DEV Community: Charles Snow

How to Prepare Images and Videos for Social Media Faster in the Browser

Charles Snow — Tue, 10 Mar 2026 08:12:46 +0000

Why uploads still look wrong

It happens to almost everyone who creates content regularly. You spend an hour editing a perfect photo or a high-quality video, hit the upload button, and then stare at the result in frustration. Your head is cut off in the thumbnail, or worse, your cinematic 16:9 video is forced into a square frame with distracting black bars at the top and bottom. Sometimes, the platform's compression algorithm turns your crisp graphics into a blurry, pixelated mess that looks like it was captured on a flip phone from 2005.

The reason for this isn't just bad luck or a slow internet connection. Social media platforms aren't just giant hard drives for our files; they are highly optimized delivery engines that have very specific requirements for how content should look and behave to provide the best user experience. Every platform, from Instagram and TikTok to LinkedIn and YouTube, has its own set of required dimensions and aspect ratios. If you don't match these exactly, the platform's automated systems will "fix" your file for you. Unfortunately, their automated fix usually involves awkward cropping or stretching that ruins the visual impact of your work and makes your brand look unprofessional.

Understanding the technical requirements of these platforms is the first step toward consistent, professional-looking content. When you take control of the dimensions before you upload, you ensure that your audience sees exactly what you intended them to see, maintaining the integrity of your visual storytelling.

Crop vs Resize vs Aspect Ratio — what's the difference?

Before we dive into the specific numbers for each platform, we need to clarify three technical terms that often get used interchangeably but mean very different things in a production environment: cropping, resizing, and aspect ratio.

Crop

Cropping is the process of cutting out a portion of your image or video. It fundamentally changes the composition of the frame. If you have a wide landscape photo and you want it to fit a vertical phone screen, you have to crop the sides. This means you are literally throwing away pixels. The challenge with cropping is making sure you don't cut out the actual subject of your content. Strategic cropping allows you to refocus the viewer's eye on the most important part of the image, which is essential when moving between different screen formats.

Resize

Resizing changes the actual pixel dimensions of the file without necessarily changing what is visible in the frame. If you take a 4000x3000 pixel image and resize it to 1000x750, you still see the same entire image, just with less total detail. The primary danger here is distortion. If you try to resize a square image into a rectangle without cropping it first, you will end up stretching or squishing the content. This is how you get people looking unusually thin or unnaturally wide in social media posts.

Aspect Ratio

This is the most critical concept for social media success. The aspect ratio is the mathematical relationship between the width and the height of your file. A square is 1:1. A standard high-definition television or YouTube video is 16:9. A vertical phone screen, like the ones used for TikTok or Reels, is 9:16.

For social media, the aspect ratio matters much more than the total pixel count. If you upload a 4K video (3840x2160) to a platform that expects a vertical 9:16 ratio, it won't matter that your video is ultra-high quality. The platform will still put black bars on the sides (letterboxing) or crop the center automatically. Your goal should always be to match the target aspect ratio first, then ensure you have enough pixels to look sharp on high-resolution displays.

Recommended sizes for major platforms

Navigating the various requirements of different social networks can feel like a full-time job. Platforms change their user interfaces frequently, which often changes the ideal dimensions for posts. For instance, what worked on LinkedIn last year might look slightly off today because they updated their mobile app's feed layout. However, there are several "safe" standards that have become the industry norm.

Here is a breakdown of the current recommended dimensions and aspect ratios for the major platforms you likely use every day:

Platform	Format	Dimensions	Aspect Ratio
Instagram	Feed Portrait (recommended)	1080×1350px	4:5
Instagram	Feed Square	1080×1080px	1:1
Instagram	Stories / Reels	1080×1920px	9:16
TikTok	Video	1080×1920px	9:16
YouTube	Thumbnail	1280×720px	16:9
YouTube	Video (HD)	1920×1080px	16:9
YouTube	Shorts	1080×1920px	9:16
X (Twitter)	Post Image	1200×675px	16:9
LinkedIn	Link Post Image	1200×627px	1.91:1
LinkedIn	Video	1920×1080px	16:9
Facebook	Link Image	1200×630px	1.91:1

It's worth noting that while these are the standard "safe" sizes, some platforms allow for a degree of flexibility. For example, Instagram can technically display images wider than 4:5, but they will be cropped in the grid view on your profile. Sticking to these specific numbers ensures your content looks great in both the main feed and when someone is browsing your history. Consistency in these dimensions helps build a more professional-looking brand presence.

For the full up-to-date list across all platforms, the Social Media Size Guide is a good reference.

A practical browser-based workflow

For a long time, the only way to properly prepare images and videos was to use heavy desktop software like Adobe Photoshop or Premiere Pro. While these tools are incredibly powerful, they are often total overkill for simple resizing and cropping tasks. They take time to load, require a monthly subscription, and have a steep learning curve that many creators don't have the time to master.

