Atul Acharya laventura

I Built an Enterprise AI Avatar Platform. Looking for Partners/Customers in India, UAE, Turkey.

18-month journey building a Soul Machines competitor. Now focusing on viable markets outside the West.

Test the Demo

Book a 15-minute live demo: [Speak with HAVVA]

Modern R Development Guide

This document captures current best practices for R development, emphasizing modern tidyverse patterns, performance, and style. Last updated: August 2025

Core Principles

Use modern tidyverse patterns - Prioritize dplyr 1.1+ features, native pipe, and current APIs
Profile before optimizing - Use profvis and bench to identify real bottlenecks
Write readable code first - Optimize only when necessary and after profiling
Follow tidyverse style guide - Consistent naming, spacing, and structure

Let's hack on `llm`!

If you have uv installed (and you should!), you can install llm globally in a uv-managed tool environment with:

uv tool install llm

If you want to use models other than OpenAI models, you'll need some extensions:

Vibe Building a Client‑Side PDF Compressor with Ghostscript‑WASM

Hosted at: https://austegard.com/pdf-compressor.html

Modern browsers are incredibly powerful. If you’ve ever tried Squoosh.app’s client‑side image compression, you know just how slick a fully‑in‑browser workflow can feel. What if we extended that idea to PDFs—shrinking large documents without ever sending them to a server?

In this post I’ll walk through how we built PDF Compressor, a 100% client‑side PDF optimizer written in pure HTML/JS/WebAssembly. We started with a Claude 3.7 Sonnet–generated plan, iterated with ChatGPT o4‑mini‑high, and ended up with a reasonabvly‑fast, vector‑preserving compressor powered by Ghostscript‑WASM.

This guide is adapted from this original post by Christopher Charles.

Project Setup

1. Get the source:

Clone the MLX Swift Examples GitHub repository:

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

Learning Rust

Getting Started + Installation | Cheat Sheet

	# Setup:
	# conda create -n whisper python=3.9
	# conda activate whisper
	# https://github.com/openai/whisper
	# pip install git+https://github.com/openai/whisper.git

	# Usage:
	# python whisper-audio-to-text.py --audio_dir my_files --out_dir texts

	import argparse