Tag: user experience

What to do About AI’s Forced Rethink of Reliability in Modern DevOps
For years, reliability discussions have focused on uptime and whether a service met its internal SLO. However, as systems become more distributed, reliant on complex internet stacks, and integrated with AI, this binary perspective is no longer sufficient. Reliability now encompasses digital experience, speed, and business impact.

For the second year in a row, The SRE Report highlights this shift. DevOps and SRE leaders across industries are redefining reliability, with AI accelerating change by revealing the limitations of legacy monitoring and requiring teams to link technical signals to real-world outcomes. Traditional availability metrics continue to fall short because they fail to capture how users actually experience reliability.

From Uptime to Experience-Driven Resilience

This year’s SRE Report reinforces that slow performance is as critical as downtime, with nearly two-thirds of respondents considering performance degradations as serious as outages. This is good news as users do not distinguish between down and slow, instead perceiving both as a disruption.

According to the report, “Speed is now one of reliability’s clearest trust signals.” Fast performance builds reputation, while slow performance quickly leads to lost conversions, customer churn, and diminished trust. Resilience is now defined not only by incident survival, but by maintaining acceptable user experience under real-world conditions such as high load, congestion, and third-party failures.

AI accelerates this transition. AI-driven features, agentic workflows, and LLM-based applications introduce new latency paths and probabilistic behaviors. A component may be “up,” but the system may still provide a degraded or unexpected experience to your users. In this context, uptime metrics may produce data, but they do not produce insight.

Where Legacy Monitoring Breaks Down

Traditional monitoring tools were designed for systems fully controlled by internal teams. Today, modern digital experiences rely on:
- Third-party providers and managed services
- SaaS platforms and APIs
- Internet routing, CDNs, and millions of connected devices
- AI/ML components whose behavior changes over time
The SRE Report highlights that most teams still rely heavily on dashboards and alerts to detect performance issues. While familiar and trusted, these tools are good with the “what” but not so much the “why”, especially when the root cause lives outside the application boundary.

This gap becomes even more pronounced with AI systems. Only a small minority of respondents say they feel highly confident in monitoring the reliability of AI/ML components. For most teams, AI remains a black box, introducing new failure modes, subtle regressions, and cascading performance issues that legacy alerting was never designed to catch.

AI not only introduces new reliability challenges but also reveals the fragmentation in current observability methods. Siloed metrics, disconnected tools, and static thresholds cannot keep up with systems that learn, adapt, and rely on changing external conditions. In other words, AI is best poised to amplify whenthe underlying data is connected and integrated.

AI as the Integrator and the New Operating Model for Reliability

Increasingly, AI is being used to:
- Correlate performance, network, and user experience signals
- Surface patterns humans would miss across tools and domains
- Assist during incident response with context-rich suggestions
But AI only helps if someone owns it. Teams need people who can lead AI systems in production—setting guardrails, validating what the system suggests, tuning it over time, and being accountable when it’s wrong. That’s the new job: not “watch dashboards all day,” but guide the automation and keep it effective as the environment changes.

The report also warns against expecting automatic benefits. AI shifts the work: maintaining model drift, validating recommendations, governing agent behavior, and measuring impact. The bigger question is whether reliability work is changing business outcomes, not just engineering metrics.

Connecting Reliability to Business Outcomes

The most significant gap in the data is organizational, not technical. Only about a quarter of teams consistently assess whether performance improvements impact business metrics such as revenue or NPS. Even fewer quantify the financial cost of downtime or severe slowness.

This is a missed opportunity. The report is clear: when reliability is expressed in business terms, it gains influence. Teams that quantify the cost of delay can prioritize more effectively, justify investment, and elevate reliability from an operational concern to a strategic one.

AI can assist in this area as well. By correlating user experience data with business KPIs, AI-driven analytics help answer key executive questions:
- How much revenue does a 500 ms slowdown cost?
- Which customer journeys are most sensitive to performance?
- Where should we invest to protect trust and growth?
When reliability becomes a shared language between engineering and leadership, it shifts from being a cost center to a competitive advantage.

What This Means for SRE and DevOps Leaders

AI is prompting a reevaluation of reliability, though the goal of building trust remains unchanged. What is changing is how trust is established and measured.

For modern SRE and DevOps leaders, that means:
- Expanding reliability beyond service level objectives to speed and experience level objectives
- Acknowledging that internet stack and AI dependencies are first-class reliability risks
- Using AI thoughtfully to amplify integrated signals, or integrate edge cases
- Tying reliability work directly to customer and business outcomes
The 2026 SRE Report makes it clear that reliability has moved from the server room to the boardroom. In an AI-driven environment, reliability is how trust is earned, and the teams that understand that will be the ones that scale with confidence.
February 20, 2026
Meta Introduces Confucius Code Agent: A New Approach to AI-Powered Software Engineering

A research team from Meta and Harvard has released the Confucius Code Agent (CCA), a software engineering agent designed to work with large-scale codebases. The system achieved a 54.3% resolve rate on the SWE-Bench-Pro benchmark, outperforming previous research frameworks and matching commercial results from leading AI companies.

What makes this release notable isn’t just the performance numbers. The team built CCA on top of a new development platform called the Confucius SDK, which addresses a problem that’s plagued AI coding agents: They often struggle when moving from research demos to production workloads.

Why Agent Architecture Matters

The research makes a clear point about AI coding assistants. Raw model capability isn’t everything. How you structure the agent around the model, what the team calls “agent scaffolding,” determines whether it can handle real software engineering work.

The paper demonstrates this with concrete numbers. When the same Claude model runs on different frameworks, performance varies significantly. CCA with Claude 4 Sonnet achieves 45.5%, while the baseline SWE-Agent with the same model reaches only 42.7%. The difference comes entirely from the agent architecture, not the underlying AI model.

According to Mitch Ashley, VP & Practice Lead, Software Lifecycle Engineering, Futurum, “Confucius Code Agent shows that the limiting factor in AI-powered software engineering is no longer the model. The performance gap comes from how agents are structured to reason over code, manage context, and separate machine-facing signals from human-facing artifacts. This work demonstrates that agent scaffolding can materially change outcomes even when the underlying model is identical.”

“For software teams, this reframes the build decision. Choosing a model is table stakes. The real differentiation moves to agent architecture that supports long-lived work, persistent memory, and controlled tool execution across real codebases. Confucius makes clear that production-grade AI development systems will be judged by how well they operationalize models, not by raw benchmark scores alone.”

