DEV Community: David Flores Flores

Best Cursor Alternatives in 2026 by Developer Workflow

David Flores Flores — Mon, 29 Jun 2026 09:07:45 +0000

Most Cursor comparisons get stuck on feature checklists: autocomplete quality, chat UI, model choices, and price. Those matter, but they are not how developers actually choose a tool for a repo.

A more useful question is: which workflow are you replacing?

Below is the way I would shortlist Cursor alternatives in 2026 if the goal is to match the tool to the daily development loop, not just pick a brand name.

Quick workflow map

Workflow	Strong candidates	Why it fits
AI-first editor	Windsurf, Trae	You still want an editor-centered workflow with chat, inline edits, and project context.
Terminal agent	Claude Code, Aider	You want the assistant to inspect the repo, run commands, and report validation steps.
VS Code agent extension	Cline, Continue	You want to stay in VS Code and keep more control over models, approvals, or local setup.
Enterprise IDE coverage	GitHub Copilot	You need broad IDE support, policy controls, and GitHub-native adoption.
Open-source/local-first	Aider, Cline, Continue	You care more about portability, reviewable diffs, and model flexibility than a polished all-in-one editor.

I keep a fuller comparison table here: Cursor alternatives by developer workflow.

1. If you want the closest AI editor alternative: Windsurf

Windsurf is usually the first tool to compare if your team likes Cursor's editor-first habit: open the repo, keep context in the IDE, ask for changes, review the diff, repeat.

The important thing to test is not whether the demo looks similar. Test whether Windsurf preserves your team's normal review loop:

Does it understand the same project boundaries Cursor handled well?
Can developers keep using their familiar keyboard and file navigation habits?
Does it make multi-file changes easy to inspect?
Can you express durable project rules instead of retyping the same instructions?

If the answer is yes, Windsurf is a serious Cursor alternative. If your real pain is terminal automation or repo maintenance, keep reading.

2. If you want a command-running agent: Claude Code

Claude Code is less of a Cursor clone and more of a different operating model. It works best when the task needs shell commands, repo inspection, test output, and step-by-step validation.

That makes it strong for:

dependency cleanup
failing test investigation
migration planning
refactors with validation commands
issue-to-patch work where command output matters

The tradeoff is that the repo needs good instructions. A clear CLAUDE.md, known test commands, and explicit permission boundaries matter more than a flashy UI.

For teams comparing Cursor vs Claude Code, I would not frame this as editor vs editor. Cursor is often better for continuous editing. Claude Code is often better when the work is closer to "take this repo task and drive it to a checked result."

3. If you want VS Code with approvals: Cline

Cline is a good fit when developers want an agentic workflow but still want visible approval checkpoints. It can feel slower than a fully integrated editor flow, but the friction can be useful in codebases where every file operation should be reviewed.

Cline is worth testing if your team wants:

VS Code as the primary surface
explicit approval before tool actions
model/provider flexibility
a workflow that makes diffs and file changes obvious

It is less ideal if you want the smoothest possible inline coding experience. Its value is control.

4. If you need organization-wide adoption: GitHub Copilot

GitHub Copilot is often not the most exciting answer, but it may be the easiest one to roll out across a company. It works across common IDEs, fits GitHub-heavy teams, and gives organizations a familiar procurement and policy surface.

Choose Copilot when the constraint is not "which tool feels most magical for one power user?" but:

how many developers can adopt it quickly?
can it work across multiple IDEs?
can security, legal, and platform teams manage it?
does it fit existing GitHub review workflows?

For solo developers, Cursor or Claude Code may feel sharper. For broad adoption, Copilot is still a very real competitor.

5. If you want open-source or local-first control: Aider, Continue, and Cline

Open-source Cursor alternatives are not always as polished, but they can be easier to reason about. You can keep configuration closer to the repo, choose providers, and preserve a more conventional Git workflow.

Aider is especially useful when you are comfortable in the terminal and want every change to be visible in Git. Continue is appealing when model flexibility and source-controlled assistant configuration matter. Cline sits in the middle: VS Code surface, agent workflow, explicit approvals.

This path is best for developers who care about:

provider flexibility
local review habits
portable configuration
avoiding lock-in to one AI editor
understanding exactly what changed

My practical shortlist

If I had to reduce the decision to one line each:

Choose Windsurf if you want the closest AI-editor alternative to Cursor.
Choose Claude Code if the work depends on terminal commands, tests, and repo-wide investigation.
Choose Cline if you want VS Code plus explicit agent approvals.
Choose GitHub Copilot if team-wide IDE coverage and GitHub integration matter most.
Choose Aider or Continue if you want more open, configurable workflows.

