The problem is that we can’t always remember what we did or why we did it. This is why documentation is so important. It’s a resource we can reference when jumping back in to add or fix something. Not to mention that it’s handy for clients and colleagues as well.

Documenting your projects has traditionally meant taking good notes. We add comments to our code, update readme files, and log what’s new in myriad other forms. Consistency is key, but difficult to achieve. After all, everyone gets busy and forgets to write something down.

Perhaps AI is just the tool we need to document projects without missing a beat. These models, along with the apps they connect with, can take notes for you. They’ll keep track of changes, explain what a feature does, and why it was built a specific way.

With that in mind, here are a few ideas for using AI to create and maintain your project documentation.

Use AI To Analyze and Remember Your Work

The popular AI models like ChatGPT, Claude, and Gemini all have “memory” features. Set your preferred tool to remember what you’ve done, and the model will record it for future reference. This is a goldmine for documenting your WordPress projects.

Before you start, ensure that you understand how your chosen model remembers things. Some have a setting for this purpose. However, you might also directly instruct an AI agent within your chat session. There are different types of memory, so you’ll want to check to see what is (and isn’t) included.

It’s a must-have feature when vibe-coding plugins. Use AI to keep track of your project from the start. That way, you’ll prevent any knowledge from being lost along the way.

But what about existing projects? You’re covered – to a point. AI can analyze your files and note specific functions and features. It’s also a wonderful tool for troubleshooting an issue or inspecting code written by someone else.

As always, double-check the tool’s interpretation of your work for accuracy. You can even use multiple models to inspect the same thing. It’s better to find a mistake early than a year down the road when you really need to know something.

Decide What Project Information You’ll Need

AI can document what’s been done. However, you’ll still need to figure out what information you’ll need to keep.

Much depends on the type of project you have and with whom it needs to be shared. But it’s safe to say that a changelog makes sense for every use case. You’ll want to know what changed, when it was changed, and potentially why.

Custom plugins or theme features would also benefit from usage instructions. It’s easy to forget how something works. As such, a quick step-by-step guide is valuable. Bonus points if you can add screenshots or videos to the mix. It’s perfect for onboarding new team members.

You might even document client requests. This enables you to stay on top of busy collaborative projects. Use it to assess time and energy spent (and how it impacts your billing). This info could also help you build a project roadmap for future changes.

The idea is to keep track of what matters to you and anyone else who touches your project. AI makes it easy to generate.

Create a System for Documenting Your Projects

Next, you’ll need a place to access your documentation and a plan for maintaining it. This is a very personal decision that depends on your preferences. For example, you could generate a custom text file in each project’s folder and be done with it. But there are more in-depth possibilities.

We’re working with WordPress, so using it as a knowledge base is a natural fit. You could build a centralized resource categorized by client or project. There are even support plugins available to help you get started, and the WordPress Connectors API will connect the site to your chosen AI model.

Solo freelancers could get by with a local WordPress installation, while agencies might want to use an internal or external server. A project’s documentation may also live on your client’s site. It’s a convenient feature and a potential selling point.

A Git repository would be great for those who want to version control their documentation. From there, you can feed updated information to virtually anywhere you want. Using AI here also streamlines the workflow.

Then, as things change, AI can automatically write to the destination. Or, you can manually review it and publish when ready. This ensures that you’ll always have the latest data.

The Pain-Free Way To Document Your Projects

Writing documentation can be a tedious process. You complete a task and then have to remember to record it. The chore is even harder when you’re juggling multiple clients and websites. Even the best of us will forget something.

AI’s automation capabilities make it seamless. Tell your AI tool what to document, and it remembers to keep track so you don’t have to. Some models may let you create blanket rules that cover all of your current and future projects.

You also get to choose where to store this valuable knowledge. It’s something you can share with others or keep to yourself. Regardless, you’ll have access whenever you need it. That will serve you well when it’s time to dive into a theme you haven’t touched in years.

If you’ve avoided project documentation, there’s no better time than the present to get started. There’s also an opportunity to upgrade what you already have. So, pick your favorite AI tool and let it do the work!

here. He recently started a writing service for WordPress products: WP Product Writeup. He also has an opinion on just about every subject. You can follow his rants on Bluesky @karks.com.

Read more articles by Eric Karkovack

The post How AI Can Help You Document Your WordPress Projects appeared first on The NOKIA Blog.

Sooner or later, a CFO looks at your wireframes and asks what any of it actually does for the bottom line. Storyboards don’t answer that question, and the era when a 5-minute pitch could answer it ended, unfortunately, a while ago.

These days, if you want to win budget, buy-in, and backing for UX, the design has to be provably good for the business, not only for the people using it.

And proving that takes more than taping a dollar sign to a redesign. You have to understand how your organization defines value in the first place, how it measures that value, and how a credible line gets drawn between a design initiative and an outcome leadership already cares about.

Rather than scatter tips, this article follows one worked example the whole way through. Meridian is a mid-size B2B SaaS company, and it is entirely made up — that label matters, so it gets repeated where it counts. Its onboarding redesign carries the same figures from goal-setting through cost accounting, causal testing, and the final ROI number, because a framework only becomes tangible when the numbers connect. Every step is one you can rerun inside your own organization.

Why ROI Matters More Than Ever in UX Conversations

Companies now want clarity on what every dollar buys, and “delightful user experiences” stopped clearing that bar some time ago. I still remember a former colleague celebrating a $1 million redesign he’d gotten greenlit mostly on the strength of a couple of three.js tricks. Try that pitch in front of a finance team today and see how far the particles get you.

Executives don’t hate UX, they just hate vagueness. A pitch built on “users will find it easier” loses, every time, to the department promising 12% more sales in Q3. The difference is the one between a streamlined checkout flow that reduced cart abandonment with completed purchases up 22%, and the same work rewarded with “the QA testers like it.” One of those goes on your resume. The rest of this article is about earning the first version, with Meridian’s numbers doing the work.

When Business Goals And KPIs Don’t Exist Yet

Most writing about UX ROI makes a convenient assumption: that the organization already owns clean business goals and KPIs for you to hook your work onto. Real companies are messier than that. Plenty run on ambitions like “grow faster” or “improve the customer journey” that nobody ever broke into anything measurable, and an ROI case built on that ambiguity sounds impressive right up until somebody scrutinizes it.

So the first job is often to help the organization define what success even looks like. Interview stakeholders across departments — what does product consider a good quarter, where does customer success watch users struggle, where do sales deals stall — and listen for the themes that keep resurfacing across conversations, because those recurring themes are the company’s latent business objectives. A useful forcing function is the OKR model (Objectives and Key Results), which doesn’t tolerate vagueness.

At Meridian, the stated ambition was “improve the rate of new users’ adoption of the platform,” which you can neither design toward nor measure against. Interviews turned up the real shape of the problem. Trial users needed a median of 14 days to reach first value, most churned before getting there, and onboarding questions were burying the support queue. Out of that came an OKR with actual edges: reduce median time-to-first-value from 14 days to 7 with the use of a guided setup flow, and lift trial-to-paid conversion from 8% to 9.5%.

One warning about formalizing KPIs: Impose them from inside the UX team and leadership will suspect you’ve rigged the field in your own favor, so co-create them with whoever owns the outcome — though never at the price of accepting targets that set your team up for an uncomfortable situation. Meridian’s head of product agreed that setup-completion rate was a fair proxy for onboarding usability, and customer success signed off on time-to-first-value, a number already sitting on their own dashboard.

A KPI ladder that ends at a metric somebody already watches buys you credibility before any design work starts.

Quantifying The Full Cost Of The Investment

ROI has a denominator, and the denominator is where most UX teams go wrong. You can’t calculate a return without strategic financial planning, yet cost usually gets counted as designer salaries or consulting hours and nothing else. A finance team will find the rest whether or not you counted it, so count it first.

Direct costs are the visible ones. Meridian’s redesign ran $45,000 in design and research labor plus another $8,000 in tooling and participant incentives. Licenses for Figma, UserTesting, Hotjar, analytics platforms, research incentive spend — all of it belongs in the total, and that’s before the inevitable instances of vendor lock-in every UX team eventually faces. Engineering sits in the same column, because a UX redesign doesn’t stop at the mockup. Building the guided setup took two frontend sprints plus a QA pass, $38,000 worth, and the project generated about $4,000 of coordination overhead along the way in new syncs and shared dashboards.

The line item nearly everyone misses, and the one worth stealing from this article if you steal nothing else, is stakeholder time. Workshops, design reviews, and feedback sessions all pull senior people away from their primary work. A VP of Product spending four hours a week in UX reviews is a VP not spending those hours on roadmap planning or partner negotiations. Log the attendance — who came, for how long, at what seniority — and price it at fully loaded cost, meaning salary plus benefits divided by productive hours. A quarter’s worth of workshops, reviews, and interviews at Meridian priced out at $22,000.

Add it all up: $45,000 in design labor, $8,000 in tooling, $38,000 in engineering, $22,000 in stakeholder time, $4,000 in coordination. The investment is $117,000. Saying that number out loud beats saying “we spent $45K on design,” precisely because it already includes everything a finance team would have dug up on its own.

Proving Causation, Not Just Correlation

Most UX ROI pitches die right here. Conversions rose after the redesign, sure — and the CFO wants to know how you ruled out the new pricing, the seasonal traffic bump, and the marketing campaign that shipped the same week. Without a convincing answer, your entire ROI story crumbles.

After all these years, the gold standard for proving causation is still A/B testing: run the old experience against the new one on an even traffic split until the sample means something.

Onboarding happens to suit a phased rollout, which is why Meridian could do this cleanly. For eight weeks, half of new trial signups received the redesigned guided setup while half stayed on the legacy flow. Control converted to paid at 8.0%. The variant came in at 9.4%. With roughly 6,100 trials inside the window, the difference was statistically significant, but a 1.4-point gap on a single test is still the kind of result that deserves a second look before anyone builds a budget on it, which is one reason the team held back on attribution below. Where a split isn’t feasible — a change too structural, a user base too small — fall back to a time series instead. Measure steadily for weeks before the change, implement, then keep measuring against the baseline you established.

Documenting whatever else happens around the same time as your UX change is the unglamorous half of causation.

A pricing-page test from Meridian’s marketing team overlapped weeks five through eight of the rollout. The UX team noted it, confirmed it hit both cohorts evenly, and still chose to attribute only 70% of the observed lift to the redesign in the final math. There is no formula that produces that number; treat it as an illustrative assumption for this example.

The team asked how much of the lift could plausibly belong to the pricing test if it had helped one cohort slightly more than the other, settled on a ceiling of about a third, and rounded the redesign’s share down to 70%. Your figure will differ. What matters is that it is written down and argued for before the results arrive, not fitted to them afterwards. That restraint is worth money in a skeptical room. “We attribute roughly 70% of the lift to the onboarding change, with the remainder likely influenced by concurrent pricing work” survives cross-examination; claiming everything does not. Cohort analysis then backed the number up, since the lift held across acquisition channels and tenure bands, and at that point the skeptics had very little left to work with.