A modern, faster workflow relies on specialized browser-based tools. Imagine you are a content creator who just finished a 10-minute video. You need to create a YouTube thumbnail, an Instagram Reel to promote the video, and a LinkedIn post to share it with your professional network. Instead of setting up three different projects in a heavy video editor, you can use a streamlined browser workflow:

Start with your high-quality source file. Always keep your original, uncompressed file as your starting point. Working from an already compressed file will lead to "generation loss," where the quality degrades every time you save it.
Check the requirements. Use a reference guide to see the exact pixels needed for each specific platform you are targeting. As we saw in the table above, these vary significantly.
Apply the crop and resize. Adjust your source file to match the target aspect ratio first. If you are turning a horizontal video into a vertical Reel, you'll need to decide which part of the frame to keep. This is where you ensure your subject remains in the center.
Compress and export. Social media platforms have strict file size limits. A 500MB video might be high quality on your hard drive, but it will take forever to upload and might get heavily compressed by the platform anyway, often resulting in lower quality than if you had compressed it yourself. Compressing it before uploading gives you more control over the final visual result.

This workflow is significantly faster because it eliminates the "context switching" of opening and closing different apps. You can process your files one after another in a few clicks right in your browser tab.

When to use ImageVideoFit

When you want to move quickly without sacrificing quality, specialized tools are your best friend. ImageVideoFit is built specifically for this workflow. It handles the tedious parts of resizing so you don't have to memorize pixel counts or do the math for aspect ratios every time you want to post.

The Image Resizer for Social Media handles platform presets automatically. Instead of typing in "1080" and "1350," you just pick "Instagram Portrait" and the tool sets the correct dimensions for you. You can upload your image, select the platform, and the tool will show you a preview of exactly how it will be cropped. You can move the crop area around to make sure the subject of your photo is perfectly framed.

For video content, which is increasingly dominant on social media, the Video Resizer for Social Media supports TikTok, Reels, YouTube Shorts, and many other formats. It allows you to take a horizontal video and quickly convert it to the vertical format required for mobile-first platforms. This is essential for repurposing long-form content into bite-sized clips for different networks.

Key facts and service limits

ImageVideoFit is designed to be accessible and fast for everyone. You don't need to sign up for an account or provide an email address to start using the basic tools, which makes it ideal for quick edits on the fly.

Free tier: You get up to 5 processes per day. This is usually plenty for most individual creators or small business owners. Images can be up to 25MB, and videos can be between 100MB and 200MB depending on the specific tool you are using.
Pro tier: For those managing multiple client accounts or high volumes of daily content, the Pro tier supports files up to 500MB and allows batch processing of up to 50 images at once.
Privacy and processing: All files are processed on secure servers. To keep the service fast and protect your privacy, files are automatically deleted within 1 hour of processing. Your data doesn't sit around on a server indefinitely.
No watermarks: One of the biggest frustrations with free tools is the forced watermark. ImageVideoFit does not add any watermarks to your exports, even on the free tier, ensuring your content looks professional.

When you need more than resizing

Sometimes, your content creation workflow involves more than just fitting an image into a social media box. You might need to perform broader file manipulations before you even get to the resizing stage. For example, you might have a PDF report that you want to turn into a series of images for a LinkedIn carousel, or an audio recording that needs to be converted to a different format for a podcast platform.

For these broader file conversion workflows—covering PDF, audio, video format conversion, and even OCR or AI transcription—I also use FastlyConvert. It covers over 40 different file types and is a great companion tool for when you need to handle deeper file transformations before finalizing your social media assets.

Final checklist

Before you hit that "Post" or "Publish" button, run through this quick checklist to ensure your content is fully optimized for your audience:

[ ] Check the required dimensions. Are you sure you're using the latest specs? A quick double-check can save you from a blurry post.
[ ] Resize to exact pixel dimensions. Don't settle for "close enough." Exact matches prevent the platform's engine from re-scaling your file and introducing artifacts.
[ ] Use the correct aspect ratio. If you're posting a Reel or a TikTok, it must be 9:16. Don't let your content get letterboxed with black bars.
[ ] Compress before uploading. This saves you time on the upload and often results in better final quality than letting the platform's heavy-handed compression take over.
[ ] For video, check resolution and file size. High-resolution video is great, but make sure the file size is within the platform's limits to avoid frustrating upload errors.
[ ] Test on mobile. Most social media is consumed on phones. If you have the chance, check your draft on a mobile device to see how the framing looks in a vertical orientation.

By following these steps and utilizing the right browser-based tools, you can cut your preparation time in half while significantly improving the look and feel of your social media presence. Stay focused on the aspect ratio, match the pixels exactly, and your content will stand out for all the right reasons.

Why I Built a Multilingual SaaS Frontend Without React, Next.js, or a Build Step

Charles Snow — Tue, 10 Mar 2026 04:06:52 +0000

This frontend ships 40+ conversion tools in 7 languages, handles auth, Stripe payments, and trial gating — and the build command is literally echo 'Static site - no build needed'.

For FastlyConvert, I skipped React, Next.js, and every bundler on the market. The entire frontend is plain HTML files, vanilla JS modules, Tailwind via CDN, and Vercel rewrites.

That sounds like a nostalgic choice, but it was mostly a practical one: the product is a tool-heavy, SEO-sensitive SaaS with lots of landing pages, lots of long-tail intent, and a real need for predictable HTML output.

This post is not an anti-framework manifesto. It is a case study in what you gain, what you lose, and where plain HTML plus edge logic still works surprisingly well.

The architecture in one sentence

Each tool page is a standalone HTML document, shared behavior lives in a handful of global JS modules, and Vercel handles clean URLs plus language-aware routing at the edge.

That means pages like pdf-to-word, meeting-transcription, and text-to-speech can be shipped as normal HTML files while still behaving like a modern SaaS frontend with auth, pricing, trial gating, localization, and structured SEO.