Three Design Perspectives

The Confucius SDK separates agent design into three distinct areas: Agent Experience (AX), User Experience (UX), and Developer Experience (DX). This separation addresses a common mistake in agent frameworks, where human-readable logs get passed directly to the AI model, creating noise that degrades performance.

Agent Experience focuses on what the AI model sees and how information gets structured for reasoning. The system compresses verbose output into distilled summaries, keeping prompts concise while preserving essential information.

User Experience handles how humans interact with and observe the agent. Users see detailed execution traces and readable logs, but this information stays separate from what the agent processes internally.

Developer Experience covers the tools needed to build, inspect, and improve agents. The SDK provides modular interfaces for prompts, tools, and memory, making it easier to run ablations and iterate on designs.

Four Key Mechanisms

The SDK implements four mechanisms that address the core challenges of large-scale software engineering.

First, hierarchical working memory manages context across long sessions. When conversations grow too large, an “Architect” agent summarizes earlier turns into structured plans. This compression preserves key decisions and error traces while keeping recent interactions in their original form. The approach prevents context overflow without losing important reasoning chains.

Second, a note-taking agent converts interaction traces into persistent Markdown notes. These notes capture both successful solutions and failure modes, creating a knowledge base that improves performance across sessions. When the team tested this on 151 tasks, the second run showed improved efficiency: average token cost dropped from 104k to 93k, and the resolve rate increased from 53% to 54.4%.

Third, the extension system handles all tool use through modular components. Extensions define how the agent parses outputs, invokes tools, and manages side effects. This separation makes behaviors easier to observe, test, and reuse across different agents.

Fourth, a meta-agent automates agent development through a build-test-improve loop. It generates configurations, wires together components, evaluates candidates on test tasks, and refines prompts based on observed failures. The production version of CCA is itself the output of this automated refinement process.

Performance and Evaluation

The team evaluated CCA on multiple benchmarks. On SWE-Bench-Pro, CCA with Claude 4.5 Opus achieved 54.3%, exceeding the 52.0% reported by Anthropic for their proprietary system. With Claude 4.5 Sonnet, CCA reached 52.7%, significantly ahead of the Live-SWE-Agent baseline at 45.8%.

Ablation studies isolated the contribution of each mechanism. On a 100-example subset, removing advanced tool use while keeping context management dropped performance from 51.6% to 51.0%. Removing context management entirely reduced it further to 44.0%. The results confirm that both mechanisms contribute independently to overall performance.

The team also tested CCA on long-context scenarios by grouping tasks by the number of files modified. Performance remained stable even when touching 10+ files, though it degraded moderately as edit volume increased. This suggests the agent can handle multi-file refactoring but faces challenges with cumulative localization uncertainty.

Comparison with Claude Code

Beyond standardized benchmarks, the researchers compared CCA with Anthropic’s Claude Code on real PyTorch issues. Both systems used the same Claude Sonnet 4.5 model and identical GPU resources, isolating the effect of agent architecture.

The comparison revealed different problem-solving styles. CCA typically identifies root causes through systematic analysis within a single reasoning chain. Claude Code delegated investigations to separate subagents that performed exhaustive analysis without access to the main context.

This architectural difference affected solution characteristics. CCA generally produced simpler fixes, while Claude Code often implemented more comprehensive but potentially over-engineered solutions. On one CUDA memory issue, CCA’s minimal fix matched what the PyTorch team eventually merged, while Claude Code added more elaborate memory-management logic.

What This Means for Development Teams

The research suggests that organizations building AI coding assistants should invest as much in agent architecture as in model selection. The gap between research-grade and production-grade agents isn’t just about scale—it’s about how you structure memory, manage context, and coordinate tools.

The Confucius SDK provides one framework for addressing these challenges, but the core insights apply broadly. Separating what the agent sees from what users see improves both. Persistent memory across sessions enables learning from past mistakes. Modular tool systems make behaviors easier to test and improve.

For development teams considering AI coding assistants, these architectural patterns offer a starting point. The question isn’t just which model to use, but how to scaffold it for sustained, reliable performance on real codebases.

January 12, 2026
From Code to Confidence: Building AI Apps That Earn User Trust
As a developer, you’ve probably felt the excitement of deploying your first AI-powered feature. The model performs exceptionally well in testing with solid accuracy metrics, and stakeholders are delighted. Then, reality hits: Users start complaining about biased recommendations, confusing outputs or worse — publicly calling out your application for generating harmful content.

A recent Applause survey found that 65% of users experienced issues with AI applications between January and March 2025, including bias, hallucinations and incorrect responses. Trust has officially become the new battleground for user experience, and traditional testing approaches are no longer sufficient for AI applications.

The good news? By shifting from assumption-driven development to deliberate, user-centered testing processes, you can build AI applications that don’t just work — they earn genuine user confidence.

Most developers approach AI testing in the same way they handle traditional software development: They write unit tests, check edge cases, validate outputs and ship the product. However, AI applications are fundamentally different. They’re probabilistic, not deterministic. They evolve with new data. Most critically, they interact with humans who judge them not just on functionality but on fairness, transparency and trustworthiness.

Many teams attempt to fill resource gaps with automated testing alone. Your recommendation engine might show 95% accuracy in your automated test suite, but real users from different demographic groups may experience vastly different outcomes. Your automated tests missed what matters most — how your AI behaves across the full spectrum of human diversity. This gap between lab performance and real-world trust is a business-critical issue that can sink your application before it gains traction.

Implement Human-Centered Testing From Day One

The solution isn’t more automated tests — it’s involving humans into your testing process from the beginning. Your AI is only as trustworthy as the data and people involved in its training. If your training data comes from a narrow slice of users, your AI will reflect those limitations.

Build testing communities that accurately reflect your actual user base, encompassing diverse ages, backgrounds, languages and accessibility needs. Don’t rely solely on your internal team or contractors who share similar perspectives. Establish regular testing cycles in which human evaluators from diverse demographic groups assess your AI’s outputs for fairness. Track metrics like response quality across user segments, not just overall accuracy.

Bias isn’t a one-time check — it’s an ongoing concern requiring continuous monitoring. As your model learns from new data, new biases can emerge or existing ones can amplify. Users don’t just want your AI to be right — they want to understand why it makes specific decisions. This is especially crucial for AI applications that affect essential life decisions, such as healthcare, finance or hiring.

Build explanation capabilities into your AI architecture from the outset. Test these explanations with real users to ensure they’re understandable, not just technically accurate.

Create Inclusive Feedback Loops

Traditional feedback loops in software development often miss the nuanced ways AI impacts different users. Creating inclusive feedback mechanisms requires intentional design.

