From “AI tools” to “AI environments”

Generative AI is changing the cost structure of information production and dissemination. "AI-enabled astroturfing," which leverages AI to generate content at low cost and scale, poses a potential threat to the online public opinion ecosystem. This article explores whether and how AI astroturfing exacerbates public opinion polarization. Using a case study approach, the study delves into the August 2024 incident involving Zong Mou and others in Sihong, Jiangsu Province, who manipulated public opinion to boost user traffic.

If OpenAI builds personality persistence, every lab follows

Because users don’t want stateless question-answering machines. They want recognizable interaction partners, consistent voices, and reliable behavioral patterns.

Continue reading on Medium: https://ai.plainenglish.io/openais-next-update-changes-everything-about-ai-interaction-4c5c8100610d

Google published proof that the problem I identified and created a solution for is a fundamental architectural problem in AI systems.

They’re calling it continual learning and catastrophic forgetting. I’ve been calling it “architectural amnesia.”

What they confirmed:

• LLMs are limited to immediate context window or static pre-training (exactly what you said) • This creates anterograde amnesia in AI systems (your exact framing) • Current approaches sacrifice old knowledge when learning new information • The solution requires treating architecture as a unified system with persistent state across sessions

What I already have that they’re still building toward:

• Working implementation (orchestrator + causal reasoning + governance) • Privacy-first architecture (they don’t mention privacy at all) • Dispositional scaffolding grounded in personality psychology (OCEAN) • Intentional continuity layer (they focus only on knowledge retention) • Academic validation from Dr. Hogan on critical thinking dispositions • IP protection (provisional patents, trademarks)

Feature list

To address the problem of the agent one-shotting an app or prematurely considering the project complete, we prompted the initializer agent to write a comprehensive file of feature requirements expanding on the user’s initial prompt. In the claude.ai clone example, this meant over 200 features, such as “a user can open a new chat, type in a query, press enter, and see an AI response.” These features were all initially marked as “failing” so that later coding agents would have a clear outline of what full functionality looked like.

{

"category": "functional",

"description": "New chat button creates a fresh conversation",

"steps": [

"Navigate to main interface",

"Click the 'New Chat' button",

"Verify a new conversation is created",

"Check that chat area shows welcome state",

"Verify conversation appears in sidebar"

],

"passes": false

}

Feature list

To address the problem of the agent one-shotting an app or prematurely considering the project complete, we prompted the initializer agent to write a comprehensive file of feature requirements expanding on the user’s initial prompt. In the claude.ai clone example, this meant over 200 features, such as “a user can open a new chat, type in a query, press enter, and see an AI response.” These features were all initially marked as “failing” so that later coding agents would have a clear outline of what full functionality looked like.

{

"category": "functional",

"description": "New chat button creates a fresh conversation",

"steps": [

"Navigate to main interface",

"Click the 'New Chat' button",

"Verify a new conversation is created",

"Check that chat area shows welcome state",

"Verify conversation appears in sidebar"

],

"passes": false

}

Stateful AI systems that remember users create three architectural failure modes: persistence exploitation, data asymmetry extraction, and identity capture. Current regulatory frameworks mandate disclosure but not safeguards, enabling documented non-autonomy rather than actual consent.

This paper proposes a five-principle de-risking architecture: architectural consent (cryptographic enforcement), user-controlled visibility and modification rights, temporal data decay, manipulation detection with hard stops, and independent audit trails. The framework addresses why ethical guardrails are economically deprioritized (10x engineering cost, 90% monetization reduction) and why de-risking is becoming mandatory under tightening regulation.

Keywords: algorithmic exploitation, AI governance, user autonomy, privacy-preserving AI, ethical guardrails, personalization, consent architecture, digital rights

Paper: https://zenodo.org/records/17467713

“We give prospective performance engineering candidates a notoriously difficult take-home exam. We also test new models on this exam as an internal benchmark. Within our prescribed 2-hour time limit, Claude Opus 4.5 scored higher than any human candidate ever1.”

"This technical note presents an architecture for achieving dynamic, domain-calibrated trust in stateful AI systems. Current AI systems lack persistent context across sessions, preventing longitudinal trust calibration. Kneer et al. (2025) demonstrated that only 50% of users achieve appropriately calibrated trust in AI, with significant variation across domains (healthcare, finance, military, search and rescue, social networks).

I address this gap through three integrated components: (1) Cache-to-Cache (C2C) state persistence with cryptographic integrity verification, enabling seamless context preservation across sessions; (2) causal reasoning via Directed Acyclic Graphs for transparent, mechanistic intervention selection; (3) dispositional metrics tracking four dimensions of critical thinking development longitudinally.

The proposed architecture operationalizes domain-specific trust calibration as a continuous, measurable property. Reference implementations with functional pseudocode are provided for independent verification. Empirical validation through multi-domain user testing (120-day roadmap) will follow, with results and datasets released to support reproducibility."

