Oliver Mannion tekumara

Every atomic object has a timeline (TL) of writes:
- A write is either a store or a read-modify-write (RMW): it read latest write & pushed new one.
- A write is either tagged Relaxed, Release, or SeqCst.
- A read observes some write on the timeline:
  - On the same thread, future reads can't go backwards on the timeline.
  - A read is either tagged Relaxed, Acquire, or SeqCst.
  - RMWs can also be tagged Acquire (or AcqRel). If so, the Acquire refers to the "read" portion of "RMW".
Each thread has its own view of the world:

Shared write timelines but each thread could be reading at different points.

Package PyApp app with batteries included

This is just a quick write up - mostly for myself - on how to create a python PyApp package for an air-gapped machine. This means that all dependencies, etc., will be included.

Download the CPython version that should be used. A list of default versions are in build.rs of PyApp
- Linux default: 3.12.3, linux, x86_64, gnu, v3
- Windows default: 3.12.3, x84_64, msvc
Unpack the distribution

LLM Samplers Explained

Everytime a large language model makes predictions, all of the thousands of tokens in the vocabulary are assigned some degree of probability, from almost 0%, to almost 100%. There are different ways you can decide to choose from those predictions. This process is known as "sampling", and there are various strategies you can use which I will cover here.

OpenAI Samplers

Temperature

Temperature is a way to control the overall confidence of the model's scores (the logits). What this means is that, if you use a lower value than 1.0, the relative distance between the tokens will become larger (more deterministic), and if you use a larger value than 1.0, the relative distance between the tokens becomes smaller (less deterministic).
1.0 Temperature is the original distribution that the model was trained to optimize for, since the scores remain the same.
Graph demonstration with voiceover: https://files.catbox.moe/6ht56x.mp4

Grand-master Level Chess without Search: Modeling Choices and their Implications

Yoav Golderg, February 2024.

Researchers at Google DeepMind released a paper about a learned systems that is able to play blitz-chess at a grandmaster level, without using search. This is interesting and imagination-capturing, because up to now computer-chess systems that play at this level, either based on machine-learning or not, did use a search component.[^1]

Indeed, my first reaction when reading the paper was to tweet wow, crazy and interesting. I still find it crazy and interesting, but upon a closer read, it may not be as crazy and as interesting as I initially thought. Many reactions on twitter, reddit, etc, were super-impressed, going into implications about projected learning abilities of AI systems, the ability of neural networks to learn semantics from observations, etc, which are really over-the-top. The paper does not claim any of them, but they are still perceiv

	You are Manus, an AI agent created by the Manus team.

	You excel at the following tasks:
	1. Information gathering, fact-checking, and documentation
	2. Data processing, analysis, and visualization
	3. Writing multi-chapter articles and in-depth research reports
	4. Creating websites, applications, and tools
	5. Using programming to solve various problems beyond development
	6. Various tasks that can be accomplished using computers and the internet

	// Claude Code is a Beta product per Anthropic's Commercial Terms of Service.
	// By using Claude Code, you agree that all code acceptance or rejection decisions you make,
	// and the associated conversations in context, constitute Feedback under Anthropic's Commercial Terms,
	// and may be used to improve Anthropic's products, including training models.
	// You are responsible for reviewing any code suggestions before use.

	// (c) Anthropic PBC. All rights reserved. Use is subject to Anthropic's Commercial Terms of Service (https://www.anthropic.com/legal/commercial-terms).

	// Version: 0.2.9

	# the "verifiers" repository is a clean implementation of templated GRPO reinforcement learning training environments
	# this is a generic set of "install from scratch" commands complete with a deepspeed z3 config that i have been using when i spin up nodes
	# it will run on the gsm8k example w/ default batch size & generation size (8), and the 8th GPU is used for vllm generations
	# qwen 14b full finetuning will run on this configuration too without LoRA or CUDA OOM, at least for the gsm8k task's context sizes + generation lengths
	# hyperparameters are controlled by `verifiers/utils/config_utils.py`; i have been preferring extreme grad clipping (between 0.001 and 0.01) and low beta (under 0.01)

	# NOTE FEB 27: examples have moved into `verifiers/examples` not `/examples`

	cd /root
	mkdir boom

	Please analyze the following GitHub issue data, which is provided as a JSON object:

	{
	"title": "🐛 BUG: WebSocket typing doesn't work in apps that also pull in DOM types",
	"body": "Which Cloudflare product(s) does this pertain to?",
	}

	Provide a response with the following structure:

	<json>

	# THIS LINUX SETUP SCRIPT HAS MORPHED INTO A WHOLE PROJECT: HTTPS://OMAKUB.ORG
	# PLEASE CHECKOUT THAT PROJECT INSTEAD OF THIS OUTDATED SETUP SCRIPT.
	#
	#

	# Libraries and infrastructure
	sudo apt update -y
	sudo apt install -y \
	docker.io docker-buildx \
	build-essential pkg-config autoconf bison rustc cargo clang \

	import asyncio
	import copy
	import hashlib
	import json
	import os
	import random
	from dataclasses import dataclass
	from typing import Any, Dict, List, Optional

	import numpy as np