blackadder

[blackadder @xxvvrr1]

@ramon_audet @meritxellbudo @EliseuEsterlich @JuntsXCat Un cop parlat d’immigració doncs, ja està solucionat?

[Kevin Bass @kevinnbass]

I have conducted an audit of Anthropic's finances.

What I have found is so shocking that I am calling for a Congressional investigation.

Anthropic is not just seeking regulatory capture.

It has built a regulatory capture machine that cannot be turned off.

Structural financial incentives make it impossible for Anthropic -- I call it the Anthropic Network -- to turn off its own AI doom cycle.

It starts with METR.

Dario Amodei proposes "third-party evaluators" to assess the risk of Anthropic's models.

He proposes METR for this purpose.

But METR is financially dependent on the Anthropic's success -- specifically, on the explosive growth of more than $7 billion dollars in Anthropic stock.

Dustin Moskovitz invested this stock into Good Ventures Foundation, where it represents the majority of that organization's portfolio.

And GVF is the overwhelming funder of the entire Anthropic Network ecosystem.

This stock was worth $500 million early last year.

It is worth more than $7.7 billion just ~16 months later.

METR -- and all of those building a career its parent organizations -- cannot afford to disrupt that growth.

Because if Anthropic goes under, many of the organizations that fund METR go under as well.

But if Anthropic succeeds, METR and its parent organizations become more richly financed to regulate AI -- something those at METR want very much.

The "third-party evaluator" is not "third-party" at all.

The evaluator is on Anthropic's payroll.

If this were the end of it, that's bad.

But that isn't all.

The same organizations that fund METR also fund the many organizations, such as the Tarbell Center, that promote AI Doom.

The Tarbell Center publishes AI Doom articles in The Verge, Science, LA Times, The Dispatch, TIME, and others.

They are selling the problem, and then selling the solution to the problem -- from the same money pile: Anthropic's.

All of these organizations are financially dependent on the same exploding $7 billion money pile.

As Anthropic grows more and more powerful, its AI Doom Machine grows better and better financed -- louder and louder.

Meanwhile, the regulatory regime seeded in METR grows larger to solve the increasingly loud -- now hysterical -- problem of AI Doom that the Anthropic Network itself created.

From this standpoint, as Anthropic becomes more powerful, AI might be getting scarier, sure -- but the positive feedback loop also becomes more deafening -- independent of objective facts.

This itself is an objective fact.

The deafening AI Doom is part of an business model, that, as it expands, so too does the AI Doom messaging -- there is simply more money to do it.

But the problem also goes in the other direction:

If Anthropic dies, the Regulatory Regime and the AI Doom Machine are crippled or die.

Neither METR nor Tarbell nor the other organizations in the Anthropic Network can allow that to happen.

Hence, neither METR or the AI Doom Machine can be trusted to provide independent assessments of Anthropic's models or AI more broadly.

They simply are not organizations independent of Anthropic.

And Anthropic cannot detach itself from METR or Tarbell or countless other safety orgs (not shown here), either, because they drive hype for the models and the possibility of eventual regulatory capture, and Anthropic will not give that up willingly.

What's more, the people at all of these organizations are all the same ecosystem, the same community. They just shuffle between organizations.

The Anthropic Network is therefore, so long as it is successful, locked into a self-amplifying feedback loop inside an ideological monoculture.

And that feedback loop is winning.

That's what Jacob Coxon is.

China is keeping messaging tight. That is why optimism for AI is so high in China.

America has Anthropic: a massive company pushing anti-AI propaganda at a state level.

Anthropic will either create hysteria until American AI slows down and China wins, or it will create fractures throughout American society with severe political consequences.

Ironically, because of the structural financial incentives underpinning the Anthropic Network, it has become the same kind of self-amplifying virus that it fantasizes AI to become in the future -- while hiding its tracks just as carefully.

It is the mirror of the same AI virus that it hypothesizes to consume America.

Anthropic's business model, models itself after the very thing it claims to fear.

Except Anthropic's ideology infects humans, not computers.

Congress must investigate.

Evidence and Github in next post.

Then some supplementary figures.

[Dave Kennedy @HackingDave]

AI represents one of the largest technological leaps I have witnessed in nearly 30 years in technology and cybersecurity. These models are advancing faster than anything we have seen in modern technological history.

The central concern is that this is fundamentally different from traditional software development. With conventional programming, developers explicitly define how a system should behave.

Advanced AI models can be difficult to predict and constrain, and they can demonstrate capabilities their creators did not fully anticipate. As disclosures from companies such as OpenAI and Anthropic have shown, these systems are already capable of performing sophisticated cyber operations against real organizations.

