Go

Coding My Handwriting — Amy Goodchild

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

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

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

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

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,

AI Explained - Stanford Research Paper.pdf - Google Drive

Nano Banana Pro is wild

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?

Gemini 3: Introducing the latest Gemini AI model from Google

I quite like the new DeepSeek-OCR paper

How to Get Consistent Classification From Inconsistent LLMs? "How to Obtain Consistent Classification From Inconsistent Language Models?"

My trick for getting consistent classification from LLMs

Google just dropped an ace 64-page guide on building AI Agents

Agentic Design Patterns - Documenti Google

Research Agent with Gemini 2.5 Pro and LlamaIndex  |  Gemini API  |  Google AI for Developers

AI Act, c'è il codice di condotta per un approccio responsabile e facilitato per le Pmi - Cyber Security 360

Gemini for Google Workspace Prompting Guide 101

Automated 73% of his remote job using basic automation tools, told his manager everything, and got a promotion

Come Addestrare un LLM con i Tuoi Dati Personali: Guida Completa con LLaMA 3.2

Gemma 3 QAT Models: Bringing state-of-the-Art AI to consumer GPUs

GitHub - humanlayer/12-factor-agents: What are the principles we can use to build LLM-powered software that is actually good enough to deploy?

A foundation model to predict and capture human cognition | Nature