vektormemory

I unplug my headphones and I shit in silence

groovy man...

I don't push the play anymore, I sit in silence

I just enjoy the silence...

I feel like all spiritual man like, "Wow."

shitting in silence

see you on the playa man

Just sit in silence for a moment and see what happens. You might actually like it.

Like wow man, groovy

Its me, its been awhile - are you sitting in silence?

It me, its been awhile

Dreamy man, like silence wow man

I'm sitting in silence man wow

Wow man wow man

Bunch of fukn clowns... Lol

Just sit in silence for a moment and see what happens. You might actually like it.

Wow man really fukn inspirational lol

bunch of fukn idiots...

Your post doesn't even have any info or make sense? Jfc I hate the internet... Lol

Most people who try to let an AI agent operate on a real VPS end up in a few places.

They either lock it down so hard the agent can barely do anything useful (read-only, no writes, ask a human to copy-paste the command back), or they hand over a standing SSH key and just hope nothing gets hacked. Or the worst scenario, an agentic mess of deletes and rewrites of sensitive data without any backups taken.

Neither of those counts as running an autonomous agent. One is a chatbot with a read-only window into your server. The other is a loaded gun with the safety off, cowboy style.

There is another way, and it’s the only one that actually holds up once you’re doing real work on a real system. We run it daily against our own production infrastructure. Here’s exactly how it works, with a real situation from live work we did today, not a hypothetical.

The three things that have to be true at once

An agent operating on your infrastructure needs to do three things at the same time, or the whole setup falls apart.

It has to act, not just suggest. If every command gets copy-pasted by a human into a separate terminal, you’ve built a slower way of doing the work yourself.

It has to fail safely. If a write command goes wrong, there needs to be a way back that doesn’t involve your users telling you the site is down.

And it has to remember. If the agent forgets every fix and every incident the moment a session ends, it re-solves the same problems from scratch, over and over. That quietly costs more time than doing it by hand.

Most setups manage one of these. Getting all three right at once is the actual hard part, and it’s why “just give the AI a terminal” either stays useless or eventually causes a real incident.

How we actually do it

Every command gets classified before it runs.

Read-only work, checking logs, listing files, checking a process, runs immediately with no friction.

Anything that writes to disk, restarts a service, or installs a package comes back as a pending action with the exact command shown, and nothing executes until it’s approved by the sloppy human.

Some might say that's painful as they want the agent to loop forever; it is 100% necessary to stop a Chernobyl-agentic meltdown of your VPS.

Every write gets its own approval, not a session-wide green light. That’s the real difference between “the agent has SSH access” and “the agent proposes commands a human confirms,” and it stops mattering as an abstraction the first time something almost goes wrong.

Nothing gets touched without a backup first. Before any file changes, a copy gets taken automatically. That single habit is the reason you can let an agent make a real change with actual confidence instead of crossed fingers. If it screws up, which it eventually will from bad human context, bloat, or just loop errors, you go back to the previous saved backup.

Keys don’t live on the server being administered. Credentials sit in an encrypted vault and get pulled only for the exact moment they’re needed, then get destroyed right after: written, used, shredded, in one step, so there’s never a window where an interrupted session leaves a live key sitting on disk. If a managed server is ever compromised, there’s no standing key on it for an attacker to find and reuse somewhere else.

And it remembers. Not within a single chat session, but across days and across whichever AI tool you happened to have open. A fix made three months ago in a different tool is still recallable today, because the memory isn’t tied to any one app’s chat window. The agent has real-time access to recall thousands of saved memories with past actions.

A weekend project about LLM guardrails, copyright, and why proving your code is yours turned out to be a lot more complex than it should be.

This is a firsthand look into an experimental weekend project, not legal advice. If any of this matters to your actual business, talk to an actual lawyer in your jurisdiction. I use multiple LLMs daily as idea generators for code, production work, and research.

So don’t read the next few paragraphs as naive surprises. I’m not pointing fingers at the model providers or pretending I didn’t know what I was walking into over the last 4 years of use. I’m just trying to work within the tools we’ve actually been given, ethically, and see how far that can get you.