Here is the stack at a glance:

Layer	Tool	Role
Pages	Standalone HTML	Each tool owns its markup, schema, and SEO
Styling	Tailwind CDN	No build step, no PostCSS
Logic	Vanilla JS (IIFE modules)	Auth, payments, i18n, trial gating
Routing	`vercel.json` rewrites	Clean URLs mapped to real files
SEO	Edge Middleware	Patches canonical, title, description per language
i18n	Custom `window.i18n`	7 languages, attribute-based DOM hydration

Why I did not use a framework

The main reason was control.

I wanted:

fully rendered HTML for every public page
clean URL routing without a client router
predictable metadata for search engines and social previews
the ability to ship many conversion pages without a JS-heavy runtime
simple deployment on Vercel without a frontend build artifact

This repo leans hard into that tradeoff. package.json has a build script that literally returns Static site - no build needed, while the actual product surface lives in index.html, pages/**/*.html, assets/js/*.js, and vercel.json.

Clean URLs without a frontend router

Instead of a SPA router, the project uses Vercel rewrites and redirects to map public URLs to real HTML files.

This is one of the most important parts of the stack, because it lets the site keep human-friendly paths like /pdf-to-word while the actual file lives under pages/pdf/pdf-to-word.html.

Here is the pattern in vercel.json:

{
  "rewrites": [
    { "source": "/:lang(fr|ja|es|pt|zh-CN|zh-TW)/:path*", "destination": "/:path*?lang=:lang" },
    { "source": "/pdf-to-word", "destination": "/pages/pdf/pdf-to-word.html" },
    { "source": "/meeting-transcription", "destination": "/pages/audio/meeting-transcription.html" }
  ]
}

That one choice removes a lot of application complexity.

There is no client-side route resolution to debug. Crawlers get real HTML per route with no client router, while localized UI text is still applied client-side and the edge layer patches canonical, title, description, and H1 for language-prefixed URLs. Product pages stay individually editable. And if one page needs a weird SEO experiment or custom schema, it can own its own markup.

The downside is obvious too: duplication. When you have a lot of standalone pages, you need discipline around shared scripts, naming, and conventions.

Edge middleware became the SEO control plane

The interesting part is not just the rewrites. It is what happens after the request arrives.

middleware.js does several jobs that a framework would normally hide inside server rendering logic:

protects admin routes
normalizes ?lang=en away from default-language URLs
redirects ?lang=fr to /fr/...
patches canonical, <html lang>, <title>, and meta description for /{lang}/... routes
updates og:url and adds or replaces og:locale without trying to rewrite every Open Graph field

This is the core idea:

const seoData = SEO_META[pathKey]?.[lang];
if (seoData) {
    html = html.replace(/<title>[^<]*<\/title>/i, `<title>${escapeHtml(seoData.title)}</title>`);
    html = html.replace(
        /<meta\s+name="description"\s+content="[^"]*"\s*\/?>/i,
        `<meta name="description" content="${escapeHtml(seoData.desc)}" />`
    );
}

This worked well for a simple reason: I did not want seven physical copies of every page just to localize meta tags.

The public HTML files stay mostly English-first, while the edge layer adjusts canonical URLs, titles, descriptions, og:url, and og:locale for localized traffic. That is a pretty efficient middle ground between a purely static site and a full SSR app.

A custom i18n system was enough

The localization setup is also deliberately simple.

Two shared JS files provide translation dictionaries and expose a global window.i18n. The pages use HTML attributes for text binding, so the markup stays clean while the language layer remains centralized.

In practice it looks like this:

<!-- HTML stays clean and readable -->
<button data-i18n="converter.startConversion">Start Conversion</button>
<p data-i18n-html="converter.filesUpTo">Files up to {size}</p>

// Client-side hydration on page load
window.i18n.t('converter.startConversion')
// → "Start Conversion" (en)
// → "開始轉換" (zh-TW)
// → "Iniciar conversión" (es)

The extended dictionary covers seven languages: en, fr, zh-CN, zh-TW, ja, es, pt.

That setup would feel primitive in a large component-driven app, but in a static multi-page product it is surprisingly effective. The translation source is big, but the behavior is straightforward and easy to inspect.

There is also an SEO upside: localized UI text and localized metadata are handled separately. UI strings come from the client-side i18n layer, while search-facing metadata is patched at the edge. That separation makes the intent of each layer much clearer.

Shared business logic still matters, even on a static site

A static frontend does not mean a dumb frontend.

This repo still has to handle authentication, pricing, trial limits, user state, and conversion-specific UX. The difference is that those concerns are organized as plain browser scripts instead of framework components.

Some examples:

An auth modal module (~1200 lines) handles login/register state, Google OAuth callbacks, JWT token expiry checks, referral code capture, and CSRF-protected requests.
A payment module (~1100 lines) manages Stripe checkout sessions, subscription tiers, file-size limits (100MB free / 500MB pro), and toast notifications.
A conversion helper (~170 lines) detects the current tool type from the URL path and coordinates trial-limit gating with the upgrade modal.

The conversion gating flow is especially pragmatic:

function handleLockedResponse(data) {
    if (!data || !data.locked) {
        return false;
    }

    if (window.TrialUI && window.TrialUI.showUpgradeModal) {
        window.TrialUI.showUpgradeModal(CONVERSION_TYPE);
    }

    return true;
}

That is not glamorous code, but it is honest product code. A user hits a limit, the UI reacts, and the behavior stays shared across many tool pages.

