AUAggy

Running Claude Code Locally with Ollama and Open-Source Models as a Free Alternative to the Anthropic API

Claude Code's API costs add up fast for heavy users, often $50 to $200+/month on Opus 4.5/4.6. Ollama (v0.14.0+) now supports the Anthropic Messages API natively, which means Claude Code can run against local open-source models at zero cost, with no data leaving the machine.

This guide covers the full setup: installing Ollama and Claude Code, choosing a model that fits 16 GB of RAM, connecting the pieces, and understanding the real tradeoffs.

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The Cost Problem

alextusinean

// This code was tested on a TikTok video with over 3000 comment. It got a little bit laggy during the loading phase, but it
// worked out very good at the end. But even on a high-end machine, I wouldn't recommend running this on any TikTok with more than 10k comments
with({
    copy
}) {
    /* 
        This is the critical section. Every selector that contains 'jsx' (so almost everyone) is subject to change in the future,
        since TikTok decides to changes those random numbers after 'jsx-'. If they start to break again,
        instead of trying to update them with the new number, just remove the 'jsx-.+' class entirely, because the selector
        specified in the trailing comment should suffice (not tested at all).

anshulxyz

Privacy Policy for [Bot Name]

This Privacy Policy governs the collection, use, and sharing of personal information by [Bot Name], a Discord bot developed by [Developer Name]. By using [Bot Name], you agree to the terms of this Privacy Policy.

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We collect information that you provide to us through your use of the bot, such as your Discord user ID and username, server and channel information, and message content. We may also collect usage data, such as the frequency and duration of your use of the bot.

How We Use Your Information

anshulxyz

TERMS OF SERVICE AGREEMENT

This Terms of Service Agreement (the "Agreement") is entered into between [Anshul Chauhan] ("Bot Owner") and the user ("User") of the [Bot Name] Discord bot (the "Bot").

By using the Bot, the User agrees to be bound by the terms of this Agreement. If the User does not agree to the terms of this Agreement, they should immediately discontinue use of the Bot.

1. Use of the Bot: The Bot Owner grants the User a non-exclusive, non-transferable, limited license to use the Bot for personal or non-commercial purposes.
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am17an

#!/usr/bin/env python3
import argparse, json, sys, time
from urllib import request

PROMPTS = [
    {"name": "code_python",      "prompt": "Write a Python function that returns the n-th Fibonacci number using memoization. Include a docstring."},
    {"name": "code_cpp",         "prompt": "Write a C++ template function `clamp(x, lo, hi)` that returns x clamped to [lo, hi]. No std::clamp."},
    {"name": "explain_concept",  "prompt": "Explain how speculative decoding works in large language model inference, in three short paragraphs."},
    {"name": "summarize",        "prompt": "Summarize in two sentences: The Industrial Revolution began in Britain in the late 18th century, transforming manufacturing through mechanization, steam power, and the factory system. It spread to continental Europe and North America during the 19th century."},
    {"name": "qa_factual",       "prompt": "Q: What are the four fundamental forces of physics?\nA:"},

mattpocock

agent-proxy — see the bloat in Claude Code's requests

A zero-dependency logging proxy that sits between Claude Code and the Anthropic API. It forwards every request untouched, streams the reply straight back (so the CLI is unaffected), and writes a readable Markdown document for each request — led by a ranked table of what is eating your context.

Run it

BennisonDevadoss

{
  "vim.showMarksInGutter": false,
  "vim.foldfix": true,
  "vim.surround": true,
  "vim.easymotion": true,
  "vim.easymotionKeys": "asdghklqwertyuiopzxcvbnmfj",
  "vim.leader": "<space>",
  "vim.normalModeKeyBindingsNonRecursive": [
    {
      "before": [":", "w", "<CR>"],

BennisonDevadoss

[
  // ╔══════════════════════════════════════════════════════════════════════════════════╗
  // ║                   VSCODE VIM LAZYVIM KEYBINDINGS                                  ║
  // ║                   Modifier Key Bindings (Ctrl, Alt, Shift)                        ║
  // ╚══════════════════════════════════════════════════════════════════════════════════╝
  //
  // This file contains keybindings that use modifier keys (Ctrl, Alt, Shift) and special
  // characters that need VSCode's key binding system (not Vim's).
  //
  // IMPORTANT: DO NOT put space-leader bindings here (<leader>...)

soyprofesor

Visual Studio 2026 18.x
Professional: NVTDK-QB8J9-M28GR-92BPC-BTHXK
Enterprise: VYGRN-WPR22-HG4X3-692BF-QGT2V

Product	Year	Version	Product Keys
Visual Studio 2022	2021	17.x	
Professional: TD244-P4NB7-YQ6XK-Y8MMM-YWV2J
Enterprise: VHF9H-NXBBB-638P6-6JHCY-88JWH

Visual Studio 2019	2019	16.x	

karpathy

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.