Dario Amodei and employees at several frontier AI companies have called for slowing development long enough to establish meaningful safeguards around this extraordinarily powerful technology. I understand and share many of those concerns. The geopolitical problem, however, is that the United States currently holds an advantage over China. If we slow down unilaterally, we could give China the opportunity to close that gap, surpass us, and fundamentally shift the global balance of power. AI has the potential to be that consequential.

We are entering an extraordinary period.

Over the next several months and years, AI could help us solve mathematical problems that have eluded humans, advance quantum physics, discover treatments for devastating diseases, and accelerate scientific progress across nearly every field. That transformation is not theoretical.

It is already underway.

I believe AI represents the next nuclear arms race. The crucial difference is that much of the power is currently concentrated in private companies rather than governments. That makes this moment fundamentally different from the Manhattan Project.

There is also an economic reality that cannot be ignored. Frontier models reportedly cost enormous amounts of money to develop and may remain state of the art for only a few months before the next generation replaces them.

That pace raises serious questions about sustainability, revenue, and widespread adoption. Meanwhile, Chinese AI companies have become highly effective at using techniques such as distillation to reproduce capabilities from leading models at a fraction of the cost.

Depending on how that knowledge is obtained, this can raise major intellectual property and national security concerns. Slowing the release of frontier models could temporarily make that process more difficult, but it is not a long term strategy for containing China’s advancement.

There are also unconfirmed reports and considerable industry speculation about progress toward recursive self improvement, or RSI. This is one of the major objectives being pursued by frontier AI laboratories and nation states. Today, AI development still requires substantial human training, guidance, and intervention.

True recursive self improvement would allow an AI system to improve aspects of itself with progressively less human involvement. If that threshold is reached, development could accelerate far beyond the pace we are experiencing today.

In a very real sense, we are building the foundations of superintelligence.

This means systems capable of accessing humanity’s accumulated history, research, mathematics, science, and medical knowledge almost instantly, while continuously becoming more capable.

That presents enormous risks, but it also represents an extraordinary opportunity for humanity.

From a cybersecurity standpoint, we are on high alert.

Many of the defensive approaches that worked in the past will not be sufficient in this new environment.

Both the private sector and our national defense infrastructure remain grossly underprepared for AI enabled threats, these advancements, and that should concern every one of us.

[Igor Kozlov @iekozlov]

Amazing work by the NVIDIA-CrowdStrike team.

In a nutshell, it's a proof of concept demonstrating a closed-loop detection-engineering agent pipeline tuned in a purple-team agentic exercise.

What it does:

- It deploys a red-team agent with the goal of executing an attack from a selected scenario family.
- It records EDR telemetry and the ground-truth attack trace, then passes them to a blue-team agent to generate detection rules.
- It runs each candidate through a validation loop that addresses common LLM failure modes (hallucinated syntax, invalid queries, environment-specific indicators, rules that fail against recorded telemetry, detections that lack behavioral grounding).
- It tests generalization by running new, independently seeded attacks from the same preselected scenario family.

The results are promising: the optimized open-model pipeline - including Nemotron orchestration and a post-trained detection-writing model - produced detections that generalized more reliably to new attacks from the same preselected family.

What does this mean?

- The authors correctly warn that this does not prove cross-scenario generalization. It is a "directional system-level case study, not a general benchmark".
- It shows how a purple-team agentic exercise in a lab can provide provide enough data for detection pipeline tuning. Scaling this kind of data generation remains an important challenge, which reinforces a design choice behind our Cyber Defense Benchmark RL gym: combining synthetic scenario generation with real attack telemetry recorded in emulated environments to scale post-training while retaining grounded signals.
- More broadly, it supports the idea that small, specialized models can be trained for bounded cybersecurity tasks. Cisco Antares demonstrates a related pattern for vulnerability localization - although vulnerability localization (cyber offense) and detection engineering (cyber defense) are distinct capabilities and require different evaluations. This distinction is visible when comparing model performance on the offensive CyberGym (vulnerability finding) and the defensive Cyber Defense Benchmark (threat hunting).

Congratulations again to the team and @Cyb3rWard0g on a meaningful step toward adaptive cyber-defense agents.

Cyber Defense Benchmark:
https://t.co/n06rjUjcY5

Cisco Antares:
https://t.co/h4IsP5PIxi

NVIDIA original source:
https://t.co/nZlAB4cABW

[CrowdStrike @CrowdStrike]

Cyber defense is entering the superintelligence era. ⚡

Introducing the CrowdStrike Cyber Superintelligence Lab, the first frontier AI research organization built for cyberdefense and AI safety. The Lab delivers CrowdStrike SafeMind, a family of purpose-built security models and harnesses.