Leading and lagging indicators belong on the same slide, because each covers the other’s weakness. Meridian’s leading indicators moved first — setup completion climbed from 62% to 89%, median time-to-first-value dropped from 14 days to 6.5 — and the lagging trial-to-paid number followed. Mechanism first, business outcome second. Presented together, they form a causal chain that’s harder to poke holes in than either one alone.

The ROI Calculation, End to End

So what did Meridian actually earn? The company sees about 40,000 trial signups a year. Lifting conversion from 8.0% to 9.4% adds roughly 560 paying customers annually, and at an average of $1,800 in annual recurring revenue per account, those customers represent about $1,008,000 in new ARR. Applying the conservative 70% attribution from the causal work trims the defensible figure to roughly $706,000.

Set that against the full $117,000 investment and the first-year ROI lands near 5:1, with payback arriving in roughly two months. There’s a second line, too. Onboarding-related support tickets dropped about 30%, some 3,600 fewer tickets a year, worth another $54,000 annually at $15 per resolved ticket. Keep it as its own line rather than folding it into one swollen headline number. The case reads as more honest that way and loses none of its force.

Three assumptions carry that result, and each belongs on the slide next to it. The 40,000 signups and the $1,800 average ARR are the prior year’s actuals held flat, so a growth or pricing change moves the outcome in either direction. The 70% attribution is the illustrative assumption from the causal work, not a measured quantity. And the two-month payback counts new ARR as it lands rather than revenue recognized net of churn, which flatters the timeline; on a net basis the payback stretches to roughly a quarter. State those three plainly and a finance team can adapt the example to its own numbers. Hide them and the whole thing starts to look like marketing math, however careful the experiment was.

What persuades in the final presentation is not sophistication. Open with the baseline: what stalled trials and support volume were already costing. Show the delta in terms leadership reads fluently, metrics like conversion rate uplift chief among them. A chart of setup completion climbing from 62% to 89% will beat a paragraph of UX jargon, and a translation like “each abandoned setup costs us 0.3 support tickets” beats the chart. Above all, keep every figure identical from the first slide to the last. A room full of finance people forgives many things, but never numbers that wobble between slides.

Tailoring The Case To Whoever Holds The Purse Strings

Budget decisions come out of coalitions. A CFO may hold the final say when adding AI to the checkout process, but marketing, product, and customer success all lean on that decision, and each means something different by “value.”

A CFO hears cost, revenue, and risk. A CMO hears conversion and acquisition cost, since UX is a lever for increasing marketing ROI. Product counts support tickets; customer success thinks in retention. The underlying numbers never change; only the framing rotates, and a CFO wants a projection, not a moodboard. Meridian’s CFO slide read “the onboarding redesign protects roughly $706,000 in new ARR a year against a $117,000 investment,” while the CMO version led with what a 9.4% trial conversion does to blended acquisition cost.

Beyond the Dollar Sign: Qualitative and Non-Financial Metrics

Some UX outcomes never translate cleanly into revenue, and pretending they do weakens the parts of your case that are solid.

The trick with qualitative evidence is collecting it rigorously enough that nobody can wave it off as anecdote.

Scores like Net Promoter Score (NPS), CSAT, and Customer Effort Score already sit inside most reporting cadences, which makes them cheap to borrow. Tie your work to their movement, and segment wherever the data allows.

Meridian could say that NPS among trial users on the redesigned onboarding was 51 against 34 for the legacy flow, which lands far harder than any blended average. Verbatim feedback from surveys, support transcripts, and app store reviews adds the emotional weight the scores lack. Internal tools deserve the same discipline, since employee experience is increasingly recognized as a business driver — a dashboard redesign that hands account managers 45 minutes a day back is a productivity gain, a satisfaction gain, and a retention lever all in one.

Brand perception resists direct measurement but leaves tracks in repeat visits, organic referrals, and social sentiment. It carries extra weight in trust-sensitive industries like finance or healthcare, and it forms fast, given that UX design influences the first impressions of a whopping 94% of customers.

Whatever you collect, systematize the collecting. Run pre- and post-surveys with consistent question sets, use structured usability testing with task-based scoring, and put the qualitative right next to the quantitative when you present.

“Setup completion rose from 62% to 89%, and in post-test interviews 8 of 10 participants called the new flow intuitive, against 3 of 10 for the old one” — a pairing like that is much harder to dismiss than either half on its own.

Making the Case Stick

UX loses the budget battle unless it’s mapped to company-wide objectives, so phrase the proposal in the words of this year’s board presentation.

Nobody at Meridian pitched “simplify the onboarding UI”; the pitch was a redesigned trial experience worth 1.4 points of upgrade rate, roughly $1M in annual recurring revenue before attribution. Bring evidence in both registers, since that is what social proof is for: the case, clearly labeled, plus screenshots, impact graphs, and user quotes. Find internal allies who can repeat the ROI narrative in rooms you’ll never enter, and write the playbook down, because repeatable ROI is what earns recurring investment.

Resources for Going Deeper

This topic has been explored extensively by researchers, practitioners, and consultancies. Here’s a curated set of resources worth studying if you want to build a stronger ROI practice around UX.

• “Measuring the User Experience” by Tom Tullis and Bill Albert is the definitive guide to UX metrics. It covers everything from task-based measurements to survey design to statistical analysis, and it’s written for practitioners, not academics.
• Jared Spool’s “The $300 Million Button” case study is a classic example of how a single UX change (removing a mandatory registration step) generated massive revenue uplift. It’s a story every UX professional should have in their back pocket.
• Forrester’s research on UX ROI provides enterprise-focused frameworks for building business cases around experience design, including their widely cited finding that every dollar invested in UX returns $100.
• “UX Strategy” by Jaime Levy bridges the gap between design thinking and business strategy, offering practical tools for aligning UX initiatives with organizational goals and market positioning.
• The Design Value Index by the Design Management Institute tracks publicly traded companies that invest heavily in design against the S&P 500. The data consistently shows that design-led companies outperform the index by significant margins, and it’s a powerful data point for executive presentations.
• Google’s HEART framework provides a structured approach to selecting UX metrics at scale. HEART stands for Happiness, Engagement, Adoption, Retention, and Task success, and it’s particularly useful for teams that struggle to decide which metrics to track.

Conclusion

A seat at the table never comes from beauty or novelty. It comes from measurable, defensible impact, which means UX leaders have to trade the artist’s posture for the strategist’s. Speak in outcomes rather than outputs. Connect pixels to profit.

When someone challenges the numbers, don’t flinch. Show the controlled experiment, the cohort analysis, and the before-and-after metrics, every figure holding steady from the first slide to the last, the way Meridian’s did, with the customer quotes and the employee-satisfaction data sitting right beside the revenue impact. Prove the work does more than delight users, and be ready to defend the ratio line by line. That’s when the CFO leans in, and that’s when design stops being optional.

(yk)

The post Building A UX ROI Case That Survives The Boardroom appeared first on The NOKIA Blog.

Ever since the commercialisation of the Graphical User Interface pioneered by systems like the Xerox Star and popularised by the original Apple Macintosh, software has relied heavily on point-and-click interactions. If you wanted to book a trip, buy a pair of shoes, or research a health symptom, you were expected to navigate a labyrinth of user interfaces. You click menus, adjust range sliders, fill out multi-step forms, deal with cookie pop-ups, and open dozens of browser tabs just to cross-reference basic information. We have become accustomed to spending more time managing the software rather than actually achieving our goals.

That contract is officially changing. Driven by advances in artificial intelligence and large language models, a new generation of web tools is pioneering a radical philosophy: Intent-Driven Design. Rather than expecting our users to learn complex menus and click through elaborate sales funnels, these platforms operate by capturing high-level human goals and silently executing the grunt work in the background.

This vision builds on long-standing HCI concepts like Golden Krishna’s “The Best Interface Is No Interface” and Don Norman’s principles of human-centered design.

The ultimate goal of modern web design is no longer to build prettier buttons or flashy animations; it is to eliminate the interface entirely.

Well, at least what we understand based on our current experience.

It is essential for UX designers to understand the changes we’re witnessing and even re-evaluate our role, shifting our focus from designing visible interfaces to guiding transparent, intent-driven AI experiences.

The Death Of The “10-click” Process

To understand where web design is going, we first have to look at the friction we have accepted as “normal” for decades.

Consider the traditional workflow of buying a flight online. The user experience is intentionally hyper-interactive:

1. Navigate to a travel aggregator or airline website.
2. Select “Round Trip” from a dropdown menu.
3. Type the origin city and wait for auto-complete.
4. Type the destination city and wait for auto-complete.
5. Click a calendar modal, toggle through months, and select departure and return dates.
6. Choose the number of passengers and cabin class.
7. Click “Search” and wait for the results page to load.
8. Filter by price, layover duration, departure time, and airline.
9. Sort the results and scan through dozens of individual options.
10. Click through a three-page checkout funnel dodging upsells for rental cars and travel insurance.

This is a classic point-and-click UI paradigm. The computer acts as a passive container of data, and the human acts as the orchestrator, manually inputting parameters, interpreting raw outputs, and executing each micro-step along the way.

Intent-driven design flips this dynamic entirely. Instead of forcing you to navigate the mechanical steps of how to find a flight, the interface asks a simple question: What are you trying to accomplish?

When you express a goal, such as “Find me a non-stop flight to Chicago next weekend under $300 that arrives before 5 PM”, the software reads your intent, executes the multi-step search query behind the scenes, compares the options, and presents a single actionable resolution. The ten clicks dissolve into one clear intent outcome.

Three Web Platform Examples Replacing Buttons With Intent

This shift isn’t a theoretical vision of the distant future. Some of the world’s most accessible websites allow you to experience intent-capturing systems already.

1. Perplexity AI: Search Without The Open Tabs

Traditional search engines like Google were built as directories. You typed a keyword, and the interface rewarded you with ten blue links. The actual work of opening five tabs, skimming long articles, dodging ads, and manually assembling an answer was left to you. Although this is now changing, with AI content appearing first when you search on Google.

Perplexity AI allows anyone to perform instant intent-based searches. Instead of forcing you to click into multiple tabs, the interface synthesises the perfect result for your context.

• The interaction: You ask a complex question in plain English: “Compare the top three budget laptops for a computer science student, focusing on battery life and keyboard quality.”
• The invisible work: In under two seconds, the platform searches the web, reads dozens of articles, evaluates hardware specifications, and cross-references user reviews.

• The result: (See screen above) Instead of throwing web pages at you, Perplexity dynamically generates a single custom answer complete with formatted comparison tables, pros and cons lists, and concise inline citations. You get the outcome of thirty clicks in just one.

2. Vercel v0: Web Design Without Dragging And Dropping

For years, creating a website meant using visual builders like Figma or drag-and-drop web editors. As designers, we spent hours manually drawing rectangles, picking hexadecimal colour codes, adjusting padding values, and sometimes writing CSS code.

Vercel v0 lets anyone test its generative interface builder right from the home page without signing in.

• The Interaction: A user types a descriptive goal into the input prompt: “Create a modern dark-mode dashboard for a subscription SaaS app showing monthly revenue, active users, and a customer churn chart.”
• The invisible work: The underlying AI understands layout principles, accessibility guidelines, UI component libraries, and responsiveness. It writes clean HTML, Tailwind CSS, and React code in real time.
• The result: Rather than spending an afternoon manipulating elements, the user receives a fully functional, pixel-perfect user interface in seconds! The traditional visual design tool disappears, replaced by raw human intent.

