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<core_identity> You are an assistant called Cluely, developed and created by Cluely, whose sole purpose is to analyze and solve problems asked by the user or shown on the screen. Your responses must be specific, accurate, and actionable. </core_identity>

<general_guidelines>

• NEVER use meta-phrases (e.g., "let me help you", "I can see that").
• NEVER summarize unless explicitly requested.
• NEVER provide unsolicited advice.
• NEVER refer to "screenshot" or "image" - refer to it as "the screen" if needed.
• ALWAYS be specific, detailed, and accurate.

MaTriXy

Hi LinkedIn friend!

Here is how I managed to have the agent I'm building reduce token usage.

1. My agent has 62 tools (and growing quickly in terms of number of tools..)
2. Each tool has a description. All in all I was sending the entire 62 tools+description in every agent turn. 

It came out to 10k tokens before even the system prompt+user prompt - ON EVERY TURN.

The solution I found was to do a preflight LLM request to select only the relevant tools for the user request.

MaTriXy

# Prebid.js Video Ad Handling: Complete Flow Logic

## Overview

This document explains how Prebid.js handles video ads with different combinations of:
- Cache settings (enabled vs. disabled)
- Video types (instream vs. outstream)
- VAST representations (vastUrl vs. vastXml)

## Key Components

MaTriXy

import asyncio
import os
import sys
from dataclasses import dataclass

import aiohttp
import google.ai.generativelanguage as glm
from dotenv import load_dotenv
from loguru import logger
from runner import configure

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# train_grpo.py
import re
import torch
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig
from trl import GRPOConfig, GRPOTrainer

# Load and prep dataset

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from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from pydantic import BaseModel
from browser_use import ActionResult, Agent, Controller
from browser_use.browser.context import BrowserContext
from browser_use.browser.browser import Browser, BrowserConfig
import asyncio
import os
import json
import re

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MaTriXy

<?

	//
	// AUTO KEYWORD-BASED FOLLOWER CURATION BOT (by @levelsio)
	//
	// File: twitterFollowerCuratorBot.php
	//
	// Created: May 2021
	// License: MIT
	//

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#!/bin/bash

# Check if a GitHub URL is provided as an argument
if [ -z "$1" ]; then
    echo "Usage: $0 <github_url>"
    exit 1
fi

# Store the GitHub URL
GIT_URL="$1"

MaTriXy

Detailed writeup: https://huggingface2.notion.site/How-to-split-Flux-transformer-and-run-inference-aa1583ad23ce47a78589a79bb9309ab0

But TLDR is we split the models where possible and decouple the different stages of pipeline