Facebook

Staff Software Engineer & Tech lead for Service Discovery @Meta (in Core Infrastructure)

Apache Zookeeper.

Software Engineer

Microsoft

Shipped Windows 10 Photos app ( C++ )
- Designed and implemented image analysis pipeline. This pipeline brings together various imaging algorithms together and also supports versioning.
- Performance improvements of as much as 4x across various components of the analysis pipeline.
- Solely architected the app to support multiple windows in the Photos app.
- Integrated edit pipeline from windows 8.1 Photo editor into the viewer to show users enhanced version of their photos.
- Created an internal first party API to allow other apps to use the crop control in the Photos app.
- Worked with multiple teams in windows platform to get new features added to improve Photos app. 
- Mentored junior developers.
- Connected OneDrive album experience with Photos app album experience allowing users to manage or publish albums to OneDrive.
- Added album share so that users can share their favorite albums with friends and family.
- Working on getting more features in for the Photos app.

Software Development Engineer II

Shipped windows raw codec as part of windows update on windows 8.
- Performance improvements on arm 
- Deployment schema for windows update.

Photo Editor - Windows 8.1 Photos app. ( C++ 11, Direct2D, SIMD, XAML, PPL)
- Core member for the design of image editing effect pipeline, rending engine and a variety of edit features.
- Added the direct2d rendering engine in the photo editor.
- Performance improvements for edit features using vectorization and multi core programming.
- Ensured 30FPS on most devices.
- Added image straighten, blemish removal + local area pixel fix engine, revamped crop & selective focus and other contributions

Video Trim - Windows 8.1 Photos app. ( C++ 11, XAML, PPL)
- Implemented the complete video trim feature for the photos app.

Camera - Windows 8.1 Camera App ( C++ 11, PPL, XAML)
- Improved startup performance by more than 50%
- Improved performance of other core scenarios by significant amount.
- Feature contributions.

Stood up a TFS + git based build system for the team.

Software Development Engineer

Georgia Institute of Technology

Worked on web applications for marketing studies. (Php, MySQL)

Web Developer

Added Auto Collage in Windows Photo gallery. (C++, COM)

Software Development Intern

I am a Staff Engineer & Techlead at Meta. I have 
- Mentored many senior & staff engineers and managers. 
- Designed & built multiple systems with critical focus on scalability & reliability. 
- Solved many cross team & organization misalignments with my inter-personal skills. 

I love writing about the importance of soft skills combined with stories from my 10+ years of experience. If this interests you, follow me and subscribe to my newsletter Techlead mentor.

If you are looking for guidance on navigating your career ladder, you can reach out to me.

Raviraj Achar

Trivial Business

Providing interim software engineering management (VP Engineering/CTO) for startups and SMEs.
Providing leadership and software development coaching and mentoring to software engineering leaders and their teams.

Example projects:

■ Interim CTO redefining the IT infrastructure and software systems of a business turnaround.
■ Interim CTO of an angel backed start-up.
■ Leading the development of a large scale website for the BBC.
■ Advising on technology strategy for businesses.
■ Advising a consultancy on technology deliver and project turnaround.
■ Advising a technology company on the use of machine learning and NLP to access sentiment on financial news.

Providing training, mentoring and coaching to Staff+ and Engineering Managers on leadership and soft skills.

Providing training, mentoring and coaching software engineers. Delivering the training both on-site and remote. Topics included: Go, Python, C++, NLP and Machine Learning.

Chief Technology Officer

Coding Chats

Podcast Host

Coding Challenges

Writing weekly coding challenges that help software engineers level up by building real-world software applications.

Delivering live software engineering training.

Software Engineering Coach

Acast

Acast is a podcast advertising company. They help creators and advertisers connect.

AWS, CDK, Typescript/JavaScript and node.js

Senior Software Engineering Manager

Met Office

The Met Office is the United Kingdom's national weather service, providing critical weather services and world-leading climate science, helping you make better decisions to stay safe and thrive.

AWS (Cloud) and Python for handling large meteorological data sets.

Contract Software Engineer

Sky is Europe's largest media company and pay-TV broadcaster.

Building serverless solutions to support Sky’s broadcasting activities using Python 3.8, Java, AWS and the Serverless Framework.

Cirrus Logic

Cirrus Logic is a fabless semiconductor supplier that specialises in analog, mixed-signal, and audio DSP integrated circuits.

Managing several teams developing Data Engineering Tools to gather, process and distribute data to support the development, testing and deployment of machine learning algorithms using Python, Conda, PyTest, Numpy, Pandas, Dask and Dask-Distributed.

Starting to build a new team whilst designing, developing and deploying software tools to manage big data for the development and evaluation of biometric algorithms.

