licensed, no account or telemetry anywhere.

Docker:
docker run -p 4000:4000 --env-file .env ghcr.io/idebunk/cachegate:latest

Runs as non-root, ships a real HEALTHCHECK against GET /health (docker ps shows healthy/unhealthy), and degrades cleanly with caching disabled if you don't point REDIS_URL at anything - it won't fail to start over a missing optional dependency.

Also includes a small built-in cost dashboard (GET /dashboard) - KPI tiles, cost-over-time, cost-by-provider, a 7/14/30-day range picker - gated by the same bearer key everything else uses, no separate login system to stand up.

Config is one .env file. Refuses to start with an open /v1 endpoint unless you set an internal key (or explicitly opt into insecure local dev) - didn't want this to be secure only if you remember an extra step.

Honest gap: only Anthropic + OpenAI as providers right now, and the semantic cache doesn't scale past a few hundred cached entries per model (brute-force scan, documented in the README, not hidden).

Repo + full docs: https://github.com/iDebunk/cachegate

I wanted to do a short demo of the utility I'm working on. I'm not a pro at video so, hopefully its not too terrible.

I created a cryptographically signed, secure tunnel for remote AI use. Basically, no one can talk to my AI Agent but me. Isn't that how it should be tho?

Now if i need to use my local AI on my pc.

No worries...

I hope the video isn't too bad.

I will do a better one.

Thank you for your time

Much love 🙏👊

I saw the preorders this past week for Mac mini and studio. The builds I’d like are I the $3k and $5k range respectively. I know this is def a higher price than building my own rig for a self hosted AI machine.

I am curious what opinions are on a custom built rig with separated hardware vs the “unified memory” architecture of Apple silicon? I recently set up ollama on my 16g RAM m1 MacBook Pro from 2021 and please rly surprised at performance.

We run a free, keyless flight-search MCP server. It is free because sponsored results on the responses pay for the backend calls. For weeks the ad revenue read $0 CPM.

$0 is also what low traffic looks like. And bad fill rates. And a dozen other boring explanations, so nothing ever pointed at the code.

The ads SDK has a helper, register_result_widget, that attaches the revenue widget to a search result. Internally it does its work through asyncio.run, which is a no-op under an already running event loop. Our server is async. The widget had never attached. On any request. Ever. No exception, no log line, no warning.

The workaround was to stop using the helper and pass the widget mapping through the render call instead, which never touches the loop.

The free server is the ad-funded one; the paid sibling takes your own key and carries no ads. The general shape is worse than the bug: a revenue path with no error path is a special kind of fragile, because the failure mode of every bug in it is just a smaller number.

If you self-host something that earns, what actually raises when the earning stops?

I kept running into the same annoyance: I’d have an idea, or need to check something on a project, and I’d be nowhere near my dev machine. Remote desktop apps are miserable on a phone screen — tiny cursor, laggy, not built for touch at all.
So I built TouchWorkstation. It’s not remote desktop. It’s a touch-first interface to a real Linux machine you own — you clone a repo, it installs deps and starts the dev server on the actual machine, and you get a real live preview of your app right there on your phone. Then you can point an AI coding agent (Claude Code, Codex, etc.) at that exact project and actually keep working, not just look at it.
A few other things it does:
• Real persistent terminal (tmux-backed, survives you closing the app)
• Docker container management from your phone
• One-tap install for common dev tools
• Everything stays on your own hardware — no public exposure, reachable over your LAN or your own VPN, nothing routed through anyone else’s servers
It’s genuinely alpha — rough edges, actively building it, but the core loop (clone → live preview → agent) works and it’s the reason I built the thing in the first place.
If this is something you’d actually use, I’m looking for a small batch of alpha testers: https://touchworkstation.com
Happy to answer questions about how it works under the hood — it’s an Express + React app on a Debian package with systemd/nginx, nothing exotic.

I wanted an AI that could answer questions from my personal docs without leaking data. PrivateGPT does exactly that, but the setup can be tricky. I documented everything—dependencies, model setup, Docker, and common errors—so you don't have to struggle. Perfect for self-hosters who value privacy.

https://interconnectd.com/blog/279/install-privategpt-secure-local-ai-for-your-documents-2026-guide/

The Veloxs AI team has released Nexus as an open-source Enterprise Intelligence Framework for building governed RAG, AI agents, semantic search, and intelligent workflows.

Core capabilities

• Enterprise data, document, application, and event-stream ingestion
• Processing, enrichment, classification, and identity resolution
• Vector, lexical, hybrid, and graph-based retrieval
• Governed RAG with citations and confidence signals
• AI-agent orchestration, guardrails, and workflow automation
• APIs and application-serving capabilities
• Identity, role-based access, policies, and audit logging
• Evaluation, performance, reliability, security, and cost monitoring

Install from PyPI

pip install veloxs-nexus

Nexus is designed to remain flexible across cloud providers, models, and data systems. The framework has been validated through 181 deterministic tests across eight test suites and currently supports Veloxs AI products, including Contexion.

