Tokens are getting more expensive

Companies that rely on AI models are finding that despite the cost of these models decreasing over time, their margins are not improving as expected. This is because the demand for the latest and best models remains high, and the cost of using these models is not decreasing in the way that companies had anticipated, due to the increasing complexity and token consumption of newer models.

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One Dataset. No Warning. Google Took Everything. You're Not Safe Either

Mark Russo, the developer of a privacy app called Punge, had his entire Google account suspended after he downloaded a dataset used in AI research, which allegedly contained CSAM, resulting in the loss of access to his email, backend infrastructure, and other essential tools. The suspension was done without warning or explanation, and Russo is now warning that this could happen to anyone, highlighting the need for more transparency and accountability in content moderation.

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How I use Claude Code to implement new features in an existing complex codebase

The author shares their approach to using Claude Code, an AI coding tool, to implement new features in a complex codebase, emphasizing the importance of human oversight and adherence to a set of guidelines outlined in their CLAUDE.md file. The guidelines cover best practices for implementation, testing, database management, code organization, and tooling, with the goal of maintaining a clean, production-grade codebase and reducing bugs introduced by AI.

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Show HN: ArchAltect, AI to generate roadmaps in a clean UI

ArchAltect is an AI-powered project intelligence platform that transforms abstract web project concepts into concrete, actionable plans, guiding users from conception to completion with comprehensive roadmaps and real-time progress tracking. The platform offers a range of features, including complete project lifecycle management, secure and reliable data storage, and permanent access to past projects, making it a game-changer for turning ideas into successful projects.

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'A black hole': New graduates discover a dismal job market

Recent graduates are facing a challenging job market, with many describing their search as a "black hole" and feeling disheartened after months of applying to hundreds of jobs with little to no response. The national economic data supports their experience, with the unemployment rate among recent graduates increasing to 5.3% and hiring slowing down across the economy, making it one of the toughest job markets for new graduates since 2015.

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Agentic Web: Weaving the Next Web with AI Agents

The Agentic Web is a new phase of the internet characterized by autonomous, goal-driven interactions between AI agents, which can plan, coordinate, and execute complex tasks on behalf of users, enabling a more interactive and automated web experience. A framework for understanding and building the Agentic Web is presented, covering its evolution, core technological foundations, and key dimensions, as well as discussing potential applications, risks, and research directions for developing open and intelligent ecosystems.

ArXiv

Git Context Controller: Manage the Context of LLM-Based Agents Like Git

The Git-Context-Controller (GCC) framework manages context as a versioned memory hierarchy, enabling agents to structure their memory and perform operations like checkpointing, exploration, and reflection. Agents equipped with GCC have achieved state-of-the-art performance, resolving software bugs and completing tasks with significantly higher success rates compared to those without GCC.

ArXiv

Show HN: Arch-Router – Aligning LLM Routing with Human Preferences

Existing large language model routing approaches are limited by their reliance on benchmarks that don't capture human preferences and their selection from a limited pool of models. A new framework, Arch-Router, is proposed, which uses a compact 1.5B model to map queries to user-defined domains and action types, allowing for more transparent and flexible routing decisions that align with human preferences.

ArXiv

Falcon-H1: A Family of Hybrid-Head Models Redefining Efficiency and Performance

Falcon-H1 is a new series of large language models that combines Transformer-based attention with State Space Models to achieve high performance and efficiency, with models ranging from 0.5B to 34B parameters. The Falcon-H1 models demonstrate state-of-the-art performance, rivaling larger models while using fewer parameters and less data, and are suitable for a wide range of applications, including reasoning, mathematics, and multilingual tasks.

ArXiv

Hypertokens: Holographic Associative Memory in Tokenized LLMs

Large Language Models (LLMs) suffer from information loss, which can be addressed by reframing the issue as a communication problem and using a new memory framework called HDRAM. HDRAM, which combines error-correcting codes, holographic computing, and quantum-inspired search, enables efficient key-value operations and improves associative retrieval in LLMs without requiring architectural changes.

ArXiv

Show HN: InsForge – Open-source agent-native alternative to Supabase

InsForge is an open-source, AI-native alternative to Supabase, allowing AI agents to build and manage full-stack applications autonomously with features like authentication, database, storage, and serverless functions. To get started, users can install and run InsForge using Docker, connect an AI agent, and begin building projects with natural language prompts, such as creating a todo app or Instagram clone.

Repo

Python Testing MCP Server

This project is an advanced Model Context Protocol (MCP) server that provides AI-powered Python testing tools, including intelligent unit test generation, AI-powered fuzz testing, and advanced coverage testing. The server leverages Google's Gemini AI and BAML (Boundary ML) to generate comprehensive unit tests and perform sophisticated fuzz testing on Python code, offering features such as automated testing capabilities, hybrid AI approach, and modular architecture.

Repo

Show HN: Mcp-error-formatter – Cursor-style JSON errors for any MCP/LLM tool

The mcp-error-formatter package standardizes error formatting for Model Context Protocol (MCP) tool calls, transforming JavaScript Errors into structured CallToolResult objects with auto-detection of error types and request-IDs for easy tracing. It provides a lightweight and seamless way to make MCP tools more robust and user-friendly, with zero runtime dependencies beyond uuid and compatibility with official MCP SDKs and JSON-RPC servers.

Repo

LLM Economist – Mechanism Design for Simulated Agent Societies

The LLM Economist is a comprehensive framework for economic simulations using Large Language Models (LLMs), allowing for realistic and dynamic simulations with diverse agent populations to study tax policy optimization and mechanism design. It supports multiple LLM providers, including OpenAI, Google Gemini, and Anthropic Claude, and offers various features such as multi-LLM support, multiple deployment options, and scalable architecture.

Repo

Ask HN: Can AI-generated forecasts be trusted for new economic protocols?

This protocol proposes a peer-to-peer social currency that rewards altruistic contributions and fosters trust within communities, operating through a three-layer structure of currency, governance, and implementation. The system is designed to be free, open, and non-ownership based, allowing anyone to implement and modify it, with the goal of creating a trust-based economy rooted in local communities and promoting civilizational regeneration.

Repo