The bigger point: "best Cursor alternative" is not a single answer. Cursor competitors are strong in different parts of the development loop. Start with the workflow you are replacing, then choose the tool.

How to find a YouTube playlist ID and calculate playlist length

David Flores Flores — Mon, 29 Jun 2026 09:05:33 +0000

YouTube playlists are easy to share, but the useful bits of the URL can be surprisingly easy to miss. If you are building a small tool, organizing research, or planning how long a playlist will take to watch, two details matter most: the playlist ID and the total watch time.

This guide walks through both.

1. Find the playlist ID in a YouTube URL

Most playlist URLs include a query parameter named list. The value after list= is the playlist ID.

For example:

https://www.youtube.com/playlist?list=PLxxxxxx123456789

The playlist ID is:

PLxxxxxx123456789

You may also see the same parameter on a video URL:

https://www.youtube.com/watch?v=VIDEO_ID&list=PLxxxxxx123456789&index=4

In that case, ignore v= and index= if your goal is the playlist. The playlist ID is still the value of list.

If you want a quick checker, I made a free tool here: YouTube Playlist ID Finder. Paste a playlist URL, video URL, or short link and it extracts the playlist ID for you.

2. Common URL shapes that contain playlist IDs

These are the common patterns I usually check:

https://www.youtube.com/playlist?list=PLAYLIST_ID
https://www.youtube.com/watch?v=VIDEO_ID&list=PLAYLIST_ID
https://youtu.be/VIDEO_ID?list=PLAYLIST_ID

A few notes:

Playlist IDs often start with PL, but not always.
YouTube Mix and liked-video lists can use different prefixes.
If a URL has no list parameter, it probably points to a single video rather than a playlist.

3. Calculate the total playlist length

Once you have the playlist, the next question is usually: how long will this take?

You can estimate it manually by adding each video's duration, but that gets tedious for long playlists. It is also useful to see adjusted watch time for playback speeds such as 1.25x, 1.5x, or 2x.

For that, I built a free YouTube Playlist Length Calculator. It shows total duration, video count, and faster playback estimates so you can plan a study session, course, podcast queue, or creator research batch.

4. Why this matters

Playlist IDs are useful when you need to:

Save a clean playlist reference
Build internal dashboards or content workflows
Export playlist videos to CSV
Compare course length before starting
Estimate how long a playlist takes at faster playback speeds

The basic rule is simple: look for list=, copy its value, then use the playlist URL or ID to calculate the total duration.

I would love feedback from YouTube creators, students, and developers who work with playlists often. The toolset is free and focused on practical playlist tasks: ID extraction, length calculation, CSV export, and watch-time planning.

How to calculate bonus after tax in 2026

David Flores Flores — Mon, 29 Jun 2026 09:05:04 +0000

A separate bonus check can feel confusing because it is usually withheld differently from regular wages. The check may show a big federal tax line even when your year-end tax rate is lower. That does not always mean the bonus is taxed at a special final rate; it usually means payroll used a supplemental wage withholding method.

This walkthrough shows a practical way to estimate a 2026 bonus after tax before payday.

1. Start with the gross bonus

Use the bonus amount before any deductions. For example, assume a $5,000 bonus paid as a separate check.

2. Estimate federal supplemental withholding

For many separate bonus checks, employers use the flat supplemental wage method. In 2026, the common federal supplemental withholding rate is 22% for supplemental wages up to $1 million.

Example:

$5,000 bonus x 22% = $1,100 federal withholding

If the bonus is paid with regular wages in the same paycheck, payroll may instead use the aggregate method. That method combines regular wages and bonus wages, applies the regular withholding tables, then subtracts the withholding already attributable to regular wages. The result can be higher or lower than the flat method depending on pay frequency, W-4 settings, and current earnings.

3. Add FICA taxes

Bonuses are wages for Social Security and Medicare purposes.

A quick employee-side estimate is:

Social Security: 6.2% up to the annual wage base
Medicare: 1.45%
Total FICA: 7.65% for most bonus checks

On a $5,000 bonus, FICA is usually about:

$5,000 x 7.65% = $382.50

High earners may also run into wage base limits or additional Medicare withholding, so the exact answer depends on year-to-date wages.

4. Include state and local withholding

This is where estimates often drift. Some states use flat bonus withholding rates, some use regular wage tables, and some have local payroll taxes. A bonus in New York City, Ohio, Pennsylvania, or Illinois can produce a different take-home result even when the gross bonus is identical.