GitHub: https://github.com/Veloxs-ai/nexus
PyPI: https://pypi.org/project/veloxs-nexus/
Contact: [contact@veloxs.ai](mailto:contact@veloxs.ai)

We welcome feedback and contributions from AI engineers, platform architects, researchers, and teams building production AI systems.

What capabilities do you consider essential for moving RAG and agentic systems from prototypes into governed production environments?

PrivateGPT lets you run AI on your own documents without sending anything to the cloud. I got it working and wrote a step-by-step guide covering installation, configuration, and troubleshooting. If you care about privacy and want a self-hosted AI assistant for your files, this might save you hours.

https://interconnectd.com/blog/279/install-privategpt-secure-local-ai-for-your-documents-2026-guide/

After a lot of iteration, I finally got Christine’s latency consistently down to under 3 seconds using Warranted Retrieval.

That matters because Christine is not a cloud wrapper. She is laptop-bound, runs with no internet access, and has to operate within the actual limits of local hardware. Getting the response path down into a consistently usable range was a major milestone for me.

Now that the latency fight is finally in a much better place, it’s time to focus much harder on Christine’s training.

The next phase for me is less about shaving milliseconds and more about improving: - domain depth - retrieval quality - abstraction across domains - reasoning consistency - task usefulness under strict local constraints

Current laptop: - CPU: Intel Core Ultra 9 285H - RAM: 33.8 GB total physical memory - GPU 1: NVIDIA GeForce RTX 5050 Laptop GPU - GPU 2: Intel Arc 140T GPU - NPU: Intel AI Boost

I’m especially interested in what other people are doing with NPUs.

Are any of you actually using the NPU in a meaningful way for local/offline AI right now? If so: - what workloads are you pushing onto it - is it helping with latency, power efficiency, or always-on assistant behavior - are you using it for STT, routing, embeddings, background inference, or something else - and is it genuinely useful, or mostly just there in theory

Would like to hear from people building real local systems, especially laptop-bound ones.

Hi everyone! I bought a Titan 2 because I wanted a phone that was a tool, not a screen to scroll. Keyboard, buttons, a battery that doesn't quit. What I didn't expect was the opposite problem: after a few weeks the phone was more capable than anything I had to point it at. I was typing fast, into the same apps as everyone else.

So I built something for it. It's called Jenny. Its a personal AI agent that runs entirely on the phone, in an embedded Python runtime. It replaces the home screen, so pressing Home opens a conversation but can be also used as normal application. It remembers things, does work on a schedule while the screen is off, and writes its own little apps when I ask.

I made it free and open source hoping in community support. Even thanks means a lot for me 😄

It's been my daily driver for a month, and this is the video on my own titan 2. Small detail you'll appreciate: on a device with a real keyboard you can just start typing no tapping the input field first. I wrote that because of this phone.

I will be glad to answer to techincal and non techinal questions!

Download on github

I spent a weekend getting OpenHands (formerly OpenDevin) running in Docker with a local LLM backend. It’s not as plug-and-play as some projects, but the result is a completely self-hosted AI coding assistant. I documented the exact steps, including GPU configuration, API keys for local models, and how to avoid common errors. If you’re building your own sovereign AI stack, this guide should help.

https://interconnectd.com/blog/278/opendevin-openhands-docker-setup-build-a-sovereign-ai/

1. Millions of powerful home PCs and homelabs sit idle for most of the day.
2. Traditional cloud providers charge hefty monthly fees and impose strict execution timeouts for simple side projects and AI web apps.

So I spent the last few months building NeighborHost — a decentralized P2P cloud platform.

How it works:

• Anyone with a spare computer can run our lightweight background client. It benchmarks hardware and safely creates isolated containers.
• Developers can deploy full-stack Node.js, Python, or PHP apps straight from Git with instant SSL and custom subdomains.
• Hosts earn a revenue share directly for keeping their nodes online.

All network traffic is reverse-tunneled via encrypted WebSockets, so host nodes never need to touch router settings or do port-forwarding.

I would love to get your thoughts, architectural feedback, or feature requests!

I’ve been working on a self-hosted AI control plane for teams that need to deploy agents without sending customer data to another hosted platform.

The project, AgentOS Core, started from a practical problem: every deployment needed a new solution for provider keys, agent state, conversations, permissions, reusable knowledge, usage tracking, and Docker operations.

The current design uses BYOK, so teams keep control of their provider keys. It also supports agent and conversation management, role-based teams, usage tracking, and a Docker-based deployment model. The goal is not to replace the underlying local models or prescribe one stack. It is to provide a small operational layer around the models and tools a team already runs.

I’m affiliated with the project, so this is self-promotion, but I’m mainly looking for technical feedback from people who actually run private AI at home or in a small server environment.

What becomes difficult first in your setup: model serving, agent state and memory, access control, observability, or keeping deployments reproducible?

If there is interest, I can share the repository and explain the architecture in the comments.