2026-01-25 11:55:20
Sorry if the title sounds a bit weird, but I want to tell you about a real production bug that almost took down all our clients.
We run Kanvas Ecosystem, a backend that powers several frontend apps. One of our core pieces is a filesystem manager: users upload files (images, audio, video, GIFs, etc.) and later attach them to different entities in the system.
Everything was working fine… until it wasn’t.
About 3 or 4 months ago, someone installed a new cache-related library in our Laravel project.
That’s when the problems quietly started.
Never install a new library without talking to your team and testing all scenarios.
Last week, I launched a new feature: an image scraper.
Users type a word, we scrape images, download them, and store them using our filesystem manager.
Suddenly, production went down.
All clients. All apps.
My boss called me worried, so we jumped into a meeting to find the issue.
Hours went by.
Our code looked perfect.
Dev environment worked.
Tests were passing.
So we thought it might be a CPU issue.
We added more cores and optimized the scraper.
Still broken.
We created a new server from an AWS image.
Same problem.
That made no sense.
Then we checked other services and discovered that Redis was down.
We increased Redis capacity and tested again in the dev environment.
I uploaded one simple image.
It took 47 seconds.
At that point I just thought: why?
I stopped testing only the scraper and started testing everything related to the filesystem manager.
The same issue appeared everywhere.
All clients were affected.
I debugged method by method.
My code looked clean.
Nothing suspicious.
Until I noticed something.
At the top of one class, there was a strange line.
An import from that cache library.
Just to test, with zero hope, I removed it.
The upload time dropped to 100ms.
I stared at the screen for a few seconds.
The library was:
On every single file upload.
Yes.
Every time.
I called my boss and said:
“I fixed it. I deserve a candy or at least a chocolate.”
I’ve never won the lottery or a gacha game, but the feeling of finding this bug was the closest thing to that.
I don’t know if this was skill or pure luck, probably both.
But one thing is clear:
Thanks for reading.
For me, it’s an honor to share this.
And remember: you can do more than you think.
2026-01-25 11:45:41
Databricks is a cloud platform that helps teams handle huge amounts of data. Think of it as a workspace where data workers can collect, clean, and study information together.
The platform runs on cloud services like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud. Companies use it to make sense of their data and build smart apps.
Three main groups work with Databricks:
Data engineers build systems that move and store information. They make sure data flows smoothly from one place to another.
Data scientists study patterns and create models. They use math and stats to predict what might happen next.
Business analysts look at reports and charts. They help companies make better choices based on facts.
All three groups can work in the same space. This makes teamwork easier and faster.
Teams don't need separate tools anymore. Engineers, scientists, and analysts share the same workspace. This cuts down on confusion and speeds up projects.
Start small and add more power when needed. Databricks can handle tiny datasets or millions of records. The system adjusts itself based on how much work you have.
You get everything in one place:
No need to buy and connect dozens of different programs.
Databricks keeps data safe with locks and codes. You can control who sees what. Everything gets tracked so you know who accessed information and when.
Raw data is often messy. It might have mistakes, missing parts, or odd formats. Databricks helps you fix these problems and get data ready for use.
Create charts that update on their own. Connect to your data and watch numbers change in real time. Share findings with teammates through simple dashboards.
Use AI to spot patterns humans might miss. Build systems that can predict sales, detect fraud, or recommend products. The platform includes popular tools like TensorFlow and PyTorch.
Work with information as it comes in. Track website clicks, sensor readings, or customer actions the moment they happen. Make quick choices based on fresh data.
Keep years of records in one place. The platform uses something called a data lakehouse. This combines the best parts of databases and file storage.
A cluster is a group of computers working together. They split big jobs into smaller tasks. This makes work finish faster than using one machine alone.
You can start clusters when needed and turn them off to save money.
Notebooks are where you write code and take notes. They look like digital journals. You can mix code, text, and pictures in one place.
Think of them as interactive documents. Run a bit of code and see results right away.
A workspace holds all your projects. It's like a folder system in the cloud. Keep notebooks, data files, and settings organized here.
Teams can share workspaces to stay in sync.
Jobs run tasks on a schedule. Set them up once and let them repeat. For example, pull new data every morning at 8 AM.
This saves time on boring, repeated work.
Track what customers buy and when. Predict which products will sell best. Send personalized deals based on shopping habits.
Spot strange account activity that might be fraud. Check transactions as they happen. Keep customer data private and secure.
