Python: Profiling, tooling, AI assisted LLM debugging and Security

[arunsingh]

Talk title

Python: Profiling, tooling, AI assisted LLM debugging and Security

Short talk description

This talk is a practical deep dive into the modern Python developer tooling landscape, covering profiling, performance analysis, code review, security, supply-chain trust, and shift-left engineering practices. It is designed for Python developers, backend engineers, platform teams, security engineers, and technical leaders who want to build faster, safer, and more reliable systems. Attendees will learn how to understand Python codebases more deeply, identify performance bottlenecks, use the right tools for review and security, and connect engineering productivity with better business outcomes. Key takeaways include a clear map of Python tooling, profiling strategies, security best practices, and emerging trends shaping the python ecosystem.

Long talk description

This talk is a comprehensive deep dive into the modern Python developer tooling landscape, with a focus on profiling, performance engineering, code review, security, and supply-chain trust. Python is increasingly used for systems programming, cloud infrastructure, developer platforms, blockchain, embedded systems, AI infrastructure, and high-performance backend services. As Python adoption grows, teams need more than language knowledge: they need practical workflows, mature tooling, and repeatable engineering practices that help them build reliable software at scale.

The session will explore how developers can understand python codebases more deeply, identify performance bottlenecks, evaluate memory and CPU behavior, improve build and runtime efficiency, and use profiling as a regular part of the development lifecycle. It will also cover the growing landscape of python tools for linting, formatting, dependency review, unsafe-code analysis, security auditing, supply-chain risk management, and continuous code quality.

This talk is intended for python developers, backend engineers, platform engineers, DevOps and DevSecOps teams, security engineers, engineering managers, and technical leaders who want to bring performance, quality, and security earlier into the software delivery process. Attendees will come away with a practical map of the Python tooling ecosystem, a better understanding of how to shift optimization and security left, and concrete ideas for improving developer productivity, codebase maintainability, and business outcomes through disciplined Rust engineering practices.

What format do you have in mind?

Talk (20-25 minutes + Q&A)

Talk outline / Agenda

Introduction to the Problem - 5 mins
Why profiling matters in Python, where performance bottlenecks usually hide, and how slow code impacts developer productivity, infrastructure cost, and business outcomes.

Core Concepts and Theory - 10 mins
Overview of Python performance fundamentals: CPU vs memory profiling, tracing vs sampling, concurrency limitations, I/O bottlenecks, runtime behavior, and how to choose the right profiling tool.

Live Demo / Code Walkthrough - 15 mins
Walk through a Python codebase, identify bottlenecks using profiling tools, interpret results, and apply practical optimizations step by step.

Best Practices and Pitfalls - 10 mins
How to make profiling part of shift-left development, avoid premature optimization, benchmark correctly, review performance-sensitive code, and use profiling insights in CI/CD and team workflows.

Q&A - 10 mins
Open discussion on Python tooling, optimization strategies, real-world performance issues, and how teams can improve developer productivity through better profiling practices.

Key takeaways

1. Clear understanding of Python profiling concepts, including CPU profiling, memory profiling, tracing, sampling, benchmarking, and when to use each approach.

2. Practical knowledge of how to identify performance bottlenecks in a Python codebase using common profiling and observability tools.

3. Best practices for bringing performance optimization into shift-left development, code review, CI/CD workflows, and developer productivity initiatives.

4. Common pitfalls to avoid, including premature optimization, misleading benchmarks, ignoring I/O bottlenecks, and optimizing code without measuring first.

5. Resources and next steps for further learning, including recommended Python profiling tools, documentation, workflows, and ways to apply profiling practices in real-world projects.

What domain would you say your talk falls under?

Core Python

Duration (including Q&A)

15 min for talk + 10 mins for Code demo walkthrough + 5min Q&A

Prerequisites and preparation

Attendees should have basic Python knowledge, including functions, classes, modules, and common data structures. A laptop with Python 3.10 or above installed is recommended, along with the dependencies or profiling tools mentioned in the talks for anyone who wants to follow along during the demo. While in the venue participants can download libraries on the go with talks and get started instantly.

