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Would you run a command without knowing what it could do?
A copied installation script. An automated workflow. A shell command suggested by AI.
Convenient? Yes. Worth checking before execution? Absolutely.
That’s why I built SafeShell, an open-source tool for inspecting commands, applying security policies, and running permitted commands with sandboxing on Linux.
The goal: help developers and engineering teams work faster with more control over what they execute.
Explore the code, try it, and share your feedback:
https://github.com/HamzaGhebriche/SafeShell
Building automation or CI/CD workflows that need stronger security controls? I’m available for freelance projects in DevSecOps, secure automation, and cloud security. Let’s talk through Contra.
Thanks! CI is a useful use case. SafeShell’s CLI could be called from a GitHub Actions step, but sandboxed execution requires a Linux runner that supports its isolation mechanisms. I haven’t validated that exact workflow yet. One limitation for deployment scripts: the current...
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Proud to officially add the AWS Certified Machine Learning Engineer - Associate certification to my toolkit!
Building robust machine learning pipelines and scaling AI workloads securely on the cloud has always been a key focus of mine. Earning this certification has been a great deep-dive into mastering AWS ML infrastructure, MLOps best practices, and production-grade architectures.
Ready to bring these advanced capabilities to upcoming data science and engineering projects! Let's build something impactful. 💡💪
🔗 Verify Certificate:
Validation Number: 74c5808986a64b84addcaa9db725b8bd
Verification Link: https://aws.amazon.com/verification
#AWS #MachineLearning #AI #CloudComputing #DataAnalysis #MLOps
Congratulations on earning the AWS Machine Learning Engineer certification, Hazem khaled! Robust pipelines and cloud automation make all the difference for production-grade AI systems.
AI Agent for ServiceNow Automation (LangGraph + GPT-4o-mini + Slack, Jira, Email)
I built a LangGraph agent that turns natural-language requests into ServiceNow incidents, so users never have to open the ServiceNow UI. A StateGraph workflow pairs an LLM node (GPT-4o-mini) that understands the request with a tool node that runs the ServiceNow Table REST API. It supports caller, description, and assignment group fields, and it resolves group names to sys_ids by looking them up in the sys_user_group table, so users can just say “Network Team”. The agent is exposed through a Flask REST endpoint and an interactive CLI, with credentials kept in environment variables and error handling around every API call. I then extended the same pattern to Slack, Email, and Jira to automate workflows across platforms.
Understand: The LLM node interprets the request and extracts the caller, description, and assignment group.
Act: The tool node executes ServiceNow actions through the Table REST API.
Resolve: Group names are looked up in the sys_user_group table to get the sys_id, so users refer to teams by name.
Expose: A Flask REST endpoint and an interactive CLI give two ways to use the agent.
Secure and robust: Credentials are managed through environment variables, and API calls have error handling.
Extend: The same agent pattern was reused for Slack, Email, and Jira integrations.
Tools to tag
Python, LangGraph, OpenAI GPT-4o-mini, Flask, ServiceNow REST API, Slack API, Jira, Agentic AI, Automation
The Investigatory Powers Act 2016 (IPA 2016) and AI-driven "undress tools" (software used to generate non-consensual deepnude or sexualized imagery) operate in different legal spheres, but they cross paths through state surveillance, criminal evidence gathering, and tech vendor mandates.
While the IPA 2016 does not directly criminalize or ban the creation of deepfakes, it provides the surveillance and hacking tools the UK government uses to track the creators, distributors, and hosts of these illicit tools.