What I like about this approach

After living with this architecture, a few benefits stand out.

1. The HTML is predictable

For pages that need to rank, share well, and render fast, predictability matters. There is very little mystery about what ships to the browser.

2. Page-level ownership is easy

If a page needs a different FAQ schema, hero layout, or copy structure, I can edit that page directly without fighting a component abstraction.

3. Performance stays naturally restrained

When you do not start with a framework runtime, it is much harder to accidentally build an over-engineered page.

4. Deployment is simple

This project fits static hosting well. Vercel handles routing and headers, while the site itself remains mostly plain files.

What I do not like about this approach

This stack absolutely has costs.

1. Duplication is real

With many standalone HTML pages, repeated markup is unavoidable. Shared modules help, but there is still a maintenance tax.

2. Type safety is basically social, not technical

You do not get compiler help for naming drift or loosely coupled globals. You need strong conventions or things will rot.

3. Cross-page refactors are more manual

A framework component tree gives you stronger reuse primitives. Here, the reuse model is mostly conventions, helper scripts, and carefully shared JS modules.

4. You need to think harder about boundaries

Because everything is plain JS, it is easier to let modules grow too large. Some of these modules carry more responsibility than they probably should.

Where I think this model still works in 2026

I would not choose this architecture for every product.

I would use it when most of these are true:

the site is content- or tool-heavy
each page benefits from owning its own HTML and metadata
SEO matters more than frontend novelty
product interactions are meaningful but not app-like enough to justify a large client framework
the team values directness over abstraction

That describes a lot of utility SaaS products, especially conversion tools, calculators, documentation-heavy products, and content-backed acquisition sites.

If you want a concrete example of the result, here is the tools index: https://www.fastlyconvert.com/tools

Final thought

I do not think frameworks are the problem. Defaulting to them without asking what the product actually needs is the problem.

For this codebase, plain HTML plus shared JS plus edge middleware turned out to be a very good fit. It gave me fine-grained SEO control, straightforward deployment, and enough flexibility to support auth, payments, localization, and dozens of conversion pages without shipping a heavyweight frontend runtime.

Sometimes the most maintainable stack is not the most fashionable one. It is the one whose tradeoffs are obvious from the first read of the codebase.

Have you shipped anything production-grade without a framework recently? Or tried edge middleware for SEO? I am curious what tradeoffs you ran into — drop a comment.

Why PDF to Word Conversion Is Harder Than You Think (And How to Do It Right)

Charles Snow — Fri, 27 Feb 2026 03:22:22 +0000

“Just convert the PDF to Word.”

If you’ve ever heard that sentence, you’ve probably felt the same thing I did: surely it’s just changing a file extension, right?

Nope.

PDF is not “a Word document with different packaging.” It’s much closer to a page description language: a way to paint text and shapes at exact coordinates. That’s why copy-paste from PDFs can look like a ransom note, and why “perfect PDF→Word” is still one of the most annoying problems in software.

I built (and tested) multiple PDF conversion workflows. This post explains:

what PDFs actually store under the hood,
why conversion breaks formatting,
how different tools compare,
when OCR is mandatory,
and the three practical tricks that get you the best results.

1) What a PDF really is (and why that matters)

Most people think a PDF is a “document” like Word: paragraphs, headings, tables, lists.

But PDFs typically don’t store those semantic structures.

PDF stores how to draw a page, not how to understand it

If you crack open a PDF's content stream, this is roughly what you see:

BT
  /F1 12 Tf          % select font F1 at 12pt
  124.2 512.7 Td     % move to coordinates (124.2, 512.7)
  (Hello World) Tj   % draw the string
ET

That's a PDF text operator block. BT/ET = begin/end text. Td = set position. Tj = show string. The PDF literally says "paint these glyphs at these coordinates."

That's great for rendering. It's awful for reconstructing structure.

Word wants:

paragraphs
runs
fonts
styles
tables
numbered lists
flowing text

PDF often gives you:

coordinates
glyph shapes
embedded fonts (sometimes subset fonts)
drawing instructions

So conversion becomes: guess the structure from geometry.

Why PDF copy-paste produces weird text

If you’ve ever copied text from a PDF and got:

missing spaces,
wrong order,
random symbols,
broken lines,

that’s usually because:

characters are placed independently,
spacing is inferred,
fonts are subset/encoded,
the logical reading order isn’t stored,
multi-column layout confuses text extraction.

Your eyes understand “this is a paragraph.” The PDF might only know “these glyphs are near each other.”

Scanned PDF vs “native” PDF (the most important distinction)

There are two broad PDF types:

1) Native (text-based) PDF

text is selectable
you can highlight words
conversion can extract actual characters

2) Scanned PDF

the page is an image (or a series of images)
text is NOT really text
conversion without OCR produces junk or blank documents

Before you pick a converter, you should ask one question:
Can I select the text in this PDF?

If not, you’re in OCR territory.

2) Comparing common PDF→Word conversion approaches

I tested a few categories, because they show up in real workflows.

Option A: LibreOffice CLI (free, fast, rough edges)

LibreOffice has a CLI route that can convert documents, and it’s “good enough” for simple PDFs.

Pros

free
automatable
works offline

Cons

formatting often breaks (especially tables, columns, complex layouts)
scanned PDFs still require OCR elsewhere
“good” output is inconsistent across PDFs

If your PDF is a simple single-column report, it can work. If your PDF is a design-heavy layout, it will suffer.

