DEV Community: lixia

I Wanted One AI Tool for Everything. That Was Probably the Problem.

lixia — Tue, 29 Sep 2026 08:52:04 +0000

For a while, I thought the ideal situation would be to find one AI tool that could do almost everything.

Writing, summarizing, working with spreadsheets, taking meeting notes — and ideally handling images, video, and audio too.

It sounded convenient.

I wouldn't have to remember so many different tools or keep switching between different apps. Whatever I needed to do, I could just open the same tool and get started.

But after using AI for different kinds of tasks, I started to realize that I was probably making things more complicated than they needed to be.

When I actually use AI, I usually start with something specific I want to get done.

Maybe I need to rewrite a paragraph. Maybe I need to organize a spreadsheet. Maybe I have some images or other content to work with.

The task usually comes first. The tool comes after.

So I stopped worrying so much about finding one AI tool that could do everything.

I don't really need one tool to handle writing, spreadsheets, meetings, images, video, and everything else. I just need a tool that works well for whatever I'm trying to do at the moment.

Different tasks naturally come with different needs.

When I'm writing, I care about wording, clarity, and tone.

When I'm working with a spreadsheet, I care more about formulas, data, and structure.

And when I'm working with visual or creative content, I may need something completely different.

Tools don't have to do everything

This also changed how I think about having multiple AI tools.

I used to think that fewer tools automatically meant a simpler setup.

And I still don't want to have ten different apps open just to finish a normal task.

But having a few tools with clear purposes doesn't necessarily make things more complicated.

Sometimes it actually makes things easier.

For example, when I'm working with images, video, audio, or other creative content, I might use a tool that's more focused on that kind of work. Ezier AI is one example. It's built more around generating, editing, and enhancing creative assets.

That doesn't mean I need to use it for everything else.

And that's probably the point.

A tool doesn't have to be good at everything to be useful.

I've been looking at different AI productivity tools for different types of work, and I'm starting to think that having a clear reason for using each tool matters more than trying to reduce the number of tools I use.

I'm not looking for the perfect AI tool anymore

I still like keeping things simple.

I just don't think “simple” necessarily means using one tool for everything.

Sometimes a small group of tools, each with a clear purpose, is easier to work with than one tool that tries to cover every possible task.

If I know why I'm using a particular tool, that's probably enough.

Looking back, I think I spent too much time looking for a tool that could do everything, when I could have simply started with the task in front of me.

Maybe the goal isn't to find one AI tool that can do everything.

Maybe it's just to find the right kind of help for the thing you're trying to do.

And honestly, that feels much simpler.

I Kept Hearing About AI Workflows. What Are They Actually For?

lixia — Mon, 21 Sep 2026 07:23:41 +0000

I kept seeing people talk about AI workflows.

At first, I assumed it meant complicated automation with APIs, integrations, and a dozen different tools.

Then I realized something:

I was already using workflows. I just didn't call them that.

So, what is a workflow?

A workflow is simply a series of steps you follow to get something done.

For example, a weekly report might look like this:

Collect information → organize it → find important changes → write a summary → review it → send it

That's a workflow.

It doesn't need AI. It doesn't need automation. It doesn't even need special software.

An AI workflow simply means AI helps with one or more of those steps.

For example:

Collect information → AI summarizes it → review the result → write the final report

That's already an AI workflow.

Is it actually useful for normal people?

Not for everything.

If you send one email a week, there's probably no reason to build an elaborate workflow around it.

But repetitive tasks are different.

Think about things you do regularly:

Writing weekly reports
Summarizing meeting notes
Cleaning spreadsheet data
Organizing research
Preparing study materials
Turning notes into action items

If you keep doing the same steps, it can be useful to stop and ask:

Can part of this process be made easier?

That's where AI can help.

Is a workflow the same as automation?

No.

This was one of the things I misunderstood at first.

A workflow describes the steps.

