Tag: workflows

Two weeks since Andrej Karpathy released Autoresearch, here are some noteworthy projects to keep an eye on.

I discovered a better way of converting PDFs to Markdowns, with all mathematical formulas converted to LaTeX, on Apple silicon.

I rechecked the Days codebase with GPT 5.4 xhigh and GPT 5.4 Pro, and the pair of models has found serious issues in one aspect that I asked it to focus on in the current implementation.

A few weeks ago, OpenAI posted a blog post on harness engineering. Yesterday, it also released a component of its workflow as open-source, called Symphony.

A quick iOS Codex access tip with Agentboard, plus a strong Rust-over-Python essay for agentic programming.

I redesigned my personal website, featuring not only a simple, minimalist design, but also a streamlined process of writing and publishing new entries via CLI tools.

Arc — My new browser of choice. I love the fact that bookmarks are organized on the side panel, rather than clustered at the top of the window.

How I use LLMs by Andrej Karpathy — A must watch.

Andriy Burkov’s minimalist implementation of GRPO from scratch — Rather than using a library such as Hugging Face’s TRL.

Transformer Lab — a free, open-source LLM workspace that prepares a custom dataset and fine-tunes a model using MLX on the Mac.

From 0 to Production — The Modern React Tutorial — Theo released it last year, and I always wanted to learn from this marathon tutorial.

Unsloth.ai’s GRPO — it seems that the Unsloth implementation of GRPO uses less GPU memory, and it supports QLoRA and LoRA.

GRPO will soon be added to Apple MLX — The PR now works, using about 32 GB of memory when training Qwen2.5-0.5B.

Another simple DeepSeek R1 reproduction — This reproduction of GRPO has one distinct feature: it is exceedingly simple and quite elegant.

Fourth attempt on reproducing DeepSeek R1’s GRPO on small models — The third fourth time is the charm. I can successfully run this repo, without activating vLLM.

How to fine-tune open LLMs in 2025 with Hugging Face — Philipp Schmid a Technical Lead at Hugging Face, posted this article on fine-tuning LLMs using Hugging Face.