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GitHub - arman-bd/guppylm: A ~9M parameter LLM that talks like a small fish.

Embarrassingly Simple Self-Distillation Improves Code Generation

Show HN: 1-Bit Bonsai, the First Commercially Viable 1-Bit Large Language Models

PrismML — Concentrating Intelligence

GitHub - microsoft/VibeVoice: Open-Source Voice AI

GitHub - karpathy/autoresearch: AI agents automatically conducting research on single-GPU nanochat training

Vibe Coding for Business: How to Use AI for Software Development Without Risks

GitHub - Pinperepette/snakebite: Detect malicious PyPI packages using heuristic analysis and LLM-powered filtering to uncover credentials

AI in anesthesia: how KOI reduces ASA-PS classification errors by 89%

GitHub - 666ghj/MiroFish: A Simple and Universal Swarm Intelligence Engine, Predicting Anything. A concise and universal swarm intelligence engine, predicting everything.

GDPR and Artificial Intelligence: Practical Guide for European Businesses

How to choose a private AI infrastructure for your business

Introducing Mistral Small 4 | Mistral AI

GitHub - andrewyng/context-hub

Coding My Handwriting — Amy Goodchild

GitHub - jundot/omlx: LLM inference server with continuous batching and SSD caching for Apple Silicon — managed from the Mac

On-Premise vs Cloud AI: Which to Choose for Your SME — Complete Analysis

Natural Language to SQL: query your enterprise databases without writing code

My head of SEO, Claude Coworker

Building AI Coding Agents for the Terminal: Framework, Integration, Context Engineering, and Lessons Learned

GitHub - NousResearch/hermes-agent: The agent that grows with you

GitHub - bytedance/deer-flow: An open-source SuperAgent framework that researches, codes, and creates. With the help of sandboxes,

spent the entire day testing Qwopus (Claude 4)

GitHub - z-lab/paroquant: [ICLR 2026] ParoQuant: Pairwise Rotation Quantization for Efficient Reasoning in Large Language Model Inference

GitHub - Search code, repositories, users, issues, pull requests...: Apple Silicon (MLX) port of Karpathy's autoresearch — autonomous AI research loops on Mac, no PyTorch

AI for Professional Firms: Complete GDPR Guide + 15 Concrete Use Cases

AI Act 2026: a practical guide for European SMEs

MicroGPT is a compact, open-source language model designed for efficient text generation and understanding. It is built to be lightweight and can run on a variety of devices, including personal computers and even some mobile devices. MicroGPT is intended for tasks such as text completion, summarization, translation, and more, making it a versatile tool for developers and researchers working with natural language processing.

GLM-5

AI Costs for SMEs: Complete Cost Breakdown and ROI Calculator

Step 3.5 Flash: Fast enough to think. Reliable enough to act.

ORCA vs ChatGPT: why your enterprise chatbot must be private

Keycloak

GitHub - zai-org/GLM-OCR: GLM-OCR: Accurate × Fast × Comprehensive

ChatGPT Alternatives for Business: Complete GDPR, Cost, and Security Comparison

GitHub - EricLBuehler/mistral.rs: Fast, flexible LLM inference

Why your business needs private AI (not ChatGPT)

GitHub - alexziskind1/llama-throughput-lab: Interactive launcher and benchmarking framework for llama.cpp server throughput, featuring tests, sweeps, and round-robin load tools.

GitHub - qwibitai/nanoclaw: A lightweight alternative to Clawdbot / OpenClaw that runs in Apple containers for security. Connect

How to Implement AI in Your Business: The Complete Roadmap from Zero to Production

GitHub - moltbot/moltbot: Your own personal AI assistant. Any operating system. Any platform. The lobster way. 🦞

GitHub - aiming-lab/SimpleMem: SimpleMem: Efficient Lifelong Memory for LLM Agents

GitHub - mikekelly/claude-sneakpeek: Obtain a parallel build of Claude code that unlocks feature-flagged capabilities such as swarm mode.

GitHub - virattt/ai-hedge-fund: An AI Hedge Fund Team

moonshotai/Kimi-K2.5 · Hugging Face

Welcome - Poké Documentation

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

NVIDIA PersonaPlex: Natural Conversational AI With Any Role and Voice - NVIDIA ADLR

GitHub - different-ai/openwork: An open-source alternative to Claude Cowork, powered by OpenCode.

GitHub - google/langextract: A Python library for extracting structured information from unstructured text using large language models (LLMs) with precision.

GitHub - memodb-io/Acontext: Data platform for context engineering. A context data platform that stores, observes, and learns. Join

GitHub - rberg27/doom-coding: A guide on how to use your smartphone to code anywhere at any time.