Basic familiarity with the command line will be helpful for running scripts, installing packages, and using profiling tools. Prior experience with Python profiling or performance optimization is not required; the talk will introduce the key concepts, tools, and workflows from the ground up.

Resources and references

https://docs.google.com/presentation/d/1GU2ys1h7FzXv0GwKEgj17uhC12dy01o64m1PQdQSi04/edit?usp=sharing
github: github.com/arunsingh
[relevant repo will be made public before the event]

Link to slides/demos (if available)

https://docs.google.com/presentation/d/1GU2ys1h7FzXv0GwKEgj17uhC12dy01o64m1PQdQSi04/edit?usp=sharing

Twitter/X handle (optional)

@aruns89

LinkedIn profile (optional)

No response

Profile picture URL (optional)

https://github.com/arunsingh

Speaker bio

Arun Singh is a tech company founder, by skills he is systems architect, principal engineer, software developer, SRE, and expert code reviewer with deep experience building reliable, scalable, and performance-conscious software systems, currently he build AI Infrastructure hyperscale system software. His work spans backend engineering, platform engineering, site reliability engineering, developer productivity, observability, security, quantum computing algorithms and code quality practices across modern software teams.

As a principal engineer and system architect, Arun has worked closely with engineering teams to improve architecture, review complex codebases, identify performance bottlenecks, and bring reliability and optimization earlier into the development lifecycle. His experience connects directly with this talk’s focus on Python profiling, tooling, code review, and shift-left developer productivity.

Arun has previously spoken at developer and reliability engineering communities including BangPypers meetups in Bangalore, SRE and platform engineering meetups, SRECon-related forums, HillHacks, Bay Area Python meetups, PyBay, and other technical community events. He enjoys sharing practical engineering lessons drawn from real-world systems, production incidents, and hands-on developer workflows.

Outside work, Arun is an avid mountaineer, trekker, and ultramarathon long-distance runner. He brings the same endurance, curiosity, and love for difficult terrain into both engineering and outdoor adventures.

You can reach Arun at arunsingh.in@gmail.com, and follow him on X at @aruns89.

Availability

23/05/2026

Accessibility & special requirements

NA

Speaker checklist

• I have read and understood the PyDelhi guidelines for submitting proposals and giving talks
• I have read and acknowledged the PyDelhi accessibility guidelines and will ensure my presentation materials (slides, videos, demos) follow these recommendations
• I will make my talk accessible to all attendees and will proactively ask for any accommodations or special requirements I might need
• I agree to share slides, code snippets, and other materials used during the talk with the community
• I will follow PyDelhi's Code of Conduct and maintain a welcoming, inclusive environment throughout my participation
• I understand that PyDelhi meetups are community-centric events focused on learning, knowledge sharing, and networking, and I will respect this ethos by not using this platform for self-promotion or hiring pitches during my presentation, unless explicitly invited to do so by means of a sponsorship or similar arrangement
• If the talk is recorded by the PyDelhi team, I grant permission to release the video on PyDelhi's YouTube channel under the CC-BY-4.0 license, or a different license of my choosing if I am specifying it in my proposal or with the materials I share

Additional comments

No response

[github-actions]

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[vaibhavjain-in]

Hi @arunsingh

Thanks for your submission. We would be very interested in a dedicated talk on "Advanced Python Profiling & Performance Engineering." We suggest evaluating the Security and AI Debugging components to allow enough time for a high-quality, 15-minute live demo of interpreting profiling results.

Please let us know if you are willing to tighten the scope to focus exclusively on Python Performance. If you choose to keep the current "all-in-one" structure, we feel it may be too broad for our technical deep-dive sessions.

You can add another session in coming meetups on Security.

Also, your GDrive links are not shared yet. Please share the deck link.

[arunsingh]

@vaibhavjain-in I will pass event for this time, I would not be able to tighten the scope, will speak some other time. Though, thanks for reviewing session.