AI Angst

The author discusses the significant investment in generative AI, with billions of dollars being poured into startups and infrastructure, and expresses concern about the pressure to generate revenue to justify these costs. The author also highlights the environmental cost of genAI, including the massive carbon footprint of data centers, and examines the potential impact of genAI on various sectors, including coding, education, and professional communication, raising questions about the long-term viability and benefits of these technologies.

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Trusting your own judgement on 'AI' is a risk

The author credits reading Robert Cialdini's "Influence: The Psychology of Persuasion" as a teenager with teaching them about the limitations of human reasoning and the ease with which people can be manipulated, even if they consider themselves intelligent. This lesson has stuck with the author, who now observes that software developers, in particular, are prone to falling for manipulative tactics and cognitive hazards, as evidenced by their willingness to adopt new technologies and tools without sufficient evidence or critical evaluation.

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Anthropic's AI-generated blog dies an early death

Anthropic's AI-generated blog, "Claude Explains," has been shut down just a week after being profiled by TechCrunch, with the company redirecting the blog's address to its homepage. The blog, which was a pilot project to test the capabilities of Anthropic's Claude AI models in generating explainer-type content, had received criticism on social media for its lack of transparency about which parts of the content were written by humans and which by AI.

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Enterprises are getting stuck in AI pilot hell, say Chatterbox Labs execs

Enterprises are struggling to move beyond the pilot phase of AI adoption due to security concerns, rather than issues with model performance, according to Chatterbox Labs executives. To overcome this, companies need to commit to ongoing security testing and governance, rather than relying on vendor assurances or basic guardrails, to ensure the safe and secure deployment of AI systems.

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Lisp Machines: A Cult AI Computer's Boom and Bust [video]

The YouTube video "A Cult AI Computer's Boom and Bust" by Asianometry discusses the rise and fall of Lisp machines, specialized computers designed to run the Lisp programming language, which were popular in the 1980s. The video explores the history and significance of Lisp machines, including their innovative features such as graphical user interfaces and networking, and how they ultimately failed to compete with more general-purpose computers.

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Why is AI hard and Physics simple?

The fields of artificial intelligence and physics are being compared, with the suggestion that physical intuition and theoretical physics approaches can be applied to machine learning. The principle of sparsity is seen as a key connection between the two fields, and a new book on deep learning theory is proposed as a step towards bridging the gap between physics and AI.

ArXiv

A quantum algorithm for Khovanov homology

Khovanov homology is a topological knot invariant with significant mathematical and physical implications, but its computational complexity is not well understood. This work proposes a novel quantum algorithm to efficiently approximate Khovanov homology, overcoming previous limitations and introducing new connections between Khovanov homology and graph theory to analyze its spectral gap.

ArXiv

Reinforcement Pre-Training

Reinforcement Pre-Training (RPT) is a new paradigm for large language models that reframes next-token prediction as a reasoning task trained using reinforcement learning, offering a scalable method to leverage vast amounts of text data. RPT improves language modeling accuracy and provides a strong pre-trained foundation for further reinforcement fine-tuning, with results showing that increased training compute consistently improves next-token prediction accuracy.

ArXiv

Creating General User Models from Computer Use

The General User Model (GUM) is an architecture that learns about a user by observing their interactions with their computer, allowing it to make inferences about their knowledge, preferences, and intentions. GUMs can be used to enable a range of applications, including proactive assistants, context-aware chatbots, and personalized notification systems, and have been shown to make accurate and calibrated inferences about users in evaluations.

ArXiv

Lossless Compression of LLMl-Generated Text via Next-Token Prediction

The volume of data generated by large language models (LLMs) is growing rapidly, and effective compression of this data is becoming increasingly important, but it presents unique challenges due to its complexity and diversity. Researchers have found that LLMs can be used as efficient compressors of their own outputs, achieving remarkable compression rates that far surpass traditional methods, with some experiments showing compression rates exceeding 20x.

ArXiv

Show HN: Glowstick – type level tensor shapes in stable rust

The glowstick crate provides a safe and easy way to work with tensors in Rust by tracking their shapes in the type system, supporting various operations like matmul, conv2d, and reshape. It integrates with popular Rust ML frameworks like candle and Burn, and although it's currently pre-1.0 and subject to breaking changes, it offers features like expressible tensor shapes as types and human-readable error messages.

Repo

Show HN: Mcp-hacker-news – An MCP server for accessing Hacker News data via LLMs

The Model Context Protocol (MCP) server for Hacker News is a bridge between the official Hacker News API and AI-powered tools, enabling them to fetch and interact with live Hacker News data via standardized MCP endpoints. This server integrates with tools like Claude and Cursor, allowing for seamless interaction with Hacker News posts, comments, and user information through standardized Model Context Protocol endpoints.

Repo

I built a knowledge system that gives AI perfect codebase memory

Octocode is a powerful code indexer and semantic search engine that builds intelligent knowledge graphs of your codebase, combining advanced AI capabilities with local-first design to provide deep code understanding and intelligent assistance for developers. It offers features such as semantic code search, knowledge graph building, multi-language support, AI-powered features, and integration with AI assistants, and can be installed and used through various commands and configurations.

Repo

Show HN: Fortune Cookie MCP

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Repo

Show HN: Pyleak – Detect asyncio issues causing AI agent latency

Pyleak is a Python library that detects leaked asyncio tasks, threads, and event loop blocking, providing detailed stack trace information to help identify and fix issues. It offers various usage options, including context managers, decorators, and configuration options, making it suitable for testing and real-world applications to ensure asynchronous code runs efficiently and without leaks.

Repo