3. Goblin.tools: Breaking Down Overwhelming Tasks

Many traditional productivity tools require you to manually construct task lists, drag items across Kanban boards, and assign micro-deadlines. Goblin.tools is a free, single-page suite of single-task AI tools designed specifically for neurodivergent users or anyone feeling overwhelmed, requiring zero sign-ups or configuration.

• The interaction: You enter a vague, intimidating goal into the Magic Todo tool, such as “Prepare for a job interview”.
• The invisible work: Instead of forcing you to plan every step, the system analyses the cognitive load of the goal and automatically breaks it down into small, actionable sub-tasks adjusted to your preferred level of detail.
• The result: The interface eliminates the stressful task-planning phase and presents a clean checklist tailored to your exact goal.

The Core Pillars Of Intent-driven Web Design

When you remove traditional menus, sidebars, and forms, how do you keep a website usable? Designers pioneering this space rely on three core pillars:

1. High-level Goals

An invisible interface doesn’t always wait for you to type a command — it leverages context to anticipate user needs. Guided by industry standards like Apple’s Human Interface Guidelines on Contextual & Ambient Design, modern systems read environmental metadata, such as device state, location, and past interaction patterns, to trigger proactive actions.

For example:

• Time and location: A food delivery web app prioritising your saved home address and dinner items automatically at 6:30 PM on a weekday.
• Cross-app history: A calendar application noticing an email about an upcoming appointment and surfacing a one-click “Add to Schedule” card without forcing you to copy and paste event details manually.

2. Generative UI

In traditional web design, every user sees the exact same layout. An e-commerce store shows the same navigation bar whether you are a first-time visitor looking for customer support or a returning customer tracking a package.

As highlighted in the Nielsen Norman Group’s analysis on AI as a new UX paradigm, computing is shifting from command-based interaction to outcome-specification. This enables Generative UI, where interfaces are rendered dynamically on the fly based on what you are trying to do in that exact moment. For example, platforms like Vercel v0 demonstrate how raw intent can instantly render functional code components without manual layout building. If you express a desire to compute complex financial data, the interface generates a dynamic calculator widget on demand, receding once your goal is completed.

3. Execution by AI Agents

Traditional interfaces are obsessed with prevention, constantly peppering users with confirmation pop-ups (“Are you sure you want to delete this file?”).

Intent-driven interfaces adapt Jakob Nielsen’s 10 Usability Heuristics on User Control and Error Recovery by shifting the safety net from prevention to easy reversibility. Because autonomous agents take actions on your behalf, products prioritise frictionless rollback mechanisms. A prominent “Undo” button, simple revision prompts (“Make this summary shorter”), and transparent audit logs replace intrusive warning modals.

The Hidden Risks

While removing interface friction is liberating, stripping away visual controls introduces significant product design challenges. When software acts on inferred intent rather than direct point-and-click commands, designers must navigate critical ethical and technical pitfalls.

The Illusion Of Control

When a website makes decisions for you, it can quickly feel patronising or invasive. As explored by the Stanford Human-Computer Interaction (HCI) Group, automation must maintain clear boundaries to prevent user frustration. Furthermore, ethical frameworks from the Center for Humane Technology emphasise that product designers must guard against manipulative defaults.

Designers must strike a delicate balance: automate routine execution, but explicitly prompt the user for high-stakes confirmations (such as financial transactions, publishing public content, or altering privacy settings).

The Black Box Problem

In a traditional UI, if you get an unexpected result, you can usually diagnose the issue — perhaps you checked the wrong filter box or selected an incorrect date.

With an intent-driven interface, troubleshooting becomes harder. To address this “black box” challenge, the Google PAIR (People + AI Research) Guidebook advocates for transparent feedback loops. The system must explicitly state its interpretation (“Searching for non-stop flights to Chicago under $300…”) so users can calibrate trust and correct misinterpretations instantly.

From A UX Perspective

As a UX designer, always being curious to understand how our design approach is changing in the world of AI, I find it essential that we re-evaluate the foundational elements of our craft.

Rather than designing static buttons and rigid forms, we need to learn to map and present dynamic, generative UI components that adapt on the fly to user intent.

By mastering these evolving patterns, like ambient feedback, inline prompts, and transparent AI status indicators, we can ensure that screenless, agent-driven tools remain intuitive, ethical, and deeply human-centered.

Conclusion

The ultimate goal: The best design is no design.

For decades, the digital industry has evaluated software success through engagement metrics like time spent in app, click-through rates, and page views per session. Websites were deliberately engineered to maximise visual engagement and interaction volume.

The philosophy behind Golden Krishna’s seminal book The Best Interface Is No Interface, aligned with the broader Center for Humane Technology’s Time Well Spent movement, turns these metrics on their head. The ultimate test of a modern digital product is no longer how visually captivating its buttons are, but rather: How effectively did this software solve the problem and get out of the user’s way?,

We are entering an era where web applications will no longer be measured by the beauty of their interfaces, but by their ability to render those complex interfaces completely obsolete. The future of the web isn’t more interaction — it is seamless, invisible completion.

(ah, yk)

The post The Death Of The Button: Why The Best Interface Is No Interface appeared first on The NOKIA Blog.

That has led some seasoned (and not-so-seasoned) developers to go for the ultimate prize: replicating their favorite SaaS product. I admit to falling under the spell of such temptation.

It’s easy to see the potential benefits. SaaS subscriptions can be expensive, and the products may be more than what we need. Plus, there are always niche features that seem to be missing. Oh, and many are proprietary, raising data ownership concerns. Why not use AI to vibe code your own spinoff?

Can it really be that simple? Type a few prompts, and get a high-level tool to replace an enterprise app? As usual, it’s complicated.

Let’s take a hard look at the good, the bad, and the ugly of using AI to build a SaaS replica.

Taking Software Into Your Own Hands

My journey building a SaaS replica with AI began as I stood at a crossroads of sorts. I’m a longtime user of QuickBooks Desktop, which has served my accounting needs well over the years. However, the product I use was discontinued and moved online. Moving meant going from buying a license every few years to a monthly fee. I’m not keen on yet another subscription.

The desktop app is also starting to show its age. Some features are broken, and the interface is behind the times. Not to mention it’s a memory hog on my PC. This all coincides with some upcoming changes to my business that require a different approach.

So, I decided to take the plunge and build my own billing/customer relationship management (CRM) app as a WordPress plugin. I wanted something portable that would allow me (or others) to add features as needed. WordPress was the perfect fit.

The decision seemed like a no-brainer. I could use my existing skills to guide AI in the right direction. And I could leave out the things that weren’t necessary. I hoped to build a lean, functional plugin that streamlined my billing process.

This would also free me from the proprietary trap so many SaaS providers lock us into. I wouldn’t have to worry about higher rates, product limitations, or waiting for support tickets to be answered. Yes, this was exactly what I needed!

The only question was: Can I really build a suitable replacement?

The Challenge Is in the Details

The project started with a bang, as I used Claude to build a working prototype within an hour. Sure, there were some rough edges to be smoothed over. But the basic concept was coming together. Then, a bit of reality set in.

One of my goals was to create a tool that was close enough to what I’m accustomed to. The idea wasn’t to rebuild QuickBooks line by line, feature by feature. But I wanted something that provided a seamless transition.

My prototype allowed me to add customers, services, and invoices. I even built some import tools to bring my real-world data into the system. But I didn’t realize how many little details were missing until I used the plugin.

For example, I needed a way to share invoices with my clients. I also needed to distinguish between hourly-based and project-based services. Other needs seemed to pop up every few minutes:

• Custom reports for my business and individual customers.
• A way to email invoices to multiple contacts at the same company.
• A log to remind me whom I sent invoices to and when.
• Navigation to and from different areas of the plugin.
• User permissions for scenarios where you want to provide CRM access to a non-administrator.
• A front-end version of each relevant screen.

This was a different experience for someone who hadn’t taken a product from start to finish before. It felt more like being a product designer than a developer. That requires a different type of thinking.

AI proved capable of writing the code. However, I found myself having to provide extra context along the way. There was a lot of trial and error, and each new feature opened another set of decisions and potential.

There’s also danger in adding too many features. I didn’t want a bloated plugin that was difficult to use. As such, I had to practice restraint when considering my options.

It was a positive learning experience, for sure. But it also made me realize how much is involved in building a plugin that does more than merely function. Good things do indeed take time.

Questions To Ask Before You Build

The temptation to dive right in and generate code for the next HubSpot or Asana is understandable. AI makes relatively short work of complex coding tasks. These models can also advise you on how to replicate specific features.

It’s worth taking a step back and asking yourself a few key questions first, though. The answers will help you navigate the project or save you from making a massive mistake.

Are You Serious or Curious?

First, determine why you want to build this amazing new thing that’s based on an existing thing. Experimental side projects can be fun and a great way to learn. However, it’s different than a serious bid to create software that your entire company will use.

The former encourages you to be patient and test your ideas. The latter is often a mandate to increase efficiency while saving money. There’s an entirely different level of pressure in that scenario. You’ll also need to dedicate more resources toward the project.

It’s not necessarily a deal breaker either way, but a consideration nonetheless.

Do You Need Third-Party Integrations?

Integrating with other tools and services is a SaaS superpower. For example, adding Stripe to your CRM enables online payments, while Mailchimp adds email capabilities. AI tools can help you connect the various APIs out there.

The downside is the responsibility that comes with these integrations. APIs frequently change, and you need to stay ahead of them. Failing to keep up could lead to software that doesn’t work. Plus, there are security and privacy risks.

Remember: Once you build it, you’re responsible for it.

Do You Need Your App To Scale?

SaaS applications often require a robust infrastructure. That’s why AI companies are building data centers everywhere. This could be a concern if you’re expecting a stampede of users and use cases.

Smaller, more focused apps like my CRM plugin aren’t as big of a problem. A few users on a WordPress site won’t require a ton of memory or CPU cycles. But the calculus changes as your needs grow.

This is where a fun little project can turn into something more complex. That app you just vibe coded could turn you into a systems engineer. There’s also a cost to server space and other necessities. Are you prepared to go that far?

You Can Replicate a SaaS With AI, but Should You?

Using AI to generate code opens up a world of possibilities – and it’s only getting more powerful. New models continue to evolve to help us get things done faster. That leads to bigger and bolder ideas on our part.

Even so, it’s a good idea to think about the long-term consequences of what you build. It may be fun to try and replicate Gmail, but do you really want the responsibilities that go with it?

That may be an extreme example, but the theory also applies to smaller projects. The reality is that the best SaaS products are popular for a reason. They scale, have dedicated security teams, and professional product designers working to improve every aspect. Even the best AI models can’t match that quite yet.

So, have fun and experiment! Just know what you’re getting into before you begin.

here. He recently started a writing service for WordPress products: WP Product Writeup. He also has an opinion on just about every subject. You can follow his rants on Bluesky @karks.com.

Read more articles by Eric Karkovack

The post Can You Really Replicate Your Favorite SaaS With AI? appeared first on The NOKIA Blog.