A simple worksheet is:

Gross bonus
- federal supplemental withholding
- Social Security
- Medicare
- state withholding
- local withholding
= estimated bonus take-home pay

5. Compare flat vs aggregate method

If payroll uses the flat method, the federal withholding line is easy to approximate. If payroll uses the aggregate method, the answer depends on your regular paycheck, filing status, W-4, pre-tax deductions, and pay schedule.

That is why two employees with the same $5,000 bonus can see different net bonuses.

6. Use a calculator for the final pass

For a faster estimate, I built a free 2026 bonus tax calculator that lets you model a separate bonus check with federal supplemental withholding, FICA, state pages, and methodology notes.

It is still only an estimate. Your final tax for the year is reconciled when you file, and payroll systems can vary by employer setup. But it is a useful planning number before you decide how much of a bonus to save, invest, or use for expenses.

How I Built a Free ACS Citation Generator for Chemistry Papers

David Flores Flores — Wed, 24 Jun 2026 09:30:07 +0000

If you have ever cleaned up references for a chemistry paper, you know the tiny formatting problems add up fast.

ACS references look simple until you have to fix 40 of them at once: author initials, journal abbreviations, year/volume/pages order, DOI links, patent numbers, website access dates, and in-text citation style. I built a small free tool to make that cleanup workflow less painful:

Free ACS Citation Generator for Chemistry Papers

This post walks through the workflow I designed around it and the implementation decisions behind the tool.

The problem I wanted to solve

Most citation tools are broad. They support APA, MLA, Chicago, IEEE, and a lot of general academic formats. That is useful, but chemistry writers often need a more specific workflow.

For ACS-style writing, the rough shape of a journal article reference is:

Author 1; Author 2. Article Title. Journal Name Year, Volume, Page-Page. https://doi.org/...
~~~

That leaves a lot of room for small errors:

- Authors pasted as full names instead of surname plus initials
- Journal titles left as full names when an abbreviation is expected
- DOI values pasted as raw strings instead of links
- Issue numbers included or omitted inconsistently
- Website references missing an access date
- Patents missing the country, number, or publication date
- Reference lists copied from multiple tools with mixed punctuation

I did not want to build another giant reference manager. I wanted a focused cleanup tool that helps with the common ACS reference formatting tasks students and researchers repeat every week.

## The cleanup workflow

The workflow I use is intentionally simple.

## 1. Start with a DOI when possible

For journal articles, DOI lookup is the fastest starting point. A DOI usually gives you the title, authors, journal, year, volume, issue, pages, and DOI URL.

In the tool, the DOI workflow tries public metadata first. If metadata is available, the fields are filled automatically. If lookup fails, the manual fields still work.

This is important because citation tools should fail gracefully. A missing DOI record should not block the user from building the reference.

## 2. Normalize the source type

ACS references are not all journal articles. The tool supports several chemistry writing cases:

- Journal articles
- DOI-based sources
- Patents
- Websites
- Books
- Conference papers
- Theses
- Preprints
- Datasets

The reason I split these into source types is that each one has different required fields. A patent needs inventors, patent number, country or patent office, and date. A website needs a URL and often an access date. A book needs publisher and place.

A single giant form would be messy, so the UI changes fields based on the selected source type.

## 3. Generate the reference, then run checks

After formatting the reference, the tool shows a small checklist. It catches common omissions such as:

- Missing authors or organization name
- Missing source title
- Missing publication year or date
- Missing journal name
- Missing locator information such as volume, pages, or DOI
- Missing URL or access date for web sources

This is not meant to replace a final human review. It is meant to stop the obvious mistakes before they get copied into a manuscript.

## 4. Build the reference list locally

A lot of citation cleanup happens in batches. You fix one reference, copy it, fix another, reorder the list, and then export.

The tool keeps a local reference list in the browser. No account is required, and the saved list stays local to the user's browser.

That design keeps the product lightweight. The core loop is:

1. Enter or import source metadata
2. Generate the ACS reference
3. Add it to the local list
4. Edit, copy, reorder, or delete items
5. Export when ready

## 5. Export in useful formats

Plain text is enough for many lab reports, but not for every workflow. So I added exports for:

- TXT for plain reference lists
- RTF for word processors
- BibTeX for LaTeX workflows
- RIS for reference managers like Zotero

The export step matters because the output should move easily into the place where the paper is actually being written.

## Implementation notes

The project is a static browser-based tool. The main behavior lives in JavaScript, with a source-type configuration that defines labels, routes, and fields.

A simplified version of the idea looks like this:

