Fred Bliss fblissjr

// ##################################################################################
// UNIFIED ADAPTIVE ENGINE - MULTI-PHASE EXECUTION PROTOCOL v1.1
// Target Fidelity: {required_fidelity_level} | Resource Profile: {active_resource_profile_name}
// Session ID: {session_guid} | Cycle ID: {execution_cycle_guid}
// Timestamp (Cycle Init): {iso_timestamp_initiation}
// Protocol Objective: End-to-end processing of Natural Language request through state configuration,
// detailed simulation, and finalized output generation within a single execution context.
// ##################################################################################

Test Time Scaling with MLX LM and R1-based LLMs

Install MLX LM:

pip install mlx-lm

And run:

Notes / Links about Stable Diffusion VAE

Stable Diffusion's VAE is a neural network that encodes images into a compressed "latent" format and decodes them back. The encoder performs 48x lossy compression, and the decoder generates new detail to fill in the gaps.

(Calling this model a "VAE" is sort of a misnomer - it's an encoder with some very slight KL regularization, and a conditional GAN decoder)

This document is a big pile of various links with more info.

Loom Design Document: OpenAI API Modification for Local LLMs

1. Overview

This document outlines the necessary changes to modify Loom's OpenAI API implementation to support local Language Models (LLMs) that use the OpenAI API specification. This modification will allow users to interact with local LLMs using the same interface as the official OpenAI API.

2. Design Goals

Allow users to specify a custom base URL for OpenAI API calls
Maintain compatibility with the existing OpenAI implementation

Loom LLM Provider Integration Design Document

1. Overview

This document outlines the process of adding the MLX LLM provider to Loom and demonstrates how to extend this approach to easily add other providers in the future. The goal is to create a flexible and extensible system that maintains consistency with existing provider implementations.

	// 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

	#!/usr/bin/env bash

	# --- Configuration ---
	declare -A PRESETS
	PRESETS["legacy_default"]="85 75" # Wired Limit: 85%, LWM: 75% (Original script's behavior)
	PRESETS["conservative"]="80" # Wired Limit: 80%, LWM: High (Ample RAM for OS)
	PRESETS["moderate"]="90" # Wired Limit: 90%, LWM: High (Recommended for general MLX use)
	PRESETS["aggressive"]="95" # Wired Limit: 95%, LWM: High (Pushes GPU memory usage)
	PRESETS["mlx_tuned"]="92" # Wired Limit: 92%, LWM: High (Optimized for MLX heavy tasks)

	# Tailwind CSS LLMs.txt Documentation

	> This document provides a comprehensive overview of Tailwind CSS utility classes, examples, and customization options. It covers various CSS properties like layout, spacing, typography, backgrounds, borders, effects, transitions, transforms, and more, explaining how to use Tailwind's utility classes to style web elements effectively and responsively.

	This document details the documentation of Tailwind CSS utilities. It explains how Tailwind scans source files for classes, the importance of using complete class names, and how utility classes can be applied conditionally using variants for states (hover, focus), responsive breakpoints, dark mode, and other conditions. It also covers customization via theme variables and adding custom styles.

	Core Concepts (from styling-with-utility-classes.mdx & responsive-design.mdx):

	* Utility-First: Style elements by combining many single-purpose utility classes directly in HTML.
	* Constraint-Based: Utilities general

	import torch
	from safetensors.torch import save_file

	INPUT_NAME = "model_weights.ckpt"
	OUTPUT_NAME = "model.safetensors"

	state = torch.load(INPUT_NAME, map_location="cpu")
	new_state = {}

	for key, value in state.items():

Fred Bliss fblissjr

Test Time Scaling with MLX LM and R1-based LLMs

Notes / Links about Stable Diffusion VAE

Loom Design Document: OpenAI API Modification for Local LLMs

1. Overview

2. Design Goals

Loom LLM Provider Integration Design Document

1. Overview

2. Adding MLX Provider

2.1 Update Common Types (common.ts)

	===========
	; A072257: a(n) = ((6n-17)4^n - 1)/3.
	; -6,-15,-27,21,597,4437,25941,136533,677205,3233109,15029589,68506965,307582293,1364546901,5995058517,26127717717,113100805461,486762960213,2084490794325,8887718991189,37749899220309,159795689903445,674367131702613,2838206015165781,11915774014084437,49914895870022997,208666782734832981,870695927958295893,3626898899909039445,15084056351939581269,62642068416972019029,259791645704742851925,1076060070966390510933,4451814236455238456661,18397552756179659478357,75951394266153460520277,313250310030353132508501,1290780171984369691743573,5314236415389307413812565,21861408571364544242603349,89863485924687435319825749,369125350255666774676952405,1515187027250335232298407253,6215490613912013463556019541,25480932475290743991673640277,104399609979733736516492809557,427501960233217988265164232021,1749621922190004121857428903253,7156944013788545162616803513685,29261601355268295351215565657429,119581706621529640207855669040469,488468031287944396043396301804885,1994436944359