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In this tutorial, we deploy the 1-bit Bonsai-27B language model using the PrismML fork of llama.cpp, which provides the specialized CUDA kernels required to decode the model’s Q1_0_g128 GGUF quantization format. We begin by validating the GPU runtime, installing the required Python dependencies, compiling the CUDA-enabled inference binaries, and downloading the compressed model weights from Hugging Face. We then test the model through llama-cli, launch an OpenAI-compatible local inference server, and interact with it through a reusable Python client that supports standard completions, streamed responses, multi-turn conversations, and code generation. We also examine optional configurations for throughput benchmarking, quantized key-value caching, long-context inference, speculative decoding, and multimodal extensions. Copy Code Copied Use a different Browser import os import sys import time import json import shutil import subprocess import multiprocessing WORK_DIR = "...

Moonshot AI’s Kimi team and kvcache-ai have open-sourced AgentENV (AENV) , a distributed platform for running agent environments at scale. AgentENV powers agentic reinforcement learning (RL) training for Kimi K3 , Moonshot’s 2.8-trillion-parameter Mixture-of-Experts model. The code ships under an MIT license . Why Environment Infra Holds Back Agentic RL Agentic RL does not just sample text. It requires the model to act inside a real computer. Every rollout needs an isolated Linux environment with a filesystem, a network stack, and live processes. That requirement creates a hard trade-off. Containers start fast but share the host kernel, which weakens isolation for model-generated code. Full virtual machines isolate properly but boot slowly and hold memory while idle. AgentENV targets exactly that gap. It runs Firecracker microVMs, then makes idle, restart, and branching cheap enough to run at training scale. Inside AgentENV’s Firecracker Architecture ...

In this tutorial, we build an advanced workflow around Anthropic’s financial-services repository and reproduce its skill-driven architecture in pure Python. We begin by installing the required libraries, cloning the repository, and programmatically mapping its agents, vertical plugins, partner integrations, managed-agent cookbooks, and financial analysis skills. We then parse the repository’s SKILL.md files into a searchable registry and construct a reusable SkillAgent that injects selected financial playbooks into the Anthropic Messages API while supporting an iterative tool-use loop for Python calculations and file generation. Using this architecture, we execute a synthetic discounted cash flow valuation, generate a WACC and terminal-growth sensitivity heatmap, perform comparable-company analysis with formatted Excel output, draft a private-equity investment committee memo, and inspect a managed-agent deployment specification without sending a live deployment request. Copy Cod...