Mert

Every morning used to look the same: scroll LinkedIn alerts -> open a JD -> tailor my resume -> Easy Apply or an external form -> repeat. Lots of context-switching, easy to miss steps, and hard to track what I already applied to.

So I documented the whole process in a resume + application workspace:

What the agent does

• Scans today’s LinkedIn job listings (Browser MCP)
• Saves each JD and scores fit against my base resume
• Tailors a truthful, JD-aligned resume (no invented experience)
• Exports a PDF and pre-fills Easy Apply / external forms

What I still do

• Review the tailored resume and fit score
• Answer role-specific questions
• Click Submit (by design – I stay in control)

What’s in the repo

• base-resume.md — single source of truth
• RUNBOOK.md — step-by-step daily batch
• jobs/<company-role>/ — JD, analysis, tailored resume, PDF
• applications.csv + queue — no duplicate applies
• Cursor rules so tailoring stays consistent every run

One prompt to start the day:

Run today's job application batch per RUNBOOK.md

It’s not “auto-apply everything.” It’s prep at machine speed, decisions at human speed – which is how I want to job search as a PM.

If you’re job hunting with Cursor, I’d start with a runbook + a base resume + a hard rule: never submit without you.

Your AI copilot demo worked. The model answered every question in the sandbox, latency was fine, and the stakeholders nodded. Then you connected it to production and the answers turned vague, wrong, or quietly incomplete. The reflex is to blame the model — swap one vendor for another, try a fine-tune, wait for the next release. That rarely fixes anything, because the model was probably never the problem.

Enterprise AI rollouts stall on data, not intelligence. Customer information is spread across a CRM, a billing platform, two or three support tools, a warehouse, and a legacy system nobody wants to touch. The model reasons perfectly well over whatever you hand it. It just cannot see a coherent picture of your business, so it answers from fragments.

The model was never the bottleneck

Picture a support copilot fielding a simple question: what is the status of the Acme account? To answer, it needs the subscription tier from billing, open tickets from the help desk, the renewal date from the CRM, and maybe usage data from a product database. Four systems, four different identifiers for the same company. Salesforce calls it account 0014x, Stripe calls it customer cus_J4k2, Zendesk calls it organization 360A. None of them match, and none of them know the others exist.

Hand a frontier model all four records and it will reconcile them fine. The hard part is getting all four to the model in the first place — correctly joined, fresh, and filtered to what this specific user is allowed to see. That is an integration problem and a governance problem. It is not a model-capability problem, and no amount of model shopping makes it go away.

This is why AI agents in the enterprise underperform the demo. The demo ran on one clean dataset. Production runs on your real data estate, and that estate is fragmented. A CIO AI strategy that budgets for model licenses and GPU time but not for the data plumbing is funding the wrong half of the project.

What AI data integration actually involves

“Connect the data” sounds like a weekend of API work. It is not. Here is what the phrase actually covers.

Entity resolution. You need one canonical identity per customer, per product, per contract — deciding that cus_J4k2 in Stripe and account 0014x in Salesforce are the same company, and handling the cases where “Acme Inc”, “Acme, Inc.”, and “ACME” need to collapse too. Without this layer, every cross-system question returns a partial answer.

Schema and semantic alignment. The field status means an invoice state in billing, a ticket state in support, and a deal stage in the CRM. If your retrieval layer hands all three to the model under the same label, it will conflate them. Someone has to map fields to a shared vocabulary.

Freshness. A copilot answering from a vector index rebuilt nightly will confidently quote yesterday’s data. For account questions, stale is the same as wrong. You have to decide which facts need real-time lookups and which can be cached.

Governance and permissions. This is the one teams skip, and the one that causes incidents. The agent must inherit the access rules of the person asking. A support rep’s copilot should not surface another customer’s revenue — and an agent crawling every system with a single service account will happily cross that line.

The most expensive enterprise AI failure is not a wrong answer — it is a correct answer shown to the wrong person. An agent querying your systems through a broad service account bypasses every row-level permission your applications enforce. Propagate the requesting user’s identity and access scope all the way down to the retrieval layer before you let an agent touch production data.

The work to do before you wire up a copilot

You do not need to unify your entire data estate before shipping anything. You need to unify enough of it for one workflow.

Pick a single workflow. “AI across the company” has no definition of done. “A support copilot that answers account-status questions” does. Scope to one job with measurable success.

Build a thin canonical layer. For the entities that workflow touches — customers, subscriptions, tickets — create one resolved view with a stable ID, even if it starts as a single materialized table. You are not building a data warehouse; you are building the smallest join that makes the workflow correct.

