tlkahn

In Emacs, the minibuffer is typically used for single-line input, so pressing RET (Enter) usually submits the input. However, if you need to input a line break (newline) in the minibuffer, you can do so by using the following methods: Method 1: Use C-q C-j

Press C-q (quoted-insert), followed by C-j (newline).

This inserts a literal newline character into the minibuffer.

Method 2: Use C-o

Press C-o (open-line), which inserts a newline and moves the cursor to the new line.

tlkahn

The reward model equation represents the negative log-likelihood loss for training the reward model $r_{\phi}$:

1. $r_{\phi}(x, y)$ is the reward model with parameters $\phi$.

2. $(x, y_w, y_l)$ is a tuple from the dataset $\mathcal{D}$, where:

   • $x$ is the input
   • $y_w$ is the preferred output
   • $y_l$ is the less preferred output

3. $\sigma(z) = \frac{1}{1+e^{-z}}$ is the logistic function.

tlkahn

• Deep Q Learning (DQN) _{^{(Mnih et al. 2013)}}
• DQN with Fixed Q Targets _{^{(Mnih et al. 2013)}}
• Double DQN (DDQN) _{^{(Hado van Hasselt et al. 2015)}}
• DDQN with Prioritised Experience Replay _{^{(Schaul et al. 2016)}}
• Dueling DDQN _{^{(Wang et al. 2016)}}
• REINFORCE _{^{(Williams et al. 1992)}}
• Deep Deterministic Policy Gradients (DDPG) _{^{(Lillicrap et al. 2016 )}}
• Twin Delayed Deep Deterministic Policy Gradients (TD3) _{^{(Fujimoto et al. 2018)}}
• Soft Actor-Critic (SAC)

tlkahn

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

# Mock dataset
def create_mock_dataset(vocab_size, seq_len, num_pairs):
    preferred = torch.randint(0, vocab_size, (num_pairs, seq_len))
    non_preferred = torch.randint(0, vocab_size, (num_pairs, seq_len))

tlkahn

Below is a working example of how to create (1) a simple AppleScript application that registers a custom URL scheme, and (2) links that open a given PDF at a given page in Skim.

────────────────────────────────────────────────────────

1. Create the AppleScript ────────────────────────────────────────────────────────
2. Open “Script Editor” (in /Applications/Utilities).
3. Create a new document and paste this script:

---

-- This handler is called when a URL with the scheme "skimpdf://" is opened.

tlkahn

from icalendar import Calendar, Event
from datetime import datetime, timedelta

cal = Calendar()
cal.add("prodid", "-//My Calendar//mxm.dk//")
cal.add("version", "2.0")

classes = [
    (
        1,

tlkahn

import random

def quirky_text_converter(text):
    """
    Convert normal text to a quirky, alternating case with intermittent capitalization.

    Parameters:
    text (str): The text to convert.

    Returns:

tlkahn

import random
from typing import List, Tuple
import ell


@ell.simple(model="gpt-4o-mini", temperature=1.0)
def random_number() -> str:
    """You are silly robot. Only respond with a number."""
    return "Come with up with a random number"

tlkahn

from typing import List, Tuple

class Polynomial:
    def __init__(self, neg: List[float], pos: List[float]):
        """
        Initialize the polynomial.
        - neg: Coefficients for negative exponents, ordered from x^{-1}, x^{-2}, ...
        - pos: Coefficients for non-negative exponents, ordered from x^{0}, x^{1}, ...
        """
        self.neg = neg.copy()  # e.g., [a1, a2, ...] for a1*x^{-1} + a2*x^{-2} + ...

tlkahn

import numpy as np

def gaussian_elimination(A, b):
    """
    Gaussian elimination is a method for solving systems of linear equations. It involves transforming a matrix into row echelon form through a series of elementary row operations. Key points:

1. Used to solve linear systems: $Ax = b$
2. Steps:
   - Convert to augmented matrix [A|b]
   - Transform to row echelon form