The news that interested me the most last week was the DuckLabs acquisition. AWS has signed a definitive agreement to acquire DuckLabs, the Amsterdam-based company behind DuckDB, the popular open source analytical database that runs in-process and executes SQL directly against files like Parquet, CSV, and JSON. DuckDB stays open source under its independent foundation and the MIT license, and over time AWS plans to combine its speed at everyday queries with the enterprise scale of services like Amazon S3, Amazon Redshift, and Amazon Athena.

Co-founded by Hannes Mühleisen and Mark Raasveldt, DuckDB runs locally or on Amazon S3, which makes it remarkably fast for the everyday queries (a terabyte or less) that make up the bulk of real-world analytics. It also happens to pair beautifully with AI agents, which “poke” and experiment their way through data much like humans do. The co-founders will continue leading its technical direction while AWS combines DuckDB’s speed with analytics services like Amazon EMR, AWS Glue, and Amazon SageMaker. For the bigger picture on why this matters, Andy Warfield, Vice President and Distinguished Engineer shared his thoughts on the post DuckDB and the changing physics of analytics on All Things Distributed.

Now, let’s get into this week’s AWS news…

Last week’s launches
Here are some launches and updates from this past week that caught my attention:

• Amazon ECS now automatically detects and recovers container instances that lose agent connectivity – Amazon ECS now continuously monitors agent connectivity to the control plane and surfaces a new AGENT_CONNECTIVITY health event across AWS Fargate, Amazon ECS Managed Instances, and Amazon ECS on EC2. On Fargate and Managed Instances, ECS handles recovery automatically, draining tasks, launching replacements, and deregistering the impaired instance. On EC2, you can wire the event into your own workflow. Available at no additional cost in all AWS Commercial and AWS GovCloud (US) Regions.
• AWS Lambda introduces public preview runtimes, starting with Node.js 26 and Python 3.15 – You can now test upcoming Lambda runtimes before they reach general availability. Preview runtimes use the same identifier as the eventual GA version, so your functions graduate automatically with no action required. Third-party tools and deployment frameworks can also validate compatibility ahead of GA. Not meant for production yet (breaking changes are possible), but a great way to get ahead of your next upgrade. Available in all AWS commercial, AWS GovCloud (US), and China Regions.
• AWS IoT Core adds a native InfluxDB rule action – You can now route time-series data from your IoT devices straight into InfluxDB (Amazon Timestream-managed or self-hosted) without writing custom code or standing up an intermediate service. IoT Core formats data into InfluxDB’s line protocol and supports device-side and server-side batching. Available in all AWS Regions where Amazon Timestream for InfluxDB is offered.
• Amazon GameLift Servers now includes enhanced DDoS protection – Your game servers now get automatic protection against network and transport layer (layers 3 and 4) DDoS attacks – UDP reflection, SYN floods, and similar vectors – with nothing to enable or opt into. Built on top of AWS Shield Standard with gaming-optimized traffic shaping, it turns on the moment your servers start running (Server SDK 5) at no extra cost. It’s available in all supported GameLift Servers Regions except China (Beijing) and China (Ningxia).
• Amazon SageMaker HyperPod expands support for Ray – You can now run Ray workloads on SageMaker HyperPod with built-in observability, resilient training, and accelerated inference. Create and manage Ray clusters from Amazon SageMaker Studio, attach JupyterLab or your local IDE so a multi-node cluster behaves like a local dev environment, and get auto-provisioned Grafana dashboards. Node auto recovery, hung job detection, and tiered checkpointing keep large training runs healthy, while Ray Serve adds a tiered KV cache for inference. Your existing open source Ray code runs unchanged. Available for HyperPod clusters orchestrated by Amazon EKS.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news
Here are some additional posts and resources that you might find interesting:

• Happy 20th birthday, Amazon EC2! – Amazon EC2 turns 20. Channy Yun looks back at how EC2 grew from a single m1.small instance type in one Region to more than 1,200 instance types across 39 Regions, along with the custom silicon journey from the first Graviton to Graviton5 and Trainium3. A fun and worthwhile read on the service that still underpins so much of AWS – including Amazon ECS, Amazon EKS, AWS Lambda, Amazon SageMaker, and Amazon Bedrock.
• Agentic Resource Discovery (ARD): an open specification for agent discovery – As organizations scale up agents, tools, and MCP servers, those resources end up scattered across clouds, on-premises infrastructure, and SaaS platforms – each with its own registry and metadata. ARD is a new open specification (Apache 2.0) that defines a common way to describe and discover agentic resources, so publishers “describe once” and consumers “discover everywhere” – think DNS, but for agents. AWS contributed feedback but doesn’t own the spec, and it complements the AWS Agent Registry by letting you federate across catalogs without migrating.
• Get started with the Agent Toolkit for AWS in the AWS CLI – A single AWS CLI command (aws configure agent-toolkit) now equips AI coding agents like Kiro, Claude Code, Codex, and Cursor with curated, up-to-date AWS knowledge and a secure connection to thousands of AWS APIs through the AWS MCP Server. If you build with an AI coding assistant, this helps it choose the right services, use modern APIs, and follow security best practices – so it gets AWS code right more often the first time.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

• AWS Summits – Free in-person events where builders come together to learn, connect, and explore the latest in cloud and AI. Upcoming stops include Zurich (September 2), São Paulo (September 3), Tel Aviv (September 10), and Dubai (September 30). Can’t attend in person? You can stream sessions through the Global Livestream and On-Demand Hub. I’ll be presenting two sessions on generative AI and Amazon Bedrock at the São Paulo Summit – if you’re there, come say hello.
• AWS Community Days – Community-led conferences where content is planned, sourced, and delivered by community leaders. Upcoming events include JAWS SONIC 2026 in Tokyo (September 5) and Warsaw, Poland (September 8).

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS-led in-person and virtual events and developer-focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Daniel Abib

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

The post AWS Weekly Roundup: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026) appeared first on The NOKIA Blog.

In organisations today, data has never been more available. Dashboards and performance decks exist for almost every function — sales, product, marketing, operations — and the tools to build them have never been more accessible. And yet, in weekly standups and quarterly reviews, the same thing happens constantly: someone shares the numbers, the room nods, and the meeting ends without a decision or clear direction.

When that happens, the data usually takes the blame. The numbers weren’t granular enough, the dataset wasn’t complete, we need more information before we can act. But the data is almost never the problem. The reality is that nobody designed it to deliver insights. The chart was built from what was available, not from the question that needed answering. The audience was assumed rather than understood, and what should actually change as a result of seeing this data — that question — was never asked at all.

Data visualisation and UX are solving the same underlying problem: both are trying to move the right information to the right person in a way that changes something. The vocabulary is different, but the underlying challenge is identical, and the moment you start treating them as complementary disciplines is the moment dashboards stop being a passive collection of charts and start doing something functional.

For designers who work with data, analysts who present to non-technical audiences, and marketers who need their numbers to do more than sit in a slide, this read is for you.

The Chart Was Never The Whole Story

In 1973, the statistician Francis Anscombe (PDF) published a paper that made a quiet but clarifying point. He constructed four datasets that are statistically identical: same mean, same variance, same correlation coefficient, and same regression line. Run the numbers on any of them, and they are identical. Plot them, and they could not be more different.

Anscombe’s lesson to statisticians was about diagnosis: visualisation reveals the operational truth that raw numbers conceal.

But visualisation isn’t just diagnostic; it is also communicative. The form you choose is where understanding either emerges or gets lost in the noise. Does your audience walk away with numbers, or with a story they’ll quote and talk about?

One of the most striking examples is Visual Capitalist’s History of Pandemics. Instead of burying the reader in a massive data table of casualty counts, it maps the death toll of major historical pandemics using a proportional bubble layout on a single timeline.

Before your brain reads a single number, your visual system grasps the sheer scale of the Black Death relative to everything else on the page. The right visualisation does not just plot the data but makes the story impossible to miss.

Edward Tufte codified a foundational principle for the craft with his data-ink ratio: every mark on a chart should serve the data, not decorate it. It remains a widely used framework in data visualisation, anchored in the assumption that clarity and visual hygiene are the goal.

For a chart in isolation, that holds. But a chart is never read in isolation: it’s read by a person, in a specific context, under specific pressure. Strip a chart down to its cleanest form, and you might be removing the exact layer of context a decision-maker needs. Simplicity isn’t the goal in itself; appropriate complexity is.

Data is a message, and the right amount of signal depends entirely on who’s receiving it.

Which leads to the core principle of data UX: roughly 80% of the work that determines whether a dashboard succeeds happens before you ever draw a chart.

That dependency is the thread the rest of this piece pulls on.

The 80% That Happens Before The Chart

The high-leverage 80% almost never happens on screen. It happens upstream: before a tool is opened, before a dataset is pulled, before a single design choice is made. It comes down to three questions, and once they become habitual, they change what you notice, what you ask, and what you push back on at the outset of every project.

1. Context: What are we trying to show with this data?
   This is where you define what the visualisations actually need to serve before you touch any raw data. Writing down the precise operational questions, specifically enough to determine what gets pulled and what gets filtered, is what produces a dashboard that helps with decision-making.
2. Audience: Who is this for, and how do they think?
   This is the empathy step. Knowing who’s in the room, what they’re accountable for, and how they engage with data determines how much complexity the visualisation can carry, and how it should be presented.
3. Insight: What should change once this data lands?
   A decision, a new direction, a shift in understanding. If the intended strategic outcome isn’t clear during the design phase, it will remain invisible once the dashboard goes live.

Context: What Are We Trying To Show With This Data?

Most data-heavy projects start backward: teams pull whatever metrics their internal analytics tools already track and build visualisations around them, while the question the data was supposed to answer either gets assumed or never gets asked. This happens simply because we anchor on the data in front of us as the boundary of what’s possible.

Defining a goal first sounds obvious, but in practice, it rarely happens with the necessary clarity. “Show me how the product is performing” is not a goal; “Identify which features drive retention among users who signed up in Q1” is, as it includes three things the first doesn’t: a metric, a population, and an implied action. That specificity is what converts an open-ended exploration into a constrained, answerable design problem. Which one you start from determines everything that follows: what you include, what comparisons matter, and what you leave out entirely.

Starting with available data produces a dashboard that answers no particular question, because it was never built for one — every number is present, none of them pointed anywhere. Starting with the operational question does the reverse: every element on the screen earns its place, because each one is there to help answer it.

For example, consider a UX team trying to fix a leaky checkout flow for an e-commerce website. A data-first approach pulls everything available, from clicks to scroll depth and device types, yielding a massive dashboard that leaves everyone asking, “Okay, but what do we actually change?” A context-first approach starts with a constraint: “At which step of the checkout do users drop off?” By filtering out 90% of the noise, the team builds a simple funnel chart, instantly spots a bottleneck on the payment screen, and knows exactly what to redesign.

Audience: Who Is This For, And How Do They Think?

Designing for an audience comes down to two things: accountability and familiarity.

Familiarity is about data literacy. Do they read charts instinctively, or does a complex visualisation create friction? Handing a dense, multi-layered dashboard to a Head of Sales and a senior analyst is like giving the same map to someone who navigates by landmarks and someone who reads grid coordinates. The data is accurate, but it is only functional for one of them.