Name a system of record per field. Decide that billing owns subscription tier, the CRM owns renewal date, the help desk owns ticket state. When systems disagree, the agent needs a rule, not a guess.

Instrument retrieval. Log every record the agent pulled for every answer. When it is wrong — and early on it will be — you want to see whether it retrieved bad data or reasoned badly over good data. Those are different bugs with different fixes.

A fast diagnostic: take five answers your copilot got wrong and trace each one. If most failed because the right record was missing, unreachable, or stale, your problem is data integration. If most failed because the model had the right records and still reasoned poorly, then — and only then — is model choice worth revisiting.

Structured systems are only half the fragmentation. The other half is institutional knowledge — runbooks, policies, past decisions, onboarding docs — scattered across old wikis, shared drives, and chat threads. An internal copilot retrieving from five half-maintained wikis produces five half-right answers. Consolidating that knowledge into one searchable, permissioned workspace is unglamorous, and it is among the highest-leverage things you can do for retrieval quality.

Enterprise AI adoption is gated by data fragmentation, not model quality. The teams shipping working copilots did not win on model choice — they won by doing the entity resolution, schema alignment, and permission propagation that the demo let them skip. Do that work on one workflow, prove it, then widen. The model will be ready when you are.

Originally published at pickuma.com. Subscribe to the RSS or follow @pickuma.bsky.social for new reviews.

The Problem We Were Actually Solving

Selling digital products in a restricted country is like trying to navigate a minefield. The platform’s one-size-fits-all approach to payment processing assumes a world where every market has equal access to mainstream payment methods. But that’s not my reality. My customers need a way to pay that doesn’t involve jumping through hoops or filling out additional paperwork. I needed a solution that would handle all this complexity behind the scenes, so customers could focus on what matters: buying my product.

What We Tried First (And Why It Failed)

I tried integrating a popular payment gateway that promised to handle international transactions. Sounds great, right? Well, it came with a catch: a hefty processing fee that would eat into my profit margins. And then there were the issues with incomplete payments, timeouts, and errors – all symptoms of a system that wasn’t designed to handle my unique use case. I was so focused on finding a solution that I overlooked the fact that this payment gateway wasn’t meant for digital products sold using cryptocurrencies.

The Architecture Decision

I finally landed on a combination of OpenZeppelin’s ERC-20 library, Web3.js, and the MetaMask extension. My system would create a unique cryptocurrency address for each transaction, using the customer’s email address as the wallet identifier. Once the transaction was complete, the system would generate a download link for the product and send it to the customer via email. It wasn’t pretty, but it worked. I spent countless hours tweaking the error handling to prevent timeouts and ensure seamless execution. In the end, my setup allowed me to bypass the platform’s restrictions and deliver my product to customers worldwide.

What The Numbers Said After

The new system was put to the test with over 1,000 transactions processed in the first month alone. My product sold more units than ever before, and my customers were able to complete payments without issue. Here are some key metrics: MRR (monthly recurring revenue) jumped by 300%, activation rate saw a 25% increase, and churn rate dropped by 15%. The addition of a 24/7 support system helped address customer inquiries and reduced the number of payment-related errors.

What I Would Do Differently

Looking back, I wish I had more time to spend on implementing a customer wallet system, where customers could store their payment methods securely. This would eliminate the need for unique cryptocurrency addresses and simplify the checkout process. However, given the tight deadline, I prioritized getting the system up and running over long-term scalability. In retrospect, a more advanced wallet system could have prevented some of the errors I encountered during peak transaction periods. As I continue to refine my automated crypto payment and delivery system, I’ll make sure to invest in better design and architecture. It’s a constantly evolving landscape, and staying ahead of the curve is crucial when you’re operating in a restricted country.

Modern websites are overloaded.

Not broken.

Not slow.

Not outdated.

Just overloaded.

More sections. More animations. More popups. More sliders. More integrations. More “engagement tools”.

And somehow, despite all of that, many websites still struggle with the one thing that actually matters:

👉 helping users take action.

I’ve worked on websites for years — WordPress projects, WooCommerce stores, service websites, SEO optimization, performance fixes. And the same pattern appears again and again.

Most websites don’t fail because they lack functionality.

They fail because users get tired before they reach the point of conversion.

The Problem Isn’t Missing Features

When business owners feel that a website underperforms, the first instinct is usually:

“We need to add something.”

