Lei Wang wangleiphy

name	sci-brainstorm
description	Use when exploring a research idea, starting from a paper, concept, or question — conducts deep literature survey via arxiv and web search, discovers cross-field connections, then challenges the idea through Socratic and adversarial discussion

Scientific Research Brainstorming

Research-first brainstorming. Every phase begins with autonomous search — never ask the human what the literature says; go find out. Staged posture: Socratic while the idea is forming, adversarial once it has shape. Produces a research direction document.

Entry

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.

After installing jax, run with:

git clone https://gist.github.com/jackd/99e012090a56637b8dd8bb037374900e
cd 99e012090a56637b8dd8bb037374900e
python dirty_test.py

	"""
	The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp

	using JuMP, HiGHS, Graphs, LinearAlgebra, Test

	# Find one valid assignment with integer programming
	function lattice_sites(num_sites::Vector{Int}, multiplicity::Vector{Int}, num_atoms::Vector{Int})
	model = Model(HiGHS.Optimizer)
	JuMP.set_silent(model)
	@variable(model, x[1:length(num_sites), 1:length(num_atoms)] >= 0, Int)
	for (ia, na) in enumerate(num_atoms)
	@constraint(model, sum(x[i, ia] * num_sites[i] for i in 1:length(num_sites)) == na)
	end

	#!/usr/bin/env python

	import math
	import matplotlib.pyplot as plt
	import torch
	import torch.nn as nn

	from sklearn.datasets import make_moons
	from torch import Tensor
	from tqdm import tqdm

	# requires OMEinsum version >= 0.7
	using Distributed
	using OMEinsum, CUDA

	println("find $(length(devices())) GPU devices")
	const procs = addprocs(length(devices())-nprocs()+1)
	const gpus = collect(devices())
	const process_device_map = Dict(zip(procs, gpus))
	@info process_device_map

	# zhangqiaorjc@google.com

	# `jax.distributed.initialize` is available in jax-0.2.25.
	# $ pip install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_releases.html # Note: wheels only available on linux.

	# Run this script on 2 GPU nodes, assuming 10.128.0.6 is the master node
	# python nvidia_gpu_pjit.py --server_addr="10.128.0.6:1456" --num_hosts=2 --host_idx=0
	# python nvidia_gpu_pjit.py --server_addr="10.128.0.6:1456" --num_hosts=2 --host_idx=1
	from absl import app
	from absl import flags

	macro _threads(ex)
	return quote
	if (Threads.nthreads() > 1) && (length(st) > 4096)
	$(Expr(:macrocall, Expr(:(.), :Threads, QuoteNode(Symbol("@threads"))), __source__, ex))
	else
	$ex
	end
	end \|> esc
	end