Build real-time knowledge graph for documents with LLM

CocoIndex is a tool that makes it easy to build and maintain knowledge graphs with continuous source updates, and it uses Large Language Models (LLM) to extract relationships between concepts in documents. The process involves adding documents as a source, extracting summaries and relationships using LLM, and then exporting the data to a graph database like Neo4j to build a knowledge graph.

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GOP sneaks decade-long AI regulation ban into spending bill

House Republicans have added a provision to a spending bill that would ban state and local governments from regulating artificial intelligence (AI) for 10 years, effectively halting all local oversight of AI by US states. The move has been criticized by tech safety groups and Democrats, who argue that it would leave consumers unprotected from AI harms and is a "giant gift to Big Tech".

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AI Is Like a Crappy Consultant

The author, a self-proclaimed AI/LLM skeptic, tried using AI to help with coding in Swift and SwiftUI, and concluded that AI should be treated like an untrustworthy consultant, providing guidance but not being trusted to make big decisions or work unsupervised. The author found that AI excels in certain areas, such as finding syntax errors, but is not reliable for tasks that require critical thinking, architecture, or complex problem-solving, and is best used for grunt work or tasks that can be closely supervised.

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DeepSeek’s founder is threatening US dominance in AI race

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Chrome's New Embedding Model: Smaller, Faster, Same Quality

Chrome's latest update features a new text embedding model that is 57% smaller than its predecessor, reducing from 81.91MB to 35.14MB, while maintaining virtually identical performance in semantic search tasks. This size reduction was achieved through quantization of the embedding matrix from float32 to int8 precision, resulting in a model that is more storage-efficient without compromising search quality, and bringing benefits such as reduced storage footprint, faster browser updates, and improved resource efficiency.

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Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions

This survey categorizes memory representations and introduces six fundamental memory operations, mapping them to relevant research topics to provide a structured perspective on memory in AI systems, particularly in large language models. By reframing memory systems through these operations and representation types, the survey clarifies the functional interplay in AI agents and outlines promising directions for future research.

ArXiv

LithOS: An Operating System for Efficient Machine Learning on GPUs

LithOS, a novel operating system approach, is introduced to optimize GPU utilization and energy efficiency in datacenters for machine learning, providing fine-grained management and strong isolation. The system achieves significant improvements in performance, including reduced tail latencies and increased aggregate throughput, while also offering substantial GPU capacity and energy savings with minimal performance hits.

ArXiv

IterGen: Iterative Semantic-Aware Structured LLM Generation with Backtracking

Large Language Models (LLMs) often produce flawed outputs, and current libraries for structured LLM generation lack the ability to correct or refine outputs mid-generation. IterGen, a new library, addresses this issue by enabling iterative, grammar-guided LLM generation that allows users to move forward and backward within the generated output, making corrections during the process and improving overall efficiency and accuracy.

ArXiv

CRANE: Reasoning with Constrained LLM Generation

Constrained large language model (LLM) generation can enforce formal grammar, but strict enforcement often diminishes the model's reasoning capabilities. The proposed CRANE algorithm balances correctness and flexibility, and experiments show it significantly outperforms state-of-the-art constrained and unconstrained decoding strategies, with up to 10% points accuracy improvement on challenging benchmarks.

ArXiv

Type-constrained code generation with language models

Large language models (LLMs) often produce uncompilable code due to typing errors, but a new type-constrained decoding approach leveraging type systems can guide code generation and enforce well-typedness. This approach has been shown to reduce compilation errors by more than half and increase functional correctness in various code-related tasks, demonstrating its effectiveness and generality across different LLMs and model sizes.

ArXiv

Show HN: HelixDB – Open-source vector-graph database for AI applications (Rust)

HelixDB is an open-source, high-performance graph-vector database built in Rust, designed for RAG and AI applications, offering fast and efficient performance, native support for graph and vector data types, and reliable storage. It provides a range of features, including a query language, CLI tool, and SDKs for TypeScript and Python, and is available under the AGPL license, with commercial support and managed service options also available.

Repo

Simple GPT in pure Go, trained on Jules Verne books

The gpt-go project is a simple implementation of the GPT model in pure Go, trained on Jules Verne books, and can be run and trained on a local machine. The repository provides a companion to the "Neural Networks: Zero to Hero" course, with explanations and examples of neural network concepts, including self-attention mechanisms, and allows for customization and experimentation with different datasets and models.

Repo

Show HN: OpenMemory – Make your MCP clients more context-aware

Mem0 is an intelligent memory layer that enhances AI assistants and agents by enabling personalized interactions, remembering user preferences, and adapting to individual needs. It offers a range of features, including multi-level memory, developer-friendly APIs, and cross-platform SDKs, making it suitable for various applications such as customer support, healthcare, and productivity.

Repo

I built a type-safe .NET casting library powered by AI

ArtificialCast is a lightweight, type-safe casting and transformation utility powered by large language models, allowing seamless conversion between strongly typed objects using type metadata, JSON schema inference, and prompt-driven reasoning. It provides a suite of transformation utilities that can generate objects from type definitions, transform between different models, merge and split structured data, and perform natural language-powered queries, but is fundamentally unsafe and can fail in ways that resemble success.

Repo

Show HN: LLM-God – An LLM Multiprompting App

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Repo