PocketTM vs Agent Lab

Welcome to PocketTM App Summary At the grocery store, do you need to perform a Threat Model? No problem, PocketTM is here to help you. Want to save time while still covering everything you need as part of performing a Threat Model? Introducing PocketTM, an iOS application designed to guide security professionals through comprehensive security threat assessments using Microsoft's STRIDE framework. The app provides a structured approach to identifying potential security vulnerabilities during system design phases, transforming complex threat modeling into an accessible, step-by-step process. Pocket Threat Model (PocketTM) is a comprehensive threat modeling companion for security professionals, developers, and architects. Browse and assess threats using the industry-standard STRIDE framework plus a dedicated AI & Machine Learning section covering today's most critical AI risks. Select relevant threats, explore guided discovery questions, review mapped mitigation strategies, and generate shareable HTML reports — all from your pocket. STRIDE Framework — Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege AI Threat Modeling — Prompt Injection, Training Data Poisoning, Adversarial Attacks, AI Agent Risks, Model Theft, Hallucination & Output Integrity, Supply Chain, Bias & Ethics, Sensitive Data Exposure, Shadow AI, AI Social Engineering, Vector & Embedding Attacks, Fine-Tuning Subversion, Multimodal Attacks, AI Denial of Service 702 discovery questions · 101 mitigation strategies · 49 threat risks · Shareable HTML reports

Agent Lab is a learning and experimentation playground for AI agents, built for AI learners, agent developers, and product managers. It doesn't just run agents — it makes every step of an agent's execution visible, so you can finally understand how agents actually work. Unlike an ordinary AI chat app, Agent Lab is about learning, experimenting, debugging, and visualization. On every run you can see the prompts, tool calls, retrieval, tokens, and latency — nothing is hidden. Get started fast · On-device local models: download a small Qwen model on first use (Wi-Fi recommended), then run it fully offline — no API key and no second machine required. Cost is always 0, and tokens and latency are shown for real. · Download on demand: grab other open-source models your device can actually run, with capability gating based on your chip and memory. · Cloud models: works with Chat Completions-compatible APIs (DeepSeek, Moonshot, OpenRouter, and more) and Anthropic Claude — just enter your key, base URL, and model. Three built-in demos · Customer Support Agent — demonstrates tool calling and workflow with a built-in mock order system, no real backend needed. · RAG Knowledge Base — comes with curated reference docs (LangGraph, MCP, LLM APIs, Python) and lets you upload your own PDF / Markdown / TXT, then inspect the retrieved chunks, similarity scores, and final answer. · Research Agent — shows how an agent plans on its own: Planner → Search → Read → Summarize → Answer. Unified visual debugging Every demo offers three views: · Timeline — the full step-by-step sequence of LLM and tool calls · Graph — the workflow graph, highlighting the active node as it runs · Console — expand any step to see System / User / Assistant prompts, the complete LLM request and response JSON, tool requests and responses, tokens, estimated cost, latency, and errors Security and privacy · API keys are stored encrypted on-device via the system Keychain — local only, never uploaded to any server. · Uploaded knowledge-base files and the vector indexes built from them stay on your device and can be deleted anytime. · On-device inference runs fully offline; your data never leaves the device. No sign-up, no login — open it and go. Make every step an agent takes visible.