Bash is All You Need
- Every agent is a while loop that keeps calling the model until it says 'stop'.
- Agent: One loop & Bash is all you need
One Handler Per Tool
April 4, 2026About 1 min
Chrome DevTools MCP is a powerful browser automation tool that allows AI assistants to directly interact with Chrome browsers. Through this MCP server, AI can:
systemd (generate unit files, manage lifecycle).podman build (powered by Buildah) supports Dockerfiles; optional Docker REST API socket for tooling compatibility.podman generate kube exports manifests; podman play kube runs manifests locally; smoother dev→K8s workflows.fuse-overlayfs for rootless; supports volumes and image management similar to Docker.podman-compose; with the Docker-compatible socket, many docker compose workflows can work, though features may vary by version.Purpose: Code coverage analysis tool
Measures how much of your source code is exercised by your unit tests
Tracks:
Generates visual HTML reports showing what parts of code are untested
| Algorithm | Type | Generative/Discriminative | Linear/Non-linear | Notes |
|---|---|---|---|---|
| Naive Bayes | Classification | Generative | Linear | Assumes feature independence, uses Bayes' theorem |
| Logistic Regression | Classification | Discriminative | Linear | Uses sigmoid function, mainly for binary classification |
| Decision Trees | Classification | Discriminative | Non-linear | Handles both numerical and categorical features |
| SVM | Classification | Discriminative | Linear/Non-linear | Uses kernel trick for non-linear, finds optimal hyperplane |
| k-NN | Classification | Discriminative | Non-linear | Instance-based, sensitive to feature scaling |
| Neural Networks | Classification | Discriminative | Non-linear | Can model complex patterns, requires large datasets |
| Linear Regression | Regression | Discriminative | Linear | Predicts continuous values, sensitive to multicollinearity |
| k-Means | Clustering | - | Linear | Unsupervised, sensitive to initial centroids and outliers |
| PCA | Dimensionality Reduction | - | Linear | Unsupervised, maximizes variance, does not consider class labels |
| Hierarchical Clustering | Clustering | - | Non-linear | Unsupervised, builds nested clusters, linkage methods affect results |
| Perceptron | Classification | Discriminative | Linear | Simple model, only for linearly separable data |
| Multi-Layer Perceptron | Classification/Regression | Discriminative | Non-linear | Can solve XOR problem, uses backpropagation for training |
# server.py
from mcp.server.fastmcp import FastMCP
# Create an MCP server
mcp = FastMCP("Demo")
# Add an addition tool
@mcp.tool()
def add(a: int, b: int) -> int:
"""Add two numbers"""
return a + b
# Add a dynamic greeting resource
@mcp.resource("greeting://{name}")
def get_greeting(name: str) -> str:
"""Get a personalized greeting"""
return f"Hello, {name}!"
if __name__ == "__main__":
mcp.run(transport="stdio")
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