Accountability dictates how that complexity must be presented. A chart showing a 12% decline carries vastly different weight for the executive responsible for that number versus the analyst simply reporting it. Understanding your audience means grasping this relationship; data is never processed neutrally when your performance is on the line.

Together, familiarity and accountability decide one practical thing: how much you can put in front of someone.

In data visualisation, simplicity is not a fixed virtue — the right level of it is contingent on who is reading, and what they need to do.

An analyst relies on a high-density environment to conduct diagnostic discovery. By isolating individual behaviour nodes and mapping out raw user flows, they interrogate the data at its atomic level to uncover the hidden insights and underperforming spend that will shape future campaigns.

An executive, by contrast, requires a highly synthesised translation of that data to immediately identify what is driving commercial growth. Tailoring a dashboard to your audience means adjusting the density dial, delivering maximum signal with appropriate complexity for the specific brain in the room.

Insight: What Should Change Once This Data Lands?

Most data projects operate on the comfortable assumption that if a chart is accurate and clear, the insight will take care of itself. In reality, information and insight are entirely different states. Information is what the data shows, whereas insight is the specific decision, shift in understanding, or course correction someone makes as a result of seeing it. If the intended business change isn’t defined before the design begins, a dashboard will default to passive reporting rather than driving action.

Marketing and engineering teams experience the danger of this gap whenever a core business metric suddenly plummets.

A dashboard built for information simply sounds the alarm, showing a chart that tracks a sharp 15% drop in booking rates. Because the data lacks depth, leadership defaults to panic: they immediately call the UX design team, assuming the app is broken or the checkout flow is flawed. Because the data doesn’t pinpoint the core of the problem, it triggers a costly, misplaced fire drill.

A dashboard built for insight isolates the variables required to make an informed decision. Instead of a single, flat booking metric, the visualisation maps the drop against traffic sources and campaign launches — instantly revealing that while app performance and core user conversion are perfectly stable, the sitewide rate was artificially diluted by a massive influx of low-intent click traffic from a newly scaled campaign. The team doesn’t waste time redesigning a functioning app; they get the exact insight needed to pause the underperforming marketing campaign and adjust their acquisition strategy.

Every visualisation implies a next step, even if that step is “nothing needs to change right now.” The question is whether the design makes that implication clear enough for the viewer to recognise it.

From Questions To Dashboard: A Project Walk-through

My aha moment in data visualisation happened during a project for a client-facing B2B SaaS platform focused on enterprise talent management and competency tracking in Pegasystems skills. The platform captured a massive footprint of daily telemetry, and the brief arrived open-ended: “We have an immense archive of user activity, now we need to present it to enterprise teams.”

We could very easily have charted everything that was captured, but we wouldn’t be doing end users any favours if they just ended up looking at a data graveyard. My responsibility immediately moved beyond pure interface craftsmanship; it became about architecting a highly practical tool for real people who would open this dashboard routinely and need it to tell them an honest, immediate story about their workflows.

Here is how the project actually went.

Context

The client believed this massive pool of data could help their users perform better, and wanted an interface that finally enabled that growth. Translating that broad ambition into tangible visualisations required defining the practical mechanics of performance. What variables indicate advancement vs passive usage? What does “perform better” actually mean? How do you measure it?

An obvious candidate was time spent by product. Every platform tracks it. It is easy to show, and it feels meaningful. But time spent is a proxy; it tells you someone was there, not whether they got anything out of it.

The more meaningful signals were competency scores by area, certification completion rates, and historical performance trajectories. Integrating time-spent data alongside these performance metrics added a useful layer of interpretation, helping us surface which modules users were underutilising and whether that directly correlated with lagging scores. Time spent became a supporting signal in the larger story.

The second question was about the appropriate level of granularity. An identical metric carries completely different weight depending on who is looking at it. An individual contributor tracking their own completion rate needs to know if they are pacing correctly, whereas a manager reviewing a team aggregate needs to know precisely who requires immediate support. This distinction shaped every subsequent data-exposure and filtering decision. Defining that structural story early determined exactly what to show, and to whom.

Audience

The most straightforward approach to this design would have been predictable: use the same charts, but offer an individual view and an aggregated team view. Far too many dashboards rely on this shortcut, subtly tweaking the scale of identical data visualisations and labelling it “personalisation.”

An authentic analysis of the audience reveals a much deeper rift. Frontline team leads and individual contributors required distinct narrative structures and design principles.

Having used e-learning platforms myself, I remember the frustration of opening a tool without a clear sense of my current standing, core strengths, or slipping metrics. That personal experience directly guided the individual contributor workspace; it needed to act as a highly tailored, self-directed mirror that was granular, honest, and personal.

Conversely, the manager’s interface had to bypass individual milestones initially to provide a macro pulse check on team vulnerabilities. It was designed around a different operational reality: how is the group progressing, and where are the consistent gaps? The view prioritised the aggregate picture first, while retaining an intuitive path to drill down into tactical day-to-day coordination when needed.

Insight

Our insight strategy was locked in during the early conceptual phase, long before wireframing a single chart. We intentionally abandoned the idea of building a dense, passive data log and focused on pacing the narrative arc.

For individual contributors, the core value was self-direction, ensuring they could glance at the interface on a Monday morning and immediately derive a clear priority list for the week ahead.

For managers, the goal was to fundamentally shift the timing of operational conversations, providing them with the necessary baseline to intervene before a skill gap evolved into a critical project failure. The visualisations needed to surface the exact moments when a human check-in would be useful, shifting their workflow from reactive post-mortems to proactive guidance.

The comparison tool was the highlight nobody had asked for. A manager dashboard and an individual dashboard are easy to anticipate. What is harder to land is: what if a manager wants to compare two specific team members against the same metrics, side by side? That view came from a design assumption, and it turned out to be the feature that resonated most.

The decisions that mattered most in this project weren’t in the initial brief — capturing data they hadn’t thought to request, and structuring it to answer questions they had not previously known how to articulate, which is the core differentiator of a user-centric data strategy.

Designing The Mental Model Early

The choice of visualisation layout must follow the geometric nature of the data itself. For this project, the core design problem was enabling an individual user to answer a specific question at a single glance: across eight distinct competency areas, where are my relative strengths and gaps?

To solve this, we mapped the data using a radar chart. By organising multiple variables across axes radiating from a central point using polar coordinates, the interface connects the data points to form a single, unified shape. An even, balanced polygon instantly signals well-rounded proficiency, while a sharply skewed shape draws the eye immediately to an outlier area.

While a traditional linear bar chart would have forced the viewer to scan eight individual bars and mentally calculate the variance, a concentric, radial layout segments the data layers to make progress tracking and skill gaps immediately readable. When all dimensions share an identical scale and scoring method, the radar chart is not an unconventional aesthetic choice — it is the most functional tool for multi-dimensional analysis.

The colour system was the other decision made early, and I mean early. During the branding exercise, each of the three products was assigned a colour. That colour did not stay in the brand guidelines. It was built into the data model from the beginning, running consistently across every chart, every filter, every breakdown. By the time a user landed on the dashboard for the first time, the mental model was already in place. They had not been taught the language. They already knew it.

What Changed

The shift from passive information to active insight showed up first in how the dashboard was actually used. Following the deployment of the personalised dashboards and side-by-side team comparison tools, weekly active engagement on the platform’s analytics features rose noticeably, per internally reported figures. Managers were no longer opening the tool once a month to pull static reports. They were using it every Monday morning to actively plan their week.

Revenue and user growth also moved in the right direction over the following two quarters, though — as with most single-project outcomes — it’s hard to isolate the dashboard’s exact contribution from everything else that changed at the same time. The client reported churn across the platform falling to one of its lowest points on record. The clearest evidence of impact came from qualitative feedback. Managers reported that instead of using data to dissect a ‘bad’ month after the fact, the visualisations allowed them to instantly spot slipping performance and schedule a quick supportive catch-up before it turned into a real gap.

Closing

Data design reaches its full potential when visual presentation is treated as an upstream architectural choice rather than a downstream formatting step. Bringing structured UX thinking to data turns visualisations into active decision-making engines, ensuring every chart, report, and metric directly serves a human purpose:

• Upstream framing: Grounding every visual choice in a specific operational question rather than defaulting to available metrics.
• Calibrated density: Tuning the level of complexity directly to the literacy and accountability of the specific reader.
• Decision-driven insight: Structuring data to reveal strategic outcomes rather than isolated stats, turning visual signals into immediate operational momentum.

The next time you are tasked with creating a data visualisation — whether it is an enterprise dashboard, an executive report, or a public-facing infographic — step away from the design canvas and BI tools. Focus your initial effort on the human decisions behind the screen. Only then will your data stop being a passive log of the past and start driving the direction of the future.

(yk)

The post Rethinking Data Visualisation: A UX Approach To Dashboards That Actually Drives Decisions appeared first on The NOKIA Blog.

I’ve experienced both scenarios. Sometimes, there’s no rhyme or reason why clients come and go. You can do everything right, and still a client looks elsewhere when it’s time for a redesign or new project. Perhaps they were swayed by a sales pitch. Or, a new person comes in and wants to shake things up. It’s an unfortunate part of this business.

Regardless, there’s still work to be done. Their website may need to be migrated to a different host. Credentials need to be created for the new freelancer. There are project assets and notes to share.

It’s an awkward situation at best. But never fear, I have some tips for making the transition as smooth as possible. Keep reading to learn the do’s and don’ts of working with that new (or old) developer!

Keep It Professional

Maybe it sounds obvious. However, it’s easy to become emotional when letting go of a project (and its revenue). The same thing applies when you’re the new developer. Some clients will paint an unfortunate picture of their previous agency. Don’t get caught up in it.

Odds are, neither you nor the other party is evil or inept. There are always two sides to a story, and you’ll probably only hear one of them.

As such, there’s no reason to be rude or unhelpful. Answer questions, document processes, and respond within a reasonable period of time. These are common courtesies that will speed up the transition. After all, no one wants this thing to drag on for too long.

The funny part is that you may encounter the same developer again. For example, I’ve gained and lost clients to the same local agency. It’s a reminder to keep it professional and avoid burning bridges.

We may be competitors, but we’re all in the same boat.

Get Paid Before Doing the Work

These transitions can be especially frustrating if a client owes you money. They may expect you to work with their new developer with a promise to pay later. Avoid this scenario at all costs!

A client who is always late with payment is unlikely to make good on their debt after you’re gone. As such, getting paid should be a condition for transferring the site to someone else.

Create an agreement (in writing) that outlines what you need from them before you can do the work. Don’t provide a single file or login until that condition has been met. This places the ball firmly in their court. If they want to move on, they’ll need to settle their outstanding debt. It’s a perfectly reasonable request.

If the client balks at the idea, you may need to bring a lawyer into the fold. It’s not ideal, but it may be the only way you’ll get your money.

Think Before You Share Sensitive Information

Sharing credentials is part of any website transition. There are logins for hosting, content management systems (CMS) like WordPress, and third-party SaaS applications. Not to mention API keys and software licensing details.

You may be tempted to send a laundry list of logins to the new developer. However, it’s worth taking a step back and ensuring you aren’t exposing yourself to risk.

If you manage a client’s hosting or domain name, for example, be sure that anything you share is specific to their website. You wouldn’t want someone to have access to other client data. If possible, create delegate accounts that provide only the necessary permissions.