Option B: `pdf2docx` (Python library)

This is a popular approach for developers: script conversion in a pipeline.

from pdf2docx import Converter

cv = Converter("input.pdf")
cv.convert("output.docx")
cv.close()

Pros

developer-friendly
can perform well on simple PDFs
easy to integrate into backend jobs

Cons

complex PDFs break quickly (tables, mixed layouts)
output quality depends heavily on the input structure
still not a magic solution for scanned PDFs

It’s a great tool when you control the PDF source. It’s less great when users upload random PDFs from everywhere.

Option C: Adobe Acrobat (expensive, but often the most reliable)

Acrobat is the “it usually works” option.

Pros

generally strong fidelity
handles complex cases better
mature OCR features

Cons

cost (often around $20/month depending on plan)
not automatable for many teams
not ideal for quick, occasional conversions

It’s the tool you buy when the output must look perfect and you can justify the subscription.

Option D: Online converters (convenient vs privacy risk)

Online tools exist because the need is universal:

no install
works anywhere
quick for non-technical users

But they also raise a real concern:
What happens to your files after upload?

That’s why if you build an online converter, you have to be explicit about:

how files are processed,
retention window,
deletion policy,
and whether files are used for anything else.

What I built (and the engineering tradeoffs)

When I built FastlyConvert's PDF converter, the main engineering decisions were:

Server-side processing over client-side WASM. Client-side sounds appealing (no upload = no privacy concern), but PDF parsing is memory-hungry and slow in the browser — especially for large files or scanned docs that need OCR. Server-side gave predictable performance across all PDF types.

Auto-deletion within 24 hours, not instant. Instant deletion sounds better for privacy, but it breaks the user flow — if the download fails or they close the tab, the file is gone. A 24-hour window balances usability with data minimization.

Separate pipelines for native vs scanned PDFs. Routing scanned PDFs through OCR first (instead of one-size-fits-all) significantly improved output quality for both paths.

Here's the simplified comparison table:

Approach	Cost	Setup	Best for	Formatting fidelity	Scanned PDFs
LibreOffice CLI	Free	Medium	simple PDFs, dev pipelines	Low–Medium	Needs OCR
pdf2docx (Python)	Free	Medium	simple PDFs, controlled sources	Medium	Needs OCR
Adobe Acrobat	$$$	Low	high-stakes formatting	High	Yes (with OCR)
Online converter	$–$$	Lowest	fast, anywhere	Medium–High (varies)	Needs OCR / separate flow

The key: the “best” tool depends on whether your PDF is native or scanned, and how important formatting fidelity is.

3) OCR: the lifesaver for scanned PDFs

If the PDF is a scan, conversion without OCR is like trying to “translate” a photo into a Word doc.

You need OCR: Optical Character Recognition.

Tesseract vs commercial OCR

Tesseract

free
runs locally
decent for clean scans

A minimal example:

# Convert a scanned PDF page to searchable text
tesseract scanned-page.png output -l eng --oem 1 --psm 6
# --oem 1 = LSTM neural net mode
# --psm 6 = assume a single uniform block of text

But it can struggle with:

low-resolution scans,
skewed pages,
noisy backgrounds,
complex layouts,
mixed languages.

Commercial OCR engines

usually handle messy scans better,
have stronger layout detection,
often support more languages and fonts robustly,
but cost money and are typically server-based.

When you need OCR (simple rule)

If you cannot select text in the PDF → you need OCR.
If text is selectable but messy → OCR might still help (sometimes).

How to improve OCR accuracy (3 practical tips)

1) Resolution matters

300 DPI is a common baseline for good OCR. Below that, accuracy drops fast.

2) Contrast matters

Faint gray scans are harder than crisp black text on white background.

3) Language matters

OCR engines do better when you specify the language instead of guessing.

I’m planning a deeper guide on scanned PDF workflows — follow me here to catch it when it’s published.

4) Three tricks to keep formatting sane

“Perfect conversion” is rare. But good workflows exist.

Here are three techniques that save the most time.

Trick #1: Table-heavy PDFs → consider Excel first

If the PDF is basically tables (invoices, financial statements, reports),
PDF→Word often produces:

broken cells,
misaligned columns,
weird spacing.

In those cases, convert to Excel first, clean the table there, then paste into Word if needed. Dedicated PDF→Excel tools handle tabular extraction much better than PDF→Word tools do.

Trick #2: Mixed text + images → choose higher fidelity over smaller output

Layout-heavy PDFs (brochures, designed resumes, marketing pages) are the hardest.

What helps:

choosing a “high fidelity” or “preserve layout” mode (when the tool offers it),
accepting that some edits will be manual,
and avoiding “over-compression” before conversion.

If your PDF is huge, compress it after you confirm conversion quality — not before.

Trick #3: Pure text documents → TXT can be cleaner than DOCX

This sounds weird, but it’s true:

If the PDF is mostly text and your goal is “editable content,”
sometimes extracting plain text gives you a cleaner starting point than a messy DOCX.

DOCX conversion tries to reconstruct layout. TXT skips layout and gives you content.

Then you can restyle it in Word/Docs the way you actually want.

5) Batch processing: where PDF conversion becomes a real workflow problem

Once you move beyond “one PDF,” the pain multiplies.

Scenario 1: HR receives 200 PDF resumes

They need:

names,
job titles,
years of experience,
searchable keywords.

If half of those resumes are scanned PDFs,
they need OCR in the pipeline.