So the site gets:

• another popup
• another slider
• another CTA
• another plugin
• another fancy section

At first, it feels productive.

But over time, the website slowly turns into a collection of competing elements — all fighting for attention at the same time.

And users feel that chaos instantly.

Even if they can’t explain it.

Friction Is Often Invisible

This is what makes UX problems dangerous.

Most website owners don’t notice them because technically everything “works”.

The menu works.

Buttons work.

Forms work.

But the experience still feels tiring.

That’s friction.

And online, even small friction matters.

Too Many Choices = No Action

One of the most common issues I see is overcomplication.

For example:

• too many menu items
• multiple competing buttons
• long blocks of text above the actual offer
• homepage sections trying to explain everything at once

Instead of guiding users, the website overwhelms them.

People don’t want to study a website.

They want clarity.

The Mobile Experience Gets Worse First

This becomes especially obvious on mobile.

A desktop layout that feels “rich” often becomes exhausting on a phone.

You’ve probably seen websites where:

• popups cover content
• sticky elements fight for space
• sliders take half the screen
• buttons are hard to tap
• pages feel endless

Technically, nothing is broken.

But the experience feels heavy.

And users leave quietly.

More Features Often Mean More Confusion

There’s also a hidden technical side to this problem.

Every new feature introduces:

• more logic
• more UI elements
• more edge cases
• more maintenance

Over time, websites stop feeling intentional.

Instead, they become a timeline of past decisions:

“Let’s add this too.”

And eventually:

• the interface loses hierarchy
• important actions become less visible
• users stop knowing where to focus

Good UX Often Feels Simpler Than Expected

Interestingly, the websites that convert best are not always the most impressive.

Very often, they simply:

• explain things clearly
• reduce hesitation
• remove distractions
• guide attention naturally

Good UX rarely screams:

“Look how advanced this website is.”

Instead, users feel:

“This is easy.”

And that feeling matters more than most design trends.

What Reducing Friction Actually Looks Like

In practice, improving UX is often less about adding and more about removing.

Simplify the first screen

Users should immediately understand:

• what you do
• who it’s for
• what action to take next

Not after scrolling.

Not after reading five sections.

Immediately.

Reduce competing elements

If everything is highlighted, nothing stands out.

Clear hierarchy matters:

• one primary CTA
• one clear direction
• one main message

Make mobile feel effortless

Not “responsive”.

Effortless.

Test the website like a normal user:

• with one hand
• under sunlight
• while scrolling quickly

You’ll notice friction immediately.

Remove unnecessary steps

Every extra click, popup, or decision reduces momentum.

Sometimes improving conversion is as simple as:

• shortening a form
• simplifying navigation
• removing one distracting block

Focus on confidence, not effects

Animations and visual effects can help.

But clarity and trust matter more.

Users don’t convert because a transition looked smooth.

They convert because they feel comfortable moving forward.

A Real Pattern I Keep Seeing

Some of the best-performing improvements I’ve seen were surprisingly small.

Not full redesigns.

Just:

• cleaner structure
• better spacing
• simpler navigation
• clearer messaging
• less visual noise

And after that:

• users scroll more
• forms perform better
• conversion rates improve

Often without adding a single new feature.

Final Thought

A website doesn’t become better because it does more.

It becomes better when users can move through it without friction.

That’s a huge difference.

Modern web development often focuses on:

• features
• integrations
• complexity
• visual trends

But real users usually want something much simpler:

• clarity
• confidence
• ease

Sometimes the best thing you can add to a website…

is less.

👋 About the author

I work with WordPress, SEO, UX, and website performance — helping businesses build websites that are not only technically optimized, but easier to use and better at converting visitors into customers.

More about my work: https://pbb.design

I’ve been living inside Kiro CLI for months now. The ACP integration in my JetBrains IntelliJ IDEA is fantastic for coding sessions : I get the full agentic power of Kiro right where my code lives. And the new Terminal UI with its syntax-highlighted markdown, collapsible tool outputs, and crew monitor is genuinely impressive and I love it.

But here’s the thing. Not everything I do with an AI agent is coding. Sometimes I’m researching an architecture pattern. Sometimes I’m exploring a hypothesis about a system design. Sometimes I’m doing deep analysis on a technical decision and I want to see tool calls, and streaming responses in a comfortable visual layout, the kind of experience you get on claude.ai or ChatGPT. A web chat, basically. Except one that talks to Kiro’s full agentic capabilities instead of a vanilla LLM.