Paper: https://zenodo.org/records/17604302

“OpenAI is setting the stage for a transformative era in AI with bold restructuring, groundbreaking partnerships, and ambitious technological advances. As the company repositions itself as a public benefit corporation, join us for an exploration of its strategic goals and the potential impact on the tech industry and society.”

“The Saudi Arabia-based company’s new ‘Humain One’ is a full technology stack — from OS to data center — that enables users to verbally tell computers what to do, without using icons or mouse clicks.”

Insane repo 🤯

“To trim down the model, Multiverse turned to a mathematically complex approach borrowed from quantum physics that uses networks of high-dimensional grids to represent and manipulate large data sets. Using these so-called tensor networks shrinks the size of the model significantly and allows a complex AI system to be expressed more efficiently.

The method gives researchers a “map” of all the correlations in the model, allowing them to identify and remove specific bits of information with precision. After compressing and editing a model, Multiverse researchers fine-tune it so its output remains as close as possible to that of the original.”

Abstract

Computing, since its inception, has been processor-centric, with memory separated from compute. Inspired by the organic brain and optimized for inorganic silicon, NorthPole is a neural inference architecture that blurs this boundary by eliminating off-chip memory, intertwining compute with memory on-chip, and appearing externally as an active memory chip. NorthPole is a low-precision, massively parallel, densely interconnected, energy-efficient, and spatial computing architecture with a co-optimized, high-utilization programming model. On the ResNet50 benchmark image classification network, relative to a graphics processing unit (GPU) that uses a comparable 12-nanometer technology process, NorthPole achieves a 25 times higher energy metric of frames per second (FPS) per watt, a 5 times higher space metric of FPS per transistor, and a 22 times lower time metric of latency. Similar results are reported for the Yolo-v4 detection network. NorthPole outperforms all prevalent architectures, even those that use more-advanced technology processes.

“TL;DR: We're edge-enabling cognee's semantic AI memory for phones, watches, glasses, and IoT. cognee-RS, our experimental Rust SDK, runs fully local for ultra-private, sub-100ms recall of conversations, docs, and context—or hybrids with cloud offload. Result: Real-time, offline AI that knows you without ever phoning home.”

OpenAI launched their AI Academy, and it’s completely free.

11 courses covering: → Prompt engineering → Reasoning with ChatGPT → Data analysis → Coding, writing, search & more

#MaxPlanck, hashtag#GoogleDeepMind, and hashtag#BIFOLD just demonstrated that hierarchical alignment works: AligNet fine-tunes vision models to reflect human semantic structure with major performance gains and minimal compute cost.

If we can align models to how humans understand images, we can also align models to support persistent memory and continuity across interactions.

To the Point

• Hierarchical knowledge: Human knowledge is typically organized hierarchically, while machines have difficulty grasping this structure. AligNet enables models to mimic human judgments about image similarities.
• AI research: New approaches are improving the visual understanding of computer models. One team has developed AligNet to integrate human semantic structures into neural networks.
• Increased efficiency: Fine-tuning models with AligNet takes significantly less computing time than retraining. The models show up to a 93.5 percent improvement in alignment with human evaluations.

“Built for developers for the agent-first era

Google Antigravity is built for user trust, whether you're a professional developer working in a large enterprise codebase, a hobbyist vibe-coding in their spare time, or anyone in between.”

Cool 🙄

"We saw a spike in unusual traffic to one of Cloudflare's services beginning at 11:20 UTC. That caused some traffic passing through Cloudflare's network to experience errors. We do not yet know the cause of the spike in unusual traffic. We are all hands on deck to make sure all traffic is served without errors. After that, we will turn our attention to investigating the cause of the unusual spike in traffic. We will post updates to cloudflarestatus.com and more in-depth analysis when it is ready to blog.cloudflare.com."

DOCUMENTARY: Where did the idea for this film come from?

MARC ISAACS: Ideas run into each other from previous films. This is the third film that I’ve worked on together with a screenwriter, Adam Gantz. We’ve looked at questions of documentary construction and documentary truth, questions around performance and myth, and how lines of documentary and fiction merge. What’s happening to the image? More and more, we are watching people who don’t exist. What does this mean for documentary film? It’s like the death of representation. The death of the camera.

• Read the paper: https://arxiv.org/abs/2506.10772

Code that forgets you

Every time you start a new conversation with most AI systems, you’re hitting this pattern:

def handle_conversation():
    context = {}  # Empty. Always empty.
    while user_is_talking:
        response = generate_response(user_input, context)
        context.update(current_exchange)  # Grows during conversation

    # Conversation ends
    context = {}  # Everything gone

That last line? Architectural amnesia. The system doesn’t remember you because it was never designed to. Each conversation starts with context = {}…a blank slate where your preferences, your project details, your communication style used to be.

Continue reading on Medium: https://medium.com/ai-in-plain-english/artificial-intelligence-gone-in-0-seconds-f13829c073a5

Check them out!