Principal Software Architect

Software development for High Performance Computing using Python, SciPy, Pandas, NumPy, Dask, Dask-Distributed and C.

Principal Software Engineer

Morningstar

Morningstar Real-Time Data provides real-time. low latency financial market data feeds.

Leading a globally distributed team of 40+ software engineering and QA staff developing and supporting real time low latency market data feed handlers and supporting infrastructure for several hundred financial exchanges. 
Using C++, C# and Python on Windows.

Head of Software Engineering

Aria Networks

Aria Networks provides artificial intelligence driven network planning and optimisations tools for major telecommunications companies, over-the-top providers and global enterprises.

■ Defining the technical strategy to deliver the business strategy and products.
■ Leading the software development and QA teams delivering a customised network capacity planning and optimisation tool for telecommunications networks (IP, MPLS, OTN, SDN and NFV).
■ Using C++, Boost, QT, JavaScript and Python to build software based around an Artificial Intelligence (AI, specifically Evolutionary Computing/Genetic Algorithms & Neural Networks) 
engine that runs on Windows, Mac and Linux.

VP Engineering

Helping you become a better software engineer through coding challenges that build real applications.

I am on a mission to level up one million software engineers and software engineering leaders!

I’m doing that by: 

1. Helping you improve your coding skills through Coding Challenges that have you building real applications.

2. Helping you improve your software development and soft skills with posts and articles about software development.

You can find the coding challenges here ➡ https://codingchallenges.fyi

You can follow me here on LinkedIn for the posts and articles!

I also post some longer articles on my Developing Skills newsletter here ➡  https://developingskills.substack.com/


My BACKGROUND:

I'm a software engineer and sometimes a manager of software engineers. I've worked as both a senior individual contributor (Staff+) and a senior manager (VP Engineering, Head of Software Development).

I've worked across many tech stacks. I've spent most of the last six years building distributed systems with Python and Flask deployed on AWS using CloudFormation and some CDK. 

Recently there has been some TypeScript/JavaScript, React and Next.js on the frontend with Node.js and Express on the backend. Deployed on AWS using CDK.

For most of my career before that I worked in C++. I'm coming the full circle now and currently learning Rust.

I have a passion to help individuals, teams and departments reach their full potential.

John Crickett

I don’t mind if you let an LLM write all your code, as long as you understand all your code.

Treat it the same way you’d treat a teammate’s pull request. Read it. Question it. Make sure it fits your team’s standards and can be understood by the people who will maintain it.

If it’s good, ship it. If it’s not, refine it or rewrite it. Simple.

AI should accelerate you, not replace your judgement. The quality of your work still depends on how well you can read, reason about, and explain the code you ship.

The rule stays the same: you own what goes to production, no matter who typed it.

CTO / VP Engineering / Head of Software Engineering

Here's my problem with people claiming they use AI to write 13,000 lines of production code per day and who suggest it is a repeatable process.

I can't write a clear enough specification for enough functionality to fill 13,000 lines of code per day, every single day.

And I've never met anyone else who can either.

The bottleneck isn't writing code, it's knowing what code to write.

Let's discuss how we tackle that bottleneck, with or without AI.

19 books on Software Architecture - that you can get for around 4% of the cover price right now!

👉 Modern Software Engineering
👉 Understanding Software Dynamics
👉 Software Requirements Essentials
👉 Righting Software
👉 Balancing Coupling in Software Design
👉 Architecture for Flow
👉 Effective Software Architecture
👉 Software Architecture and Decision-Making
👉 Domain Driven Design Distilled
👉 Functional and Concurrent Programming
👉 Software Architecture in Practice
👉 Practical Object-Oriented Design
👉 Serverless as a Game Changer
👉 The Async-First Playbook
👉 Principles of Web API Design
👉 Strategic Monoliths and Microservices
👉 Domain Storytelling
👉 Continuous Architecture in Practice
👉 Patterns for API Design

You get all of these worth over £618 for around £25 right now through Humble’s Book Bundle and support charity, details here: https://lnkd.in/en2eN_kG

Unskilled and inexperienced software engineers take simple requirements and create complex code.

Skilled and experienced software engineers take complex requirements and create simple code.

The difference is that experience maintaining a complex system, teaches them the benefits of simple code and gives them the skills to do so.

This is also why skilled, experienced software engineers are best positioned to leverage AI-assisted software engineering: they know how to turn a complex set of high level requirements into a simple set of low-level requirements that can be used to prompt and AI-agent and have it build simple code.

Tips for AI-Assisted software development:

If you don’t get it, don’t ship it.

AI can write code fast, but it can’t think about the future. Pasting code you don’t fully understand leads to tech debt, surprises, and skills that get rusty. 

Understanding the code keeps your skills sharp and your product reliable.

