AI is eating our brains. MIT study: Your brain on ChatGPT
The study reveals that using Large Language Models (LLMs) like ChatGPT in educational settings may lead to a cognitive cost, as participants using LLMs exhibited weaker neural connectivity and lower engagement compared to those relying solely on their brains or search engines.
MiniMax-M1 open-weight, large-scale hybrid-attention reasoning model
MiniMax-M1 is the first open-weight hybrid-attention reasoning model, featuring a Mixture-of-Experts architecture and a lightning attention mechanism, enabling it to handle 1 million tokens and outperform competitors in complex tasks.
Making 2.5 Flash and 2.5 Pro GA, and introducing Gemini 2.5 Flash-Lite
Gemini 2.5 now includes Flash and Pro models, which are generally available, alongside the introduction of 2.5 Flash-Lite, the fastest and most cost-efficient model yet.
AMD's CDNA 4 Architecture Announcement
AMD’s CDNA 4 architecture enhances matrix multiplication performance for machine learning by optimizing lower precision data types, while maintaining a competitive edge in vector operations through a familiar chiplet design.
Timescale Is Now TigerData
TigerData, formerly Timescale, has evolved from a time-series database to a modern PostgreSQL solution, catering to the needs of over 2,000 customers and achieving mid 8-digit ARR with significant year-over-year growth.
Andrej Karpathy's YC AI SUS talk on the future of the industry
Andrej Karpathy emphasizes a transformative era in software development, coining Software 3.0 as a paradigm shift where neural networks, particularly large language models (LLMs), are now programmable in natural language, making programming more accessible to everyone.
LLMs pose an interesting problem for DSL designers
LLMs challenge traditional DSL design by raising the question of necessity; if LLMs can generate code efficiently, why invest in creating domain-specific languages that eliminate boilerplate?
Is There a Half-Life for the Success Rates of AI Agents?
AI agents exhibit an exponentially declining success rate on longer tasks, characterized by a unique half-life, suggesting that as task duration increases, the likelihood of failure rises due to the accumulation of subtasks, as shown in Kwa et al. (2025) arXiv.
The Unreasonable Effectiveness of Fuzzing for Porting Programs
Fuzzing with LLMs has proven effective for automating the porting of programs from C to Rust, allowing for significant reductions in manual coding effort and potential errors during the process.
Time Series Forecasting with Graph Transformers
Graph Transformers enhance time series forecasting by leveraging interconnected data from relational databases, allowing for more accurate predictions through a comprehensive end-to-end pipeline that integrates various signals from the entire graph structure.
Real-time action chunking with large models
Real-time chunking (RTC) enables robots to execute actions without delays or discontinuities, significantly improving performance in dynamic tasks like striking a match or plugging in an Ethernet cable, even under high latency conditions exceeding 300 milliseconds.
[D] CausalML : Causal Machine Learning
CausalML is a significant advancement in Causal Machine Learning, offering a comprehensive 140-page survey that explores its methodologies and applications. Read the survey here.
Revisiting Minsky's Society of Mind in 2025
Minsky’s vision of intelligence as a modular society of agents is gaining traction in AI development, as researchers pivot from monolithic models to multi-agent systems that enhance robustness and scalability.
Reasoning by Superposition: A Perspective on Chain of Continuous Thought
Continuous chain-of-thought (CoT) techniques in Large Language Models (LLMs) outperform discrete methods by enabling parallel breadth-first search, solving directed graph reachability with D steps, where D is the graph's diameter, compared to O(n²) steps for discrete CoTs.
[R] Breaking Quadratic Barriers: A Non-Attention LLM for Ultra-Long Context Horizons
The proposed non-attention based architecture for large language models (LLMs) can efficiently manage context windows of hundreds of thousands to millions of tokens, overcoming the limitations of traditional Transformer designs that face quadratic memory and computation overload.