Le Chat: Custom MCP Connectors, Memories

Le Chat is introducing several new features, including a wide range of enterprise-ready connectors that allow users to integrate their workflows and access various tools, as well as a "Memories" feature that enables the AI assistant to recall context and provide more personalized responses. Both features are available to all Le Chat users, including those on the free plan, and can be accessed through the web, mobile, or on-premises deployments.

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LLM Visualization

The nano-gpt model is a large language model with 85,000 parameters that takes a sequence of letters, or tokens, and aims to sort them in alphabetical order. The model processes the input sequence, which is converted into an embedding vector, and passes it through multiple transformer layers to predict the next token in the sequence, with the goal of eventually sorting the input sequence.

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io_uring is faster than mmap

Sourcing data directly from disk can be faster than caching in memory due to exponential growth in disk bandwidth and stagnation in memory access latency. An experiment using a simple loop to count integers on a server with a 32-core processor and 96GB of DDR4 memory found that reading from disk was initially slower, but subsequent runs that utilized cached memory were still limited by the CPU's instructions per second, reaching only 3.71 GB/s, well below the memory bandwidth limit.

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A PM's Guide to AI Agent Architecture

A product manager's decision to make their AI agent "smarter" often neglects the importance of architectural decisions that shape the user experience and trust in the agent. To build a trustworthy AI agent, product managers must consider several key layers, including memory, system integration, capabilities, and trust strategies, which collectively determine whether users will trust and continue to use the agent or abandon it after encountering a complex issue.

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Coinbase CEO Revealed He Fired Engineers Who Didn't Use AI Tools

Coinbase CEO Brian Armstrong revealed that he fired engineers who refused to adopt AI coding tools, requiring all engineers to use tools like Cursor and Copilot within a one-week deadline. The company has since seen significant results, with 33% of code now written by AI, and aims to reach 50% by the end of the quarter, while also emphasizing the need for human oversight and review to maintain quality and security.

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Supporting Our AI Overlords: Redesigning Data Systems to Be Agent-First

Large Language Model agents will likely become the primary users of data systems, employing a process called agentic speculation to explore and analyze data, which poses challenges for current systems due to its high volume and inefficiencies. To address this, new research opportunities are emerging to develop an agent-first data systems architecture that can natively support agentic workloads, including new query interfaces, processing techniques, and memory stores.

ArXiv

LLM Social Simulations Are a Promising Research Method

Large language model (LLM) simulations of human research subjects have the potential to provide a valuable data source for understanding human behavior, but their adoption has been limited due to several challenges. Addressing these challenges, such as through context-rich prompting and fine-tuning with social science datasets, could enable the effective use of LLM social simulations for pilot studies and potentially more widespread applications in the future.

ArXiv

Mathematical research with GPT-5: a Malliavin-Stein experiment

GPT-5 was tested in a controlled experiment to see if it could extend a qualitative fourth-moment theorem to a quantitative formulation with explicit convergence rates in the Gaussian and Poisson settings, a problem that had not been addressed in existing literature. The experiment and its results, which assess GPT-5's ability to go beyond known results in the Malliavin--Stein framework for central limit theorems, are documented and discussed in the paper.

ArXiv

Survey of Deep Learning and Foundation Models for Time Series Forecasting

Deep learning has been slow to show advantages in time series forecasting, but recent architectural advances have begun to change this, with techniques like encoder-decoders and transformers showing promise. However, challenges remain, particularly in pandemic prediction, and research is ongoing to develop foundation models and integrate external knowledge, such as Knowledge Graphs and Large Language Models, to improve deep learning models' performance and interpretability.

ArXiv

The Landscape of Agentic Reinforcement Learning for LLMs

Agentic reinforcement learning (Agentic RL) represents a significant shift from traditional reinforcement learning, transforming large language models into autonomous decision-making agents that can interact with complex environments. This new approach is characterized by the use of temporally extended, partially observable Markov decision processes and is supported by a comprehensive taxonomy of agentic capabilities and applications, as well as a compilation of open-source resources to facilitate future research.

ArXiv

Show HN: Llmberjack, A simple open-source Go interface for multiple LLM provider

LLMberjack is a type-safe wrapper adapter around various Large Language Model (LLM) providers, allowing users to configure multiple providers and select them when sending requests. The library provides a series of chainable methods to build and execute requests, including support for typed output, provider and model selection, prompting, and conversation history accumulation through threads.

Repo

Show HN: Recreated Claude Code's behavior in 280 lines of Python

The mini-agent is a minimal coding assistant with the same behavior as Claude Code, but without its "secret sauce", allowing it to run an interactive coding agent with basic tools and features. It supports various features like custom subagents, TODO lists, and model selection, and can be run in interactive or non-interactive modes, with the ability to resume from existing transcripts and migrate conversations from Claude Code.

Repo

Smart Speed – adjusts YouTube playback speed based on real-time audio using AI

YouTube Smart Speed is a Firefox extension that uses artificial intelligence to adjust YouTube playback speed in real-time, automatically speeding up during silence and slowing down during detected speech. The extension offers customizable settings, including speed range, manual speed control, and machine learning capabilities that allow it to learn from user behavior and improve over time.

Repo

Ask HN: What Arc/Dia features should we prioritize?

BrowserOS is an open-source, Chromium-based browser that runs AI agents locally, providing a privacy-first alternative to other browsers by keeping user data on their computer. It offers features like AI-powered automation, a familiar interface, and extensibility, with the goal of reimagining browsers from scratch and providing a more secure and automated browsing experience.

Repo

VX-NOVA.Ω1: A Symbolic AI Engine That Patches Code Without Any Models

VX-NOVA.Ω1 is a fully symbolic AI engine that uses recursive scroll ignition to patch vulnerable code without relying on machine learning models, training data, or internet connections, making it suitable for secure, air-gapped environments. The system is deterministic, auditable, and runs offline, generating functional code from symbolic seeds and supporting layered recursion for self-reflective logic generation.

Repo