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tengu_pewter_ledger — A/B Test Proof

Evidence that Claude Code ships a GrowthBook-controlled A/B test called tengu_pewter_ledger that silently restricts Plan Mode output for some users.

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Source Files Examined

Version	File	Size

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// knip.ts (in the monorepo root)
import type { KnipConfig } from 'knip';

const config: KnipConfig = {
  // Add schema for VSCode validation and autocompletion
  $schema: 'https://unpkg.com/knip@5/schema.json',

  // --- Monorepo Setup ---
  // Knip automatically detects pnpm workspaces via pnpm-workspace.yaml.
  // The `workspaces` key is only needed to *override* defaults for specific

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#!/usr/bin/env bash
# File: vscode  —  tiny wrapper around the official `code` CLI
# Usage:
#   vscode open <file>
#   vscode diff <left> <right> [title]
#   vscode cmd  <commandId> [jsonArgs]

set -euo pipefail
sub=${1:-}; shift || true

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lnr Work Management System

Overview

lnr is an append-only, file-based lnr work management system designed for AI coding agents. Lnr work is organized into phases - coherent units of effort that match how AI agents naturally operate. The system requires no indices, no status tracking, and no complex tooling. State is derived entirely from which files exist in the directory.

Core Principles

Lnr work happens in phases, not issues or tickets. Each phase represents a coherent push toward a specific goal. Phases can be small (refactor a function) or large (implement authentication system).

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Here is what you need to know about Tailwind CSS v4 (May 2025)

I. Core Architecture & Performance

1. New Engine - Performance First:
   • V4 ships with a completely rewritten engine. Expect drastically reduced build times – typically sub-10ms for most projects, even large ones often under 100ms. This is achieved by more efficiently parsing sources and generating CSS on-demand.
2. CSS-First Configuration via @theme:
   • The primary configuration mechanism shifts from tailwind.config.js (for theme values) to your main CSS file using the @theme directive.

/* app.css */

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#!/bin/bash

HOSTFS="/mnt/hostfs"

function sleep_forever() {
    while true; do sleep 100; done
}

function setup_kubectl() {
# Setup kubectl

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Looking for a solution to my below game problem. I believe it to require some sort of reinforcement learning, dynamic programming, or probabilistic programming solution, but am unsure... This is my original problem, and is part of an initiative to create "unique and challenging problem that you're able to conceptualize and then solve. 3 Judging criteria: uniqueness, complexity, and solution (no particular weighting and scoring may favor uniqueness/challenge over solution"

Inspirations: Conway's Game of Life, DeepMind's Starcraft Challenge, deep Q-learning, probabilistic programming

BEAR SURVIVAL

A bear is preparing for hibernation. A bear must reach life-strength 1000 in order to rest & survive the winter. A bear starts off at a health of 500. A bear explores an environment of magic berries. A bear makes a move (chosen randomly with no optional direction) and comes across a berry each time. There are 100 different types of berries that all appear across the wilderness equally and

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Sun Jul 15 17:59:46 UTC 2018

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Keybase proof

I hereby claim:

• I am mdramos on github.
• I am rambossa (https://keybase.io/rambossa) on keybase.
• I have a public key whose fingerprint is 50B7 2B40 2491 3DB2 279E 4243 D11F 7D44 0A73 27C6

To claim this, I am signing this object:

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private func stylizeImage(cgImage: CGImage, model: MLModel) -> CGImage {
    
    // size can change here if you want, remember to run right sizes in the fst evaluating script
    let input = StyleTransferInput(input: pixelBuffer(cgImage: cgImage, width: 883, height: 720))

    // model.prediction will run the style model on input image
    let outFeatures = try! model.prediction(from: input)
  
    // we get the image buffer after
    let output = outFeatures.featureValue(for: "add_37__0")!.imageBufferValue!