DEV Community: Simuletic

My weeks got easier when I stopped using GIANT to-do lists

Simuletic — Fri, 14 Aug 2026 08:26:12 +0000

My weeks got easier when I stopped using GIANT to-do lists

I'm a developer, which means I have a strange talent for turning very simple problems into systems.

A task needs doing?

Easy.

Create a project.

Add a label.

Set a priority.

Put it in a sprint.

Maybe create a recurring rule.

Perhaps we need a dashboard.

Before long, managing the tasks becomes another task.

I eventually realized that my biggest productivity problem wasn't that I needed a better to-do list.

It was that one giant to-do list has no concept of capacity.

A list doesn't tell you when anything will happen

Imagine this list:

Fix authentication bug
Answer customer emails
Finish App Store screenshots
Go to the gym
Review a pull request
Book hotel
Write release notes
Fix landing page
Call dentist
Test Android build
Research new feature
Buy groceries

It's technically organized.

Everything is captured.

Nothing is forgotten.

But there's a problem.

When am I actually going to do any of it?

A normal to-do list treats all twelve tasks as if they're competing for attention right now.

So every time I look at it, I have to make the same decision again:

What should I do next?

That decision gets surprisingly expensive when you make it twenty times per day.

I started treating the week like a capacity constraint

Instead of maintaining one backlog, I started forcing tasks into actual days.

Monday:

Answer customer emails
Review PR
Gym

Tuesday:

Fix authentication bug
Test Android build

Wednesday:

Finish App Store screenshots
Buy groceries

Thursday:

Fix landing page
Research feature

Friday:

Write release notes
Book hotel
Call dentist

Nothing revolutionary happened.

But something important did.

I could immediately see when I'd created an impossible day.

Five large tasks on Tuesday?

Move two.

Friday already full?

Stop pretending that another task will somehow fit there.

The week became a very crude—but surprisingly effective—capacity planning system.

It's basically WIP limits for your personal life

Software teams figured this out a long time ago.

If you have twenty things "in progress", you don't actually have twenty things in progress.

You have twenty unfinished things.

Yet our personal task managers often encourage exactly that behaviour.

Everything sits in one enormous queue.

Everything remains visible.

Everything feels active.

Putting a task on Thursday gives it permission not to matter on Monday.

That's the part I've found most useful.

It's less about scheduling and more about removing things from today's attention.

My current planning rule: one anchor task per day

I've also started picking one task for each day that matters more than everything else.

Not five "P1" tasks.

One.

For example:

Tuesday: Ship Android build.

There might be four smaller things underneath it, but if the Android build ships, Tuesday was successful.

This stops another developer habit I have: completing six tiny tasks because checking boxes feels better than starting the difficult one.

Leave empty space

This was harder for me.

When planning a week, my instinct is to use all available capacity.

Monday through Friday: filled.

Perfect.

Until Tuesday's bug takes three hours longer than expected.

Or someone schedules a meeting.

Or a child gets sick.

Or an API decides today is the day it will return an undocumented error.

Now the entire plan slides one day to the right.

So I deliberately under-plan.

If I think I can handle five things, I schedule three or four.

It feels inefficient when planning.

It feels extremely efficient on Thursday.

The five-minute version

My weekly planning process has eventually become:

Dump everything out of my head.
Pick the 3–5 things that actually matter this week.
Assign those tasks to specific days.
Pick one anchor task for each day.
Leave some capacity unused.
At the end of the week, move, delete or finish the leftovers.

That's basically it.

I've written a more structured version of the five-minute weekly planning routine if you want to try the method yourself or try Weeklane App.

The interesting part is that you don't need a particular app to do this.

Paper works.

A Markdown file works.

Apple Reminders works.

A whiteboard works.

The important change is going from:

“Here are all the things I need to do.”

to:

“Here is what I realistically intend to do on each day.”

Why I ended up building around this idea

I now work on a small app called Weeklane that's based almost entirely around this concept.

The whole product is basically seven short lists—Monday through Sunday.

And one of the hardest product decisions has been resisting the temptation to keep adding things.

Projects?

Could be useful.

Nested subtasks?

Definitely useful sometimes.

Complex prioritization?

Useful.

Team collaboration?

Also useful.

But eventually you have built Jira again.

So I've been trying to apply the same principle to the software that I apply to the week:

Just because there's room to add something doesn't mean you should.

I'm curious how other developers handle this.

Do you keep one backlog for everything, plan tasks into individual days, use your calendar, or have some completely different system?

A Practical Workflow for Writing and Scheduling Technical X Threads

Simuletic — Fri, 14 Aug 2026 08:22:13 +0000

Technical threads are surprisingly easy to get wrong.

The problem usually isn’t a lack of useful things to say. It’s trying to choose the structure, wording, links, and publishing time in one hurried session.

While working on Tweetify, I settled on a simpler process: separate writing, reviewing, and publishing into three distinct steps.

