Building
Building a Hybrid Rule-Based and Machine Learning Framework to Detect and Defend Against Jailbreak Prompts in LLM Systems
In this tutorial, we introduce a Jailbreak Defense that we built step-by-step to detect and safely handle policy-evasion prompts. We generate realistic attack and benign examples, craft rule-based signals, and combine those with TF-IDF features into a compact, interpretable classifier so we can catch evasive prompts without blocking legitimate requests. We demonstrate evaluation metrics, explain…
Building AI agents is 5% AI and 100% software engineering
Production-grade agents live or die on data plumbing, controls, and observability—not on model choice. The doc-to-chat pipeline below maps the concrete layers and why they matter. What is a “doc-to-chat” pipeline? A doc-to-chat pipeline ingests enterprise documents, standardizes them, enforces governance, indexes embeddings alongside relational features, and serves retrieval + generation behind authenticated APIs with…
Building a Speech Enhancement and Automatic Speech Recognition (ASR) Pipeline in Python Using SpeechBrain
In this tutorial, we walk through an advanced yet practical workflow using SpeechBrain. We start by generating our own clean speech samples with gTTS, deliberately adding noise to simulate real-world scenarios, and then applying SpeechBrain’s MetricGAN+ model to enhance the audio. Once the audio is denoised, we run automatic speech recognition with a language model–rescored…
Preventing business disruption and building cyber-resilience with MDR
Given the serious financial and reputational risks of incidents that grind business to a halt, organizations need to prioritize a prevention-first cybersecurity strategy 09 Sep 2025 • , 4 min. read Threat actors are on a roll. They’ve optimized supply chains. Their ranks are growing thanks to pre-packaged services that lower the barriers to entry…
Building Advanced MCP (Model Context Protocol) Agents with Multi-Agent Coordination, Context Awareness, and Gemini Integration
class MCPAgent: “””Advanced MCP Agent with evolved capabilities – Jupyter Compatible””” def __init__(self, agent_id: str, role: AgentRole, api_key: str = None): self.agent_id = agent_id self.role = role self.api_key = api_key self.memory = [] self.context = AgentContext( agent_id=agent_id, role=role, capabilities=self._init_capabilities(), memory=[], tools=self._init_tools() ) self.model = None if GEMINI_AVAILABLE and api_key: try: genai.configure(api_key=api_key) self.model = genai.GenerativeModel(‘gemini-pro’) print(f”✅…
‘Only Murders in the Building’ Season 5 review: Who cares about murders when the vibes are this immaculate?
After five seasons of Only Murders in the Building, I’ve realized I don’t care much about the show’s murder mysteries anymore. Yes, I gasp at every end-of-episode twist and give suspicious new characters the side-eye, but I’m not watching Only Murders for its murders. I’m watching it for the building. SEE ALSO: ‘Only Murders in…
Lessons on building an AI data analyst
Malloy TL;DR Text-to-SQL is not enough. Answering real user questions requires going the extra mile like multi-step plans, external tools (coding) and external context. Context is the product. A semantic layer (we use Malloy ⎋) encodes business meaning and sharply reduces SQL complexity. Use a multi-agent, research-oriented system. Break problems down using context / domain…
Building Better Cars, One Layer At A Time
– Advertisement – Traditional manufacturing has long dictated the boundaries of automotive design, favoring symmetry, uniformity, and production-friendly shapes that could be formed by traditional manufacturing equipment. Additive manufacturing (AM), however, changes what is possible. Instead of being limited by conventional tooling, engineers can use AM to design parts with complicated shapes and features. A…
Building a Multi-Agent Conversational AI Framework with Microsoft AutoGen and Gemini API
class GeminiAutoGenFramework: “”” Complete AutoGen framework using free Gemini API Supports multi-agent conversations, code execution, and retrieval “”” def __init__(self, gemini_api_key: str): “””Initialize with Gemini API key””” self.gemini_api_key = gemini_api_key self.setup_gemini_config() self.agents: Dict[str, autogen.Agent] = {} self.group_chats: Dict[str, GroupChat] = {} def setup_gemini_config(self): “””Configure Gemini for AutoGen””” os.environ[“GOOGLE_API_KEY”] = self.gemini_api_key self.llm_config = { “config_list”: […
AI Guardrails and Trustworthy LLM Evaluation: Building Responsible AI Systems
Introduction: The Rising Need for AI Guardrails As large language models (LLMs) grow in capability and deployment scale, the risk of unintended behavior, hallucinations, and harmful outputs increases. The recent surge in real-world AI integrations across healthcare, finance, education, and defense sectors amplifies the demand for robust safety mechanisms. AI guardrails—technical and procedural controls ensuring…