Tag Archives: ai

Bandit and CircleCI

How You Can Integrate Bandit with CircleCI Why It’s Valuable Things to Watch Out For / Trade-Offs Example Snippet (Pseudo config.yml) version: 2.1jobs: security_scan: docker: – image: cimg/python:3.9 steps: – checkout – run: name: Install Bandit command: pip install bandit – run: name: Run Bandit command: bandit -r . -f json -o bandit-report.json – store_artifacts:Continue reading “Bandit and CircleCI”

Sleeper AI Agent

A “Sleeper AI Agent” typically refers to an AI system designed to remain dormant or behave normally until activated by specific conditions, triggers, or commands. This concept appears in several contexts: Security and AI Safety Context Sleeper agents in AI safety research refer to models that: Research Applications Legitimate uses include: Technical Implementation Sleeper agentsContinue reading “Sleeper AI Agent”

TensorFlow vs. PyTorch

Development Philosophy TensorFlow takes a production-first approach, emphasizing scalability, deployment, and enterprise features. Originally built around static computational graphs, though TensorFlow 2.0 introduced eager execution by default. PyTorch prioritizes research flexibility and intuitive development. Built from the ground up with dynamic computational graphs and a “Pythonic” design philosophy that feels natural to Python developers. EaseContinue reading “TensorFlow vs. PyTorch”

Skypilot in ML conext

SkyPilot is a framework designed to run large language models, AI workloads, and other batch jobs across cloud platforms. It abstracts infrastructure complexities, maximizes GPU availability through autoscaling groups across regions/zones, and aggressively pursues cost optimization with managed spot instances. SkyPilot aims to require no code changes to existing applications What is SkyPilot? ☁️ Cloud-AgnosticContinue reading “Skypilot in ML conext”

AI Agent Loop

Option One An AI Agent Loop refers to the cyclical process by which an autonomous AI agent perceives its environment, plans actions, executes those actions, and reflects on the results. This loop enables the agent to operate intelligently in dynamic environments by continually adapting its behavior based on feedback and outcomes. It is foundational toContinue reading “AI Agent Loop”

RAG pipeline building frameworks comparison

When building a Retrieval-Augmented Generation (RAG) pipeline, the “best” tool depends on your goals, level of abstraction, and control you want over the components. Here’s a breakdown of LangChain, Hugging Face, and PyTorch, to help you choose: 🧱 1. LangChain: Best for: Rapid prototyping and production-ready apps with modular components ✅ Use LangChain if youContinue reading “RAG pipeline building frameworks comparison”