So I built one with the AI-Driven Development Lifecycle (AI-DLC) methodology and Kiro CLI as my usual coding partner, I created Kiro UI a self-hosted, browser-based chat interface that connects to Kiro CLI via the Agent Client Protocol.

How it works. At its core, the architecture is simple: your browser (React 19 + Vite) connects via WebSocket to an Express server, which spawns and manages kiro-cli acp processes over stdio. Each browser tab gets its own independent agent process with its own context. The communication uses the ACP protocol.

What you get in the browser is a proper agentic chat experience: real-time streaming with markdown rendering and syntax highlighting, side-by-side diffs when the agent modifies files, collapsible tool call blocks with raw I/O inspection, an MCP server panel with live status indicators, permission management with auto-approve policies, slash command autocomplete, image and file attachments, a context usage meter showing how much of the context window you’ve consumed, …

Let me show you how to get started.

Step 1: Clone the repository and install dependencies.

git clone https://github.com/guyon-it-consulting/kiro-ui.git
cd kiro-ui
npm install

Step 2: Make sure Kiro CLI is installed and authenticated. You need kiro-cli in your PATH.

# Verify kiro-cli is available
which kiro-cli
# Should output something like: ~/.local/bin/kiro-cli

Step 3: A single command builds the frontend and starts the server:

npm start
# Open http://localhost:3000

Et voilà! You have a full-featured chat interface to Kiro’s agentic capabilities running locally in your browser.

Step 4 (optional): If you prefer a desktop app experience, Kiro UI packages as an Electron app:

npm run electron:dev              # Dev mode
npm run electron:build:mac        # macOS → DMG + ZIP (universal)

Things to know:

• Self-hosted and private : Everything runs on localhost. No data leaves your machine.
• Multi-tab sessions : Each tab spawns its own kiro-cli acp process. They’re fully independent : different contexts, different conversations, different working directories.
• Protocol-first design : Every feature is driven by ACP. The UI adapts to what the agent supports, including Kiro-specific extensions (_kiro.dev/*) for MCP servers, slash commands, and context compaction.
• Configuration : Settings live in ~/.kiro-ui/ with settings.json for preferences and trust.json for persistent tool permission rules.
• License : Apache 2.0. Use it, fork it, extend it.

Give it a try: https://github.com/guyon-it-consulting/kiro-ui. I’d love to hear how you use it and what features you’d want next.

— Jérôme

The Problem We Were Actually Solving

Our company was facing a classic problem: how to sell digital products in countries where Stripe, our primary payment processor, was unwilling to operate. Our customers were located in countries like Japan, South Korea, and Malaysia, where local payment methods are prevalent but Stripe’s infrastructure is not. We tried to use Stripe Connect to work around the issue, but we encountered a 3.4 second latency penalty on every transaction, which not only harmed customer experience but also led to a significant increase in chargebacks and failed payments.

What We Tried First (And Why It Failed)

Our initial solution was to partner with local payment aggregators, hoping to offload the complexity to a third-party provider. However, this approach proved to be a nightmare: the aggregators were unreliable, with an average uptime of 95%, and their payment processing fees were exorbitant, ranging from 5% to 15% of every transaction. Moreover, the aggregators’ APIs were poorly documented and prone to sudden changes, causing our platform to break repeatedly. We soon realized that relying on local payment aggregators was not a viable long-term solution.

The Architecture Decision

After months of research and experimentation, we decided to implement Unchained Commerce, an open-source platform that allows us to integrate with local payment methods directly. We chose Unchained Commerce for its flexibility, scalability, and low-latency architecture, which enabled us to reduce our transaction latency to under 200 milliseconds. With Unchained Commerce, we can support over 100 local payment methods worldwide, including popular options like Alipay, WeChat Pay, and Kakao Pay.

What The Numbers Said After

Since implementing Unchained Commerce, our platform has seen a significant improvement in transaction success rates, with a 30% reduction in failed payments and a 25% increase in overall revenue. Our customers are now able to pay with their preferred local methods, resulting in a 40% increase in first-time purchases. Our platform’s average latency has decreased by 90%, and our payment processing fees have been reduced by 50%.

What I Would Do Differently

In retrospect, I would have invested more time and resources in researching alternative payment processors and local payment methods from the outset. While Stripe’s limitations were a major obstacle, they also presented an opportunity to explore uncharted territory and develop a more robust and flexible commerce platform. By taking a more open-source and modular approach to payments, we were able to create a system that is not only more scalable but also more adaptable to the ever-changing landscape of international commerce.