A practical workflow is:
1) Identify scanned vs native (selectable text test)
2) OCR scanned resumes
3) Convert to Word or extract text
4) Index/search the output

Scenario 2: Legal teams archiving contracts

Legal teams care about:

text search,
consistent naming,
archivable formats,
retention policy.

They may not need perfect formatting, but they do need reliability.

That’s why tool “maturity” matters:

stable conversion,
predictable output,
clear file retention policy,
and support for batch/bulk handling.

Privacy note (server processing)

FastlyConvert processes PDFs on the server to generate the converted DOCX/XLSX outputs. Files are uploaded temporarily for processing and automatically deleted within 24 hours. Transfers use HTTPS encryption. Please upload only files you own or have permission to use.

Need to convert PDFs today?

If you’re dealing with PDF headaches, here are the shortcuts:

FastlyConvert supports PDF → Word/Excel/PPT/Image, offers a free trial, and deletes files automatically within 24 hours.

What’s your go-to PDF conversion workflow? Have you tried scripting it with pdf2docx or Tesseract, or do you just throw money at Acrobat? I’m curious what other devs rely on — drop your setup in the comments.

I Replaced My Meeting Notes With AI Transcription — Here’s What Actually Works (and What Didn’t)

Charles Snow — Mon, 23 Feb 2026 15:01:11 +0000

I used to finish a meeting feeling productive… and then lose another 30 minutes rebuilding what just happened.

My notes were always the same story:

half sentences,
missing action owners,
zero context for “we decided X”,
and that one moment I needed to remember—gone.

So I replaced my meeting notes workflow with AI transcription.

Now, most meetings turn into a usable transcript in a few minutes, and I spend about 5 minutes doing a quick human pass (titles, action items, key decisions). It’s not perfect, but it’s consistent—and it saves me 2+ hours per week.

This post is a builder story + a practical guide:

why built-in speech recognition didn’t cut it,
what I tested,
what actually improves accuracy,
and how I shipped a transcription workflow using the Whisper API (without building a giant infra project).

If you want to try the same workflow:

Real-time dictation: /speech-to-text
Upload a recording: /audio-to-text

1) Why built-in speech recognition wasn’t enough

I started with the simplest thing: whatever the browser or OS gives you.

Web Speech API: great demo, fragile workflow

Chrome’s Web Speech API feels magical… until you rely on it for real work.

The problems I hit quickly:

Punctuation is inconsistent (and varies by language).
Long sessions are fragile — tab suspends, mic permission glitches, or network hiccups can wipe progress.
It’s designed for live speech, not for audio files you recorded earlier.
It’s hard to produce something “archive-quality” (clean paragraphs, stable output, repeatable results).

For quick dictation, it’s fine. For “meeting notes you trust,” it was not.

That’s why I split the product into two workflows:

1) Real-time speech-to-text (fast capture) → /speech-to-text

2) File-based transcription (recordings you want to keep) → /audio-to-text

2) The three approaches I tested (and why I chose Whisper API)

When people say “speech-to-text,” they often mean very different things. I tested three routes.

Option A: Google Cloud Speech-to-Text

Pros: strong accuracy, enterprise-grade, lots of language support

Cons: billing adds up, you need backend + auth + quotas, more plumbing

It’s a good choice if you’re building a bigger B2B product with a backend anyway.

But my goal was: make transcription feel like uploading a file and getting a transcript—no setup.

Option B: Self-hosted Whisper

Pros: powerful model, lots of tooling, full control

Cons: you’ll want GPU for speed, deployment complexity, scaling headaches

Whisper is amazing, but self-hosting it becomes its own project:

GPU instances,
queueing,
retries,
storage,
security,
cost surprises.

Option C: Whisper API (what I shipped)

Pros: solid accuracy, fast enough, no GPUs to manage, simple backend

Cons: you must handle file upload, privacy, retention, and cost controls

This was the sweet spot. I could ship quickly and focus on UX instead of infrastructure.

Here’s the simplified comparison I kept in my notes:

Approach	Accuracy	Ongoing cost	Setup complexity	Best for
Google Cloud STT	High	$$–$$$	High	infra-heavy products
Self-host Whisper	High	$ (infra)	Medium–High	tinkerers / full control
Whisper API	High	$–$$	Low–Medium	shipping fast

3) What my accuracy tests taught me (real audio, real pain)

I stopped debating models and started testing with real recordings. Three scenarios:

Test 1: Quiet meeting room (best case)

clear voices,
minimal overlap,
consistent mic distance.

Result: Whisper API performed well and the transcript was readable with light editing.

Test 2: Café / background noise (realistic case)

Noise causes two failure modes:

missed words (especially quiet speakers),
hallucinated filler (noise interpreted as speech).

Result: accuracy dropped—less because of the model, more because of input quality.

Test 3: Accents + fast speech (hard case)

Accent + speed breaks the usual assumptions:

proper nouns get mangled,
sentence boundaries disappear,
speaker turns blend together.

Result: the biggest improvement wasn’t “switch models.” It was: prep audio + choose the correct language.

The 5 things that improved accuracy the most

These made the biggest difference in practice:

1) Set the correct language

Auto-detect is convenient, but it fails silently.

2) Reduce cross-talk

Two people talking at once turns your transcript into “best effort.”

3) Get closer to the mic

Mic distance beats most model changes.

4) Split long recordings

60 minutes → 3 × 20 minutes improves stability and review speed.

