May 13, 2026

Daily

Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model

• Needle distills Gemini 3.1 into a 26m parameter Simple Attention Network, enabling local finetuning on personal devices, achieving 6000 toks/sec prefill and 1200 decode speed in production.

Interaction Models from Thinking Machines Lab

• Interaction models represent a transformative shift in AI, enabling real-time collaboration across audio, video, and text, thus overcoming the limitations of traditional turn-based systems.

A History of IDEs at Google

• Google's IDE landscape evolved from fragmentation to a unified platform with the introduction of Cider V, which now supports 80% of development in the main codebase, enhancing productivity through better integrations and AI features.

Beyond Semantic Similarity

• Direct Corpus Interaction (DCI) allows agents to search raw data using terminal tools, bypassing traditional retrieval systems that limit access and hinder multi-step reasoning.

The US is winning the AI race where it matters most: commercialization

• The US leads in AI commercialization, leveraging cloud infrastructure, data, and strategic capital, significantly outpacing competitors like China in revenue and adoption since the launch of DeepSeek R1 in January 2025.

An idiot's guide to lead optimisation for proteins

• Lead optimisation is a critical phase in drug design, where existing molecules are refined to enhance their efficacy, leveraging machine learning to propose and test modifications efficiently, as exemplified by the Cradle-1 pipeline.

Show HN: Statewright – Visual state machines that make AI agents reliable

• Statewright enhances AI agent performance by implementing state machines that limit tool access, allowing models to focus on specific tasks and improve efficiency across various platforms like Claude Code and Codex.

Fc, a lossless compressor for floating-point streams

• fc is a lossless compressor for IEEE-754 64-bit doubles, achieving a compression ratio of 3.07 and a decoding speed of 1277 MB/s, outperforming competitors on structured data.

$δ$-mem: Efficient Online Memory for Large Language Models

• $δ$-mem introduces a lightweight memory mechanism that enhances large language models by utilizing a compact online state, allowing for efficient historical information management without the need for extensive context window expansion.

Human-level performance via ML was not proven impossible with complexity theory

• The Ingenia Theorem proposed by Van Rooij et al. claims that achieving AGI via ML is impossible, but this assertion is fundamentally flawed due to the lack of a precise definition for "human-level classifier."

TabPFN-3 just released: a pre-trained tabular foundation model for up to 1M rows

• TabPFN-3 is a pre-trained tabular foundation model capable of handling up to 1M rows in a single forward pass, significantly enhancing efficiency with 10x-1000x faster inference than its predecessors.

Elastic Attention Cores for Scalable Vision Transformers

• Elastic Attention Cores introduce a core-periphery block-sparse attention structure for Vision Transformers, reducing computational costs from N² to 2NC + C² for C core tokens, enhancing scalability at higher resolutions.

I Found a Hidden Ratio in Transformers That Predicts Geometric Stability

• A hidden ratio in transformer models, derived from Lyapunov spectral analysis, predicts geometric stability, indicating whether a model will collapse to rank-1 based on the MLP and attention spectral norms.

Learning, Fast and Slow: Towards LLMs That Adapt Continually

• Fast-Slow Training (FST) enables large language models (LLMs) to adapt continually by utilizing fast weights for task-specific learning while maintaining slow weights for general reasoning, enhancing efficiency and performance.

Solve the Loop: Attractor Models for Language and Reasoning

• Attractor Models enhance language modeling and reasoning by iteratively refining embeddings, achieving a 46.6% improvement in perplexity and 19.7% in accuracy while reducing training costs compared to standard Transformers.