GitHub - bolt-foundry/gambit: Agent framework for building, running, and verifying LLM workflows

Private AI for SMEs: The Complete 2026 Guide for European Businesses

GitHub - eigent-ai/eigent: Eigent: The Open Source Cowork Desktop to Unlock Your Exceptional Productivity.

Ask HN: What is the best way to provide continuous context to models?

Recursive Language Models

Reimagining LLM Memory: Using Context as Training Data Unlocks Models That Learn at Test-Time

Show HN: Agent-of-Empires: OpenCode and Claude Code Session Manager

You Should Write an Agent · The Fly Blog

Getting Started - SWE-agent Documentation

How to Build an Agent - Amp **Introduction** Building an agent, especially one that leverages the power of Amp, involves several key steps. Amp, which stands for Advanced Multi-Purpose Protocol, is a versatile framework designed to enhance the capabilities of agents in various domains. This guide will walk you through the process of creating an agent using Amp, from conceptualization to deployment. **1. Define the Purpose and Scope** Before diving into the technical details, it's crucial to define the purpose and scope of your agent. Ask yourself the following questions: - What specific tasks will the agent perform? - In what environments will the agent operate? - What are the key performance metrics for success? **2. Choose the Right Tools and Technologies** Selecting the appropriate tools and technologies is essential for building a robust agent. For an Amp-based agent, you might need: - **Programming Languages**: Python, Java, or C++ are commonly used. - **Development Frameworks**: TensorFlow, PyTorch, or custom frameworks compatible with Amp. - **Data Sources**: APIs, databases, or real-time data streams. - **Communication Protocols**: HTTP, WebSockets, or other protocols supported by Amp. **3. Design the Agent Architecture** The architecture of your agent will determine its efficiency and scalability. Consider the following components: - **Input Layer**: Handles data ingestion from various sources. - **Processing Layer**: Processes the data using algorithms and models. - **Output Layer**: Delivers the results to the end-users or other systems. - **Feedback Loop**: Allows the agent to learn and improve over time. **4. Develop the Core Functionality** With the architecture in place, start developing the core functionality of your agent. This includes: - **Data Ingestion**: Implementing mechanisms to collect and preprocess data. - **Algorithm Development**: Creating or integrating algorithms that will drive the agent's decision-making. - **Model Training**: Training machine learning models if applicable. - **Integration**: Ensuring seamless integration with other systems and protocols. **5. Implement Amp Protocols** Integrate Amp protocols into your agent to leverage its advanced capabilities. This might involve: - **Protocol Implementation**: Writing code to adhere to Amp standards. - **Communication**: Ensuring the agent can communicate effectively with other Amp-compatible systems. - **Security**: Implementing security measures to protect data and communications. **6. Testing and Validation** Thoroughly test

SAM Audio

We got Claude to fine-tune an open-source LLM.

Use Claude Code with Chrome (beta) - Claude Code Documentation

GitHub - microsoft/VibeVoice: Open-Source Voice AI

GitHub - GVCLab/PersonaLive: PersonaLive! : Expressive Portrait Image Animation for Live Streaming

GitHub - NevaMind-AI/memU: Memory infrastructure for large language models and AI agents

GitHub - VibiumDev/vibium: Browser automation for AI agents and humans

GitHub - yichuan-w/LEANN: RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.

GitHub - DGoettlich/history-llms: Information hub for our project training the largest possible historical language models.

LLMRouter - LLMRouter

Everything as Code: How We Manage Our Company In One Monorepo At Kasava, we've embraced the concept of "everything as code" to streamline our operations and ensure consistency across our projects. This approach allows us to manage our entire company within a single monorepo, providing a unified source of truth for all our configurations, infrastructure, and applications. **Why a Monorepo?** A monorepo offers several advantages: 1. **Unified Configuration**: All our settings, from development environments to production, are stored in one place. This makes it easier to maintain consistency and reduces the risk of configuration drift. 2. **Simplified Dependency Management**: With all our code in one repository, managing dependencies becomes more straightforward. We can easily track which versions of libraries and tools are being used across different projects. 3. **Enhanced Collaboration**: A single repository fosters better collaboration among team members. Everyone has access to the same codebase, making it easier to share knowledge and work together on projects. 4. **Consistent Build and Deployment Processes**: By standardizing our build and deployment processes, we ensure that all our applications follow the same best practices. This leads to more reliable and predictable deployments. **Our Monorepo Structure** Our monorepo is organized into several key directories: - **/config**: Contains all configuration files for various environments, including development, staging, and production. - **/infrastructure**: Houses the infrastructure as code (IaC) scripts for provisioning and managing our cloud resources. - **/apps**: Includes all our applications, both internal tools and customer-facing products. - **/lib**: Stores reusable libraries and modules that can be shared across different projects. - **/scripts**: Contains utility scripts for automating various tasks, such as data migrations and backups. **Tools and Technologies** To manage our monorepo effectively, we use a combination of tools and technologies: - **Version Control**: Git is our primary version control system, and we use GitHub for hosting our repositories. - **Continuous Integration/Continuous Deployment (CI/CD)**: We employ Jenkins for automating our build, test, and deployment processes. - **Infrastructure as Code (IaC)**: Terraform is our tool of choice for managing cloud infrastructure. - **Configuration Management**: Ansible is used for configuring and managing our servers and applications. - **Monitoring and Logging**: We use Prometheus and Grafana for monitoring,