Don’t just rely on star ratings or binary feedback. Collect qualitative insights through conversational interviews, demographic-specific focus groups and longitudinal studies that track how trust evolves. Create feedback channels that accommodate different communication preferences — some users prefer quick surveys, others seek detailed conversations and some express concerns through community forums.

Lab testing can’t replicate how users interact with AI in their daily lives. Background noise affects voice AI performance differently in a coffee shop than in a quiet office. Cultural context also influences how users interpret AI-generated content. Test your AI applications in the environments where they’ll be used and partner with diverse user communities for ongoing feedback.

Establish Continuous Trust Monitoring

Building trustworthy AI isn’t a sprint — it’s an ongoing commitment to improvement. Traditional software metrics such as uptime and response time are insufficient for AI applications. You need metrics that specifically track user trust and confidence.

Develop trust-specific KPIs such as user confidence scores, bias detection rates and explanation clarity ratings. Track these alongside your technical metrics. Your AI model should evolve based on real user feedback, not just algorithmic optimization. Create feedback loops where user insights about bias, fairness and trust directly inform your model training and fine-tuning processes.

Users are more forgiving of AI limitations when they understand them. Be upfront about what your AI can and can’t do, and how you’re working to improve it. The temptation is to iterate quickly and fix problems later, but trust issues compound rapidly — once users lose confidence in your AI, regaining it is difficult than building it right the first time.

Building trustworthy AI applications requires a fundamental shift in how you approach development and testing. Begin with these concrete actions:
- Audit your current testing approach. How diverse are your testers? Are you testing for bias and fairness or only for functionality?
- Establish human evaluation processes. Regularly identify ways to incorporate diverse human perspectives into your testing pipeline.
- Create multiple feedback channels. Develop various ways for users to share concerns about bias, fairness and trust.
- Define trust metrics. Establish KPIs that measure user confidence alongside technical performance.
The future belongs to AI applications that not only work well but work well for everyone. By embracing user-centered testing processes and building inclusive feedback loops, you’re not just shipping better software — you’re laying the foundation for AI applications that users will adopt and advocate for.

In AI development, confidence truly comes from quality, and quality comes from deliberately designing for trust from the outset.
November 6, 2025

How Observability Improves User Experience and Digital Performance

In today’s digital scenario, user experience can create or break a business. For organizations managing complex apps and cloud-native platforms, observability has emerged as an essential practice, not only a technical requirement, but a strategic driver of customers’ satisfaction, retention and business development. Let us see how the modern industry improves the insight, metrics and real-world examples, user experience and digital performance directly to take action.

What Does Observability Mean for Users?

The Observability enables engineering teams to examine marks for deep viewing, matrix, logs, and infrastructure, as well as every aspect of application behavior, to monitor all aspects of behavior. This allows comprehensive visibility for organizations:

Detect performance issues before they affect customers

Minimize downtime and outage

Troubleshooting “unknown unknown” -Sudeen, unexpected problems

Continue to optimize continuous digital experiences

Utilize tools such as middleware, Clepher and Grafana to enhance digital performance.

Core Ways Observability Elevates User Experience

Faster Incident Detection and Resolution

The observability allows teams to indicate the root cause of events with correlated telemetry, dramatically reducing mean time to detect (MTTD) and mean time to resolution(MTTR). A recent report found that organizations that leverage observability can reduce the time to respond by up to 85% during an event, leading to fewer user-support disruptions and improved trust.

Proactive Performance Optimization

Systems can optimize the speed and reliability of the application before notifying any issue to the team users, with real-time insights into health, hurdles, and delays. For example, monitoring core web vitals, such as largest contentful paint, first input delay, and cumulative layout shift, reveals that SEO directly impacts ranking and user satisfaction. Companies using observability solutions receive 20% higher conversion rate due to rapid, more stable digital experiences.

Seamless Releases and Safer Innovation

Observancy enables “shift left” practices, where developers monitor the impact of their code in the environment, such as production in a live environment. It accelerates the release cycle, reduces deployment errors, and allows teams to instill confidence in everyone without compromising credibility.

Personalized and Contextual Debugging

Modern observation equipment allows analysis of data by user segments. Teams can identify issues affecting specific user cohorts (by geography, device, or journey) and even solve micro bugs that can only affect their audiences, leading to substantial improvement and high customer satisfaction.

Reduced Friction and Improved Retention

Ends help users solve issues before they experience them, and observability helps to promote customer retention rates and reduce abandonment due to poor performance. Recent studies suggest that organizations that prefer observationalism receive up to 98% for key services, often leading to improvement in double digits in retention and brand loyalty.

Real-World Impact: Case Studies

Airbnb: Uses observability to monitor guest and host activities in real time, allowing proactive fixing of booking issues and fast response to platform performance dips—resulting in higher customer satisfaction and trust.

Spotify: Relies on observability tools to ensure seamless streaming for millions of listeners globally, quickly detecting and resolving audio delivery problems to keep users engaged without interruptions.

Slack: Utilizes real-time observability for both infrastructure and applications to minimize downtime in its collaboration platform, safeguarding productivity and user trust.

Riot Games: Improved visibility and workflow debugging across microservice architectures, allowing gamers to have smoother and more reliable experiences.

Quantifiable Benefits of Observability for Digital Performance

Here are key stats from recent industry research:

Benefit	Impact Metric
Faster MTTR	Up to 85% reduction in incident resolution time
Higher Conversion Rates	20% increase due to optimized digital experiences
Customer Retention	Double-digit improvement, up to 98% service uptime
Strategic Business Value	88% of organizations gain more time for innovation
Uptime/Resilience	Verified reliability for millions of users (e.g., Airbnb, Spotify)

Conclusion

Observability is a game-changer for digital businesses is not only about technical troubleshooting solutions, but also about providing an excellent user experience. By enabling the recovery of rapid phenomena in the user journey, active performance adaptation, and relevant insights, observability directly drives happy users, better retention, high conversion and permanent development.

For organizations wishing to excel in the rapidly changing digital world, investing in observability is one of the most obvious routes to increase user experience and achieve excellent business performance.

October 27, 2025

Redefining Engineering Excellence: How Product Skills Amplify Your Impact in the Era of AI
The rapid evolution of artificial intelligence (AI) is transforming the landscape of multiple industries, including software development. The question isn’t whether AI will change the role of software engineers — it already has. The real question is: How can engineers amplify their impact and thrive in this new reality?

