I passionately hate hype, especially the AI hype

The author passionately condemns the concept of hype, particularly in the tech industry, where it can lead to exaggerated promises, wasted resources, and negative consequences for investors, businesses, and individuals. The current AI hype is cited as a prime example, with the author arguing that only about 10% of the hype is based on useful facts, while the rest is misleading and driven by greed, and that the over-reliance on AI can hurt businesses and individuals in the long run.

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Walled Gardens Can Kill

The author, an Apple ecosystem user, had a life-altering experience when their wife fell ill and they were unable to install a geo-restricted insurance app on their iPhone due to Apple's locked-down ecosystem. This led them to use an Android emulator and eventually purchase an Android phone to access the necessary information, changing their opinion on the matter and prompting them to advocate for a more open approach, similar to the EU's Digital Markets Act.

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Dear Lewis, my CEO wants AI to do it all. How do I argue for humans?

A VP of Sales, Aiden, was tasked by his CEO to justify why human sales managers couldn't be replaced by AI, and he had 48 hours to make his case. With the help of a coach, Aiden developed the HUMAN framework, which highlights the unique strengths of human sales leaders, such as detecting unsaid cues, embodying accountability, and taking context-aware action, and ultimately used this framework to successfully argue for the value of human sales managers.

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A Realistic AI Timeline

The current state of AI research suggests that the development of general intelligence will not follow a straightforward path of increasing model size and capacity, but rather will involve specialized, opinionated training and a focus on reasoning, reinforcement learning, and mid and post-training. By 2026, generative AI is expected to become a major player, with significant revenue growth and widespread adoption, driven by advances in accuracy, democratization of reinforcement learning, and improvements in model interpretability.

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A 1980s toy robot arm inspired modern robotics

The Armatron robotic arm was a groundbreaking toy introduced in 1981 that allowed users to control a mechanical arm with twin joysticks, featuring complex movements and a unique whirring sound. The toy's designer, Hiroyuki Watanabe, a Japanese engineer and toy designer, was inspired by a newspaper clipping of a mechanical arm and drew from his experience with radio-controlled helicopters to create the Armatron's innovative control system.

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PIM-LLM: A High-Throughput Hybrid PIM Architecture for 1-Bit LLMs

PIM-LLM is a hybrid architecture that combines analog processing-in-memory and digital systolic arrays to accelerate 1-bit large language models, achieving significant improvements in performance and efficiency. The design results in up to 80x faster processing and 70% higher energy efficiency compared to conventional hardware accelerators, setting a new benchmark for PIM-based LLM accelerators.

ArXiv

How to evaluate control measures for LLM agents?

AI developers will need to implement sophisticated control measures to prevent potentially misaligned agents from causing harm, and can test these measures through control evaluations where a "red team" attempts to subvert them. A proposed framework adapts the capabilities of the red team to the advancing AI capabilities, allowing for more practical and cost-effective control measures, but also highlights the need for research breakthroughs to ensure safety, particularly for superintelligent agents.

ArXiv

"All Roads Lead to ChatGPT": How AI Is Eroding Social Interactions

The adoption of generative AI is affecting learning and help-seeking behaviors, with students often relying on AI instead of their peers for assistance, which can undermine social interactions and peer learning. This shift is leading to increased feelings of isolation and demotivation among students, which is concerning given the importance of social interactions in learning and a sense of belonging.

ArXiv

Human Trust in AI Search: A Large-Scale Experiment

Large Language Models (LLMs) that power generative search engines can significantly influence human decision making, but the causal effect of these designs on human trust has not been established. A study of ~12,000 search queries and a randomized experiment found that trust in GenAI search can be increased by reference links and citations, but decreased by uncertainty highlighting, and that trust varies by topic, demographics, and user experience, ultimately predicting behavior such as clicking and evaluating search results.

ArXiv

SDFs from Unoriented Point Clouds Using Neural Variational Heat Distances

A novel variational approach is proposed for computing neural Signed Distance Fields (SDF) from unoriented point clouds, utilizing the heat method instead of the eikonal equation. This approach yields accurate surface reconstruction and consistent SDF gradients, and is demonstrated to be effective through numerical experiments and a proof-of-concept solving a PDE on the zero-level set.

ArXiv

Less Slow C++

This repository provides practical examples of writing efficient C and C++ code, covering topics such as performance-oriented design, compiler optimizations, and parallel algorithms. The project includes a set of benchmarks that demonstrate various techniques for improving performance, such as using custom libraries, optimizing memory allocation, and leveraging parallel processing, and can be built and run on Linux, MacOS, and Windows using GCC, Clang, or MSVC compilers.

Repo

Athena – An open source production-ready general AI agent

Athena is a production-ready general AI agent that can perform various tasks, such as finding and summarizing information, automating web browsers, and executing Python code, to help users move from ideas to results effortlessly. The project is open-source and community-driven, with a roadmap that includes features like autonomous code writing, robust browser automation, and long-term memory with retrieval, and welcomes contributions from everyone.

Repo

V0, Cursor, Manus, Same.dev, Lovable, Devin and Replit Agent System Full Prompts

The author has obtained over 5,500 lines of official system prompts and internal tools from various AI models, including FULL v0, Manus, Cursor, Same.dev, Lovable, Devin, and Replit Agent. The author is also offering a free AI security audit through ZeroLeaks to help AI startups secure their systems and protect against vulnerabilities.

Repo

Show HN: Product Analytics for LLMs

Conversational Product Analytics is a toolkit for analyzing and improving multi-turn AI conversations through data-driven insights, providing a way to gain deeper insights into user experiences and systematically improve conversational AI products. The toolkit generates event schemas, tags messages with relevant events using large language models (LLMs), and uploads the tagged events to analytics platforms like Amplitude and PostHog, enabling effective product analytics for conversational AI products.

Repo

Show HN: SecureML – Privacy and Compliance Toolkit for ML

SecureML is an open-source Python library that integrates with popular machine learning frameworks to ensure AI agents handle sensitive data in compliance with data protection regulations. It provides various features, including data anonymization utilities, privacy-preserving training methods, compliance checkers, synthetic data generation, and regulation-specific presets, to help developers protect sensitive data and maintain regulatory compliance.

Repo