Performance Monitoring

3 posts

tossOriginal article

Working as a QA in a (opens in new tab)

Toss Place implements a dual-role QA structure where managers are embedded directly within product Silos from the initial planning stages to final deployment. This shift moves QA from a final-stage bottleneck to a proactive partner that enhances delivery speed and stability through deep historical context and early risk mitigation. Consequently, the organization has transitioned to a culture where quality is viewed as a shared team responsibility rather than a siloed functional task. ### Integrating QA into Product Silos * QA managers belong to both a central functional team and specific product units (Silos) to ensure they are involved in the entire product lifecycle. * Participation begins at the OKR design phase, allowing QA to align testing strategies with specific product intentions and business goals. * Early involvement enables accurate risk assessment and scope estimation, preventing the "shallow testing" that often occurs when QA only sees the final product. ### Optimizing Spec Reviews and Sanity Testing * The team introduced a structured flow consisting of Spec Reviews followed by Q&A sessions to reduce repetitive discussions and information gaps. * All specification changes are centralized in shared design tools (such as Deus) or messenger threads to ensure transparency across all roles. * "Sanity Test" criteria were established where developers and QA agree on "Happy Case" validations and minimum spec requirements before development begins, ensuring everyone starts from the same baseline. ### Collaborative Live Monitoring * Post-release checklists were developed to involve the entire Silo in live monitoring, overcoming the limitations of having a single QA manager per unit. * This collaborative approach encourages non-technical roles to interact with the live product, reinforcing the culture that quality is a collective team responsibility. ### Streamlining Issue Tracking and Communication * The team implemented a "Send to Notion" workflow to instantly capture messenger-based feedback and ideas into a structured, prioritized backlog. * To reduce communication fragmentation, they transitioned from Jira to integrated Messenger Lists and Canvases, which allowed for centralized discussions and faster issue resolution. * Backlogs are prioritized based on user experience impact and release urgency, ensuring that critical bugs are addressed while minor improvements are tracked for future cycles. The success of these initiatives demonstrates that QA effectiveness is driven by integration and autonomy rather than rigid adherence to specific tools. To achieve both high velocity and high quality, organizations should empower QA professionals to act as product peers who can flexibly adapt their processes to the unique needs and data-driven goals of their specific product teams.

figma2 min readCurated summary

How we use data | Figma Blog

Figma uses data in two broad ways: functional data to provide its service and analytics data to improve the product. The company emphasizes collecting only the information necessary to operate Figma, while using aggregated usage insights and experiments to guide product decisions. Examples include improving file sharing and identifying performance problems in the iOS app. ## Functional Data - Functional data supports core account and product operations. - Figma collects a relatively small amount of information at signup: - Email address - Name - Role - This information enables usernames, password-reset messages, file creation, and collaboration. - Figma generally does not require sensitive information such as identity documents or verification. - Payment information for paid plans is collected and processed by Stripe. ## Analytics Data - Analytics data describes how users access and use Figma. - It helps teams understand: - Which features users adopt - Which features they ignore - Where users encounter difficulties - How usage varies across platforms - Data scientists analyze these signals alongside user research, product intuition, and direct feedback. ## Improving Features Through Experiments - A/B testing is a central part of Figma’s product-development process. - Experiments test hypotheses and measure how proposed changes affect user behavior. - In one study, Figma examined its file-sharing modal, where users invite collaborators, manage permissions, and publish work. - Research showed that: - Only 20% of users opened the share modal during their first month. - Only half of those users successfully shared a file. - Figma simplified the interface and moved secondary functions into separate tabs. - The change produced: - A 2% increase in users sending invitations - A 2% increase in users invited to each file - No observed decline in users publishing work to Figma Community ## Identifying Performance Issues - Figma uses data to monitor application performance across platforms and prioritize improvements. - After launching the beta iOS app, the data team analyzed crashes by platform, scenario, and timing. - The analysis found that prototypes were a major source of iOS crashes. - About 25% of prototype crashes occurred within the first 10 seconds of loading. Figma’s approach combines minimal functional data collection with analytics, experimentation, and performance monitoring. The practical goal is to use data selectively to improve usability, reliability, and collaboration while limiting the amount of sensitive information required from users.

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datadog1 min readCurated summary

Using Datadog APM to improve the performance of Homebrew | Datadog

Datadog announces that Gartner named it a Leader in the 2026 Magic Quadrant for Observability Platforms. The provided content is primarily the website’s navigation menu and does not include the blog article’s substantive discussion, methodology, or supporting evidence. ## Gartner Recognition - Datadog highlights its position as a Leader in Gartner’s Magic Quadrant for Observability Platforms. - The linked resource appears to support or explain the recognition. ## Datadog’s Observability Portfolio The navigation indicates that Datadog’s platform spans: - **Infrastructure:** infrastructure, container, network, serverless, GPU, storage, and cloud-cost monitoring. - **Applications:** APM, profiling, dynamic instrumentation, and service monitoring. - **Logs and data:** log management, database monitoring, data-stream monitoring, and observability pipelines. - **Security:** cloud security, SIEM, vulnerability management, workload protection, and code security. - **Digital experience:** real-user monitoring, session replay, synthetic monitoring, product analytics, and error tracking. - **Software delivery and service management:** CI visibility, testing, incident response, SLOs, workflow automation, and internal developer portals. - **AI capabilities:** agent observability, AI integrations, investigation agents, and GPU monitoring. The supplied excerpt does not provide enough article text to summarize Datadog’s specific strengths, Gartner’s evaluation criteria, or the company’s evidence for being named a Leader.

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