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In this tutorial, we build a robust, multi-layered safety filter designed to defend large language models against adaptive and paraphrased attacks. We combine semantic similarity analysis, rule-based pattern detection, LLM-driven intent classification, and anomaly detection to create a defense system that relies on no single point of failure. Also, we demonstrate how practical, production-style safety mechanisms can be engineered to detect both obvious and subtle attempts to bypass model safeguards. Check out the  FULL CODES here . Copy Code Copied Use a different Browser !pip install openai sentence-transformers torch transformers scikit-learn -q import os import json import numpy as np from typing import List, Dict, Tuple import warnings warnings.filterwarnings('ignore') try: from google.colab import userdata OPENAI_API_KEY = userdata.get('OPENAI_API_KEY') print("✓ API key loaded from Colab secrets") except: from getpass import get...

What is Zero Padding Zero padding is a technique used in convolutional neural networks where additional pixels with a value of zero are added around the borders of an image. This allows convolutional kernels to slide over edge pixels and helps control how much the spatial dimensions of the feature map shrink after convolution. Padding is commonly used to preserve feature map size and enable deeper network architectures. The Hidden Issue with Zero Padding From a signal processing and statistical perspective, zero padding is not a neutral operation. Injecting zeros at the image boundaries introduces artificial discontinuities that do not exist in the original data. These sharp transitions act like strong edges, causing convolutional filters to respond to padding rather than meaningful image content. As a result, the model learns different statistics at the borders than at the center, subtly breaking translation equivariance and skewing feature activations near image edges. How Zero...

NVIDIA has released Nemotron-Nano-3-30B-A3B-NVFP4 , a production checkpoint that runs a 30B parameter reasoning model in 4 bit NVFP4 format while keeping accuracy close to its BF16 baseline. The model combines a hybrid Mamba2 Transformer Mixture of Experts architecture with a Quantization Aware Distillation (QAD) recipe designed specifically for NVFP4 deployment. Overall, it is an ultra-efficient NVFP4 precision version of Nemotron-3-Nano that delivers up to 4x higher throughput on Blackwell B200. https://ift.tt/EcH9kD0 What is Nemotron-Nano-3-30B-A3B-NVFP4? Nemotron-Nano-3-30B-A3B-NVFP4 is a quantized version of Nemotron-3-Nano-30B-A3B-BF16 , trained from scratch by NVIDIA team as a unified reasoning and chat model. It is built as a hybrid Mamba2 Transformer MoE network: 30B parameters in total 52 layers in depth 23 Mamba2 and MoE layers 6 grouped query attention layers with 2 groups Each MoE layer has 128 routed experts and 1 shared expert 6 experts are active per ...

In this tutorial, we build a memory-engineering layer for an AI agent that separates short-term working context from long-term vector memory and episodic traces. We implement semantic storage using embeddings and FAISS for fast similarity search, and we add episodic memory that captures what worked, what failed, and why, so the agent can reuse successful patterns rather than reinvent them. We also define practical policies for what gets stored (salience + novelty + pinned constraints), how retrieval is ranked (hybrid semantic + episodic with usage decay), and how short-term messages are consolidated into durable memories. Check out the  Full Codes here . Copy Code Copied Use a different Browser import os, re, json, time, math, uuid from dataclasses import dataclass, asdict from typing import List, Dict, Any, Optional, Tuple from datetime import datetime import sys, subprocess def pip_install(pkgs: List[str]): subprocess.check_call([sys.executable, "-m", ...