Automation means some of those steps happen automatically.

You can have a completely manual workflow:

Meeting notes → summary → review

Or a partially automated one:

Meeting notes → AI summary → human review

You don't need an AI agent or a complicated automation platform to benefit from the second approach.

Sometimes one useful AI step is enough.

But is it safe?

This is probably a more important question than whether a workflow is "smart."

It depends on what information you're putting into it and which services are involved.

There's a big difference between:

"Summarize this public article."

and:

"Analyze this spreadsheet containing private customer information."

Before using AI in a workflow, consider:

What information am I sending?
Which services will receive it?
What permissions does the workflow have?
Do I actually need to send this data?

For sensitive or confidential information, check the relevant tool's privacy and data-handling policies first.

And don't assume that something is safe simply because it is called an AI workflow.

Does it have to be complicated?

Not at all.

A simple workflow could be:

Collect → Summarize → Review → Send

That's it.

You don't need ten tools.

You don't need to automate every step.

You don't even need to automate anything if having a clear process already makes the task easier.

For example, suppose you prepare a project update every Friday.

Instead of reading several documents and starting from scratch, you could organize the information first, use AI to identify the important changes, then review the result yourself.

The AI handles the repetitive part.

You handle the judgment.

Don't automate a bad process

There's one rule I find useful:

Simplify the process before automating it.

If your existing workflow is confusing, adding more tools probably won't fix it.

Start by writing down the actual steps.

For example:

Collect → Clean → Analyze → Review → Finalize

Then ask:

Which step is repetitive?
Which step takes the most time?
Which step could AI help with?
Which step still needs human judgment?

You may find that only one step needs to change.

That's enough.

There are plenty of examples of AI productivity workflows that show how AI can be incorporated into everyday tasks without turning everything into a complicated automation project.

Maybe workflow is just a fancy word

After thinking about it this way, "workflow" doesn't seem particularly mysterious.

It's basically:

This is how I usually get something done.

An AI workflow is simply:

This is how I usually get something done, and AI helps with part of it.

You don't need to automate your entire life.

Pick one repetitive task.

Write down the steps.

Try AI on one part.

Keep human review where it matters.

Then see if the process is actually better.

You don't need a complicated AI system. Sometimes you just need a better way to do one annoying task.

AI Is Making Work Faster. So Why Are We Still So Busy?

lixia — Tue, 08 Sep 2026 07:32:22 +0000

AI can write the first draft.

It can summarize a meeting in seconds.

It can turn a long document into a short list of key points.

It can generate code, organize information, analyze data, and even help plan the next step.

So why does it sometimes feel like we are busier than ever?

This is one of the strange things about AI at work.

We expected AI to give us more free time. Instead, many people are discovering a different problem: when work becomes easier to produce, we simply produce more of it.

And that changes what "productivity" actually means.

AI Has Reduced the Cost of Producing Work

Before AI became part of everyday work, creating something usually required a certain amount of effort.

Writing a report took time.

Creating a presentation took time.

Researching a topic took time.

Drafting an email took time.

Because these tasks required effort, people naturally had to decide whether they were worth doing.

AI changes that equation.

When a first draft can be generated in seconds, the cost of starting becomes extremely low.

That sounds like a pure productivity win.

But there is a hidden consequence.

When it becomes cheap to create something, we tend to create more things.

Instead of writing one version of a document, we might create five.

Instead of researching one approach, we might ask AI to explore ten.

Instead of preparing one presentation, we might generate several versions.

Instead of sending one carefully written message, we may spend more time managing dozens of AI-assisted drafts.

The bottleneck moves.

The problem is no longer always:

"How do I produce this?"

It becomes:

"Which of these things actually deserves my attention?"

The Real Bottleneck May Be Attention

For a long time, productivity was largely about getting things done faster.

But AI is changing the relationship between speed and value.

If AI can produce ten possible solutions in a few minutes, the difficult part may be deciding which solution is actually useful.