Here’s the workflow.

1. Decide what the reader should learn

Before writing the opening post, complete this sentence:

After reading this thread, someone should understand ___.

Keep the answer narrow.

“Everything I learned building an iOS app” is too broad.

“How we stopped scheduled jobs from running twice” is specific enough to become a useful thread.

This sentence isn’t necessarily the opening. It’s the promise that keeps the rest of the thread focused.

2. Give every post one job

A reliable technical thread can follow this structure:

The result or problem — why should someone continue?
The context — what were you building?
The mistake or constraint — what made it difficult?
The change — what did you do?
The evidence — how did you know it worked?
The lesson — what can someone else reuse?

For example:

Our scheduler occasionally processed the same job twice.

The worker wasn’t broken. It was retrying correctly after a timeout, but the operation itself wasn’t idempotent.

We fixed it by assigning every publish request a stable operation ID and rejecting duplicate executions.

The broader lesson: if a background job can be retried, assume that it will be.

That’s more useful than stretching one observation across ten posts. The thread should be as long as the idea requires—and no longer.

3. Review the chain, not only the individual posts

A post can read well on its own and still feel confusing inside a thread.

Read the entire sequence from top to bottom and check:

Does the opening make a clear promise?
Does each post introduce one new point?
Are words such as “this,” “it,” and “that” still unambiguous?
Does every post logically lead to the next one?
Have you repeated the same introduction twice?
Does the final post provide a conclusion rather than simply stopping?

I also leave some unused characters in each post. Filling every segment to its limit makes late corrections unnecessarily difficult.

4. Check the practical details

Before scheduling, verify:

The correct X account is connected.
Links open the intended page.
Images have useful alternative text.
Code screenshots remain readable on a phone.
The date, time zone, and AM/PM value are correct.
The posts are in the intended order.
The first post can stand on its own if someone never opens the thread.

If you’re working from an iPhone, I wrote a more detailed step-by-step guide to scheduling a complete X thread from iPhone.

5. Schedule the thread—but stay available

Scheduling is helpful because it removes the pressure to finish writing at the exact moment you want to publish.

It shouldn’t remove the human part.

When possible, be available shortly after the thread goes live. Answer questions, clarify ambiguous points, and pay attention to which part people respond to. Those replies often reveal what the next useful post should be.

A short reusable checklist

Before publishing your next technical thread:

One clear reader outcome
One idea per post
A useful opening
Concrete evidence or an example
No repeated sections
Enough character space for corrections
Links and media checked
Correct account and time
A real conclusion
Time reserved for replies

The goal isn’t to publish more threads. It’s to make each one easier to understand and more useful to the people reading it.

Disclosure: I work on Tweetify, an iPhone app for drafting and scheduling X posts and complete threads. The workflow above is the same one we are designing the app around.

Check it out directly: tallytodo.com

Simuletic — Mon, 03 Aug 2026 12:40:43 +0000

Simuletic

Aug 3

Why I Built a Deliberately Boring To-Do App

#productivity #mobile #design #indiedev

3 min read

Why I Built a Deliberately Boring To-Do App

Simuletic — Mon, 03 Aug 2026 12:38:46 +0000

There are thousands of to-do apps.

Many of them now include projects, labels, priorities, subtasks, calendars, collaboration, AI assistants, dashboards, productivity scores, and more settings than some development tools.

I wanted something simpler.

So I built WeekLane, a weekly task app designed around one screen and one question:

What do I need to get done this week?

The problem with powerful productivity apps

I have tried plenty of task-management systems.

They usually work well at first. Then the system itself starts requiring work:

Organizing tasks into projects
Choosing priorities
Maintaining labels
Rescheduling overdue items
Cleaning up an increasingly large backlog
Deciding which view to use

Eventually, managing the productivity tool becomes another task.

For many people, that complexity is useful. Teams need structure, dependencies, deadlines, and collaboration.

But for an individual trying to remember a handful of important things this week, it can be unnecessary.

The idea: make the week the interface

Weeklane is based on a simple weekly layout.

Each day is shown as part of one continuous list. Tap a day to expand it, add what you need to do, and check tasks off as you complete them.

There are no complicated project hierarchies to maintain.

The week provides enough structure by itself:

Today contains the immediate work
Tomorrow remains visible
Later tasks have a clear place
Completed tasks stay visible as progress

Instead of asking users to design their own productivity system, the app provides one opinionated workflow.

Constraints can improve product design

One of the most useful lessons from building small apps is that adding functionality is often easier than protecting simplicity.

Every new feature sounds reasonable in isolation:

Recurring tasks
Categories
Notes
Time estimates
Reminders
Multiple lists
Calendar integration
AI task planning

But features interact.

Each new option adds another decision, another interface state, and another opportunity for the user to feel that they are using the app incorrectly.