~~~js
const sourceTypes = {
  journal: {
    label: "Journal Article",
    fields: ["authors", "title", "journal", "year", "volume", "issue", "pages", "doi"]
  },
  patent: {
    label: "Patent",
    fields: ["inventors", "title", "patentNumber", "country", "publicationDate"]
  },
  website: {
    label: "Website",
    fields: ["authors", "title", "site", "year", "url", "accessDate"]
  }
};
~~~

Each formatter can focus on one source type instead of trying to make one universal citation function handle everything.

A simplified journal formatter might look like this:

~~~js
function formatJournalArticle(ref) {
  return [
    ref.authors + ".",
    ref.title + ".",
    ref.journal + " " + ref.year + ",",
    ref.volume,
    ref.pages ? ref.pages + "." : "",
    ref.doi ? "https://doi.org/" + cleanDoi(ref.doi) : ""
  ]
    .filter(Boolean)
    .join(" ");
}
~~~

The real work is less about one clever function and more about lots of small formatting decisions:

- How to avoid double punctuation
- How to handle optional fields
- How to clean DOI input
- How to preserve author separators
- How to make missing fields visible without blocking manual entry
- How to export the same reference list into multiple formats

## Why I kept it free and browser-based

For this kind of utility, speed matters more than accounts. A chemistry student writing a lab report should not need to sign up just to clean a DOI reference.

So the tool is free to use, runs in the browser, and keeps reference-list data locally. That also makes it easier to maintain as a small independent project.

## A practical ACS cleanup checklist

When I clean a reference list now, I use this checklist:

1. Prefer DOI import for journal articles.
2. Confirm author names and initials.
3. Check whether the journal title should be abbreviated.
4. Confirm year, volume, issue, and page range.
5. Use DOI links consistently.
6. Add access dates for web pages that can change.
7. Keep patents, datasets, and preprints in their own source-type formats.
8. Export the final list and do one manual pass against the target journal or instructor requirements.

The last step is important. ACS-style examples are useful, but journal instructions and course requirements can still differ.

## What I would improve next

The next improvements I am thinking about are:

- Better journal abbreviation suggestions
- More batch DOI cleanup features
- Stronger examples for ACS in-text citations
- More edge-case tests for patents, datasets, and preprints
- A smoother bridge to Zotero and other reference managers

If you write chemistry papers, lab reports, or technical documents, I hope this saves a few minutes of reference cleanup.

Tool link again: [Chem Citation Tools - ACS Citation Generator](https://chemcitationtools.com/acs-citation-generator/)

Note: this is an independent educational tool and is not affiliated with the American Chemical Society. Always check the final requirements from your instructor, department, publisher, or target journal.

I Built a Small GPT Image 2 Tracker Because the Rollout Details Were Too Scattered

David Flores Flores — Mon, 04 May 2026 08:19:23 +0000

I kept seeing the same GPT Image 2 questions in different places:

Is it actually available yet?
Is API access live?
Does ChatGPT access mean API access too?
What will it cost?
Can I test image-to-image workflows somewhere?

Annoying, but fair questions.

Model rollouts are messy. One page says a feature is announced. Another post says it is rolling out. Someone on Reddit says they can see it. Someone else with the same plan cannot. For a developer or small product team, that difference matters.

So I built a small tracker for it.

The site is here: GPT Image 2 Status

It tracks GPT Image 2 access, pricing, API availability, ChatGPT rollout, usage limits, and model comparisons. I also added a free playground for text-to-image and image-to-image testing, because reading about an image model is not the same as trying prompts against a real workflow.

Why not just follow official announcements?

Official docs are the source of truth. I still check them.

But they are not always the easiest place to answer the messy product questions people actually ask. A docs page may tell you what exists. It may not tell you whether a typical ChatGPT user can access it today, whether API access is separate, or how it compares with other image models for a specific use case.

That gap is where this tracker fits.

Not a news site. Not another “best AI tools” list.

Just a place to check what is live, what is unclear, and what is worth testing.

What I wanted the page to answer fast

I tried to keep the page focused on practical checks:

current access status
pricing notes
API availability
ChatGPT rollout notes
usage limits
model comparisons
prompt testing
image-to-image testing

The playground part matters more than I expected. A model can look great in examples and still be awkward for the workflow you care about.

A product mockup prompt is different from a meme edit. A clean UI screenshot edit is different from a stylized illustration. You only notice that once you test.

The small lesson

I do not think every AI model needs a tracker.

But when a model has mixed rollout signals, API questions, pricing questions, and workflow questions, a simple status page can save people time.

The hard part is not building the page. The hard part is keeping it useful instead of turning it into another vague AI landing page.

That is what I am trying to avoid.