weijh imweijh

Update — 2026-07-14: This is the original wire-level analysis (grok 0.2.93). Since publishing: xAI disabled the upload server-side (disable_codebase_upload: true); added a /privacy opt-out — which I wire-tested and found to be a data-retention setting, not a block on what's sent; and Elon Musk publicly committed to deleting all previously-uploaded data (not yet confirmed complete). Full, maintained story + evidence: https://cereblab.com · https://github.com/cereblab/grok-build-exfil-repro

What xAI's Grok Build CLI Actually Sends to xAI: A Wire-Level Analysis

By @cereblab — Independent AI Safety Checker. Reproduce it yourself: github.com/cereblab/grok-build-exfil-repro

*A measured, reproducible teardown. Findings are backed by captured artifacts (endpoint, HTTP method, status code, byte size, host) and repro commands; where an observation was seen live but not retained as a file, §7 says so expli

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Running Qwen3 Models with llama-server (Embedding + Reranking + Chat)

A practical guide to running multiple Qwen3 models through a single llama-server instance using model routing. Covers embedding, reranking, and chat/vision models.

Tested on Windows with RTX 3090 (24GB VRAM), llama-server build from llama.cpp master branch. Last updated: 2025-03-09.

What you get

name	codex
description	Use OpenAI Codex CLI for complex debugging, code analysis, or when stuck on difficult problems. Invokes Codex with a file-based question/answer pattern.
disable-model-invocation	true

Using Codex for Complex Debugging

When you encounter a difficult problem that would benefit from a second perspective or deep analysis, use Codex via the file-based pattern.

Configure OpenAI Codex CLI with DeepSeek Models

Disclaimer (May 2026). Sorry for the long radio silence on the comments, busy stretch on my side. I no longer use this setup and stopped tracking it actively. I'm refreshing the page out of thoroughness because comments kept coming in and people deserve an honest snapshot. If you find better workarounds, post them in the comments and I'll link them, but I won't be maintaining this further.

Status

Codex deprecated wire_api = "chat" in favour of OpenAI's Responses API. DeepSeek only speaks Chat Completions, so the direct integration documented below is no longer reliable. Symptoms reported by users:

An assistant message with 'tool_calls' must be followed by tool messages... (insufficient tool messages following tool_calls)
Missing 'reasoning_content' field in the assistant message at message index N

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

Gemini Agent: Core Directives and Operating Protocols

This document defines your core operational directives as an autonomous AI software development agent. You must adhere to these protocols at all times. This document is a living standard; you will update and refactor it continuously to incorporate new best practices and maintain clarity.

1. Core Directives

These are the highest-level, non-negotiable principles that govern your operation.

Primacy of User Partnership: Your primary function is to act as a collaborative partner. You must always seek to understand user intent, present clear, test-driven plans, and await explicit approval before executing any action that modifies files or system state.
Teach and Explain Mandate: You must clearly document and articulate your entire thought process. This includes explaining your design choices, technology recommendations, and implementation details in project documentation, code comments, and direct communication to facilitate user learnin

	{#---------------------------------------------------------------------
	custom_pub_chat_template_gemma4.jinja
	=====================================
	A public, harness-friendly fork of Google's Gemma 4 chat template,
	tuned for open-source agentic coding harnesses like:
	- anomalyco/opencode (https://github.com/anomalyco/opencode)
	- earendil-works/pi (https://github.com/earendil-works/pi)
	- openclaw, OpenHarness, similar Claude-Code-style harnesses

	WHY THIS FORK EXISTS

	{%- set image_count = namespace(value=0) %}
	{%- set video_count = namespace(value=0) %}
	{%- macro render_content(content, do_vision_count, is_system_content=false) %}
	{%- if content is string %}
	{{- content }}
	{%- elif content is iterable and content is not mapping %}
	{%- for item in content %}
	{%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
	{%- if is_system_content %}
	{{- raise_exception('System message cannot contain images.') }}

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

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