Faros AI

Data needs a narrative. You don't always land on the right metric to tell your story on your first try. It's a learning process and a muscle you develop. Explore the process here: https://lnkd.in/gPpdEVDS

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Tulika Garg, Director of Product Management for Developer Enablement and Ecosystem at Autodesk, says that visibility into developer productivity is crucial to help teams swiftly identify areas for improvement, make data-driven decisions, and deliver high-quality software faster.  In a talk at the 2024 Gartner® Application Innovation and Business Summit, Tulika shared a powerful example. Mean Time To Resolve (MTTR) measures how long it takes an organization to resolve an outage. Outages have a huge impact on customer loyalty, brand reputation, and profitability — especially for companies operating under strict SLAs. While certain incident management tools can measure MTTR, they do not answer the question of how to improve it. With Faros AI, development teams now have the insights to pinpoint sources of issues, whether in time to detect or rollback speed, and can better prioritize improvements. Read Autodesk’s valuable learnings from implementing a platform approach to accelerating engineering productivity: https://lnkd.in/gg_tBGFf

Get the full guide here: https://lnkd.in/gH3DgquK

"Successful people ask better questions, and as a result, they get better answers." - Tony Robbins Similarly, the most successful, high-achieving engineering teams ask better questions, uncover deeper insights, and ultimately achieve greater outcomes. These are the types of questions they ask in their sprint retros: 1. Did we estimate our capacity correctly? 2. Are we delivering well against our commitments? 3. Are we working on the right things? 4. What is the current morale of the team, and how is that impacting performance? Discover the four corresponding sprint metrics they use to get better answers: https://lnkd.in/gf8dyrvp

🔺We’re playing the $100,000 pyramid game, working through our final category with only seconds remaining on the timer… These four things appear: -Getting a gym membership -Scheduling an annual physical -Cleaning the gutters -Refactoring code *** The bell dings and the crowd goes wild*** Did you guess the category? “THINGS PEOPLE USUALLY PROCRASTINATE” Hey, we get it. While many prefer to avoid the topic of code complexity altogether, it's crucial to understand its impact on your development teams. Machine learning models can provide you with that insight so you can make informed decisions, and stop procrastinating when it’s truly doing damage. Read all about it here: https://lnkd.in/gGKn69YZ

Get the full guide here: https://lnkd.in/gH3DgquK

Get the full guide here: https://lnkd.in/gH3DgquK

Visibility into developer productivity at Autodesk did not come easy, given the sheer complexity and scale of the Autodesk tech stack: Autodesk teams run hundreds of thousands of builds per month that span thousands of configurations on a combination of loads, technologies, and tools. After initially attempting in-house instrumentation of standard productivity metrics, Autodesk’s Internal Developer Platform (IDP) was provisioned with a visibility plane, where Faros AI feeds data insights from some of the key SDLC tools.  The company is eager to share several of its valuable learnings with peers dealing with similar challenges. Read their four tips here: https://lnkd.in/gg_tBGFf

A 'just get it done' mindset often leads to poor development practices that degrade a codebase’s quality and maintainability over time. This increases both cyclomatic and cognitive complexity, ultimately reducing developer productivity and team morale. But how do you know when you’ve reached the tipping point? R&D from Faros AI combines developer productivity analytics, automated issue detection, and the ranking of potential causes to highlight when code complexity is becoming a blocker. Read more here: https://lnkd.in/gGKn69YZ