The rabbit hole

It started with a paper I found while reading through arXiv: Verifiable Provenance and Watermarking for Generative AI, which builds an evidentiary framework mapping cryptographic provenance and watermarking schemes to the actual proof thresholds used in courts and regulation.

The finding that stuck with me, paraphrased from a conversation about the paper, was that no single scheme on its own clears the bar under realistic adversarial conditions. It’s the combination of methods that holds up, not any one of them in isolation.

And CLASP: Training-Free LLM-Assisted Source Code Watermarking via Semantic-Preserving Transformations. https://arxiv.org/pdf/2510.11251

CLASP reformulates source code watermarking into two stages: Semantically Consistent Embedding, which uses LLMs to perform semantics-aware watermark insertion from a fixed transformation space, and Differential Comparison Extraction, which recovers watermark bits through retrieval-grounded comparison against the most likely original code

That sent me down a rabbit hole for the weekend, using three frontier LLMs, Gemini, OpenAI, Perplexity, and Claude Sonnet 5, to both research the problem and try to build something real out of it as a challenge. What I found surprised me, not because the models refused things, but because of exactly which things they refused and which they didn’t.

Some even locked down, failing to proceed any further. There are always two sides to every guardrail, and it is good for when someone nefarious tries to circumvent the systems, but on the other side, what about the good ideas trying to provide preventive measures caused by the ouroboros machines themselves?

Testing the guardrails on my own code

I’ve been using LLMs since close to their public release. With years of writing Java and Python, I can count on one hand the times I’ve had genuine pushback on a code request. This weekend was different, and for a specific reason: I was trying to get an LLM to respect our proprietary licence header that we had coded in, sitting at the top of our own file.

A closer look at what Privacy Enhancing Technology actually means for vector memory and where the software you use every day really stands.

There’s a category of software that nobody threat-modeled yet, and it’s the one most of us are using every day now. Every time you tell an AI agent something, a decision, or a code action, that’s a data collection event. Somewhere, in a data center rack, a provider just wrote down and stored a piece of you.

As we’ve moved further into thinking seriously about privacy, we’ve spent a lot of time reading comments on web boards, forums, the usual places people talk honestly when nobody’s watching. There’s a real divide out there. Some users feel powerless, like the decision was already made for them somewhere upstream.

Others feel genuinely liberated by what’s happening with current technology, like a door just opened that used to be locked. We sit firmly in the second camp with a strong leaning into privacy, and this piece is really an attempt to explain why and to walk through what’s actually happening under the hood when people talk about privacy in AI software, not just assert it.

Most memory products treat this the way software treated user data in 2012. Centralize it, store it in someone else’s cloud, and call the privacy policy the privacy strategy. We built VEKTOR the other way, local-first, zero egress, your SQLite file on your machine. But “we don’t send your data anywhere” is a start, not a finish. So we sat down and asked ourselves a harder question: if we actually held ourselves to the standard the privacy engineering field uses, Privacy Enhancing Technologies, PETs, where would we land?

The honest answer is partly there, further along than most competitors, and with real gaps we can name specifically. This piece walks through where we actually stand, what we built and hardened this week to close part of the biggest gap, and what’s still being worked on.

We’re not waiting for AI companies, corporations, or governments to define this future for us. That resonates with something we believe at a basic level: people should be in control of their own sovereignty, their own software, not dictated to by Silicon Valley or by any government. It’s an engineering constraint we build against. Local, air-gapped software is your highest ground as a citizen here. No corporation can be told what to do by a government if their reach never touches the provider's cloud because your data or their model was never in it.

We all have the ability to decide what software we use, who we support, and how much privacy we hand over to companies and governments. We don’t need to feel powerless. You make a choice every time you click on a set of terms, every time you hit accept or decline on a cookie banner, every time you allow a government to pry further into your software or your home under the guise of “if you haven’t done anything wrong, you have nothing to worry about.”

Any time I hear that line, I shake my head. Privacy is a right. It’s not something you forfeit based on your ability to prove innocence. It exists to protect human dignity, autonomy, and civil liberties, not to shift the burden onto the individual to demonstrate they aren’t guilty of something.