If AI can summarize a hundred pages quickly, the difficult part is knowing which information matters.

If AI can generate dozens of ideas, the difficult part is choosing one.

This creates a new type of productivity problem.

AI is solving the problem of execution faster than it is solving the problem of deciding what deserves to be done.

That distinction is easy to miss.

A faster workflow does not automatically create better work.

Sometimes it simply creates more work.

And more output can create more things that need to be reviewed, corrected, organized, approved, or communicated.

In other words, AI can remove friction from production while simultaneously increasing the amount of production.

The "AI Workload" Problem

Imagine a manager who previously received ten project updates every Friday.

After introducing AI, employees can prepare updates much faster.

Now the manager receives thirty.

The quality of the individual updates may even be better.

But the manager still has to read them.

The same thing can happen with emails, documents, reports, presentations, research notes, and internal messages.

AI makes it easier for everyone to create information.

But humans still have limited attention.

This creates a strange imbalance:

The amount of information can grow much faster than our ability to process it.

That is why simply adding more AI tools to a workflow does not necessarily solve a productivity problem.

Sometimes it creates another layer of complexity.

More AI Tools Can Also Mean More Decisions

There is another problem that receives less attention: tool overload.

Every week, there seems to be another AI product promising to improve writing, research, meetings, presentations, coding, project management, or automation.

The natural response is to try more of them.

But every new tool introduces decisions.

Which tool should you use?

When should you use it?

What does it do better than the tool you already have?

Does it integrate with your existing workflow?

Is the extra feature actually useful?

Should your team adopt it or ignore it?

At some point, finding the right tool can become its own productivity task.

The goal should not be to use the maximum number of AI tools.

The goal should be to find AI productivity tools that actually fit the work you are trying to accomplish.

That distinction matters.

A tool can be powerful and still be the wrong tool for a particular job.

The Best AI Workflow May Include Less AI

This sounds counterintuitive.

If AI is useful, why not use it everywhere?

Because not every task has the same value.

Some tasks are repetitive and predictable.

Those are often good candidates for automation.

Other tasks require judgment, context, creativity, or responsibility.

Using AI for these tasks may still help, but the human role becomes more important rather than less important.

For example, AI can draft a customer email.

But should the email actually be sent?

AI can summarize a meeting.

But which decision from the meeting matters most?

AI can generate a report.

But are the selected metrics the right ones?

AI can suggest a project plan.

But does the plan make sense for the actual situation?

The important question is not:

"Where can I use AI?"

A better question is:

"Where does AI reduce unnecessary effort without creating unnecessary complexity?"

That is a much more useful way to think about AI adoption.

Productivity Is Becoming a Selection Problem

The traditional idea of productivity is relatively simple:

Do more in less time.

But if AI continues to make production cheaper and faster, that definition becomes less useful.

When creating something takes five minutes instead of an hour, saving another two minutes may not matter very much.

Choosing the right thing to create may matter much more.

This means modern productivity increasingly depends on selection.

Select the right task.

Select the right information.

Select the right tool.

Select the right workflow.

And perhaps most importantly, select what not to do.

That last part is easy to overlook.

AI gives us more possibilities.

But having more possibilities does not mean we need to pursue all of them.

A Better Way to Think About AI at Work

Instead of asking how many AI tools your team uses, consider asking a few different questions:

What work is genuinely repetitive?

If a task happens frequently and follows a predictable pattern, AI or automation may be able to remove unnecessary manual effort.

Where are people spending time on low-value decisions?

If employees repeatedly choose between similar options, a clearer system may be more valuable than another AI tool.

Where does human judgment matter most?

These are the areas where AI should usually support people rather than replace the decision itself.

What information is being created but never used?

If AI is generating more reports, notes, summaries, and documents than anyone can realistically consume, the solution may be producing less—not producing faster.

Which tools actually fit the workflow?