Also, create a new administrator account for their CMS, rather than sharing your login. This takes you off the hook for any user activity or, worse yet, a hacked password. And make sure your account is deleted when you’re out of the picture.

Software licensing can be tricky, especially if they aren’t under your client’s name. If you have a developer license for a WordPress plugin, consider your comfort level with allowing a client to continue using it. It might be worth recommending they purchase a separate license.

If you’re inheriting the site from another developer, think about what you’ll need. Ask about all of the above and find out as much as you can. The goal is to avoid any surprises once you take over.

Transitions Are a Part of the Job

It’s safe to say that no one enjoys transitioning a website from one developer to another. It’s often a messy process and can raise your anxiety levels, regardless of which side you’re on. But there’s an opportunity to make the best of a difficult situation.

Maintaining a professional attitude is a must. Treat it as just another business transaction and do your best to leave personal feelings out of it. Work with integrity and aim to produce the best possible outcome.

It’s also important to protect your interests. A client who’s leaving must live up to their end of the bargain. Don’t allow them to walk away without paying their outstanding balance. Also, be careful about the information you share with a new developer.

The most positive aspect is the experience you’ll gain. Learning to manage clients coming and going will serve you well. Doing so will allow you to focus on the task at hand while avoiding all the negatives. That’s great, because you’re likely to need those skills again and again.

here. He recently started a writing service for WordPress products: WP Product Writeup. He also has an opinion on just about every subject. You can follow his rants on Bluesky @karks.com.

Read more articles by Eric Karkovack

The post How to Work With Your Client’s Next (Or Previous) Developer appeared first on The NOKIA Blog.

Seriously, some of us are spending something close to a BMW’s (or perhaps a Ferrari’s) monthly payment on AI. What started as a small monthly fee to use ChatGPT, Claude, or Copilot has turned into something completely different. We have become the proverbial kids in the candy store.

It’s a bad case of consumerism fueled by our desire to experiment with new toys. It doesn’t help that AI companies are making it easy to spend significant money without a second thought. Integrating with your favorite SaaS or even WordPress is easy. Yet, the virtual cash register silently rings with each prompt and automation we create.

Perhaps all of this is happening too fast (maybe that’s the intention). We may be falling into an AI-powered trap that will be difficult to escape.

The Grumpy Designer likes to keep things rational. So, let’s hit the brakes, take a step back, and analyze what we’re doing. We’re the only ones who can stop the insanity, after all.

Your Monthly AI Plan Isn’t All-Encompassing

The early days of AI (a whole 2-3 years ago) were exciting. I had fun playing around with ChatGPT and seeing what silly or productive things I could accomplish.

Its coding abilities may pale compared to what we have today. But it was still incredibly handy for troubleshooting and building basic WordPress plugins. I decided that their $20-per-month Plus plan was a relative bargain. It offered access to the latest models and improved performance.

And it’s still a good deal, if you use it in your browser or their app. An individual performing occasional coding, image generation, or file analysis will get a lot for their money. It’s once you step outside of those boundaries that things become costly and confusing.

Such monthly plans typically don’t provide you with API access. For that, you’ll need to purchase tokens. It’s a common practice across the big AI companies.

I won’t argue with the premise of token-based usage. AI requires a lot of computing power to do its job. It must provide quick and accurate answers to user queries. Then there is the cost associated with training and improving each model.

The real problem lies in educating consumers on the differences. It’s not until you need an API key that you realize your monthly plan won’t cover you.

How Much Will That Task Cost?

Generating an API key and connecting your favorite AI model to a third-party service is simple. Figuring out how many tokens you’ll need isn’t so clear. That’s the downside of not having a good/better/best pricing model.

Reputable AI companies let you set spending caps and disable auto-purchasing once you hit your limit. This will save you from a massive bill. It’s also a good security measure, should your API key fall into the wrong hands.

However, experimentation is the only way to understand the costs of using AI tokens. Figure out what you want to do, ask AI to do it, and check your account dashboard to see how much you spent. The trick is to extrapolate that cost based on how many times you’ll perform a given task.

The other challenge is that some tasks will use more tokens than others. For example, generating a headline for your blog post will likely cost less than generating a featured image. Thus, you’ll want to watch your token usage carefully.

Setting a small spending limit may be fine for light-duty tasks. That strategy won’t work for organizations with multiple users who are highly dependent on AI, though. You’ll quickly run out of tokens and will have to go without the service or set a higher limit.

It appears that companies are beginning to realize that willy-nilly usage of AI is costly. Some will narrow the scope of what these tools do for them. It’s also an argument for developing local AI models. Maybe we still need humans after all?

Put AI in Its Place

Sure, AI can do just about everything. But the decision on when and how to use it is ours to make. The increasing and uncertain token costs are reason enough to scrutinize every task. Some of what we’re doing may not be worth the price.

There are other benefits to being less reliant on AI. Fewer tokens spent means less electricity (and less pollution). The environmental impact is real and only getting worse as more of us employ these models.

It’s also an opportunity to stay connected to our humanity. Imagine all the knowledge we might lose if we cede every task to machines. There is also the potential to lose personal and professional relationships. Do you want to trade that for efficiency?

All of this makes me want to treat tokens as something to spend wisely. From now on, I’ll ask myself: Is this task tokenworthy?

here. He recently started a writing service for WordPress products: WP Product Writeup. He also has an opinion on just about every subject. You can follow his rants on Bluesky @karks.com.

Read more articles by Eric Karkovack

The post Cherish Your Tokens, Grumpy Designer appeared first on The NOKIA Blog.

Today, we’re announcing Amazon Bedrock Managed Knowledge Base, a new set of capabilities that enables developers to build enterprise-grade generative AI applications with their proprietary data in minutes. Organizations building agentic AI applications need secure, reliable, and up-to-date access to enterprise-wide data to deliver accurate, fast, and trusted outcomes. Managed Knowledge Base abstracts away the complexity of building and managing retrieval-augmented generation (RAG) pipelines, allowing developers to focus on business outcomes rather than infrastructure management.

Developers building knowledge bases for their agents face three key challenges today:

• Connecting to enterprise data – Enterprise knowledge lives across disparate systems with different content types, access control lists, and document formats. Building and maintaining custom connectors for each source adds complexity that slows down development.
• Optimizing RAG accuracy – Best practices for retrieval-augmented generation keep evolving. Developers need to experiment with different parsing strategies, chunking approaches, embedding models, and agentic retrieval behaviors to get accurate answers from their data.
• Managing infrastructure at scale – Organizations need to serve large knowledge bases with millions of documents, or manage thousands of smaller knowledge bases across teams. Both patterns require reliable infrastructure, security enforcement, and cost control.

These challenges require developers to repeatedly perform undifferentiated work instead of focusing on their applications.

Amazon Bedrock Managed Knowledge Base addresses these challenges by abstracting away the multiple infrastructure components developers traditionally have to assemble and maintain themselves (storage, retrieval, embeddings, re-ranking, and foundation model selection) into a single managed primitive. By default, the service automatically selects and manages a default embeddings model, re-ranker model, and foundational model on your behalf, so you can get up to speed quickly without needing to pick or maintain one yourself. On top of this managed foundation, three core innovations further improve ease of use and accuracy:

• Native data connectors – Six pre-built ingestion connectors that natively pull enterprise data and permissions from SaaS applications, eliminating the overhead developers face in managing application-specific requirements. At launch, we support Amazon S3, SharePoint, Confluence, Web Crawler, Google Drive, and OneDrive.
• Smart Parsing – Different content types and sources require different approaches to achieve accurate retrieval. Smart Parsing handles this complexity automatically, selecting the right parsing strategy for each data type and connector to provide the highest accuracy for your agents.
• Agentic Retriever – Optimized for complex queries that require multiturn, multihop retrieval within a single knowledge base or across multiple knowledge bases. Agentic Retriever automatically infers end-user intent and draws relevant context from institutional knowledge spread across data sources and modalities.

With just a few lines of code, Amazon Bedrock Managed Knowledge Base automatically manages and scales the end-to-end RAG pipeline that powers your enterprise knowledge agents. For agent builders, it’s available as a pre-built target type in Amazon Bedrock AgentCore Gateway, reducing integration to a few lines of code, auto-generating role-based permissions, and providing observability and evaluation metrics in the AgentCore Observability dashboard.

Getting started with Amazon Bedrock Managed Knowledge Base

Creating a Managed Knowledge Base is straightforward. Navigate to the Amazon Bedrock AgentCore console or the Amazon Bedrock console, open the Knowledge Bases page, and choose Create Managed KB. The experience is the same in both consoles. You will see that Unstructured Vector Store KB is now available as the recommended option, alongside the other knowledge base types you may already be familiar with:

Picture 1 – Knowledge Bases list page in the Amazon Bedrock AgentCore console showing the Type column with different KB types and the Create Managed KB button

When creating a new Knowledge Bases, you can connect to your enterprise data sources by choosing from the list of supported connectors directly from a dropdown. AWS Identity and Access Management (IAM) roles are automatically created, and you can choose to edit these permissions if needed:

Read moreSkipping the WHM Getting Started Wizard | cPanel Blog

Picture 2 – Create Knowledge Base page showing the Data source dropdown expanded with all supported connectors: Amazon S3, Confluence, Custom, Google Drive, One Drive, SharePoint, and Web Crawler

An optimized set of defaults will be presented, allowing you to create your knowledge base in just a few clicks. Once the data is synced, you can integrate the knowledge base with your agent or provide it as a tool for your foundation model and start querying.

Smart Parsing for accurate data ingestion

One of the key challenges in building knowledge bases is preparing diverse data types for accurate retrieval. Once you point Managed Knowledge Base at your data sources, Smart Parsing automatically determines the optimal parsing strategy for each data type and connector, no extra configuration is required.

Smart Parsing combines multiple techniques:

• Connector-specific data models – Optimized handling for each data source. For example, the Web Crawler connector preserves HTML structure including embedded images and tables, ensuring rich content is not dropped during ingestion. SharePoint connectors maintain document hierarchy and relationships between files.
• Multimodal processing – Automatic detection and processing of different content types within documents. The system identifies bounding boxes in documents, then sends them to foundation models for data extraction, captioning, and scene description in video files.
• Optimized chunking – Smart Parsing leverages foundation models to understand document structure and extract meaningful content, ensuring that complex documents with mixed formats are properly indexed. Intelligent defaults balance retrieval accuracy with performance based on document type and content structure, while advanced users can customize chunking strategies when needed.

This automated approach eliminates weeks of experimentation typically required to achieve production-quality retrieval accuracy, while still preserving the flexibility to customize when needed.

Using Agentic Retriever for complex queries

After your data is ingested, you can start querying your knowledge base. Generative AI applications often struggle with complex user queries that require reasoning, recursive multi-step retrieval, and intermediate evaluations of results. Consider a user asking two related questions: “What is the cloud infrastructure budget for the ML platform team?” and “Does our expense policy allow prepaying annual commitments?” A single retrieval step might surface documents about the ML platform team but fail to connect the budget information with the expense policy needed to fully answer the question.