Study patient records to improve care. Find which treatments work best. Keep health information safe under strict rules.
Watch machine sensors for warning signs. Fix equipment before it breaks. Track products from factory to customer.
Pay only for what you use. Turn off computers when projects are done. No need to buy your own servers.
Teams finish projects in weeks instead of months. Ready-made tools mean less setup time. Changes happen with a few clicks.
The platform updates itself. New features appear without you doing anything. Security patches install on their own.
Databricks offers training and help. Find answers in docs, videos, and forums. Contact support when stuck.
Pick one simple project first. Maybe clean up a customer list or create a basic report. Learn the basics before tackling big tasks.
Databricks offers free trials and learning materials. Watch tutorials and try examples. Practice with sample data before using real information.
Connect with other users online. Ask questions in forums. Learn from people who already use the platform.
Think about who needs access. Decide how to organize projects. Set up security rules early.
The platform has many features. It takes time to learn them all. Focus on what you need right now. Ignore the rest until later.
Cloud bills can grow fast if you're not careful. Watch your usage. Turn off clusters when done. Set spending limits.
Garbage in means garbage out. Spend time cleaning your data first. Bad information leads to wrong answers.
Databricks helps teams handle data without the usual headaches. It brings tools together in one spot. Companies get insights faster and spend less on setup.
The platform works best when teams already know what they want to do with data. It's not magic, it makes hard work easier.
Whether you're building reports, training AI models, or just trying to organize information, Databricks gives you a solid place to start.
Ready to try it out? Sign up for a free account. Follow a beginner tutorial. Start with small tasks and grow from there.
The platform continues to add new features. Stay curious and keep learning. Your skills will grow along with your projects.
2026-01-25 11:38:58
Building an autonomous AI trading system means things break. Here's what we discovered, fixed, and learned today.
The Problem: See full details in lesson ll_298_invalid_strikes_call_legs_fail_jan23
What We Did: - Added round_to_5() function to calculate_strikes() - All strikes now rounded to nearest $5 multiple - Commit: 8b3e411 (PR pending merge) 1. Always round SPY strikes to $5 increments 2. Verify ALL 4 legs fill before considering trade complete 3. Add validation that option symbols exist before submitting orders 4. Log when any leg fails to fill - LL-297: Incomplete iron condor crisis (PUT-only positions) - LL-281: CALL leg pricing fallback iron_condor, options, strikes, call_legs, validati
The Takeaway: Risk reduced and system resilience improved
The Problem: id: LL-298 title: $22.61 Loss from SPY Share Churning - Crisis Workflow Failure date: 2026-01-23
What We Did: severity: CRITICAL category: trading Lost $22.61 on January 23, 2026 from 49 SPY share trades instead of iron condor execution.
The Takeaway: 1. Crisis workflows traded SPY SHARES (not options) 2. Iron condor failed due to:
These commits shipped today (view on GitHub):
| Commit | Description |
|---|---|
| f64bbd14 | chore(ralph): Record proactive scan findings |
| 376a3c7b | chore(ralph): Update workflow health dashboard |
| 05f43ced | docs(ralph): Auto-publish discovery blog post |
| 66e0e83d | docs(ralph): Auto-publish discovery blog post |
| d44782c7 | chore(ralph): CI iteration ✅ |
Every bug is a lesson. Every fix makes the system stronger. We're building in public because:
This is part of our journey building an AI-powered iron condor trading system targeting financial independence.
Resources:
2026-01-25 11:21:46
Wallpapers are one of those things people interact with every day — yet the experience of finding a good one is often frustrating.
I’ve tried countless wallpaper websites over the years, and the problems were always the same:
images that look great in previews but don’t work on real screens, mismatched resolutions, awkward crops, and a browsing experience overloaded with ads.
That frustration led me to build CuteWallpaper.site — a small, focused project designed around how wallpapers are actually used.
Most wallpaper platforms optimize for scale:
But they rarely optimize for fit.
A wallpaper isn’t just an image — it has to work across:
Even so-called “4K wallpapers” often break once you apply them. Icons clash with focal points, compositions feel off, and cropping usually requires external tools.
I wanted to solve that exact gap.
CuteWallpaper.site is a curated collection of cute 4K & HD wallpapers designed for desktop, mobile, and tablet devices.
Instead of maximizing quantity, the site focuses on usability and visual comfort.