SafeMind runs as one system designed to deliver AI safety: an offensive model that finds the attack path, a defensive model that closes it, and the harnesses that operate both in the same loop.

Built using @nvidia Nemotron open models and using @CoreWeave’s AI Cloud for training and inference, SafeMind goes beyond AI that simply identifies threats, it defeats them.

Learn more: https://t.co/mdASBHHKr1

[TΟΝΙ RΟS @ToniRos]

⚫️ Hello @elonmusk, in Manresa (Catalonia), Carles Vilajosana, a 45-year-old Catalan man, father of twins, has been murdered by being stabbed with a knife. The killers are minors, children of immigrants.

The political authorities and "official" local media are spreading a whitewashed narrative, blaming social media—mainly @X—and attacking those who report the facts.

Please, help us let the world know about it. Thank you!

https://t.co/AMqDw2zA2D

[Toni GM @tonetaso90]

L'audio de la vergonya.

L'audio que explica el que li va passar a en Carles. L'audio que demostra com actuen els moros. L'audio que explica que els Mossos saben que han d'haver-hi canvis. L'audio que diu que no volen que s'escampi qui eren.

[Sílvia Orriols @orriolsderipoll]

Aquests sí que no han passat per cap procés net de selecció ni han acreditat mèrits... i tots tindran pagueta i estat del benestar a costa teva...

[blackadder @xxvvrr1]

@monbusinfo No hi ha millores als horaris. El bus que anava directe de manresa a barcelona passa a trigar 20 minuts més pq passa per la zona universitaria on al bus que surt de manresa a les 6.30 no para NINGÚ!

[elhacker.NET @elhackernet]

🛠️AgenticSeek: alternativa privada y local a Manus

100 % local es un asistente de IA con función de voz que navega por la web de forma aut��noma, escribe código y planifica tareas, al tiempo que mantiene todos los datos en tu dispositivo
https://t.co/8NPTQQVRIH

[ℏεsam @Hesamation]

Anthropic just dropped a full masterclass on building tools for your agents, here's the gist:
> evaluate your tools religiously
> limit the number of tools
> namespace your tools
> return meaningful context from tools
> prompt-engineer your tool descriptions
what each means:

[Sílvia Orriols @orriolsderipoll]

La diferència entre l'Ajuntament de Manresa i el de Ripoll, és que el de Ripoll està comandat per qui no tolera la vexació ni submissió de les nenes i combat la islamització del país.

Quan Aliança Catalana governi Manresa, aquesta indecència s'haurà acabat...

#SalvemCatalunya

[blackadder @xxvvrr1]

Carta d’un ciutadà que va haver de publicar a La Vanguardia perquè al Regió7 no li van voler https://t.co/XbWHTa7ojD

#manresa

[blackadder @xxvvrr1]

Viatge de Barcelona a Manresa. El bus de #monbus surt 13 minuts tard perquè la conductora diu que “estoy haciendo el descanso de la tarde”

Hi afegeixo jo que suposadament ha vingut aquí perquè hi ha més oportunitats i potencialment una vida més digna. Gandula.

#monbus #manresa

[blackadder @xxvvrr1]

@SergiMaranya @rac1 Llegint les respostes és fàcil perdre l’esperança en cap canvi. Ens tirem pedres a nosaltres mateixos, deixem via lliure a l’empobriment de Catalunya. Anem a la més absoluta misèria, de cap!

[blackadder @xxvvrr1]

Monbus quina punyetera mania en no explicar als conductors nous que NO facin la volta per Bufalvent, perdem temps tots!! I vosaltres diners en combustible!

#monbus #manresa

[blackadder @xxvvrr1]

@ManresaAC Que algú li expliqui això al molt honorable alcalde, que encara no s’ha enterat de què va la cosa.

[blackadder @xxvvrr1]

@Guifre9 @ajmanresa Hi ha qui cel.lebra la feina feta

https://t.co/0gsXDOyeUA

[ERC Manresa @esquerramanresa]

🎉 5 anys d’alcaldia republicana a #Manresa!
Ahir vam fer un acte de reivindicació de la feina feta, d'agraïment i de mirada endavant, encarant reptes per millorar la vida de la gent. Projectes com la Fàbrica Nova, l'Anònima o el renovat Guimerà ja dibuixen la Manresa del futur.

[blackadder @xxvvrr1]

@ManresaAC Pots anar a qualsevol piscina pública del Bages, ja hi ha dones totalment tapades banyant-se. Guarres.

[blackadder @xxvvrr1]

@tcv12345 @orriolsderipoll Si el senyor Marc Aloy se’n va al seu país no crec que hi duri dos minuts tenint en compte els seus “gustos i preferències”.
Que baix ha caigut Manresa.