A tool that looks impressive in isolation may not be useful if it adds another login, another process, or another place to manage information.

These questions shift the focus from AI adoption to better work design.

AI Should Reduce Friction, Not Increase Activity

There is a temptation to measure AI success by output.

The Next Productivity Advantage Is Knowing What Not to Automate

AI adoption is still often discussed as a race.

Who has the newest model?

Who uses the most powerful agent?

Who has the most automated workflow?

Who has the largest collection of AI tools?

But the long-term advantage may belong to people and teams that can make better decisions about where AI belongs.

The best AI workflow is not necessarily the one with the most AI.

It may be the one that removes the most unnecessary work while keeping the important decisions clear.

That requires something AI cannot completely automate for us:

judgment.

As AI makes execution faster, judgment becomes more valuable.

Knowing what to automate.

Knowing what to review.

Knowing what to ignore.

Knowing which tool is actually worth using.

And knowing when doing less is the better productivity strategy.

AI can make work faster.

But faster work is only useful when we are spending that extra speed on the right things.

AI Research Workflow: How to Build a Smarter Information Pipeline in 2026

lixia — Mon, 24 Aug 2026 07:49:31 +0000

Finding useful information has become harder than ever.

Developers, creators, and teams spend a lot of time researching new technologies, comparing tools, reading documentation, and organizing knowledge.

The challenge is not finding information.

The challenge is turning scattered information into useful insights.

AI workflows can help create a repeatable system for collecting, analyzing, and organizing information more efficiently.

Why Traditional Research Takes Too Much Time

A common research process looks like this:

Search across multiple websites
Open many browser tabs
Collect useful information
Write notes manually
Create summaries

This process works, but it requires a lot of repetitive effort.

A better approach is to build an AI-powered research workflow.

A Simple AI Research Workflow

A practical AI research workflow can be divided into four steps.

1. Discover Information

The first step is collecting relevant information from reliable sources:

Documentation
Technical articles
Product pages
Research papers
Community discussions

The goal is not to collect everything.

The goal is to identify information that helps you make better decisions.

2. Summarize and Extract Key Insights

AI can help process large amounts of information and identify:

Main concepts
Important features
Advantages and limitations
Actionable ideas

Instead of manually reading every source, AI can help highlight the most valuable information.

3. Organize Knowledge

After collecting information, the next step is organizing it into reusable resources:

Notes
Templates
Checklists
Knowledge bases
Internal documentation

A good workflow transforms temporary research into long-term knowledge.

You can explore more AI productivity workflows to build better systems and improve your daily work.

4. Turn Insights Into Actions

Research only creates value when it leads to action.

Examples:

Developers can compare APIs before building new projects
Teams can evaluate tools before adoption
Creators can discover new content opportunities
Businesses can analyze market trends

The purpose of AI is not just generating answers.

The real value is helping people make better decisions faster.

Example: AI Workflow for Developers

Imagine a developer wants to learn a new technology.

A traditional approach:

Search for documentation
Read multiple tutorials
Compare examples
Write notes manually

An AI-powered workflow:

Collect documentation and examples
Ask AI to summarize key concepts
Extract common patterns
Create a reusable learning guide

This reduces repetitive research and helps developers focus on building.

Building Better Systems With AI

AI tools become much more powerful when they are combined with clear workflows.

Instead of asking:

"Which AI tool should I use?"

A better question is:

"How can I design a workflow that saves time?"

This mindset changes AI from a simple assistant into a productivity system.

I’m building WorkspaceBoosters, a collection of AI tools, workflows, and templates designed to help people work smarter with AI.

You can learn more about WorkspaceBoosters and explore practical AI workflows, tools, and resources for modern work.

Final Thoughts

AI productivity is not about replacing human creativity.

It is about reducing repetitive tasks, organizing information better, and creating systems that allow people to focus on higher-value work.

The future of productivity will belong to people who can combine AI tools with effective workflows.