As a professional with over five years of experience in software engineering, including roles such as tech lead, engineering manager and platform engineering lead at one of the world’s largest job aggregators, I have seen firsthand how much engineers dislike repetitive and routine tasks. Today, AI allows engineers to delegate these tasks to a ‘personal junior engineer’ that everyone has always dreamed about. This raises one of the many important questions: What is next for software engineers?

This is, indeed, an interesting question. To address it effectively, I propose we apply first principles thinking — a method that involves deconstructing complex problems into their most basic elements. One practical exercise within this framework is the ‘Five Whys’ technique, which can help us understand how AI can enhance software engineers’ roles rather than replace them.

Let us break it down:
- Why do engineers write code?
To build new features
- Why do engineers need to build new features?
To solve problems or meet user needs
- Why do we need to solve problems or meet user’s needs?
To create value for the business and improve user experience (UX)
- Why do we need to create value for the business?
To ensure the company’s growth and sustainability
- Why is the company’s growth and sustainability important?
It provides jobs, drives innovation and contributes to the economy
- Why does this matter at all?
As Steve Jobs said, “Innovation distinguishes between a leader and a follower.” Innovation isn’t just about solving today’s problems; it is about imagining what doesn’t yet exist and having the courage to create it. The sixth ‘why’ reminds us that we innovate not just for utility or profit but to leave a mark — to contribute something meaningful to the world.

By drilling down to these fundamentals, we see that software engineering is about creating value and innovation, allowing engineers to impact not just the ‘how’ of software development, but also the ‘what’ and ‘why’. This strategic involvement requires human insight and creativity — areas where AI cannot replace us.

The Shift From Software Engineers to Product Engineers

Traditionally, software engineers have focused on delivering clean, efficient and scalable code. While technical expertise remains critical, the rise of AI is shifting the emphasis from execution to strategy and problem-solving. Developers who incorporate product thinking into their work will position themselves as indispensable assets to their organizations.

A product engineer is more than just a code expert. They bridge the gap between development and business impact by:
- Understanding product strategy and objectives and key results (OKRs)
- Understanding user needs and designing solutions that enhance UX
- Identifying opportunities to contribute to product strategy
- Developing a product mindset
Why AI Makes Product Thinking Essential for Engineers

AI is not just a tool for automating repetitive coding tasks — it is also reshaping how products are built, tested and deployed. As AI models generate code, optimize infrastructure and even predict software failures, engineers must redefine their role in the development process.

To remain valuable, developers must shift their focus from ‘How should I build?’ to ‘What should I build and why?’ By embracing product skills, engineers can:
- Become ‘Thinkers’, Not ‘Doers’: AI can generate code, but it cannot deeply understand user pain points, business objectives and long-term scalability. Product engineers connect the dots between technology and business value.
- Lead Cross-Functional Innovation: AI-powered automation frees up engineers to drive innovation rather than get stuck in execution mode. Product-focused engineers work at the intersection of technology, business and UX. They lead strategic initiatives rather than just implement them.
- Design With AI in Mind: The best engineers do not just use AI tools — they design for them. Understanding how AI impacts user workflows, data privacy and ethical considerations gives engineers a competitive advantage in building future-ready products.
Key Product Skills Every Engineer Should Develop

For engineers looking to stay ahead in an AI-driven landscape, developing product skills is no longer optional — it is essential. Here are the key areas to focus on:

Understanding Product Strategy and OKRs

Regardless of the role you hold in a company, you are hired to deliver value. Your salary is an investment and the company expects a return. Each organization defines value differently, but ultimately, it is linked to revenue — a clear indicator that users love the product and are willing to pay for it. They exchange money for the value they receive.

How can engineers focus on delivering this value? The answer lies in understanding product strategy. Product strategy isn’t just about deciding what to do; it is also about knowing where not to waste time and energy.

To gain this understanding, engage with the product team and be curious about OKRs and product strategy. Understand the company’s goals for the next year or five years and learn how the company plans to achieve them.

For example, imagine your company sets an OKR to increase e-commerce sales by 15%. The product strategy involves enhancing the mobile app’s checkout process to reduce abandonment rates. By understanding these business objectives, you now have guidance on how to deliver value. You can generate ideas to achieve the OKR, aligning your technical skills with business goals and contributing directly to the company’s success.

User-Centric Thinking

Now that we are clear on the goal, let us revisit the second ‘why’ in our exercise: Why do engineers need to build new features? The answer is simple — to solve problems or meet user needs. How can we solve user problems if we do not truly understand who our users are and what their problems might be?

This brings us to the second important skill: Having a strong understanding of user needs. There are many frameworks available to help structure this understanding. Two that I find particularly useful are Jobs To Be Done and Customer Journey Mapping.

Now, let us return to our example of the checkout process. We know the abandonment rate is high, but why? To answer this, I suggest approaching the problem from two angles:
- Qualitative Data: Ask questions such as, ‘Do we have user insights about the current pain points in the checkout flow?’
- Quantitative Data: Analyze metrics to understand, ‘Where are users experiencing friction in the checkout flow?’
For instance, imagine users report that the payment screen is loading too slowly. By comparing this feedback with quantitative data (e.g., drop-off rates), you can validate their concerns and identify actionable solutions.

Developing Product Solutions

As you dive deeper into product thinking, you will start to identify potential solutions to business challenges. For instance, you might hypothesize that optimizing page load times could significantly reduce checkout abandonment rates.

However, it is crucial to remember that this is just a hypothesis, not a proven solution. This is where your product management skills come into play. Instead of jumping straight to implementation, approach it strategically:
- Frame your idea as a testable hypothesis
- Use frameworks such as data, insight, belief, bet (DIBB) to structure your proposal
- Outline a plan to validate your hypothesis through experiments or A/B testing
By presenting your ideas in a structured way, you demonstrate both technical expertise and strategic thinking that align with business goals.

Product Mindset

I have learned that developing a product mindset starts with curiosity and a critical, nonjudgmental approach. I always ask myself: Is what I am building really valuable? How valuable is it? I try to think like an investor — would I put my own money into this? What would I get in return?

Stay curious, I say. Question everything. Just because we have always done something one way does not mean we should keep doing it. I love digging into data to see how customers actually use our product. Trust me, it can surprise you.

For me, it is about developing a real passion for the problem. I am fascinated by payments, always looking for ways to make things simpler for merchants. Payments are complex, but our job is to make them feel simple.

I have learned to be proactive. I treat the company like it is my own. I do not wait for features to work on — I know the company’s goals. I figure out how to hit those goals, come up with ideas and pitch them.