5) Remove long silences

Dead air wastes processing and can confuse segmentation.

I wrote the full checklist here:

➡️ /blog/speech-to-text-accuracy-tips

4) Subtitles were cool… until I realized I didn’t need them

A lot of transcription posts jump straight into SRT/VTT exports.

I get it—subtitles are useful.

But here’s what surprised me: I didn’t actually need subtitle formats for my core workflow.

What I needed was:

clean paragraphs,
searchable text,
something I can copy into Notion quickly,
and enough structure to extract decisions/action items.

Export formats (TXT / SRT / VTT)

I started with TXT because it’s the fastest way to copy into Notion/Docs. But subtitles are too useful to ignore, so I added SRT and VTT exports as well.

TXT: best for notes, docs, and editing
SRT/VTT: perfect for video subtitles, timestamped reviews, and searchable archives

If your use-case is “video subtitles,” you can still do this in two steps:

1) Generate a transcript from your audio/video

2) Convert transcript → subtitles with a dedicated subtitle tool/editor

And if you only want text from video, this path still works:
➡️ /video-to-text

5) My weekly meeting workflow (the one that actually sticks)

This is the workflow that finally became habit:

1) Record the meeting (or export audio from Zoom/Meet)
2) Upload it to the file-based tool

➜ /audio-to-text
3) Download as TXT
4) Paste into Notion and do a quick “human edit” pass:

title + participants
decisions
action items (owner + deadline)
open questions

That’s it.

I’m not trying to eliminate human judgment.

I’m trying to eliminate the boring “replay the audio” work.

On average, I’m saving 2+ hours/week—and my notes are more complete because I’m not relying on memory.

6) Under the Hood: How I Built the Upload → Transcribe → Delete Pipeline

I kept the architecture intentionally boring:

The pipeline

Client uploads audio to the server
Server stores it temporarily (unique job ID)
Server calls Whisper API to transcribe
Server returns transcript (TXT)
Cleanup job deletes files on a timer

What mattered most (practical lessons)

Cost controls: enforce file size + duration limits, and rate limit abuse
Retries: uploads fail more than transcription does—make uploads resumable if possible
Queueing: long files should go async (job status + polling)
Deletion you can prove: set a retention window, log deletes, document it

Why retention matters

Because this is server processing, your users need clarity:

what gets uploaded,
how long it stays,
how it’s deleted,
and how to request manual deletion.

(And yes—being honest here helps with user trust and ad approvals.)

Privacy note (server processing)

FastlyConvert processes files on the server to generate transcripts. Files are uploaded temporarily for processing and automatically deleted within 24 hours. Transfers use HTTPS encryption. Please upload only files you own or have permission to use.

Try it (no setup)

If you want to copy this workflow:

1) Real-time dictation: /speech-to-text

2) Upload audio files: /audio-to-text

3) Video transcription: /video-to-text

4) Meeting-specific page: /meeting-transcription

5) Accuracy checklist: /blog/speech-to-text-accuracy-tips

If you’re looking for an AI transcription tool that doesn’t require deploying anything:
FastlyConvert supports 30+ languages, offers a free trial, and deletes files automatically within 24 hours.

https://www.fastlyconvert.com

I Built a File Converter That Never Uploads Your Images — Here's How

Charles Snow — Mon, 09 Feb 2026 03:47:06 +0000

Most file conversion tools upload your files to a remote server, process them, and send them back. That means your data leaves your device — which can be a problem when you're working with sensitive documents.

I wanted to explore a more privacy‑friendly approach: do as much as possible directly in the browser, and only fall back to server processing when the web platform can't do the job well. This "Hybrid (Browser‑First)" model is what I used to build FastlyConvert — a multi‑format conversion & compression suite covering PDF, image, video, and audio.

The Architecture: What Runs Where

Not every conversion can happen client‑side. Browsers are great at image operations, but heavier workloads (e.g., large video transcoding, speech recognition) still require server compute for good UX and reliability.

Here's how I split the work today:

Conversion Type	Where it Runs	Technology
Image format (JPG ↔ PNG ↔ WebP)	100% browser	Canvas API + `toBlob()`
Image resize / compress	100% browser	Canvas API + (OffscreenCanvas where available)
HEIC → JPG/PNG	100% browser	WebAssembly (e.g., `heic2any`)
PDF → Word / Excel / PPT	Server‑side	Custom parser + layout engine
Video compression / conversion	Server‑side	FFmpeg
Audio format conversion	Server‑side	FFmpeg
Audio/Video → Text	Server‑side	Whisper (speech‑to‑text)
Text → Speech	Server‑side	OpenAI TTS (MP3 output)

Rule of thumb: if the Web API can handle it natively, keep it in the browser. Everything else goes to the server with strict privacy controls (e.g., temporary storage + auto‑deletion).

Client‑Side Image Conversion with Canvas API

The simplest conversion — say JPG → PNG — needs surprisingly little code:

async function convertImage(file, targetFormat) {
  const img = new Image();
  const url = URL.createObjectURL(file);

  return new Promise((resolve) => {
    img.onload = () => {
      const canvas = document.createElement("canvas");
      canvas.width = img.naturalWidth;
      canvas.height = img.naturalHeight;

      const ctx = canvas.getContext("2d");
      ctx.drawImage(img, 0, 0);

      canvas.toBlob(
        (blob) => {
          URL.revokeObjectURL(url);
          resolve(blob);
        },
        `image/${targetFormat}`,
        0.92
      );
    };

    img.src = url;
  });
}

Key gotcha: the quality parameter only applies to JPEG and WebP. PNG is always lossless — so "quality" won't change file size much for PNG outputs.

