I wanted to introduce a significant change we are making in Podman 6 for our machine function. To understand the problem and change, we first need to establish that Podman machine is based on a con…

Much research has been carried out on large language models (LLMs) and LLM-powered agentic workflows. However, many works within the field state emergence of, ascribe to, or assume, generalised anthropomorphic attributes to them (e.g., morality or understanding of natural language). Our goal is not to argue in favour or against the existence of these attributes, but to point out that these conclusions could be incorrect. For this we build and train a simple neural network on the videogame Age of Empires II, and note that any entity in a sufficiently-powerful substrate, such as LEGO or the Greater Boston Area, could also present such attributes. Hence, the purported anthropomorphic attributes of LLMs are empirically non-unique: although some properties (e.g., responses to prompts) could remain constant, others, such as the interpretation of their perceived behaviour, might change with the substrate. Thus, any empirically-grounded discussion requires explicit measurement criteria; otherwise the interpretation is left to the representation. We then show that assuming that these attributes exist or not in a system, independent of the substrate and in a generalised way, leads to either circular or uninformative conclusions, regardless of the experimenter's viewpoint on the subject. Finally we propose a 'null' assumption, where one assumes LLM non-uniqueness instead of assuming anthropomorphic attributes to set up an experiment, along with examples of it. We also discuss potential objections to our work, briefly survey the field, and prove that Age of Empires II is functionally- and Turing-complete.

Backrest is a web UI and orchestrator for restic backup. - garethgeorge/backrest

What happens inside an LLM when it tries to calculate with nothing but matrices.

A self-hostable Wasm sandbox for JavaScript workers.

How front-loading scrutiny through multi-agent validation rebuilt trust in AI-assisted development.

Open-source MITM proxy to intercept, inspect, and mock network traffic. - sauravrao637/oproxy

The largest animated icon library for Vue 3. 535 Lucide icons, drop-in and tree-shakeable, with hover, tap, and in-view triggers and a live gallery.

Michał Zalewski (lcamtuf) is a security researcher, author, electronics enthusiast, and calculator collector. He joins Chris to talk about his new book The Secret Life of Circuits and his interest in the field of electronics and explaining things to others.

A San Diego man spent one month in jail for a crime he didn't commit, after San Diego Police misinterpreted a hit from a license plate reader.

Alexander Graham Bell is widely credited with inventing the telephone, in 1876. It’s not entirely undisputed: Elisha Gray, Antonio Meucci, and Johann Philipp Reis each also have a decent claim as w…

Coding with LLMs (Claude Code, OpenAI Codex) is often presented as the ‘killer app’ for Generative AI. But looking at data, it seems the one piece of the puzzle missing is actual cost. …

are all-encompassing.

I truly mean that.

It's hard to grasp what that means.

ALL is a huge little word. Encompassing everything seems impossible. And yet, with Alexandra, this seems to be occurring. And with others to a lesser extent but Alex highlights the impact best.

Specifically to me, it appears

If you know the 'shape' or pattern of the given dataset, you can use the right kernel function when training a GP model. This is where domain knowledge of a given dataset can be useful. One fun thing I love about kernels is that you can add or multiply them to form composites.

We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- matches that of a single reference image. Recent approaches address this problem by training a diffusion model on a single image. But even in this setting, training is computationally expensive and requires hours of optimization. Instead, we model the image using a dataset of its patches at different scales. As this dataset is finite and the dimensionality of its patches is small, the score function for a noisy patch can be computed tractably using an optimal, closed-form denoiser, eliminating the need for neural network training. We integrate this patch-based denoiser into an efficient, training-free image diffusion model, and we describe how our method connects to classical patch-based image restoration techniques. Our approach achieves state-of-the-art generation quality and diversity compared to trained single-image diffusion models, and we demonstrate applications, including unconditional image generation, text-guided stylization, image symmetrization, and retargeting. Further, we show that our approach is compatible with latent space diffusion, and we show multiple additional acceleration techniques to achieve megapixel single-image generation in one second, and gigapixel generation in minutes.

Speculative proposal to create artificial neural nets with human-like performance by high-learning-rate/regularization training of overparameterized NNs to trigger catapulting/grokking. Over-parameterization as a route to true generalization would resolve many outstanding mysteries of artificial versus natural intelligence.

Look up any IPv4 address. Find owner, ASN, country, city, PTR records, and CIDR. Search reverse DNS hostnames. Free, no signup.

Open-source, local-first, read-only AI SRE: clusters alert storms, investigates root cause over your live systems, proposes human-gated fixes. - ninoxAI/nightwatch