Read every bit of code before it hits your repo. If you can’t explain it, rewrite it or prompt the AI to try again.

Want to write better software?

Then embrace the Unix Philosophy as described in The Art of Unix Programming. It provides 17 great rules for writing better software.

With AI-assisted software development rules 1, 2, 5, 6, 7, 8 and 15 are even more important and using AI to generate code satisfies rule 14!

The rules are:

1. Rule of Modularity: Write simple parts connected by clean interfaces.
2. Rule of Clarity: Clarity is better than cleverness.
3. Rule of Composition: Design programs to be connected to other programs.
4. Rule of Separation: Separate policy from mechanism; separate interfaces from engines.
5. Rule of Simplicity: Design for simplicity; add complexity only where you must.
6. Rule of Parsimony: Write a big program only when it is clear by demonstration that nothing else will do.
7. Rule of Transparency: Design for visibility to make inspection and debugging easier.
8. Rule of Robustness: Robustness is the child of transparency and simplicity.
9. Rule of Representation: Fold knowledge into data so program logic can be stupid and robust.
10. Rule of Least Surprise: In interface design, always do the least surprising thing.
11. Rule of Silence: When a program has nothing surprising to say, it should say nothing.
12. Rule of Repair: When you must fail, fail noisily and as soon as possible.
13. Rule of Economy: Programmer time is expensive; conserve it in preference to machine time.
14. Rule of Generation: Avoid hand-hacking; write programs to write programs when you can.
15. Rule of Optimization: Prototype before polishing. Get it working before you optimize it.
16. Rule of Diversity: Distrust all claims for “one true way”.
17. Rule of Extensibility: Design for the future, because it will be here sooner than you think.

Embrace the UNIX Philosophy

Tips for AI-assisted software development:

Boring tech gives AI superpowers.

AI coding agents performs best with tools, languages, and frameworks that have been around long enough to show up consistently in its training data. 

When you push it onto the bleeding edge, it starts hallucinating, and the quality drops. You’ll see odd patterns, missing steps, or advice that just doesn’t hold up.

To get the best results, pick a well-established framework or language. Pick 'boring' tech! Ten tell the AI exactly what you’re using.

Tips for AI-Assisted software development:

Don't ask AI to build the house, ask it to make the bricks.

People struggle with AI coding tools because they're asking for too much at once. They want entire features, complete apps, full systems. The AI generates something massive, half of it doesn't work, and now you're debugging spaghetti code you didn't write.

Flip the approach. Ask AI to generate small, well-defined components. A single function. A reusable component. One API endpoint. You architect how they fit together.

Think LEGO, not construction. AI is incredible at fabricating pieces to spec. 

You're still the one building something meaningful from them.

29 great books on AI Algorithms and LLMs - that you can get for around 3% of the cover price right now!

This looks like a great deal, in particular I recommend Emmanuel Maggiori’s book: The AI Pocket Book. I'm looking forward to reading several others!

👉 Build A Large Language Model
👉 AI Agents In Action
👉 Grokking Artificial Intelligence Algorithms
👉 AI-Powered Search
👉 Generative AI In Action
👉 AI For Everyday IT
👉 Causal AI
👉 The Art of AI Product Development
👉 Starting Data Analytics with Generative AI and Python
👉 Learn AI-Assisted Python Programming
👉 AI as a Service
👉 Interpretable AI
👉 Software Testing with Generative AI
👉 Learn Generative AI With PyTorch
👉 Time Series Forecasting Using Foundation Models
👉 Effective Conversational AI
👉 How Large Language Models Work
👉 Succeeding With AI
👉 Introduction to Generative AI
👉 AI-Powered Developer
👉 Inside AI
👉 Build Financial Software With Generative AI
👉 Data Storytelling With Altair and AI
👉 Coding With AI
👉 Prompt Engineering For AI Systems
👉 Zero To AI
👉 The Complete Obsolete Guide to Generative AI
👉 The AI Pocket Book
👉 Regular Expression Puzzled and AI Coding Assistants

You get all of these worth over £1155 for around £35 right now through Humble’s Book Bundle and support charity, details here: https://lnkd.in/eAwEJAiu

What if that unrealistic estimate isn't wrong? What if you just don't know why it might be right?

Your manager says hours. You're thinking days. They must be stupid or unreasonable, right?

Maybe. Or maybe they know something you don't. A tool. A technique. Some context that changes everything.

You won't find out by complaining. You will find out by being curious. Ask them: "How do you see this getting done in that time?"

Two things happen. Either you learn something that makes you faster, or they realise they missed something and you have a real conversation about time or scope.

Either way, you win.

taplio

The Battle

Raviraj Achar

John Crickett

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taplio

The Battle

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John Crickett

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