What's helping devs thrive...

- **Non-local dev environments are now the norm — not the exception**. In a major shift from last year, **64%** of developers say they use **non-local environments** **as their primary development setup**, with local environments now accounting for only **36%** of dev workflows. - **Data quality is the bottleneck** when it comes to building AI/ML-powered apps — and it affects everything downstream. **26% of AI builders** say they’re not confident in how to prep the right datasets — or don’t trust the data they have. … ## 1. ... Great culture, better tools — but developers often still hit sticking points. From pull requests held up in review to tasks without clear estimates, the inner loop remains cluttered with surprisingly persistent friction points. … And among container users, needs are evolving. They want better tools for **time estimation (31% ** compared to 23% of all respondents**), task planning (18% for both container users and all respondents), and monitoring/logging (16%) ** vs designing from scratch (18%) in the number 3 spot for all respondents — stubborn pain points across the software lifecycle. ### An equal-opportunity headache: estimating time No matter the role, **estimating how long a task will take is the most consistent pain point** across the board. Whether you’re a front-end developer (**28%**), data scientist (**31%**), or a software decision-maker (**49%**), precision in time planning remains elusive. Other top roadblocks? **Task planning (26%)** and **pull-request review (25%)** are slowing teams down. Interestingly, where people say they need better tools doesn’t always match where they’re getting stuck. Case in point, **testing solutions and Continuous Delivery (CD)** come up often when devs talk about tooling gaps — even though they’re not always flagged as blockers. ### Productivity by role: different hats, same struggles When you break it down by role, some unique themes emerge: - **Experienced developers** struggle most with time estimation (**42%**). - **Engineering managers** face a three-way tie: **planning, time estimation, and designing from scratch (28% each)**. - **Data scientists** are especially challenged by **CD (21%)** — a task not traditionally in their wheelhouse. - **Front-end devs**, surprisingly, list **writing code (28%)** as a challenge, closely followed by **CI (26%)**. … ### The hidden bottleneck: data prep When it comes to building AI/ML-powered apps, **data is the choke point**. A full **26% of AI builders** say they’re not confident in how to prep the right datasets — or don’t trust the data they have. This issue lives upstream but affects everything downstream — time to delivery, model performance, user experience. And it’s often overlooked.