golang

The most common theme in most of my golang libraries is - “I needed it for another larger project”. Queue a side quest and boom, new library. sandboxed is no different.

sandboxed is a Go virtual filesystem for storing untrusted data as encrypted chunks. The original project driving the need for this is still too early; I’ll talk more about it when I have something substantial to share. I wanted the ability to download arbitrary binary data and protect the system from it.

This might make a few of you go “why the hell would you want to do that?”; well, wait for the other project. Others might point out that a virtual machine would be a great way to isolate the payload. No argument here. I have my reasons for wanting to avoid virtualization here.

So what does sandboxed do? It presents an fs interface for use in golang where a manifest file tracks individual files within the “file system”. Every file is streamed through a per file encryption buffer when writing data to disk. The encryption is less about security of data exfiltration and more about unpredictable scrambling of potentially malicious payloads.

You are still susceptible to malicious payloads at time of reading (or, and please don’t do this, execution) but that can be handled with care towards what you’re handling at any point.

sandboxed is mostly about protecting the host system from at-rest data triggering exploits from scanning / read attacks.

About five years ago (ok, that hurt to type. The days are long but the years are short…) I wrote SafeStop. I had made it to coordinate proper shutdown protocols across multiple services running in a large monolith application.

I decided to renew it, modernize it, and added some dependency features (so service B can shutdown after service A is shutdown, etc).

Another product of my recent golang kick.

Just released structured-parse, a multi-language parser for block labeled LLM output. It’s based on the parser I had built for arkaine.

Here’s what I mean by “block labeleled” output:

Thought: I need to search for information about robots
Action: search
Action Input: {"query": "robots and why they're so cool", "max_results": 5}

This output is not only more human readable, but also easier for LLMs to produce. But there’s a catch - LLMs tend to still introduce nondeterministic volatility towards these outputs; humans are just good about reading through that. structured-parse is a robust parser that can deal with this, allowing LLMs to reliably follow instructions and allowing your code to parse it into clean, typed data structures.

structured-parse is written in Go with exports to TypeScript/JavaScript and Python via WebAssembly; so it’s all golang at its core.

Give it a try!

Just pushed a pretty significant update to docker-harness. It was a tool I created originally to power some of my Docker containerized database tests.

I pulled out the dependencies for each database to modularize it a bit. Each database module (MySQL, PostgreSQL, Redis, Memcached) now has its own go.mod and go.sum, which means you only pull in the dependencies for the databases you actually need.

Also added some dependency updates, taking out old dependencies that weren’t needed anymore, while also ensuring that anything that has been deprecated or abandoned wasn’t being used.

Back when I was working on coppermind at the heyday of GPT3.5’s initial world shattering release, I had… difficulty finding good ways to deal with parsing the stochastic LLM outputs.

I got better at this when I started work on arkaine, eventually developing a pretty useful and reliable parsing pattern.

With an idea that would be best served as a golang app requiring interacting with LLMs I decided to do a quick port of the parser to an idiomatic golang module.

So if you need AI parsing for your golang project, check out go-arkaine-parser.