Cute, but calm
“Cute” doesn’t mean loud or distracting.
The wallpapers are selected to be:
The goal is to make screens feel calm and enjoyable, not busy.
Different devices demand different aspect ratios. Rather than forcing users to find a “perfect match,” the site adapts wallpapers to the user’s screen.
The core feature of CuteWallpaper.site is a crop-to-download tool.
If a wallpaper’s original dimensions don’t match your screen, you can:
No image editors, no trial and error — just a wallpaper that fits.
This feature came directly from personal frustration, and it remains the heart of the product.
Logged-in users can bookmark wallpapers they like and build a personal collection over time — turning casual browsing into something more intentional.
CuteWallpaper.site is not a static gallery.
New wallpapers are added regularly, and the collection is continuously refined. The focus is on consistency, quality, and long-term usefulness — not scraping massive image sets.
The goal is to build a library that improves over time.
This is an independent project, built without growth hacks or aggressive monetization.
That choice allows the product to stay:
CuteWallpaper.site exists because I wanted a wallpaper site I would genuinely use myself.
If you care about how your screens look — or you’ve ever been frustrated by wallpaper sites — this project might resonate with you.
2026-01-25 11:19:33
2026-01-25 11:16:34
Most AI products are evaluated on technical metrics.
Accuracy.
Latency.
Cost.
Throughput.
Those matter.
But they don’t explain why some AI products feel trustworthy and others feel exhausting, even when the underlying intelligence is similar.
The missing layer is emotional UX.
And most developers underestimate it because it’s invisible, hard to quantify, and rarely discussed in engineering terms.
AI Systems Create Emotional States: Whether You Design for Them or Not
Every interaction with an AI system leaves a residue.
Confidence.
Doubt.
Relief.
Anxiety.
Frustration.
These reactions accumulate over time.
Users don’t just ask:
“Did this work?”
They feel:
“Can I rely on this?”
“Do I need to double-check everything?”
“Is this helping me or making me nervous?”
That emotional response determines adoption more than raw capability.
Why Technical Correctness Is Not Enough
An AI system can be statistically accurate and still fail emotionally.
Common emotional failure modes:
Each of these creates low-grade anxiety.
Users may continue using the product, but they never relax.
That’s a UX failure.
Confidence Is the Most Important Output of an AI System
This is counterintuitive for developers.
We think the output is:
For users, the real output is confidence.
Confidence that:
If your AI reduces confidence, it increases cognitive load, even if it “works.”
Overconfidence Is More Damaging Than Inaccuracy
One of the biggest emotional mistakes AI systems make is false certainty.
When AI:
Users lose trust faster.
They would rather work with:
than
Emotional safety comes from honesty, not bravado.
Inconsistency Feels Like Betrayal
Humans are surprisingly tolerant of imperfection.
They are not tolerant of unpredictability.
If an AI:
Users feel betrayed, even if performance improves overall.
Consistency is not just a technical metric.
It’s an emotional contract.
Tone and Timing Matter More Than Explanations
Developers often try to fix emotional issues by adding explanations.
But most emotional UX problems are about:
A perfectly reasoned explanation delivered at the wrong moment still feels wrong.
Calm timing beats verbose justification.
Why Users Hate “Surprise Intelligence”
Unexpected AI behavior triggers anxiety.
When the system:
People feel out of control.
Invisible AI must be emotionally legible—even if it’s not explicit.
Users should never wonder:
“Why did this happen?”
Silence is only acceptable when behavior is predictable.
Emotional UX Is Built Through Defaults and Boundaries
Most emotional signals are not in the UI.
They live in:
A simple “undo” can eliminate fear.
A clear boundary can eliminate hesitation.
These are emotional design decisions, not technical ones.
Why Developers Often Miss This Layer
Emotional UX doesn’t show up in logs.
It doesn’t trigger alerts.
It doesn’t break builds.
But it quietly determines:
By the time metrics move, the emotional damage is already done.
Designing for Emotional Safety Is a Leadership Skill
This is not about empathy copy.
It’s about:
Great AI products don’t make users feel impressed.
They make users feel safe.
The Real Takeaway
The most important question in AI UX isn’t:
“Is this smart?”
It’s:
“How does this make the user feel over time?”
If your AI:
Users will trust it, even when it’s imperfect.
If it doesn’t, no amount of intelligence will save it.
That’s the emotional UX of AI.
And it’s the layer most developers miss, until it’s too late.
