Keeping pace is impossible, I've just now finally caught up on the "news" and "AI/news" feed in my inbox - at the expense of punting some updates to podcasts and open web tabs. There are a lot of cool developments that others have been working on, and here is where I'd like to spend any free time I get over the holidays.
I have probably bit off more than I can chew with this project.
With Scrypted / Thingino up and stable, the next goal is to replace my janky garage door sensor with a camera-based sensor. With some luck, I can extend this to every camera in the house cheaper and more accurately than Frigate, but let's not get ahead of ourselves.
What I have:
What I need:
So far: I've vibe coded up a script to use OVNIF Snapshot protocol to take a snapshot of the camera every 60 seconds.
We monetize on running the server for private re-training? Private Models? (does it matter)? Or in exchange for DP, we get anonymized data sets back for training?
A continuation of the previous blog post.
Caveat Emptor: This is a public blog of what probably should be a private note.
Tonight turned into an unexpected journey through the rabbit holes of self-hosted technologies. I decided to venture out of my comfort zone, and boy, was it an enlightening experience! Here's a rundown of gotchas:
Podman Machine on OSX has a fun caveat - the disks are not resizable! Circa this post, this behavior seems by design to ensure stability and simplicity.
https://ai.google.dev/gemini-api/docs
https://langfuse.com/ https://langfuse.com/self-hosting/configuration Turn off phone home https://langfuse.com/docs/sdk/python/decorators Next up was Langfuse, a platform for managing and deploying AI models on your own infrastructure. The setup was straightforward, but what fascinated me was the control it offers over model iterations and data privacy. If you're into machine learning but worried about cloud dependencies or data security, Langfuse might just be your knight in shining armor.
Valkey came as a surprise. An open-source alternative to Redis, it provides similar data structures and features. My exploration tonight wasn't just about setting it up (which was a breeze) but also understanding its ecosystem. Valkey offers a community-driven approach to key-value stores, which could be particularly appealing in environments where open-source contributions are paramount.
Now, Minio was where things got tricky yet incredibly educational. It’s an object storage server compatible with Amazon S3, which I installed to get a grip on handling S3-like storage locally. The real learning curve was the crash course in S3 Permissions and policy. Setting up bucket policies, managing access, and ensuring security was like solving a puzzle where every piece affects the other. It was tough but rewarding, and now I have a deeper understanding of S3's permission model, which is invaluable in cloud or self-hosting scenarios.
https://clickhouse.com/docs/en/interfaces/http Clickhouse, a column-oriented database management system, known for its speed in processing analytical queries, was next on my list. However, setting permissions proved to be wonky. Clickhouse's default user permissions are quite permissive, which I initially found odd. After a few hours of configuration, I realized this approach simplifies many operations but requires careful setup to secure a production environment. It's an excellent lesson in balancing convenience with security.
https://colab.research.google.com/github/milvus-io/bootcamp/blob/master/bootcamp/tutorials/quickstart/build_RAG_with_milvus.ipynb#scrollTo=I3FKEZs9A_uz Milvus caught my eye for its capabilities in similarity search and AI applications, particularly with unstructured data. Setting up a local instance was a breeze, and playing with vector space indexing opened up new realms of what's possible with local infrastructure. It's clear why Milvus is gaining traction for real-time analytics and AI-powered searches.
Finally, I dabbled with Python inbox processing. This wasn't about setting up something new but optimizing an existing script to handle my email with better efficiency. Parsing emails, extracting information, and organizing it into a database - it was a practical challenge, blending technology with everyday utility.
Tonight was not just about tech exploration but also about embracing the learning curve with laughter. Each of these technologies presented unique challenges and solutions, teaching me that in the tech world, the journey itself is often more valuable than the destination.
Whether you're considering going self-hosted, exploring AI, or just curious about container management, I hope my little adventure inspires you to dive in yourself. Remember, tech exploration is not just about mastering tools but about enjoying the ride and learning from each step. Here's to many more nights of tech discoveries!
Do let me know if you try any of these out or if you have any other cool self-hosted technologies to explore!
For those unfamiliar, Podman is a daemonless container engine for developing, managing, and running OCI Containers on your Linux System.
https://security.apple.com/blog/private-cloud-compute/
https://developer.nvidia.com/blog/confidential-computing-on-h100-gpus-for-secure-and-trustworthy-ai/