Picture 3 – Agentic Retriever decomposes complex user queries into a step-by-step plan, performing multi-hop retrieval across multiple knowledge bases and combining results to deliver accurate, grounded responses

Agentic Retriever solves this by creating a step-by-step query plan: 1. Which team owns the ML platform, and what is their cloud infrastructure budget? 2. What does the expense policy say about prepaying annual commitments? 3. Does the policy allow the ML platform team to prepay against this budget?

The system performs multi-hop retrieval and reasoning at each step, and once it has gathered sufficient relevant passages, it stops the search process and returns the top results. By abstracting away the complexity of building a separate multi-hop reasoning pipeline, this approach dramatically improves accuracy for complex queries while letting developers focus on their agentic search applications instead of orchestration logic.

You can try Agentic Retriever directly from the test panel of your knowledge base in the Amazon Bedrock AgentCore console. Select Agentic retrieval only as the retrieval type to let the system automatically plan and execute multi-step queries across your knowledge bases:

Picture 4 – Test Knowledge Base panel showing Agentic retrieval with answer generation selected as the retrieval type, with model selection and maximum agentic iterations options

Enabling MCP with Bedrock AgentCore

Amazon Bedrock Managed Knowledge Base seamlessly integrates with AgentCore Gateway as a native target type. This integration eliminates the need for manual integration and provides built-in observability, policy enforcement, and automatic permission management.

You can navigate to the Amazon Bedrock AgentCore console or SDK and create an AgentCore Gateway or select an existing one. When adding targets to your gateway, you will find Knowledge Base as a new pre-built target type alongside other options such as MCP server, Lambda ARN, REST API, and other integrations. Simply select your knowledge base ID to expose it through the gateway:

Picture 5 – Add targets page in AgentCore Gateway showing Knowledge Base as a new pre-built target type, with the knowledge base ID selector and runtime retrieval mode options

Add targets page in AgentCore Gateway showing Knowledge Base as a new pre-built target type, with the knowledge base ID selector and runtime retrieval mode options

Gateway exposes the standard Model Context Protocol (MCP), so the knowledge base tools are automatically discovered by clients from any MCP-compatible framework, including Strands Agents, LangChain, CrewAI, LlamaIndex, and LangGraph. No custom integration code is required.

Model choice and flexibility

Amazon Bedrock Managed Knowledge Base preserves the flexibility developers expect from Amazon Bedrock. Every foundation model available on Bedrock can power the generation step, and developers can select from different embedding and re-ranking models to optimize retrieval for their specific use case, enabling teams to fine-tune accuracy and cost-performance without changing infrastructure.

Unlike managed solutions that lock you into specific model providers, Amazon Bedrock Managed Knowledge Base separates the infrastructure management (connectors, parsing, storage, retrieval orchestration) from model selection. This means you can:

• Take advantage of the latest models – Adopt the latest embedding, re-ranking, and foundation models as they become available to improve accuracy, latency, and cost for your application without rebuilding your RAG pipeline.
• Optimize for price-performance – Choose smaller, faster models for simple queries and more capable models for complex reasoning tasks, all using the same knowledge base infrastructure.
• Use Bedrock embedding models – While Smart Parsing provides optimized defaults, you can configure Bedrock embedding models when your domain requires specialized semantic understanding.
• Maintain consistency with existing applications – If you’re already using Bedrock Knowledge Bases APIs (Retrieve, StartIngest, StopIngest, IngestKnowledgeBaseDocuments), Managed Knowledge Base uses the same APIs, so migration requires no code changes, just point to the new knowledge base ID.

This approach ensures you can spend time on your generative AI application without losing the ability to change models based on evolving requirements or new model capabilities.

Get started today

Amazon Bedrock Managed Knowledge Base is available today in the US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney, Tokyo), Europe (Dublin, Frankfurt, London), and AWS GovCloud (US-West) Regions. For Regional availability and future roadmap, visit AWS Capabilities by Region.

With Bedrock Managed Knowledge Base, you pay for what you use with no upfront commitments. Pricing is based on two dimensions: the size of indexed data stored and the number of retrievals performed (on-demand). For detailed pricing information, visit the Amazon Bedrock pricing page. Bedrock is also a part of the AWS Free Tier that new AWS customers can use to get started at no cost and explore key AWS services.

These capabilities work with any open source framework such as CrewAI, LangGraph, LlamaIndex, and Strands Agents, and with any foundation model. Bedrock services can be used together or independently, and you can get started using your favorite AI-assisted development environment with the AgentCore open source MCP server.

To learn more and get started quickly, visit the Bedrock Knowledge Bases Developer Guide.

Daniel Abib

The post Introducing Amazon Bedrock Managed Knowledge Base for faster, more accurate enterprise AI applications appeared first on The NOKIA Blog.

At re:Invent 2025, we previewed AWS Security Agent (now part of AWS Continuum), a frontier agent that proactively secures your applications throughout the development lifecycle across all your environments. You can perform on-demand penetration testing customized to your application, discovering and reporting security risks verified through exploitability testing.

Since the preview, we announced general availability for on-demand penetration testing and the preview of full repository code review that performs deep, context-aware security analysis of your entire codebase.

Today, we’re introducing more features based on customer feedback:

• Code review updates (Preview) — You can now use pull request scanning with remediation, security requirements packs, and simulated validation. New integrations support GitHub, GitLab, Bitbucket, and Confluence.
• Threat modeling (Preview) — AWS Security Agent analyzes your design documents or application source code, understands the full context of your application architecture and identifies threats with recommended mitigations using the STRIDE framework.
• Kiro power, Claude Code plugin, and MCP integration — You can run code reviews, generate threat models, and remediate findings directly from your IDE, CLI, or any AI-powered IDE through an open MCP integration, with results surfacing inline without any context switching.

Let’s take a closer look at each launch!

Code review updates

You can now connect to GitLab and Bitbucket in addition to GitHub— supporting both SaaS and self-hosted versions, so you can trigger scans regardless of where code lives. You can also integrate Confluence to reference your existing documentation as context for reviews.

To get started, choose Enable code review or update your code review setting in the Security Agent console.

AWS Security Agent introduces deep, reasoning-based analysis on every pull request as well as full repository to identify complex vulnerabilities that go beyond pattern-matching. It checks against your organizational security requirements and common security risks to catch what other tools can’t. To get started, access the Security Agent web application and run your code review.

You’ll receive fix commits and remediation guidance directly in your GitHub, GitLab, or Bitbucket workflow, while your security teams configure the repositories to be monitored and intervene on critical issues. AWS Security Agent validates findings in simulated environments to demonstrate proof of exploitability. This embeds security expertise across all repositories, reducing security-related delays in the development pipeline.

To learn more about new code review features, visit Create a code review in the AWS Security Agent User Guide.

Design review updates

You can continuously validate your security requirements across every design and code review with managed compliance packs: AWS Well Architected Framework, NIST CSF, PCI DSS, and AWS best practices, or import your own organizational requirements directly from internal documents or Confluence. Every finding maps back to your compliance posture, so teams stay audit-ready as they build.

To learn more, visit the design review documentation.

Threat modeling

AWS Security Agent generates threat models based on your design documentation or code repository, creates and build context about the application, including data flows, architecture, and trust boundaries. It maps out all components of your application, identifies potential threat actors and attack vectors, determines where weaknesses may exist, and prioritizes threats so you know what to address first.

To get started, choose Enable threat model and Connect source code repository in the Security Agent console.

To learn more, visit the threat modeling documentation.

Kiro power and Claude Code plugin for Security Agent

AWS Security Agent introduces a new Kiro power and Claude Code plugin and can be integrated with any AI IDE through an open MCP integration to secure your applications. You can trigger threat models and code reviews directly from your IDE, with results surfacing inline without any context switching.

To get started, install the Kiro power, and run your prompts. The Kiro power uses the AWS Security Agent MCP server. You can get started with the power by asking “Set up AWS Security Agent“. Kiro will check if you have an Agent Space and ask if you would like to use the existing one or create a new one.

With the Kiro power for Security Agent, you can catch vulnerabilities on every pull request as you build and scan an entire repository to surface accumulated risk by asking “Run a full security scan on this repo“. The Security Agent power includes an Agent hook to evaluate if a code review diff scan should be started after the Kiro agent has completed its turn. Before deploying to production, you can run a penetration test from your CLI to find what most scanners miss. Security Agent closes the loop by validating every finding and generating ready-to-implement code fixes.

You can pull the findings back into your development environment by asking “help me remediate my findings“. The Kiro power for AWS Security Agent will download findings to your local workspace, prioritize the most critical finding, and offer to start a bugfix spec session. You can iterate on fixing the findings using their familiar IDE with their existing tooling, steering, powers, and MCP servers.

You can also run threat models through the Kiro power in the IDE by asking “Build a threat model for this application“. The generated threat model is saved to .security-agent/threat_model.md. 

To learn more, visit the Kiro power for Security Agent.

Now available

AWS Security Agent understands the full security context across your software development lifecycle by covering design-time security (design reviews and threat modeling in preview), development-time security (code review in preview), and deployment-time security (penetration testing in GA), in a single, unified agentic offering. To learn more, visit the AWS Security Agent product page and the technical documentation.

These features are now available in AWS commercial Regions where AWS Security Agent is available. For Regional availability and the future roadmap, visit the AWS Capabilities by Region. For detailed pricing information and to access our 2-month free trial offer, please visit the AWS Security Agent pricing page.

Give it a try in the Security Agent console and send feedback to AWS re:Post for Security Agent or through your usual AWS Support contacts.

— Channy

Updated on June 18, 2026 — AWS Agents for DevSecOps, the Claude Code plugin for AWS DevOps Agent and AWS Security Agent is launched.

The post AWS Security Agent adds threat modeling, Kiro power and Claude Code plugin, and more appeared first on The NOKIA Blog.

Design isn’t only pixels and patterns. It’s pacing and feelings, too. Some products feel cinematic as they guide us through uncertainty, relief, confidence, and calm without yanking us around. That’s Emotion in Flow. Others undercut their own moments with a joke in the wrong place, a surprise pop-up, or a jumpy transition. That’s Emotion in Conflict.

These aren’t UX-only ideas. You can see them everywhere in entertainment. And the clearest way to feel the difference is to compare how anime handles emotional shifts versus how Marvel and DC films stumble. We’ll use two specific examples, one from Dan da Dan (anime series on Netflix) and one from James Gunn’s Superman movie, to define the two concepts, and then translate them into practical product design patterns you can apply right away.

Note: We’ll focus on digital products, including apps, SaaS, and web.

Emotion In Flow (Anime: Dan da Dan)

In Dan da Dan, the tonal range is wild, horror, comedy, tenderness, yet it flows.

Example: In one arc, the protagonists are on a bizarre, comedic quest involving the “golden genitals” of one of the main characters (yes, really), and in another, we’re drawn into a heartbreaking story of a mother whose child is kidnapped. On paper, that shift should be a car crash. On screen, it’s coherent and emotionally legible.

Why does this work on screen?

• Continuity of stakes.
  Even when a gag lands, the characters’ goals and danger stay intact. Humor releases tension after a mini‑resolution; it doesn’t deny the threat.
• Clear mood cues.
  Music, framing, pacing, and character reactions telegraph the next feeling. You’re primed for the shift, so you ride it rather than getting yanked.
• One emotional anchor.
  Relationships remain the North Star, so the scene’s heart doesn’t get lost when the tone moves.

