iamspoilt

Sharing this with the community because I have found self-maintained fully sandboxed environments for CLI very liberating, alleviating my security concerns of bricking my host machines while providing great persistence. Having it as a lightweight self-contained native and reproducible headless VM for development workflows is quite refreshing, and something which I don't find documented enough.

Arguably, this workflow is more ergonomic than Docker for iterative dev work because the VM’s disk persists like a normal machine, so you just install things and move on without thinking about image layers, committing containers, or rebuilding to make changes stick.

I recently wrote a blog post on using Tart for Macs for something similar that docker is doing here but with better persistence and control. My take is that the Tart approach is superior to this as it gives you a full dev machine with a single command line that allows agents to access files on host, install packages, maintain the vm and do whatever they want to do without compromising the host OS.

https://www.mrafayaleem.com/blog/sandboxing-claude-cli-with-...

Quoting Tailscale: This is our very Canadian apology: sorry you stepped on our toes. The attack didn’t exploit Tailscale, and Tailscale didn’t cause the compromise. But, we didn't stop it. Next time, we will.

Have you looked at qmd by any chance? Thoughts on that for sqlite vs qmd in this case? https://github.com/tobi/qmd

100% this! This is why I explicitly mentioned in my CLAUDE files to generously use Python for any calculations using the virtualenv. It was also my first time using Claude for tax related calculations so I did cross-check it against an online ACB calculator https://www.adjustedcostbase.ca

I couldn't find an equivalent of this for Canadians but thanks for the tip.

The copy-pasting of data from my markdowns to the tax software was the hardest path for me as well.

> I use structured JSON documents precisely for this reason, because with a well-defined schema the LLM can write TypeScript functions that must compile.

The intention of this is to reduce hallucination on information extraction, right?

Also, how do you convert your docs / information into JSON documents?

An opinionated workflow showcasing how I used Claude and Obsidian together to help with my personal tax filing in Canada

Is there an app for this I can download on my Apple TV?

As a counter argument to the kubectl example made in the article, I found the k8s MCP (https://github.com/containers/kubernetes-mcp-server) to be particularly usefuly in trying to restrict LLM access to certain tools such as exec and delete tools, something which is not doable out of box if you use the kubectl CLI (unless you use the --as or --as-group flags and don't tell the LLM what user/usergroup those are).

I have used the kk8s MCP directly inside Github Copilot Chat in VSCode and restricted the write tools in the Configure Tools prompt. With a pseudo protocol established via this MCP and the IDE integration, I find it much safer to prompt the LLM into debugging a live K8s cluster vs. without having any such primitives.

So I don't see why MCPs are or should be dead.

Orchestera (https://orchestera.com/) - Fully managed Apache Spark clusters in your own AWS account with no additonal compute markups, unlike EMR and Databricks.

Currently implemented the following:

- Automated scale in / scale out of nodes for Spark executors and drivers via Karpenter

- Jupyter notebook integration that works as a Spark driver for quick iteration and prototyping

- A simple JSON based IAM permissions managementent via AWS Parameter Store

Work-in-progress this month:

- Jupyterhub based Spark notebook provisioning

- Spark History Server

- Spark History Server MCP support with chat interface to support Spark pipeline debugging and diagnostics

Open to feedback and connecting. Docs at https://docs.orchestera.com/

Question: Why aren't we seeing similar reports for Anthropic?

Spent my 2025 building https://orchestera.com as a side project. The premise is simple - to give Data Engineers and Data Scientists the opportunity to spin up Apache Spark clusters on Amazon EKS without them needing to know all the infrastructure details, how Spark works on Kubernetes, auto-scaling etc.

The platform I am building allows users to launch Spark on Kubernetes in their own AWS account without adding any markup costs to the CPU/Memory on EC2 instances. For example, AWS EMR offering adds a 25% markup cost on top of the EC2 instance pricing. Databricks markup is even higher ranging anywhere from 30% to 100% markup.

To learn more, check out the docs for the project at https://docs.orchestera.com/

It's coming back according to Sam https://www.reddit.com/r/ChatGPT/comments/1mkae1l/gpt5_ama_w...

Manual, would do it once a month or so. Hasn't broken ever since I have been running HA. I run the full Haas OS btw.

I second that. Home Assistant "just works". I have had it running on this cheap used HP EliteDesk 705 G3 Mini Desktop for more than 4 years now without a hiccup and barely any maintenance or hygeing work on it. Just sitting in my tv stand and doing it's work.

https://homeautomation.substack.com/p/setting-up-home-assist...

Looking at your projects, seems like you still have the hacker going in you! Saw Home Assistant one! Kudos!

Having a computer engineering background, what motivated you to become a pilot and switch careers?

I have been super happy downloading the free adguard dns profile on my iphone and blocking ads across the entire spectrum. Makes reading on internet so much better. Here is the link if anyone is wondering.

https://adguard-dns.io/en/public-dns.html