For WeekLane, the main design constraint became:

A user should be able to understand the app without an onboarding tutorial.

That affected almost every decision.

The checkboxes look like checkboxes. Completed tasks are crossed out. The active day is expanded. Future days remain visible below it. Adding a task happens directly inside the list.

There is very little abstraction between the user and the task.

A mobile app should feel immediate

Another goal was to make the app feel more like opening a small paper planner than loading productivity software.

That meant focusing on:

Fast startup
Minimal navigation
Large, readable typography
Clear touch targets
Very few screens
No account required before getting started

The visual style is intentionally restrained.

The interface uses mostly neutral colors, with orange reserved for completed items and important interactions.

The result is not designed to look like a complex SaaS dashboard compressed onto a phone. It is designed to feel like a native, personal utility.

Building for a specific type of user

A common product mistake is trying to build for everyone.

Weeklane is probably not the right tool for managing a software team, a large client project, or a detailed multi-year plan.

It is for people who think in weeks.

People who want to write down:

Finish the landing page
Buy groceries
Run 6 km
Review customer feedback
Call the dentist

...and then get on with their day.

Having a narrower target also makes development decisions easier.

When considering a new feature, I can ask whether it helps someone plan and complete their current week, or whether it turns the app into another general-purpose task manager.

What I learned

The biggest lesson was that simplicity is not the absence of product decisions.

It is the result of many product decisions.

A small app still needs careful work around spacing, typography, interactions, empty states, persistence, editing, task completion, navigation, and the transition from one week to the next.

Users may never consciously notice those details.

They will notice when the app feels slow, confusing, or unnecessarily demanding.

That is the standard I am continuing to work toward: an app that quietly does its job and then gets out of the way.

Try Weeklane

Weeklane is available for iPhone.

You can learn more and download it here:

Weeklane.app

I would especially appreciate feedback from other developers and indie makers:

Does the weekly structure match how you naturally plan?
Which feature would improve the app without making it feel heavier?
At what point does a simple to-do app become too simple?

Why Synthetic Data is the Secret to Fixing Computer Vision Edge Cases

Simuletic — Mon, 04 May 2026 11:34:22 +0000

Stop Scraping Images: Why Synthetic Data is the Secret to Fixing CV Edge Cases

If you’ve ever deployed an object detection model to production, you know the pain.

Your YOLO model hits 95% mAP on your validation set. You deploy it to a live CCTV camera. Suddenly, it thinks every person holding a black umbrella is an active shooter, and every low-hanging cloud is a forest fire.

We’ve all been there. The reality of Computer Vision is that standard datasets (like COCO or OpenImages) are incredibly biased. They consist of eye-level, perfectly lit, center-framed photos. But real-world cameras look down from 15 feet in the air, through dirty lenses, in terrible lighting.

When you try to solve these "edge cases," you hit a wall: Rare data is really hard to find. You can't easily scrape thousands of images of people having medical emergencies, fighting, or wielding knives. It's either a massive privacy violation, or the data simply doesn't exist. That’s why the industry is rapidly shifting toward Synthetic Data.

Engineering the Data We Can't Find

Instead of hunting for data, we can now engineer it using 3D rendering pipelines. By randomizing environments, camera angles, lighting, and sensor noise, we can create the exact "hard negative" scenarios our models are failing on.

Here are a few areas where synthetic data is completely changing the game for CV training:

Weapon & Knife Detection (CCTV): A knife is just a few silver pixels on a 1080p stream. Real datasets are full of "knives on cutting boards." Synthetic data allows us to generate thousands of variations of knives held in hands, with motion blur, under fluorescent lighting, drastically reducing false positives.
Fall & Lying Down Detection: Getting real footage of people slipping and falling is an ethical nightmare. Synthetic avatars allow us to generate anatomically accurate 3D keypoints and bounding boxes of people slumped, fallen, or lying down in complex environments (like between store aisles).
Aggression & Fight Detection: Violence is chaotic. Synthetic data lets us simulate multi-person interactions, pushing, and striking without staging dangerous real-world recordings.
Drone vs. Bird Classification: Radars and cameras constantly confuse birds with UAVs. By rendering drones against various sky gradients and comparing them to bird flight cycles, we teach models the subtle structural differences.
Fire & Smoke Detection: Wildfire AI constantly triggers false alarms on industrial steam and morning fog. Procedural volumetric generation lets us create highly specific smoke plumes that train the AI to understand density and origin points.

Building the Solution

Data is no longer something you just "collect"—it's something you engineer.

This exact problem is why I started Simuletic. We build hyper-realistic synthetic datasets designed specifically to fix edge cases and false positives in AI training.

If you're tired of your model failing on the rare stuff, stop manually labeling blurry web scrapes. Check out some of our open-source and commercial edge-case datasets here: simuletic.com/datasets.

Let me know in the comments: what is the weirdest false positive your CV model has ever thrown in production?