GitHub - Search code, repositories, users, issues, pull requests...: 🔥 A tool to analyze your website's AI-readiness, powered by Firecrawl

Fundamentals of Building Autonomous LLM Agents This paper is based on a seminar technical report from the course Trends in Autonomous Agents: Advances in Architecture and Practice offered at the Technical University of Munich (TUM).

Introduction to the MCP Toolbox for Databases The MCP Toolbox for Databases is a comprehensive suite of tools designed to facilitate the management, optimization, and maintenance of databases. This toolbox is tailored to support a wide range of database management systems (DBMS), ensuring compatibility and efficiency across various platforms. Whether you are a database administrator, developer, or analyst, the MCP Toolbox provides a robust set of features to streamline your workflow and enhance productivity. Key Features: 1. **Database Management**: Easily create, modify, and delete databases and tables. The toolbox offers intuitive interfaces and powerful scripting capabilities to manage database schemas and objects efficiently. 2. **Performance Optimization**: Identify and resolve performance bottlenecks with advanced diagnostic tools. The MCP Toolbox includes performance monitoring and tuning features to ensure your databases run smoothly and efficiently. 3. **Backup and Recovery**: Implement reliable backup and recovery solutions to safeguard your data. The toolbox provides automated backup schedules and comprehensive recovery options to protect against data loss. 4. **Security Management**: Enhance database security with robust access control and encryption features. The MCP Toolbox helps you manage user permissions, audit logs, and secure data transmission. 5. **Data Integration**: Seamlessly integrate data from multiple sources and formats. The toolbox supports various data integration techniques, including ETL (Extract, Transform, Load) processes, to consolidate and analyze data effectively. 6. **Reporting and Analytics**: Generate insightful reports and perform in-depth data analysis. The MCP Toolbox offers advanced reporting tools and analytics capabilities to derive actionable insights from your data. 7. **Cross-Platform Compatibility**: Ensure compatibility with multiple DBMS platforms, including popular systems like Oracle, SQL Server, MySQL, and PostgreSQL. The toolbox is designed to work seamlessly across different environments. 8. **User-Friendly Interface**: Benefit from an intuitive and user-friendly interface that simplifies complex database tasks. The MCP Toolbox is designed with ease of use in mind, making it accessible to both novice and experienced users. The MCP Toolbox for Databases is an essential tool for anyone involved in database management. Its comprehensive features and cross-platform compatibility make it a valuable asset for optimizing database performance, ensuring data security, and enhancing overall productivity.

GitHub - Tencent-Hunyuan/HunyuanOCR

Effective harnesses for long-running agents Anthropic

GitHub - pixeltable/pixeltable: Pixeltable — Data Infrastructure providing a declarative, incremental approach for multimodal AI workloads

AI Explained - Stanford Research Paper.pdf - Google Drive

We present Olmo 3, our next family of fully open, leading language models

A2UI

Nano Banana Pro is making millions of interior designers obsolete I upload my floor plan and it design the whole house for me, and even generate real images for each room based on the dimension

How to Segment Videos with Segment Anything 3 (SAM3)

Introducing MagicPath, an infinite canvas to create, refine, and explore with AI

Nano Banana Pro is wild

Next up… Slide Decks! Turn your sources into a detailed deck for reading OR a set of presentation-ready slides

Presentations — Benedict Evans

Nano Banana Pro: Gemini 3 Pro Image model from Google DeepMind

Google Antigravity is not a recognized term or product associated with Google. It seems like a fictional or humorous concept. If you're referring to something specific, could you please provide more context?

GitHub - GibsonAI/Memori: Open-Source Memory Engine for LLMs, AI Agents & Multi-Agent Systems

GitHub Projects Community (@GithubProjects) on X

I’m starting to get into a habit of reading everything (blogs, articles, book chapters,…) with LLMs

Love this framing！ This is exactly what we’re building at Weco: - you write an eval script (your verifier) - Weco iterates on the code to optimize it against that eval Software 1

Supercharge your OCR Pipelines with Open Models

[2511.09030] Solving a Million-Step LLM Task with Zero Errors

Gemini 3: Introducing the latest Gemini AI model from Google

[2511.10395] AgentEvolver: Towards Efficient Self-Evolving Agent System

GitHub - rbalestr-lab/lejepa

Use Cases | Claude