nommiin

#include "stdafx.h"

#include <Windows64\4JLibs\inc\4J_Input.h>
#include <Common\App_enums.h>
#include <Windows.h>

C_4JInput InputManager;


void C_4JInput::Initialise(int iInputStateC, unsigned char ucMapC, unsigned char ucActionC, unsigned char ucMenuActionC)

karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

tatsumoto-ren

🗨️ Have a question? Join Chat!	🇯🇵 Learn Japanese	📚 Resources	❓ FAQ

---

AJATT - Japanese subtitles

• 👉 Japanese anime, drama, movie subtitles
• 🥧 GitHub repository

mkbctrl

Intent Recognition and Auto‑Routing in Multi-Agent Systems

Modern conversational AI systems often split functionality into multiple tools or sub-agents, each specialized for a task (e.g. search, booking, math, etc.). When a user sends a query, the system must interpret intent and dispatch it to the right tool/agent. There are two broad approaches: letting a general-purpose LLM handle intent detection itself, or using a dedicated router component. In practice, many practitioners use a hybrid: an initial “router” classifies the intent and then a specialized agent or tool handles the task. Below we survey best practices and examples of each approach, referencing frameworks like LangChain and Semantic Router.

LLM-Based Intent Recognition (General-Agent Approach)

A common approach is to have the LLM itself decide which tool or chain to invoke. For example, one can prompt the model to output a JSON field indicating the desired “tool” or “function” (using OpenAI’s function-calling or ChatGPT Pl

tadwohlrapp

! ============================
! YouTube Top Nav Bar
! ============================

! always show normal YouTube logo, never that distracting doodleyoodle
www.youtube.com##ytd-topbar-logo-renderer > a > ytd-yoodle-renderer
www.youtube.com##ytd-topbar-logo-renderer > a > div:remove-attr(hidden)

! hide "Search with your voice" button (YouTube users have no voice)
www.youtube.com###masthead #center #voice-search-button

dollspace-gay

Verified Spec-Driven Development (VSDD)

The Fusion: VDD × TDD × SDD for AI-Native Engineering

Overview

Verified Spec-Driven Development (VSDD) is a unified software engineering methodology that fuses three proven paradigms into a single AI-orchestrated pipeline:

• Spec-Driven Development (SDD): Define the contract before writing a single line of implementation. Specs are the source of truth.
• Test-Driven Development (TDD): Tests are written before code. Red → Green → Refactor. No code exists without a failing test that demanded it.