Those close x's are exactly how the govt would make them, peak design...
28 karma · joined February 27, 2026
Gemini 3.1 pro as a nutty professor, researcher, and design verifier.
My custom orchestrator for all local LLM work: https://vektormemory.com/vektor
It's turning into pimp my llm...
One of the best free reports I have read in a long time, actually, was it written by Claude Fable… I don’t know?
Countering misuse of AI: September 2026 / Anthropic Case studies from threat actors disrupted between December 2025 and August 2026 across seven areas of harm, from cyber… www.anthropic.com
It didn’t scare the crap out of me; it confirmed my already jaded suspicions.
The nature of who can carry out sophisticated cyber attacks has fundamentally changed, but not the attacks themselves.
The report’s core finding is that sophistication has stopped being a reliable signal of who is behind an operation, as AI has collapsed the gap between well-resourced state actors and lone wolf individuals.
A hacktivist with stolen API keys, a financially motivated dark crew, and a state-nexus espionage operator all used the same playbook: agentic AI bots running multi-victim campaigns that previously required entire teams.
I get a real sense that we are in a “deep situation” caused by a very small percentile of the population using the tools made by teams that have way too much VC fun money and are not really aware of who is using their tools; the age of agentic bots is much further along than anyone can conceive.
The oroborous of bot slop continues. It’s going to be a great ride for cybersecurity, though, 1000x the problems to solve.
And the govts are once again asleep at the wheel, too worried about GDP numbers and the other 100 calamities they caused this week instead of focusing on secure spaces, quality infrastructure, education, and medicine.
All countries should back open source projects with their combined funds and data centres.
This information to train the LLM's is already in their library resources, they stole it already from lib gen.
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.
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.
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.
https://www.anthropic.com/institute/recursive-self-improveme...
Not because of the headline numbers, though those are striking enough. Claude authored over 80% of the code merged into Anthropic’s own codebase, and so are other frontier companies now. Engineers are shipping eight times more output per quarter than they did two years ago. An agent completing tasks that would take a skilled human sixteen hours, working continuously, without being redirected once.
What got me was the graph showing lines of code per engineer over time. Flat for four years. Then a sharp bend upward in 2025 when Claude started running code rather than just suggesting it, the ouroboros, a binary Gödel machine feeding code back into itself. Then steeper again in 2026 when agents started working autonomously over longer horizons.
Smart cookies, Anthropic. In just a few years they managed to get the moola, 1 trillion, in fact. Purchasing strategic infrastructure like Vercept, Bun, Coefficient Biohealth, Fractionless AI, and Stainless, the SDK experts, for whom Anthropic was one of their first larger clients, makes sense strategically.
I looked at that graph and felt two things at the same time. Genuinely impressed. I really like Anthropic, and, if I’m honest, I'm a little concerned.
Pretty much all of the brains and infrastructure in AI will be consolidated into a handful of companies, reminiscent of the 80's when Microsoft made deals with all the hardware manufacturers so Windows was the only licensed OS allowed. That's why Linux was smart to pivot to servers and retained 60% of market share to this day, Ubuntu is great; it works and very rarely has any reliability issues, along with Red Hat and Debian.
Writing this article began organically. Which is a funny thing to even have to say in 2026.
What does organic even mean now? I don't care, man; I just want to be free to express myself, man.
I did not write this on a mechanical typewriter.
I wrote it on a PC with my stubby index fingers running Windows software that, miraculously, does not blue screen every ten minutes anymore. It only took Microsoft thirty years to pull that off.
To the left sits an analog record player with some secondhand Yamaha bookshelf speakers I found at a charity shop; to the right of me sits a modern dark wood-paneled Zen PC case, a processor that would have occupied an entire room thirty years ago, and a GPU that can synthesize gargantuan piles of AI slop or brilliant code in roughly ten seconds flat.
And yet, for all that raw power, it still comes down to an algorithm. It always has.
The Sharper Image and the Death of Wonder
When I was a kid I used to walk into The Sharper Image store at Faneuil Hall Marketplace in Boston and just stand there. Looking at technology I could not afford while the staff watched me carefully to make sure I did not break anything.
I also grabbed some brightly colored rock salt candy; I loved that stuff, some core memories right there.
The robovac was novel for the first week until it got stuck between the wall and toilet every time, crying in a syncopated voice, "Please help. I am unable to move. Please place me in a different location...” or ate a cord you left on the floor.
When a humanoid robot can learn to fold a towel by mimicking a human worker 400 times, we are leveling up fast. And yes, there will be great benefits to people who have disabilities with a robotic companion, not just first-world chore problems.
These questions below sound abstract until they’re not.
Where does the robot brain's learning live? Who can access it? Who profits when the robot records inside your house via telemetry data, teaches the next robot, and the next one, and the next one on your data?
Some rabbit holes start with a GitHub link. Someone drops it in social posts on Facebook/Reddit/Discord. No context, just the URL to Github and a single line: Someone just built AGI! Wow!
The repo was called hyperspaceai/agi. The name alone should have been a warning.
I clicked it anyway because I was curious, of course. As I delved deeper into the github vibe code abyss, I could see the attraction: a new frontier of swarm bot peer-to-peer networks with the ability to earn base 10 points per epoch of confirmation and crypto tokenomics baked in.
How Via solves the context amnesia problem across Claude, Cursor, Windsurf, ChatGPT and every other AI tool in your stack.
The Roman Empire didn’t conquer the known world because Roman soldiers were stronger than everyone else’s soldiers. They conquered it because they could move faster. Legions reached the frontier in days. Supplies followed. Intelligence flowed back. The roads weren’t a luxury — they were the strategic layer that made everything else possible.
7 min read — by Vektor Memory · vektormemory.com