The Future of Engineering

Engineers are in a unique position to lead. My advice? Take initiative. Get buy-in. Drive the product forward. Think differently. That is how you make a real difference.
July 31, 2025
Boost Your Website Core Web Vitals Through DevOps Best Practices
A website’s responsiveness and speed are critical, particularly considering that search engines such as Google have made Core Web Vitals compulsory. These metrics are not just technical jargon but actual representations of user experience.

A slow or stuttering website annoys users, causing them to abandon pages and look for alternatives elsewhere. This damages your brand, sales volumes and revenues. That’s where DevOps comes in.

DevOps methodologies that emphasize collaboration, automation and continuous improvement are critical to addressing such performance problems. The goal is not merely to pass a Core Web Vitals test but to create a better online experience than the competition.

This guide will demonstrate how DevOps teams can improve your website’s performance and overall SEO Core Web Vitals. Let’s find out more about it!

What are Core Web Vitals?

So, what exactly are Core Web Vitals? They are a group of metrics that Google employs to observe how well a website’s user experience is. Google utilizes the Core Web Vitals ranking factor as part of its ranking algorithm.

These metrics concentrate on three things: How quickly a page loads, how simple it is to interact with and how smooth it appears when loading.

If your site is not receiving enough traffic or users leave quickly, it might be due to bad Core Web Vitals. For instance:
- Largest contentful paint (LCP) impacts how quickly your primary content loads: If an image of a product or main text takes an extended period to load, users will depart without engaging.
- First input delay (FID) analyzes how quickly your site interacts: Slow interaction irritates users and leads them away.
- Cumulative layout shift (CLS) examines how steady a page is: Sudden changes in layout result in misclicks and a poor experience.
Keeping an eye on organic traffic may help confirm issues with Core Web Vitals. Various tools can help in this regard. For instance, NinjaReports offers details about your organic traffic as well as where you stand in comparison to your competitors. If it shows a drop, it could be attributed to slow load times, non-responsive pages or changing layouts. You can dig deeper to confirm and fix those issues to improve traffic.

DevOps: Your Secret Weapon for Core Web Vitals

The connection between DevOps and improving Core Web Vitals is significant. DevOps encourages a team culture, automation and continuous improvement that is vital to address performance issues. Let’s examine it closely:
- Collaboration: DevOps brings together developers, operations and SEO teams to build a quicker, more stable website. Developers pinpoint performance problems, operations deal with server issues and SEO experts optimize according to Core Web Vitals.
- Automation: Automated testing, deployment and infrastructure management ensure continuous performance monitoring. Tools can detect issues, including an image affecting LCP and prompt immediate fixes.
- Ongoing Improvement: DevOps lives for continuous fine-tuning. Performance is always monitored, tested and polished and primary web vitals are improved yearly.
How DevOps Supercharges Your LCP

Optimizing LCP using DevOps is about making the largest content element load faster on your page. Here’s how you can optimize it:
- Image Magic: Slow and unoptimized images are typically the culprit for bad LCP. DevOps teams can automate processes to compress images, optimize file formats such as WebP and cache these images at locations nearer the individuals viewing them, making them load quicker.
- SSR to the Rescue: SSR renders the initial HTML on the server instead of the user’s device, which helps the initial page load faster. DevOps can set up the right infrastructure and implement the SSR process to ensure a fast initial paint, which helps to improve Core Web Vitals.
- Lazy Loading is Your Friend: Rather than loading all the content simultaneously, lazy loading only loads the required content as the user scrolls downwards. DevOps teams can implement lazy loading in the build process. They can prepare the required scripts and ensure they are implemented to load content when individuals require it.
- Content Delivery Networks for the Win: Content delivery networks (CDNs) distribute your site’s content among servers worldwide. This allows content to be distributed from a server near the user, reducing delay and increasing downloads. DevOps sets up CDNs, ensures things are cached properly and keeps everything functioning well, all of which decrease LCP.
By doing all this, you make sure that LCP is always optimized, providing your users with a much better experience.

How DevOps Ensures a Faster FID

FID measures how fast a site responds to a user’s initial interaction, and a slow response can be frustrating. DevOps helps minimize FID by streamlining the way code and resources load.

One of the biggest problems is large JavaScript files hogging the main thread, which can be addressed with smart code splitting — splitting files into smaller, on-demand chunks to reduce the load. However, JavaScript is not the only offender; third-party scripts such as analytics and social media widgets also contribute to the slowdown. DevOps can streamline these by using lazy loading or even self-hosting key scripts.

Aside from scripts, browser caching assists by loading assets locally, allowing repeat visitors to load pages more rapidly and enjoy instant response. But even the ideal optimizations won’t work without good server performance — DevOps can handle server scalability, keeping database indexing efficient and data retrieval speedy.

To keep the FID consistently low, monitoring is always critical. Notifications for responsiveness degradation enable teams to respond in real time, keeping the site as fast as possible and easy to use.

How DevOps Improves CLS Through Teamwork

CLS quantifies how much a website’s layout shifts during loading, and DevOps plays a major role in reducing disruptions. Unsized ads and images are among the largest contributors to CLS, as they lead to elements jumping unexpectedly. By reserving space in HTML with fixed sizes, DevOps allows content to load smoothly without moving other elements.

Aside from images, adding new content on top of already existing sections can also introduce instability. In collaboration with developers, DevOps can properly arrange content placement and utilize placeholders to ensure visual consistency.

Because layout changes depend on screen sizes, automated testing on various devices is necessary to catch problems early. Moreover, keeping frameworks and libraries current avoids instability because newer versions usually include performance enhancements.

How DevOps Use Observability Tools to Ensure Site Performance

Observability tools are critical for monitoring and enhancing Core Web Vitals. They enable DevOps to have a clear picture of site performance, detect problems and fine-tune the solutions. Some key points include:
- Real User Monitoring: Real User Monitoring (RUM) tools gather performance information from real users’ browsers, giving insights into actual LCP, FID and CLS metrics. Tools such as Google Analytics provide real-world data, enabling teams to identify and fix problems.
- Synthetic Monitoring: Automated scripts simulate user interactions, allowing teams to test site performance in a controlled environment. Regular Core Web Vitals tests help identify potential problems before they impact users.
- Log Analysis: Reviewing application, server and database logs helps identify performance issues, allowing for rapid diagnosis and correction.
- Alerts: Strong alerting systems notify DevOps of performance declines, ensuring rapid response to critical issues.
Through the use of observability tools, DevOps can continually improve Core Web Vitals to optimize data-drivenly.

