LinearB is an engineering productivity platform that provides visibility into developer workflows, automation, and process metrics. It collects data across the entire development lifecycle to diagnose blockers and optimize delivery. One user reports saving 321 developer-hours per month. - Source: dev.to / 3 months ago

Most tools measure half the picture. Traditional metrics platforms like LinearB focus on quantitative signals (DORA metrics, cycle time). Survey platforms like Culture Amp capture sentiment across organizations but aren't developer-specific. DX (founded by DORA/SPACE research creators) combines developer surveys with SDLC analytics. These approaches require deliberate implementation and buy-in. - Source: dev.to / 8 months ago

LinearB is a SaaS solution that retrieves metrics overtime, some of them being used to calculate DORA Metrics. They also have a Youtube channel that advocate for DORA Metrics and more. - Source: dev.to / over 2 years ago

In helping engineering orgs get visibility into developer workflows with LinearB, Dan Lines and Ori Keren discovered that the majority of cycle time was being spent in pull request and code review. They found that:. - Source: dev.to / about 3 years ago

LinearB and there are a few cheaper alternatives. Ties in DORA metrics from gut repos and agile project management tools like JIRA. https://linearb.io. Source: about 3 years ago

AI-powered face recognition APIs are instrumental in seamlessly integrating this technology into event systems. These APIs offer the computational power needed for real-time facial analysis, enabling organizers to automate identity checks at entry points. Advanced algorithms within these APIs can handle large data volumes quickly, even in high-traffic scenarios. - Source: dev.to / almost 2 years ago

Museum security is not limited to monitoring artifacts; it also involves controlling access to restricted areas. AI-powered facial recognition systems offer a secure solution for managing entry to sensitive zones such as storage rooms, conservation labs, and exhibit preparation areas. With facial recognition, only authorized personnel are granted access, reducing the risk of unauthorized entry and potential theft. - Source: dev.to / almost 2 years ago

In the world of e-learning, personalizing the student experience is crucial for boosting engagement, comprehension, and overall academic success. One of the most innovative tools for achieving this level of personalization is AI-powered facial analysis. Through Face Analysis APIs, educators can gain valuable insights into students' engagement, attention, and emotional reactions during live or recorded lessons.... - Source: dev.to / almost 2 years ago

Face Detection and Anonymization for Privacy Protection Maintaining privacy while monitoring workers is often a concern. AI-driven APIs use face detection to verify that workers are present in designated areas, while also employing anonymization techniques to blur or obscure personal identifiers. This ensures efficient safety monitoring while respecting privacy laws like GDPR, balancing safety and privacy without... - Source: dev.to / almost 2 years ago

Traditional surveillance is often constrained by the limited capacity of humans to observe and interpret visual data in real time. AI-powered monitoring solutions greatly extend these capabilities by employing techniques like facial recognition and object detection to automatically flag suspicious behavior, unauthorized individuals, or potential threats such as weapons. These systems can operate around the clock,... - Source: dev.to / almost 2 years ago