Llms

Andreas Kling states that Ladybird is becoming a browser for real users, and those introducing changes must be responsible for them and answer for the consequences.

A security researcher created a vulnerable React Native app and spent $1,500 testing if large language models (LLMs) could exploit it, with the goal of reproducing common class of exploits found in multiple apps.

Researchers at Anthropic have made significant strides in mechanistic interpretability of Large Language Models (LLMs), enabling a deeper understanding of their inner workings. This breakthrough could lead to steering model behavior and detecting dangerous intent.

Researchers proposed AlignAtt4LLM, a system for simultaneous speech translation, using a synchronous cascade of ASR and MT. This is the first application of AlignAtt to a decoder-only LLM, with notable modifications to recover a usable policy.

A software development telemetry firm, Faros.ai, has found that the current use of LLMs is likely destroying value on average, based on data from Jira, Github, and CI/CD pipelines.

Users on Hacker News discuss the current state of app development, including the impact of AI/LLMs and the challenges of developing with frameworks like .NET Maui.

MCP (Model Context Protocol) has been criticized for eating context, having low reliability, and overlapping with existing CLI/API. Developers are starting to think differently about its potential.

A TechCrunch AI article aims to clarify AI terminology, including LLMs, RAG, and RLHF, by providing a living glossary that updates as the field evolves.

Hy3 LLM has surpassed Claude in OpenRouter's AI Model Rankings, with DeepSeek Flash V4 also performing well. Hy3's open-source release is from Chinese megacorp Tencent, but its model page is sparse and has unfavorable benchmark results.

The author analyzed 1.2M messages over 20 years, creating a structured vault of their life, and discovered insights into emotional bandwidth, endearment cycles, and friendship half-lives.

Current large language models (LLMs) cannot safely modify real software systems due to their inability to understand dependencies, invariants, and consequences of code changes.

Data quotes Kyle Ferrana as saying that raising shields is a matter of strategy, not precaution, after a hull breach occurs. This highlights the importance of taking calculated risks in decision-making.

Paul Graham believes that using AI to write emails feels like being lied to and makes him think less of the author. He criticizes founders for using AI to write in a hard-hitting journalistic style.

Researchers are searching for potential backdoors in Jane Street's large language models (LLMs), citing concerns about model safety and reliability. The investigation is ongoing, with no concrete findings reported yet.