Relying on Automation and Testing to Ensure Better Performance

Integrating automation and performance testing is essential for making Core Web Vitals SEO a natural part of the DevOps workflow. This includes the implementation of automated performance tests in the CI/CD pipeline after each code change to detect issues early on. CI/CD pipelines enable rapid testing and deployment with performance checks.

Load testing enables the replication of high-traffic conditions, uncovering bottlenecks and ensuring the site can scale for spikes. Similarly, performance budgeting, with goals for metrics such as page speed, allows teams to set automated tests and avoid degradation.

A/B testing enables teams to test new features side-by-side, seeing how they affect Core Web Vitals before deployment. With these automated flows, teams reliably deliver quality code, ensuring performance is always a consideration and never an afterthought.

Using the Combined Forces of DevOps, Developers and SEOs

Collaboration among DevOps, developers and SEO experts is required to optimize Core Web Vitals. All have their own set of skills, and if all of them collaborate, they can make a decent plan:
- DevOps and Developers: Developers construct the site, and DevOps ensures its proper deployment. Communicating frequently is the secret to catching performance problems and making sure new code doesn’t slow down the site. This means adding performance testing into the web development process, leading to improved code.
- DevOps and SEOs: SEOs are concerned with rankings in search engines related to web vitals. DevOps should collaborate with SEOs to make sure that technical work facilitates SEO activities. This means the website is fast, responsive and employs the best web optimization practices.
- Developers and SEOs: SEO experts can guide developers on search engine best practices, and developers can update the SEO team on technical aspects and site changes. This communication keeps SEO quality at the beginning of development.
- Joint Planning: Joint planning is a must for all teams. Through regular meetings, teams can exchange information, learn about each other’s problems and determine the most effective means of achieving performance and SEO objectives.
- Common Objectives and Indicators: Establishing common objectives and indicators makes teams work toward a common goal. Since everyone knows how their contribution supports the overall achievement, they tend to be more willing to accomplish the objectives.
By doing away with silos and collaborating, you can apply solutions that work for both user experience and SEO. This collaborative work ensures the ultimate success with Core Web Vitals.

Conclusion

Today, website performance and response are vital for user satisfaction and search engine rankings. Therefore, it is important to optimize Core Web Vitals. DevOps offers the perfect framework for addressing this with collaboration, automation and ongoing evolution. To know how to enhance, we must first know how to check Core Web Vitals effectively. When we apply this with DevOps best practices, we can effectively optimize these performance metrics. This results in a better page experience and future-proofs the website.
February 18, 2025
Boost User Experience With Global Network Monitoring
With the increasing global use of apps and services, ensuring a smooth user experience across different geographic locations is already a top priority for many DevOps teams. However, traditional setups, such as maintaining servers in multiple locations, can be a costly and inflexible approach, especially for smaller DevOps teams or those with tight budgets.

In this article, we’ll explore how DevOps teams of all sizes can leverage global network testing platforms to set up simple and flexible global monitoring that fits well with existing workflows without breaking the bank.

Operating on a Global Scale Requires Flexibility

To monitor apps and services from various locations around the globe, teams need access to a globally distributed infrastructure.
Typically, DevOps teams consider different setups to meet their needs, including:
- On-premises servers: Larger companies with data centers or offices worldwide can use these locations to set up network monitoring and troubleshooting.
- Cloud service providers: Infrastructure-as-a-service (IaaS) solutions allow teams to deploy virtual servers to multiple locations without needing physical access to those sites.
- Third-party services: Feature-rich monitoring tools with servers in multiple regions enable teams to leverage existing infrastructure that they don’t have to maintain themselves.
While these setups offer various benefits to different project and team requirements, they also come with drawbacks that can impact a DevOps team’s efforts:
- Cost: Infrastructure, whether self-managed or acquired via third-party vendors, can be a significant expense – especially when teams need to monitor and test from multiple locations. Moreover, third-party monitoring services may come with unneeded features, resulting in unnecessary costs.
- Flexibility: Most setups don’t allow users to change the location from which to test or monitor as needed, making it impossible to work on a real global scale. Plus, they can often not cover all the areas from which teams want to make measurements.
- Complexity: Adding new hardware, cloud instances and tools to workflows introduces new complexity and challenges, especially for DevOps teams that already struggle to complete tasks efficiently.
To address these issues, teams should consider deploying a global network testing platform that allows them to run network tests globally and overcome the challenges described.

Leveraging Global Network Testing Platforms

Global network testing platforms such as Globalping or RIPE Atlas provide a reliable solution for effortlessly performing network measurements from any location worldwide. Note, however, that these platforms are quite different from extensive continuous monitoring platforms like Kentik. They are much more streamlined and focus on delivering on-demand network testing capabilities through APIs, CLIs and other integrations.

But how do these platforms enable teams to run network tests from anywhere? They do this by leveraging a dense network of thousands of probes hosted by partners and individuals worldwide. These probes serve as the hosts from which network commands, such as ping, traceroute, mtr or dig, are executed. As a result, platform users can test and examine various aspects of their website, CDN or DNS server – even in the far corners of the world.

This image shows an example of the Globalping CLI displaying ping results to devopsy.kinsta.cloud from probes in Berlin and South America:

Transform On-Demand Testing Into Monitoring

While on-demand testing and monitoring are not exactly the same, global network testing platforms can still serve most DevOps teams’ monitoring setups.

These platforms usually provide APIs and CLI tools that let users make measurement requests programmatically so they can use these integrations for their own purposes.

Here are two practical and straightforward use cases to turn on-demand testing into monitoring:
- Teams can create customized global monitoring tools using the platform’s API or extend their existing setups. They can make measurement requests from various locations, collect the data and use it to set up alerts and draw graphs.
- For a simple and lightweight monitoring solution, DevOps engineers can write a Bash script that automatically makes measurement requests and sends emails if results are not as expected.
Choosing the Right Tool

Want to try network testing platforms but don’t know how to choose the right one for your team? Here’s what to consider:
- Integrations: What integrations does the platform offer? APIs and CLI tools are essential, allowing you to create custom implementations and integrate them with existing tools. Also, look for platforms with user-friendly web-based interfaces that enable non-technical users to run tests.
- Collaboration features: Consider platforms that support collaborative network testing, which is especially important for distributed DevOps teams. For example, Globalping offers a Slack app that brings team members together in a virtual space to solve problems faster.
- Network density: Choose a platform with a dense probe network to ensure reliable measurement results 24/7. You may also want to learn more about the probes themselves. For example, you can review the code to see whether it meets your security requirements if the project is open source.
- Rates & limits: Check the pricing structure and limits carefully, as platforms take different approaches (especially if you need to run custom measurements). Globalping, for example, is a free, open source platform with generous limits that you can extend by becoming a GitHub sponsor. On the other hand, Ripe Atlas uses a “credits” system where you can buy credits or earn them by hosting probes.
- Community & Support: Look for a platform with an active community that supports each other in forums and chat groups. Also, explore the platform’s documentation, tutorials and support channels available to help you get started and assist you in resolving issues along the way.
In summary, choosing a global network testing platform enables DevOps teams to monitor and troubleshoot their infrastructure from anywhere in the world flexibly and cost-effectively. By leveraging the capabilities of these platforms, teams can achieve higher service reliability and a better user experience, even in the most remote corners of the world.
November 10, 2023
2024—Year of the Linux Desktop? ChromeOS Reflects its Inner Penguin ¦ GNOME Rethink

Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

This week: Can the Linux desktop installed base ever break the mythical 10% barrier? Google has been refactoring ChromeOS, and GNOME is working on new window manager ideas. (more…)

August 3, 2023
3 Performance Challenges as Chatbot Adoption Grows

Today, many organizations are starting to use voice or text-enabled chatbots for the first time or already have chatbot systems in place. In fact, Gartner said 25% of organizations are predicted to make chatbots their primary customer communication channel by 2027. This is because consumer behavior has shifted to primarily digital interactions and away from traditional customer support channels.

As a result of today’s digital landscape, consumers are now accustomed to immediate gratification and expect instant responses and resolution from customer support, which makes chatbots an ideal solution for brands to rapidly serve customers. Due to this rising demand for instant customer service, chatbot systems have become more sophisticated and consumers’ reliance on them is increasing. This increase in adoption has led to higher performance expectations for bots and if those expectations are not met, customer satisfaction and brand loyalty will suffer.

Below, we’ll look at three major challenges tied to the growing popularity of chatbots as they handle larger and more diverse groups of customers, and how business leaders developers and DevOps and customer experience (CX) teams can tackle these common challenges with a continuous testing approach.

The Pain Points of Adoption: 3 Key Challenges as Chatbot Usage Grows

As the popularity of chatbots explodes for customers around the world, the underlying systems that handle natural language processing (NLP), latency, data security and other functions need to be stronger. This is due to a growing and increasingly diverse demographic of users, many of whom are sharing more of their personal information to address a wider range of customer needs. Common chatbot pain points can be summarized by three overarching challenges.

Challenge #1: Chatbots must contend with a larger and more diverse base of customers

Chatbots that are voice or text-enabled must be smart enough to operate securely while navigating countless variations in spelling, a wide range of spoken accents and vernacular, background noise, static from bad connections and more. Even in a single language, there may typically be only four or five “intents” from most customers in engaging a chatbot, but those intents may be phrased in hundreds of different ways. Now, the global growth in adoption raises the chance of poor connections and exponentially increases the number of languages and variations in phrasing that a chatbot must understand and interpret.

Challenge #2: Customers are trusting chatbots with more personal and sensitive data

As chatbots become smarter and more trusted by customers to meet their needs, people are getting more comfortable giving their personal data to chatbots. That increases the number of situations where chatbots are working with personally identifiable information (PII) or other highly sensitive data. Especially given GDPR and other data privacy regulations being enacted worldwide, there’s added pressure to ensure a chatbot system’s handling of this data remains secure and compliant across all use cases and geographic regions.

Challenge #3: Modern chatbots require 24/7 availability and reliability

In an era of flexible work and millennial and Gen Z audiences interacting with bots after business hours and on weekends, chatbots can’t afford to work in shifts. Users may be spread across many time zones or around the world, meaning there’s always a sizable portion of customers awake, online and looking for fast and efficient service. Because of this, chatbots must be able to handle spikes and heavy traffic 24/7.

The executive takeaway from these challenges is that modern chatbot investments must be built around dynamic systems capable of adjusting to shifting customer usage and performance requirements.

A Continuous Testing Mindset is Required for Top Chatbot Performance

Fortunately, by honoring a few best practices and making the right strategic technology investments in automated artificial intelligence (AI) and machine learning (ML)-driven systems, C-suite leaders responsible for CX and the teams they manage can deploy chatbots capable of keeping up with the above challenges. Perhaps the most central element for success is to embrace a continuous testing approach.

For example, when a company is undergoing digital transformation and shifting its contact centers and supporting systems to the cloud, they are moving a significant amount of data, reimagining complex integrations and rearchitecting processes. In order to maintain application stability and existing integrations in the contact center, continuous testing is critical to ensure that an organization’s systems still operate effectively throughout the cloud migration process. With continuous testing, development and testing aren’t separate processes. The testing is automated and quality assurance is involved as developers submit code.

The most effective approach to continuous testing includes end-to-end analysis across all channels. Testing chatbot behaviors should mimic real-time customer interactions. This should include challenging scenarios such as unexpected user inputs and usage spikes at random times of the day and night. Automation is critical, especially in peak demand scenarios such as Black Friday, Cyber Monday, open enrollment, etc. Load testing up to 1,000-100,000 bot requests per second, or more, can help ensure companies will be able to handle the increased traffic. Ideally, the testing approach should be comprehensive to include automated NLP score testing, conversational flow testing and security testing and performance testing and monitoring.

By promoting the continuous testing approach across contact center operations, executives can empower their teams to build and maintain chatbots that remain optimized on an ongoing basis and that form an essential part of providing exceptional CX. This will make a huge difference in performance and reliability as systems scale and adapt to the evolving demands of a growing customer base.

Continuous Testing Elevates Chatbot Reliability and Value

High-performing chatbots and the continuous testing that keeps them optimized at those performance levels can benefit any company by lowering risks and enhancing revenue. Organizations that become adept at applying continuous testing avoid 90% of the downtime from high-severity (SEV 1) errors, which can result in saving $2.2 million in costs over a three-year period. When continuous testing touches every stage of the development process, it identifies issues earlier and helps strengthen an organization’s ability to innovate faster and service its customers effectively and profitably.

January 31, 2023
8 Web Development Trends to Follow

Since the inception of the internet in 1983, many web development tools have been introduced to simplify complex processes that would otherwise take months to finish and cost companies a great deal of money.

According to internet live statistics, over 1.5 billion websites and more than five billion active daily internet users visit multiple sites around the clock. That number is expected to grow as businesses (and website owners) offer personalized experiences to users, make their websites more user-friendly and introduce navigation that visitors prefer.

