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Sep 11
Sep 10
Sep 6
sylvainkalache.com
AI handles incidents, engineers lose touch with their systems
AI-assisted incident response can lower MTTR while leaving engineers less prepared for the complex incidents automation cannot solve.
Incident ManagementCode Review
arxiv.org
Large-Language Models as a Cognitive Virus
Large-language models (LLMs) are rapidly becoming part of human culture, reshaping how information is produced, transmitted, and used. Here we propose that their diffusion can be understood through a viral analogy, with LLM use spreading through populations, becoming embedded in cognitive and cultural practices. We model transitions among uncoupled, coupled, and persistently dependent users, and show that the interplay between social transmission, recovery, and collective reinforcement can generate tipping points and technological lock-in. A central consequence is the possibility of runaway dynamics: once a critical threshold is crossed, small increases in adoption can trigger rapid population-level shifts toward persistent dependence, with abrupt losses in cognitive competence. The same framework, however, identifies conditions for cognitive immunization, based on reducing transmission and facilitating reversibility. Our results highlight how LLM adoption may involve nonlinear collective transitions with important consequences for cognitive autonomy.
Mental Health
Sep 4
Sep 3
themodernsoftware.dev
CS146S: The Modern Software Developer - Stanford University
LearningAI Engineering
cs329z.stanford.edu
Stanford CS329Z | Engineering AI Agents
Official course website for Stanford CS329Z: Engineering AI Agents — schedule, deadlines, coursework, project, and logistics.
LearningAI Engineering
arxiv.org
The Emergent Symbolic Structure of Artificial Neural Networks
Modern systems in artificial intelligence (AI) somehow excel in domains for which they seem poorly suited. Intelligence has traditionally been modeled as operating over structured combinations of symbols, such as logical formulas. However, the strongest modern AI systems are based on neural networks, which instead represent information in continuous vectors. Vectors seem inadequate for capturing the structure of language, logic, and other cognitive domains, yet neural networks achieve impressive performance in these areas. How do they do it? In this work, we propose a potential answer: Despite appearances, perhaps the internal representations of neural networks implicitly realize symbolic structure. In support of this hypothesis, we show that the vector representations of a variety of neural networks can be closely approximated with symbolic structures: we can replace the network's entire representation-generating process with a closed-form equation instantiating a symbolic structure, and the network's behavior remains largely unchanged. This finding holds for both small-scale neural networks trained to manipulate lists as well as large language models (LLMs) operating in four domains that are central in symbolic traditions: arithmetic, logic, computer code, and language. Further, our symbolic approximation allows us to modify an LLM's behavior in targeted ways via precise interventions on its internal representations, showing that the LLM's behavior is reliant on the symbolic structures we have identified. This work provides a potential way to reconcile longstanding symbolic conceptions of intelligence with the vector-based nature of modern AI.
Neural Networks
signalundefied.bearblog.dev
i wanna live an npc life
the general consensus around being an npc is that you’re giving up your autonomy—essentially becoming a side character in someone else's story or watching li...
Npc LifeMinimalism
Aug 31
Aug 28
Aug 27
Aug 26
spacex.com
SpaceX Starbase LA
A spaceport on the Louisiana Gulf Coast enabling a multiplanetary future
Star PortStarbaseSpacex
arxiv.org
Black hole singularity is a surface not a point
It is widely repeated in the popular literature and elsewhere that the singularity at the center of a black hole is a point. It is not true. Two observers who free-fall into a spherical black hole along two different angular trajectories at the same time $t$ do not encounter each other at the central singularity; rather, they lose causal contact with each other already well away from the singularity. Counterintuitively, in general relativity two points can be spatially close yet causally distant. The singularity is a surface, not a point. The story for rotating black holes is more complicated, but the same conclusion holds. For a rotating black hole, the singular surface almost certainly resides at its inner horizon, where even the tiniest classical or quantum perturbations ignite the exponential mass inflation instability, precipitating collapse to a spacelike singular surface. There are implications for quantum gravity. We argue that, whatever the ultimate theory of quantum gravity may be, the quantum states of a black hole probably reside at its effectively 2-dimensional singular surface, which coevolves unitarily with, and in thermodynamic equilibrium with, the hot atmosphere of trapped Hawking radiation that the black hole generates within its event horizon.
Astrophysics
Aug 25
latent.space
The Rise of the AI Engineer
Emergent capabilities are creating an emerging job title beyond the Prompt Engineer.
Andrew NgLearningAI codingAI Engineering · +1
larsfaye.com
AI Coding will Prevent Expertise | Lars Faye
The need for ongoing friction in long-term skill formation.
AI codingLearning
ipshipyard.com
The end of IPFS at Shipyard
We have some difficult news to share with the IPFS and wider peer-to-peer community. Our IPFS funding was cancelled
IPFS
Aug 22
cymerys.com
I'm becoming AI-blind - Rafal Cymerys
Recently I've been catching myself having these little moments at work, when I'm trying to read a document someone has sent me and my brain somehow refuses to analyze it. It feels like I'm reading it, but I'm unable to focus on its content. I sat down to analyze these situations and realized they all have a common denominator: the documents all show a strong trace to AI. My brain learned to quickly spot signs of AI-generated content, at least the low effort one, and it now ignores it and moves on without thinking much about it.
AI saturation
hai.stanford.edu
Computational Agents Exhibit Believable Humanlike Behavior | Stanford HAI
Generative agents rely on a large language model to remember their interactions, build relationships, and plan coordinated events, with implications for both gaming and social science.
World ModelsSimulationCool As F
Aug 21
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Aug 17
daringfireball.net
Anthropic’s ‘Watermark’ Text Adulteration in Claude Is a Perversion of Writing
It’s unacceptable for a tool to sacrifice an iota of clarity, coherence, meaning, quality, etc. for the purpose of embedding hidden clues within the text to suggest its provenance. The idea that anything other than *my* needs should factor into the generation of text *for me* is patently offensive.
WatermarksArtificial intelligenceSlop Detection
vectoral.com
Who Are the Token Brokers?
A look at the brokers buying unused AI credits from startups and reselling them — the marketplaces, the bulk-discount routers, and the message boards where off-market inference changes hands
Inference MarketsArtificial intelligence