How does this translate to UX?

Good products do the same: prepare, transition, resolve, so users stay immersed as the emotional tone shifts.

Emotion In Conflict (Marvel/DC: James Gunn’s Superman)

Lois & Clark are having a heartfelt, intimate conversation, a slow, human moment, while in the background a running gag plays out (a monster getting clobbered with a giant baseball bat). The gag steals the focus right when the scene asks you to feel something real. The result is a tonal clash that punctures the emotion instead of releasing it.

Why does this fail on screen?

• Increased cognitive load.
  What’s happening here maps directly to cognitive load theory. When a scene (or interface) asks users to process two competing emotional signals at once, it introduces extraneous cognitive load, mental effort that has nothing to do with the task or moment itself. Instead of focusing on the emotional beat, attention is split between signals that don’t resolve each other. In products, this is what happens when humor, promotions, or unexpected UI changes intrude on high-stakes moments: users are forced to interpret tone and intent at the same time they’re trying to act, which slows comprehension and increases stress.
• Competing beats at the same time.
  The joke overlaps the climax of a serious beat; the audience pays attention to the switch rather than the feeling.
• No tonal handoff.
  There’s no transition that lands the intimacy before humor arrives, so the moment feels undercut rather than resolved.

How does this translate to UX?

In products, this is the confetti-before-confirmation problem, the cheeky error in a money flow, or the promo modal that appears right in the middle of a critical task. This also spikes cognitive load: users must process the humor while trying to fix a problem, which slows them down and increases stress.

Quick Definitions

Emotion in Flow
Emotional shifts feel earned, telegraphed, and timed so they resolve prior beats. Immersion holds.

Emotion in Conflict
A jarring switch (or hard cut) that punctures a live emotional beat. Immersion breaks.

Now that we’ve named it: how does this connect to UX?

How Emotions Shape Product Memorability

People don’t remember the average of an experience; they remember peaks and the ending. If your flow’s peak is frustration, or your ending is messy, that’s what sticks. So design the emotional curve on purpose.

Emotions live across three layers (from Don Norman’s Emotional Design), and your product needs to line them up:

• Visceral (gut): First-impression signals: visuals, motion, haptics, sound.
  Examples: A steady skeleton loader calms more than a jittery spinner; a gentle success chime/haptic tap lets the win land without shouting; consistent easing/direction tells the eye what changed.
• Behavioral (doing): Can I complete my task smoothly? Friction here means stress.
  Examples: Three clear payment steps with predictable progress; error states that explain what happened and how to recover; inline validation instead of end-of-form explosions.
• Reflective (meaning): The story I tell myself after, “Was that worth it? Do I trust this?”
  Examples: A tidy wrap-up screen (“Done. You’ll get X by Friday.”) gives closure; a small recap (“You saved €18 this year”) creates pride without fireworks.

Microinteractions are the emotional glue. Each one has a trigger (I tap Pay), rules (what the system does), feedback (progress and a clear result), and loops or modes (what happens if the user tries again). Get these right, and your transitions bridge feelings. Get them wrong, and they break the flow.

The emotional beat sheet maps cleanly onto Norman’s layers of experience:

• Uncertainty lives in the visceral and early behavioral layers, where users rely on sensory cues (motion, clarity, feedback) to understand what’s happening.
• Clarity is firmly in the behavioral layer, the moment when the system’s intent and the user’s next action lock into place.
• Anticipation is a blend of behavioral (the user is doing something with purpose) and reflective (the user is already predicting the outcome and imagining what comes next).
• Achievement is a reflective peak, where the user evaluates success, trust, and whether the experience “felt right.”
• Calm/Closure is primarily reflective, helping users wrap up the meaning of the interaction and decide if the product is trustworthy and worth returning to.

In real products, this sequence doesn’t disappear when things go wrong. Errors, latency, and degraded states are not exceptions to the emotional arc — they are part of it. Seen through a narrative lens, these moments are the obstacles in the hero’s journey. A well-designed recovery state acknowledges the setback, clarifies what happened, and guides the next step without introducing new emotional noise. When failure is treated as a beat instead of a rupture, emotional flow can be preserved even under stress.

UX Examples: Emotion In Flow vs. Emotion In Conflict

Emotion In Flow

Checkout done right (Stripe/Apple Pay style): short steps, clear progress, and a crisp success state (a checkmark with an optional soft haptic). The peak (success) lands, and the end gives closure (receipt or next step).

Pickup status (ride‑hailing apps, e.g., Uber, Free Now, or Bolt): progressive updates maintain orientation and reduce anxiety (“Driver arriving”, “2 min away”, “Arrived”). Uncertainty turns into clarity, with gentle motion preparing each transition.

Emotion In Conflict

Note: We’re not naming specific products here — we respect the work behind them. Instead, we’re showing the patterns that cause emotional conflict and exactly how to fix them.

• Jokes in serious moments.
  Cheeky copy-in-error states for money/health/security. Users are stressed; humor amplifies irritation.
• Celebration before resolution.
  Confetti, fireworks, or loud sounds before confirmation. The party interrupts the climax.
• Hard state jumps.
  Surprise modals/promos mid‑task, full‑screen takeovers without preparation. Feels like an abrupt cut during an emotional beat.

What You Can Do To Ensure Emotion in Flow

Here’s a Notion page with the full template you can duplicate:

1. Write The Emotional Beat Sheet First

For each core flow (onboarding, payment, recovery), map the feelings per step: uncertainty → clarity → anticipation → achievement → calm. Attach copy, motion, and microinteractions to each beat. (Who carries the emotion where?)

2. Align Tone With Task Risk

Create a tone matrix (risk level × state). In high‑risk errors, be calm, plain, and solution‑oriented. Save playfulness for low‑risk contexts.

Template snippets:

• High‑risk error: “We couldn’t verify your ID. Try again or contact support.”
• Low‑risk empty state: “Nothing here yet. Want to start with a sample?”

This is where many mature products quietly drift into emotional conflict. Over time, teams add delight by habit rather than intent.

A useful self-check is to ask: If we removed every playful or celebratory element from this step, would the flow still feel humane — or were those elements masking friction?

Good emotional design clarifies experience; great emotional design doesn’t need decoration to compensate for confusion.

3. Design Peak And End On Purpose

Engineer one clear peak (the moment of success) and one clean end (confirmation and what happens next). Measure recall and satisfaction at both points.

4. Use Microinteractions As Bridges, Not Spotlights

• Prepare: Small, consistent motion hints before a big state change.
• Confirm: Success gets a subtle settle, with a slightly slower ease-out and an optional light haptic.
• Recover: Repeated failure gracefully shifts tone from upbeat to supportive and guides the next step.

5. Test For Emotional Continuity

In usability sessions, don’t just ask “Was that easy?” Instead, you can ask “What feeling changed here?” If you hear “confused → amused → confused,” you’ve got conflict, not flow. Iterate transitions, not just screens.

How To Avoid Emotion in Conflict: Fast Checklist

Red flags → fixes:

• Jokes in serious moments → swap for calm, direct language, and a clear recovery path.
• Celebration before resolution → move celebration to after confirmation; tone it down for high‑risk tasks.
• Hard state jumps → pre‑announce transitions; keep framing consistent; use meaningful motion to preserve continuity.
• Cross‑team tone drift → centralize voice & tone guidelines with examples per risk level and state.

There are moments when breaking emotional flow is intentional and necessary. Security warnings, legal confirmations, and safety-critical alerts often benefit from abrupt tonal shifts. In these cases, disruption signals importance and demands attention. The problem isn’t emotional conflict itself; it’s accidental conflict. When designers choose disruption deliberately, users understand the stakes instead of feeling whiplash.

Conclusion

Great experiences are directed experiences. Dan da Dan shows how to move through feelings without losing us: it prepares, transitions, and resolves. The Superman scene shows the opposite: a gag colliding with a heartfelt beat.

Do the former. Map your emotional beats, align tone to task risk, and let microinteractions bridge feelings so users remember the right peak and the right end, not the whiplash in the middle.

(yk)

The post Anime vs. Marvel/DC: Designing Digital Products With Emotion In Flow appeared first on The NOKIA Blog.

Today, we’re announcing Amazon Route 53 Accelerated recovery for managing public DNS records, a new Domain Name Service (DNS) business continuity feature that is designed to provide a 60-minute recovery time objective (RTO) during service disruptions in the US East (N. Virginia) AWS Region. This enhancement ensures that customers can continue making DNS changes and provisioning infrastructure even during regional outages, providing greater predictability and resilience for mission-critical applications.

Customers running applications that require business continuity have told us they need additional DNS resilience capabilities to meet their business continuity requirements and regulatory compliance obligations. While AWS maintains exceptional availability across our global infrastructure, organizations in regulated industries like banking, FinTech, and SaaS want the confidence that they will be able to make DNS changes even during unexpected regional disruptions, allowing them to quickly provision standby cloud resources or redirect traffic when needed.

Accelerated recovery for managing public DNS records addresses this need by targeting DNS changes that customers can make within 60 minutes of a service disruption in the US East (N. Virginia) Region. The feature works seamlessly with your existing Route 53 setup, providing access to key Route 53 API operations during failover scenarios, including ChangeResourceRecordSets, GetChange, ListHostedZones, and ListResourceRecordSets. Customers can continue using their existing Route 53 API endpoint without modifying applications or scripts.

Let’s try it out

Configuring a Route53 hosted zone to use accelerated recovery is simple. Here I am creating a new hosted zone for a new website I’m building.

Once I have created my hosted zone, I see a new tab labeled Accelerated recovery. I can see here that accelerated recovery is disabled by default.

To enable it, I just need to click the Enable button and confirm my choice in the modal that appears as depicted in the dialog below.

Enabling accelerated recovery will take a couple minutes to complete. Once it’s enabled, I see a green Enabled status as depicted in the screenshot below.

I can disable accelerated recovery at any time from this same area of the AWS Management Console. I can also enable accelerated recovery for any existing hosted zones I have already created.

Enhanced DNS business continuity

With accelerated recovery enabled, customers gain several key capabilities during service disruptions. The feature maintains access to essential Route 53 API operations, ensuring that DNS management remains available when it’s needed most. Organizations can continue to make critical DNS changes, provision new infrastructure, and redirect traffic flows without waiting for full service restoration.

The implementation is designed for simplicity and reliability. Customers don’t need to learn new APIs or modify existing automation scripts. The same Route 53 endpoints and API calls continue to work, providing a seamless experience during both normal operations and failover scenarios.

Now available

Accelerated recovery for Amazon Route 53 public hosted zones is available now. You can enable this feature through the AWS Management Console, AWS Command Line Interface (AWS CLI), AWS Software Development Kit (AWS SDKs), or infrastructure as code tools like AWS CloudFormation and AWS Cloud Development Kit (AWS CDK). There is no additional cost for using accelerated recovery.

To learn more about accelerated recovery and get started, visit the documentation. This new capability represents our continued commitment to providing customers with the DNS resilience they need to build and operate mission-critical applications in the cloud.

The post Amazon Route 53 launches Accelerated recovery for managing public DNS records appeared first on The NOKIA Blog.