Website development has changed and is still evolving quickly. As a website owner, it’s imperative to watch out for new trends that are being introduced in the industry. Neglecting to adopt current trends might affect your website’s functionality, user experience and network. You risk falling behind your rivals and losing visitors to websites with superior user experiences and network scalability.

The best way to maintain competitiveness is to make sure your website is consistently updated, fresh and that it follows current trends. Here are eight to watch out for.

Progressive Web Apps (PWAs)

PWAs speed up website loading times and enable offline functionality in multiple applications. PWAs have already been used by businesses like Starbucks, Uber, Pinterest and Twitter to improve user experience.

It is crucial to transform web apps into PWAs, given the increased shift toward smartphones and tablets for various online functions. PWAs offer a superior user experience, increased engagement and conversion rates and lower development costs.

Motion UI

Motion UI is used to create fully responsive site designs. Motion UI enables developers to produce motion in a native app environment, and with a built-in motion, it can be used for any design project. A website needs to be appealing and offer the information a user seeks. Your target audience is more likely to notice websites that look good.

Motion UI is easy to implement as the developer doesn’t need to be an expert in JavaScript. It also assists people in finding the precise information they need and improves the effectiveness of site design.

Privacy Center

Privacy is a core component of ensuring your customers have a safe user experience when navigating your website. Privacy Center is an intuitive way of cataloging your privacy policy and notices, terms of service, cookie policy, refund policy, data subject requests and more on a single page.

Privacy Center enables users to obtain information regarding a company’s data activities from a single source without having to navigate multiple pages and scroll through endless paragraphs to identify the information they need.

Privacy Center is a growing trend for web developers as data privacy laws impose strict obligations on companies, necessitating the need to implement intuitive privacy centers that address all concerns of users.

Dark Mode

Although dark mode was first introduced in 2016, it wasn’t until 2020 and 2021 that it gained popularity as a standard practice in web building. Essentially, dark mode refers to a site’s color scheme. In other words, the website has a darker background with light-colored text and other UI elements.

Dark mode typically lessens eye strain, especially in dim lighting. When dark mode is used instead of light mode on a mobile device, it uses less battery. People with light sensitivity or vision impairments have also found it to be beneficial.

Accelerated Mobile Pages (AMP)

Accelerated mobile pages, better known as AMP, is an initiative that resulted from Google and Twitter working together to provide quicker mobile pages. Pages that are AMP-optimized load quicker, thus ranking better than pages that take more time to load. Compared to non-AMP pages, which may take up to 22 seconds to load, AMP pages do so in roughly two seconds.

In web development, AMP is a simple search engine optimization method that reduces bounce rates, adapts to any browser and doesn’t need sitemaps to be discovered by search engines.

AI Chatbots

AI chatbots are intuitive, enabling businesses to operate more quickly over voice or text chats while enhancing the consumer experience. The technology provides a more human-like experience to visitors with the help of machine learning (ML) and Natural Language Processing (NLP) that understand the user’s intent and responds accordingly. Many well-known enterprises already use AI chatbots to communicate with their clients.

Industry experts predict that AI chatbots or self-learning bots are the way of the future, and organizations can save money by using them instead of human customer support representatives, especially for simple queries and FAQs. AI chatbots are a long-term investment as they process orders, respond to users’ questions, connect them with the relevant department and quickly resolve any complaints, resulting in an improved user experience.

Voice Search Optimization

The practice of optimizing web pages to show up in voice searches is known as voice search optimization. Voice assistants and the internet of things (IoT) have helped voice recognition devices become increasingly widespread.

Google launched voice search in 2012. Since then, the market for voice search tools—such as virtual assistants like Alexa and Siri—has seen exceptional growth. The trend is only expected to grow since virtual assistants are not currently being integrated into many internet-enabled devices.

By 2023, eight billion digital voice assistants are predicted to be in use worldwide. Due to this enormous expansion, voice search optimization will become indispensable among the newest web development technologies.

Push Notifications

When users browse, push notifications—clickable pop-ups—appear before them. Companies employ this technology as a speedy route for communicating information, such as promotions and announcements. Push notifications can be integrated on any device, including laptops, cellphones and tablets.

Push notifications are an effective tool that significantly improves users’ experience, drives visitors to your website, improves click rates, aids in grabbing users’ attention and improves conversion rates. Web applications from internet behemoths like Facebook and Google already use the technology.

Conclusion

Though it has been around for decades, web development continues to innovate and improve as organizations strive to influence the future delivery of web experiences. To give your users the best experience, raise the search rating of your website and reach new heights, it is crucial to keep up with these trends.

August 26, 2022

Tag: user experience

From Uptime to Experience-Driven Resilience

Where Legacy Monitoring Breaks Down

AI as the Integrator and the New Operating Model for Reliability

Connecting Reliability to Business Outcomes

What This Means for SRE and DevOps Leaders

Why Agent Architecture Matters

Three Design Perspectives

Four Key Mechanisms

Performance and Evaluation

Comparison with Claude Code

What This Means for Development Teams

Implement Human-Centered Testing From Day One

Create Inclusive Feedback Loops

Establish Continuous Trust Monitoring

What Does Observability Mean for Users?

Core Ways Observability Elevates User Experience

Real-World Impact: Case Studies

Quantifiable Benefits of Observability for Digital Performance

Conclusion

The Shift From Software Engineers to Product Engineers

Why AI Makes Product Thinking Essential for Engineers

Key Product Skills Every Engineer Should Develop

The Future of Engineering

What are Core Web Vitals?

DevOps: Your Secret Weapon for Core Web Vitals

How DevOps Supercharges Your LCP

How DevOps Ensures a Faster FID

How DevOps Improves CLS Through Teamwork

How DevOps Use Observability Tools to Ensure Site Performance

Relying on Automation and Testing to Ensure Better Performance

Using the Combined Forces of DevOps, Developers and SEOs

Conclusion

Operating on a Global Scale Requires Flexibility

Leveraging Global Network Testing Platforms

Transform On-Demand Testing Into Monitoring

Choosing the Right Tool

The Pain Points of Adoption: 3 Key Challenges as Chatbot Usage Grows

Challenge #1: Chatbots must contend with a larger and more diverse base of customers

Challenge #2: Customers are trusting chatbots with more personal and sensitive data

Challenge #3: Modern chatbots require 24/7 availability and reliability

A Continuous Testing Mindset is Required for Top Chatbot Performance

Continuous Testing Elevates Chatbot Reliability and Value

Progressive Web Apps (PWAs)

Motion UI

Privacy Center

Dark Mode

Accelerated Mobile Pages (AMP)

AI Chatbots

Voice Search Optimization

Push Notifications

Conclusion