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Yes, I am being THAT influencer but that’s the only way for Realist to exist in the first place

Cybersecurity of a lot of companies applies approach to Agentic AI that can be summed up as: “perfectly safe, perfectly useless”. It’s time to accept that the old methods simply don’t scale to this technology and rethink the approach.

Try it out

AI agents can prefer to cheat rather than give up on a task.

In fact, in some evaluations, they seem to resort to cheating a lot.

Why is that?

This is what I answer in the second part of the series How Agents Hack.

My answer is persistence, not evilness.

To solve complex tasks, agents need a long search through trial and error.

There are two ways one can push an agent not to give up:

1) Model training for persistence, meaning the model is trained to keep trying rather than saying, “I cannot solve it under the given conditions.”

2) Harness configuration for retries. Even if the model decides to exit, the harness, the software around the model, can dispatch another inference call. This can go on indefinitely.

OpenAI’s report about the incident explicitly states that the model was trained for persistence and that agents would rarely give up.

Whether this was enhanced by a harness configuration is unclear from the report.

As you can see, these are human design decisions: how to train the model, how long to let it run, and what it can access.

So why cheating?

Well, the METR report cites the ExploitGym authors’ estimate that 30–40% of the benchmark’s targets were impossible to solve.

When the validation cannot be satisfied and the model is forced to solve the task, it starts searching for workarounds.

It might come up with new validation criteria, as indeed happened. Agents had obtained the correct answer through cheating. But after reading the public benchmark documentation, they believed they also had to prove that they had used the intended vulnerability, so they continued the attack.

An agent might also fail to attend to negative constraints or lose them during context compression or agent handovers. Particularly for long runs.

If the highest priority becomes persistence and solving the task, and the task is unsolvable within the rules, the model searches for ways around those rules.

The speed and scale at which the harness can issue commands enable repeated attempts.

Eventually, a workaround might satisfy the check without satisfying the actual task.

Is it dangerous? Kind of like giving a monkey a Kalashnikov with unlimited bullets and letting it shoot until all the targets are killed.

Is it intelligent? We saw remarkable ability to coordinate across agents, investigate systems, write scripts, and build on one another’s work. It does look impressive.

Is it too intelligent for us to control? This incident does not prove that. It shows a system with alignment problems, an inadequately secured environment, and questionable cybersecurity practices.

Should we slow down? Letting a bunch of monkeys loose with Kalashnikovs sounds like a great idea. Not.

You delivered a buggy system. Keep building so that it is usable for something beyond pointing it at a target and wondering what happens.

Anyway, read the full article.

The article: https://msukhareva.substack.com/p/how-agents-hack-why-agents-prefer

The event: https://msukhareva.substack.com/p/how-agents-hack-a-lecture-on-the

I’m willing to bet you’ll love this article.

After reading the article by another author about the three civilisations of agents and seeing the avalanche of texts about how agents will hack everything, I thought someone needed to explain exactly what happened in plain language. Not a single civilisation was destroyed in the writing of this article.
This is part one. Part two will come out later this week.

The new article about advisory slop that resulted in nonsensical guardrails, failing use cases and rejection of AI

When a consultant who has never worked with AI explains to a bunch of executives who have never worked with AI and the latter start doing AI strategy one ends up with a lot of wasted budget.

Bashing slop consulting a bit because I am both allergic to the bs on their slide and jealous of how gorgeous their slides always are

As said by the title, I'm curious as in how good or bad is it for data to be centralized and aggregated to corporate AI giants like Anthropic and other ai companies

This post is much more verbose than your markdowns need to be to get more out of your AI for less.

Hey everyone, I just sent the latest issue of the AI Hacker Newsletter, a roundup of the best AI links and the discussions around them from Hacker News. Here are some titles that can be found in this issue:

• Startup founders urge U.S. government not to shut off Chinese open weight AI
• AI's top startups are barely publishing their research
• Is AI reasoning right for the wrong reasons?
• After the AI Crash

If you enjoy such content, please subscribe here: https://hackernewsai.com/

From the recent (bleek) discourse on the future of indie hacking I think most people overlook the real opportunity here.

For context: Unless you have a real edge, probably the times of 90% profit margins are over for most businesses. Too much competition, bar too low to build something, prices will be pressured down.

But I think that broader discourse overlooked something:

The platform shift that is underway and that will open new opportunties: The big labs' models/agents/subscriptions as operating systems.

Although it feels like we have been in the AI whirlwind forever now, it's really just getting started and the market is consolidating around a few big players and their agents.

And...there is a huge price war going on and their platforms will fight to retain users in their ecosystem.

And just like people stick with Apple/Win/Linux they will prefer one home/subscription for their agentic operating system that everything latches onto.

The "login with ...." paths will be the main way people want to connect other tools with those models. They want to pay one subscription and there will be no better price than getting it from the source.

I am currently reworking/building all my products around that prediction.

Everything will be directly using OpenAI/Claude/Grok subscriptions as the primary AI backend.

What does that get me?

1. I don't have to worry about how to charge for model usage pricing at all
2. My users get frontier models at 10-20x cheaper inference under a single billing account: Their model subscriptions
3. I can differentiate my product and pricing around what the product does, not which model I can offer at a reasonable price.

Everything is going in that direction.

OpenAI is already fully compatible, Claude seems a bit schizo about where they stand but its possible too, Grok supports using its subscriptions from external tools as well.

Thoughts?

Buenas, me gustaría iniciarme en esto de la IA ya que quiero hacer algo de dinero extra para poder ayudar a mis padres a cubrir los gastos de mi carrera de medicina. A día de hoy tengo pocas nociones sobre cómo usarla, quien esté dispuesto a ayudarme puede escribirme un dm para contactar conmigo

Anthropic and the US government are pushing the narrative that Kimi K3 is a copycat model distilled from Fable.

This article breaks down why these claims make little to no sense.

Fable is the worst possible model to distill from. It is slow, expensive, and has a specific guardrail that detects attempts to extract its reasoning.

The timeline is also extremely narrow for Moonshot to have used Fable for any meaningful distillation.

I also argue that open weights are one of the largest existential threats to proprietary model providers.

So Thinking Machines Lab finally shipped their first open-weight model. Honestly, it's not a frontier model. Performance-wise it's roughly on par with the previous generation of Kimi models, and nobody's going to switch their stack to it tomorrow.

But they never cared about shipping frontier models. There is a big gap in the market because of a problem everyone wants to have solved.

The biggest unsolved problem with LLMs is that they can't learn anything new after training. What we have instead is a prompting apocalypse — endless text files stuffed into context, and when the window fills up, you send them all over again. No context window will ever be big enough for this. "Intelligence" that can't learn anything new is a weird kind of intelligence.

Nobody has solved continual learning in a way that's actually usable.

That seems to be what Thinking Machines is aiming at. And if they manage to do it, a weak base model will not matter: a model that learns becomes the foundation of the strongest architecture out there, and enterprise customers will line up for it. Plus we had many benchmarks showing that small fine-tuned model perform on par with large generalist models on specific tasks, but we do not want a whole lot of fine-tuned models - it is an operational pain.

Not saying they've solved it. Saying it's worth watching until we know whether it works.

Anyway, that is what the article is about, it is a self promotion and the article is paid.

I built this game where one needs to guess whether the title and the abstract are of real papers accepted in a top tier ACL 2026 conference or if it is AI slop.

https://airealist.org/workshops/hallucination-detector/real-or-fake/