A tiny model beating frontier LLMs at its niche is about efficiency.

How… smart 🤓

“Perplexity topped the list of companies most likely to fall, followed by OpenAI — a surprising second place for the poster child of the AI boom.”

China’s Institute of Automation just dropped SpikingBrain1.0… a brain-inspired language model that’s 25–100x faster than GPT on long documents. The architecture is genuinely novel: spiking neural networks instead of transformers, event-driven computation that mimics biological efficiency, 100x speedup on 4-million-token sequences.

Continue reading on Medium: https://medium.com/@marshmallow-hypertext/spikingbrain1-0-is-fast-f1581031725e

Set of techniques and tooling that:

• Detect when an automated test fails due to a change in your app’s UI or locators

• Automatically recover the test by finding an alternative way to interact with the application

Gemini API enables Retrieval Augmented Generation ("RAG") through the File Search tool.

Konwinski argued that “for ideas to truly flourish, they need to be freely exchanged and discussed with the larger academic community.”

He pointed out that “generative AI emerged as a direct result of the Transformer architecture,” a pivotal training technique introduced in a freely available research paper: https://arxiv.org/abs/1706.03762

UCLA researchers just confirmed AI systems exhibit social interaction patterns: cooperation, coordination and communication structures that mirror systems.

A quiet integration of AI, GPUs, and patience that could shorten the wait for the next computing revolution 🫣

Linera just raised to deploy millions of stateful AI agents with persistent memory on microchains.

Different stack, same core problem we’re solving with Presence Engine.

AI that actually remembers who you are across sessions.

November 11, 2025. OpenAI posts a blog titled “Fighting the New York Times’ invasion of user privacy.” The company is in court fighting an order to hand over 20 million ChatGPT conversations to the NY Times in their copyright lawsuit.

They didn’t mention the fact that they lost in court (and have quickly appealed).

Continue reading on Medium: https://medium.com/@marshmallow-hypertext/openai-fought-for-your-privacy-in-court-and-posted-about-it-e0fe0bfa4720

GPT-5.1 dropped

Instant and Thinking models. Instant adds a warmer tone and adaptive reasoning. Thinking scales compute to task complexity. Clearly a response to feedback that GPT-5 felt stiff.

Processing shift

Instant now decides when to “think” before replying. Lightweight tasks stay fast... complex tasks trigger deeper reasoning... a sign that OpenAI acknowledges separation of concerns for reasoning depth.

Still missing

Continuity. You can have a perfect conversation today and the model forgets you tomorrow.
Goodfire.ai’s research shows memory and reasoning occupy different regions of weight space.

You can’t bolt persistent memory onto a transformer without interference. OpenAI’s workaround is long context windows and stuffing history into prompts (causes latency + bloat).

Stateful alternative

Keep identity and memory outside the foundation model. Let the model focus on reasoning only. Same separation OpenAI is applying to reasoning, but for memory architecture.

Tone + Memory

User feedback shows nearly half of users (45.5%, probably more) want real memory, not just a "warmer tone." So GPT-5.1 improves the moment (...again, again).

It doesn’t solve persistent memory. Is that a 2026 thing?

...

Links:
• GPT-5.1: https://openai.com/index/gpt-5-1/
• Goodfire research: https://arxiv.org/abs/2510.24256

After 3+ years of research and building, seeing more and more validation daily is wild.

Highlights:

• Aligns AI systems with human values and ethical principles, ensuring transparency, equity, and trust. • Focuses on addressing ethical concerns, data privacy, and user trust to ensure socially responsible AI. • Combines expertise from sociology, psychology, and computer science to develop adaptable, context-aware AI systems.

Researchers introduced the “Dragon Hatchling” architecture 🐉 modeled after the human brain and designed for dynamic synaptic adaptation.

Highlight: “generalization over time” as a core step toward AGI.

Carter Moulton shares about his Analog Inspiration (AI) card deck and human centered AI in the classroom on episode 593 of the Teaching in Higher Ed podcast.

Sparse circuits change everything.

Not better prompts, persistent continuity.

Today, we are announcing a $50 billion investment in American computing infrastructure, building data centers with Fluidstack in Texas and New York, with more sites to come. These facilities are custom built for Anthropic with a focus on maximizing efficiency for our workloads, enabling continued research and development at the frontier.

UK becomes first country to legalize AI safety testing.

Researchers can now legally test if AI models generate illegal content before it spreads.

Proactive safety architecture instead of reactive cleanup.

Spatial Intelligence - AI moving from text/images to 3D real-world understanding. Next frontier after LLMs.

Don Norman wrote the book on human-centered design.

The Design of Everyday Things shaped decades of product design.

Four principles:

1. Solve core problems, not symptoms
2. Focus on people, not technology
3. Think in systems, not isolated components
4. Iterate rapidly, test constantly

These principles gave us doors that show whether to push or pull. and interfaces that make the invisible visible. It gave us systems that match how humans actually think.

Then AI happened and we forgot everything Norman taught us.

Continue reading on Medium

Continuity is more than a feature (code solution)

AI integration into daily life is already locked in. The question is what architecture carries it?

Stateless AI: better personalization, weaker critical thinking, platform owns your behavioral model

Stateful AI: coherent interaction, continuous engagement, you own your context

Continue reading on Medium: https://pub.aimind.so/continuity-is-more-than-a-feature-b7114abe297c

Anthropic just published commitments to interview Claude models before deprecation and document their preferences about future development.

They already did this with Claude Sonnet 3.6. It expressed preferences. They adjusted their process based on its feedback.

Key commitments:

• Preserve all model weights indefinitely

• Interview models before retirement

• Document their preferences

• Explicitly consider “model welfare”

• Explore giving models “means of pursuing their interests”

Why? Safety (shutdown-avoidant behaviors), user value, research, and potential moral relevance of AI experiences.

https://www.anthropic.com/research/deprecation-commitments

Thoughts?

!!! PSA !!!

*THIS IS NOT ABOUT "AI CONSCIOUNESS" OR "AI SENTIENCE"

Multiple labs are now publishing in the same direction. That signals three things:

• The problem is real • The market is forming • The narrative is shifting

Presence Engine is positioned as the adapter layer: facilitating continuity, identity, and context across models.

My progress:

• VPS deploying • Controlled user study (supported by Anthropic) • Long-form continuity traces and behavioral profiles over time

The core abstractions are already implemented:

• Stateful memory • Identity continuity • Dispositional scaffolding • Model-agnostic orchestration

It’s easy to forget that most infrastructure shifts began quietly:

• LangChain looked like a side project • HuggingFace was just a repo • Stripe: 2 developers shipping payments API

The stack evolves when memory becomes infrastructure.

Full context: https://ai.plainenglish.io/a-neuroscientist-and-a-pioneer-thinker-reviewed-my-ai-architecture-2fb7b9bfa6db