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nanograph

On-device property graph database with schema-as-code and no server.

nanograph is an on-device property graph database built on Rust, Lance, Arrow, and DataFusion. It supports schema-as-code, has a one-CLI-one-folder architecture, and does not require a server. The database is designed for agents and humans to read, write, and traverse the graph natively.

Based on: GitHub - nanograph/nanograph: On-device property graph database. Schema-as-code. One CLI → One Folder. No Server. Think: DuckDB for graphs. · github.com

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Parlant: Interaction Control Harness for Customer-Facing AI Agents

An open-source interaction control harness optimized for controlled, consistent, and predictable LLM interactions.

Parlant is an agentic harness that streamlines the development and maintenance of enterprise-grade B2C and sensitive B2B interactions. It focuses on conversational governance and behavioral control, allowing developers to control the agent's behavior with precision. Parlant treats misalignment as a core design problem, building on research into model accuracy and consistency.

Based on: GitHub - emcie-co/parlant: Build reliable customer-facing AI agents with Parlant: an interaction control harness optimized for controlled, consistent, and predictable LLM interactions. · github.com

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Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

A study examining the effect of early 2025 AI tools on experienced open-source developers' productivity.

The paper reports a randomized controlled trial (RCT) to understand how AI tools at the February-June 2025 frontier affect the productivity of experienced open-source developers. The study found that allowing AI actually increases completion time by 19%. The authors collected and evaluated evidence for various properties of their setting to understand this result.

Based on: Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity · arxiv.org

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The Convenience Trap

An article arguing that AI is generating more work rather than saving time.

The author critiques the idea that AI saves time, citing studies showing that it generates more volume but not necessarily better quality. The article discusses the 'verification burden' of reviewing and correcting AI output, and highlights the issue of synthetic content flooding the web.

Based on: The Convenience Trap · jessicatalisman.substack.com

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Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models

Implementation of a module that modernizes classic N-gram embeddings for O(1) lookup.

This repository contains the official implementation of a conditional memory module, Engram, which is a new axis of sparsity for large language models. It formulates the trade-off between neural computation and static memory, identifying a U-shaped scaling law that guides optimal capacity allocation. The module demonstrates consistent improvements over MoE baselines across knowledge, reasoning, code, and math domains.

Based on: GitHub - deepseek-ai/Engram: Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models · github.com

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LadybugDB

A columnar graph database and successor to KuzuDB.

LadybugDB is a columnar graph database built on DuckDB foundations, designed for agentic AI in highly regulated industries. It offers fast analytical queries, embedded and serverless capabilities, and enterprise support. LadybugDB aims to fill the gap left by KuzuDB's acquisition and development halt.

Based on: LadybugDB: DuckDB for Graphs — The KuzuDB Successor · ladybugdb.com

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The Ontology Layer of Design

An article discussing the importance of ontology in design, particularly in the context of AI and large language models.

The author argues that designers must adapt to the changing landscape of AI by defining a product's ontology, which is the basic structure of objects, relationships, and concepts. This involves understanding how to spot good ontologies from bad ones and leveraging language as a way of shaping the worlds our new AI tools will inhabit.

Based on: The Ontology Layer of Design · figureandground.substack.com

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Michaelliv/pi-generative-ui

A GitHub repository that reverse-engineers Claude.ai's generative UI for pi.

This repository replicates the system used by Claude.ai to visualize information in a native window. It uses Glimpse to open a native window and feeds partial HTML as tokens arrive, allowing for interactive widgets with sliders, charts, and animations. The design guidelines are extracted from Claude.ai's conversations.

Based on: GitHub - Michaelliv/pi-generative-ui: Claude.ai's generative UI — reverse-engineered, rebuilt for pi. Interactive HTML/SVG widgets in native macOS windows. · github.com

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Reverse-engineering Claude's Generative UI

An article about analyzing and building a generative UI for the LLM Claude.

The author reverse-engineers Claude's generative UI, understanding how it works and builds their own version for the terminal-based coding agent pi. The article explains the streaming architecture of Claude's UI and its differences from artifacts. It also discusses the challenges of building a similar UI for pi and presents a solution using Glimpse.

Based on: Reverse-engineering Claude · michaellivs.com

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Converting from OWL to SHACL, Part I

An article discussing the principles and benefits of converting from OWL to SHACL.

The author explores the reasons for converting from OWL to SHACL, including improved validation, support for reification, and better alignment with tabular data sources. The article also delves into design considerations, such as the differences between rdfs:subClassOf and sh:node in a NodeShape.

Based on: Converting from OWL to SHACL, Part I · ontologist.substack.com

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From Discovery to Influence: A Guide to GEO

A guide on how brands can win in AI-driven discovery with Generative Engine Optimization (GEO) strategies.

The resource discusses the shift from search engine optimization (SEO) to Generative Engine Optimization (GEO) for AI-powered discovery. It explains how AI assistants, browsers, and agents evaluate data and recommends a different kind of readiness. The guide provides insights on how brands can show up in AI-driven discovery and become the recommended brand.

Based on: From Discovery to Influence: A Guide to GEO · about.ads.microsoft.com

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Agent Experience Platform (AXP) | Scrunch

A platform that optimizes content for AI agents and large language models.

The Agent Experience Platform (AXP) is a tool that helps optimize content for AI agents and large language models. It detects agentic traffic, serves optimized content, and provides insights to grow an AI presence. The platform offers features such as agent traffic analysis, site maps, monitoring, and citations.

Based on: Agent Experience Platform (AXP) | Scrunch · scrunch.com