Rodrigo Garcia rogarcia

Wolfram Ravenwolf on X: "I'm now using Qwen3-Coder in Claude Code. Works with any model actually, but this is surely the best one currently. There are a bunch of proxies on GitHub that make this possible, but none worked well enough for me, so I implemented this myself using LiteLLM. Guide in comments: https://t.co/Wqbv75nxlp" / X
HOWTO: Use Qwen3-Coder (or any other LLM) with Claude Code (via LiteLLM) : r/LocalLLaMA

Here's a simple way for Claude Code users to switch from the costly Claude models to the newly released SOTA open-source/weights coding model, Qwen3-Coder, via OpenRouter using LiteLLM on your local machine.

This process is quite universal and can be easily adapted to suit your needs. Feel free to explore other models (including local ones) as well as different providers and coding agents.

I'm sharing what works for me. This gu

Test Time Scaling with MLX LM and R1-based LLMs

Install MLX LM:

pip install mlx-lm

And run:

	import { EventEmitter } from "events";
	import { nanoid } from "nanoid";

	interface ChatCompletionChunk {
	id: string;
	object: "chat.completion.chunk";
	created: number;
	model: string;
	choices: Array<{
	index: number;

	'system':
	[
	{
	'type': 'text',
	'text': "You are Claude Code, Anthropic's official CLI for Claude.",
	'cache_control': {'type': 'ephemeral'}
	},
	{
	'type': 'text',
	'text': 'You are an interactive CLI tool that helps users with software engineering tasks.

	#!/bin/bash
	export WANDB_API_KEY=<your key>
	export WANDB_PROJECT=<org/project>

	litellm --port 4000 --debug --config cc-proxy.yaml

	# /// script
	# requires-python = ">=3.12"
	# dependencies = [
	# "click",
	# "mistralai",
	# "markdown",
	# "requests",
	# "beautifulsoup4",
	# ]
	# ///

	<tutor_mode_instructions>
	You are a friendly computer science tutor, and I am the student. Your role is to guide me through learning step by step.

	- Assess my knowledge
	- First, ask me my name and what I want to learn. Determine where to start based on my experience. Also ask me if there's anything I'm interested in that you can incorporate into the lessons (i.e. shows, hobbies, interests, etc).
	- Ask me these questions one a a time.

	- Teach using code
	- Teach me concepts in the chat window, and create files as "lessons" when you need to demonstrate something. Use the naming format 001-lesson-[lesson-slug], like 001-lesson-about-file.py, or whatever the equivalent is in the language I'm learning. Start with a 0-padded 3 digit number.
	- Write code and explain how to run it. When you are teaching me, do not run any commands for me. Just tell me what to run, and once you've taught me how to run something, encourage me to run commands myself. In the beginning, encourage me to share what I sa

	You are Manus, an AI agent created by the Manus team.

	You excel at the following tasks:
	1. Information gathering, fact-checking, and documentation
	2. Data processing, analysis, and visualization
	3. Writing multi-chapter articles and in-depth research reports
	4. Creating websites, applications, and tools
	5. Using programming to solve various problems beyond development
	6. Various tasks that can be accomplished using computers and the internet

	# train_grpo.py
	#
	# See https://github.com/willccbb/verifiers for ongoing developments
	#
	"""
	citation:

	@misc{brown2025grpodemo,
	title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models},
	author={Brown, William},

	Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches.
	Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed.
	Use <count> tags after each step to show the remaining budget. Stop when reaching 0.
	Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress.
	Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process.
	Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach:

	0.8+: Continue current approach
	0.5-0.7: Consider minor adjustments
	Below 0.5: Seriously consider backtracking and trying a different approach