You can see this exact approach in action with FastlyConvert's image converter — JPG, PNG, and WebP conversions all happen client‑side with zero server upload.

Handling HEIC (iPhone Photos) in the Browser

HEIC has been the default photo format on iPhones for years, but most browsers can't decode HEIC natively. For this, a WebAssembly approach works well.

I used heic2any (WASM‑based):
https://github.com/nicolo-ribaudo/heic2any

import heic2any from "heic2any";

async function convertHeic(file) {
  const blob = await heic2any({
    blob: file,
    toType: "image/jpeg",
    quality: 0.92,
  });

  return blob;
}

This runs entirely in the browser — no server upload needed — which is exactly the kind of task where browser‑first shines. This powers FastlyConvert's HEIC to JPG converter — your iPhone photos never leave the browser.

Server‑Side: Video/Audio Processing with FFmpeg

For large video compression and audio transcoding, the browser can do it in theory (WASM FFmpeg exists), but in practice it's often:

too slow on low‑end devices,
too memory‑heavy for big files,
and hard to make reliable across browsers.

So I run FFmpeg server‑side for video/audio tasks, and focus on:

clear presets (e.g., quality vs size),
predictable outputs (e.g., MP4 H.264 as the default),
and privacy policies (auto‑deletion).

Example UX pattern that worked well: give users 3–4 "compression modes" (High Quality / Balanced / Max Compress) instead of asking them to tune bitrate and CRF on day one. FastlyConvert's video compressor uses exactly this approach — users pick a preset, and the server handles FFmpeg processing with 24‑hour auto‑deletion.

Server‑Side: AI Transcription with Whisper (Audio/Video → Text)

For speech recognition, browser‑only options still don't match Whisper's quality and language coverage at scale.

The key architectural decisions I made:

Process only what the user requests (no extra analysis).
Auto‑delete uploaded files after a short retention window.
Keep the API simple, and return clean text + language metadata.

Pseudo‑code sketch:

@app.post("/api/transcribe")
async def transcribe(file: UploadFile):
    temp_path = save_temp(file)

    # schedule deletion
    schedule_delete(temp_path, hours=24)

    result = whisper_model.transcribe(
        temp_path,
        task="transcribe",
        language=detect_language(temp_path)
    )

    return {"text": result["text"], "language": result["language"]}

You can test this with FastlyConvert's speech‑to‑text tool, which supports 90+ languages and exports to TXT, SRT, or VTT.

Server‑Side: PDF Conversion — Why It Can't Be Client‑Side (Yet)

One category I haven't mentioned is PDF conversion — things like PDF to Word, PDF to Excel, or PDF to PowerPoint. This is firmly server‑side, and here's why:

PDF is not a "document" in the way most people think. It's closer to a printed page frozen in digital form — every character is placed at exact X/Y coordinates. There are no "paragraphs" or "tables" in the Word sense, just positioned text fragments and drawn lines.

Converting PDF → Word means reverse‑engineering the visual layout back into a flow‑based document model. That requires:

Table detection (recognizing grid patterns from drawn lines)
Font extraction or substitution
Column boundary detection
OCR for scanned pages

This is compute‑heavy and requires libraries that don't run in browsers well. The tradeoff: server processing with the same auto‑deletion policy.

i18n: Supporting 7 Languages Without a Framework

FastlyConvert is plain HTML + vanilla JS (no React/Next). For i18n, I used a simple attribute‑based approach:

document.querySelectorAll("[data-i18n]").forEach((el) => {
  const key = el.getAttribute("data-i18n");
  el.textContent = translations[currentLang][key] || el.textContent;
});

This supports:

English (en)
French (fr)
Japanese (ja)
Spanish (es)
Portuguese (pt)
Simplified Chinese (zh-CN)
Traditional Chinese (zh-TW)

No build step. No runtime framework. Just static pages + lightweight scripts.

Performance: Why No Build Step

Yes — it's "old school", but it's extremely effective for SEO‑driven tool pages:

Users land directly on specific tools (e.g., /video-compressor, /text-to-speech)
Static files are edge‑cached (Vercel/CDN)
No framework hydration overhead
Fewer moving parts = fewer deploy surprises

The tradeoff is duplication across many HTML pages, but for a conversion suite where speed + SEO matter most, it's a tradeoff I'm happy with.

Key Takeaways

Use Canvas API for common image conversions — no server needed.
WebAssembly (like heic2any) unlocks formats browsers can't decode natively.
For heavy tasks, a hybrid browser‑first approach gives better UX than "all‑server" or "all‑WASM".
Auto‑deletion policies are non‑negotiable for file processing products.
Vanilla HTML/JS can still win on performance for tool‑style websites.
PDF conversion is genuinely hard — the format gap between PDF and Word is much wider than most people realize.

Try It Out

If you want to see these patterns in production:

FastlyConvert — the full converter suite
PDF to Word — server‑side conversion with formatting preservation
Image Converter — 100% browser‑based, zero upload
AI Speech‑to‑Text — Whisper‑powered transcription, 90+ languages
Video Compressor — FFmpeg presets with auto‑deletion

All free to use, no account required.

Have you built anything with client‑side file processing? I'd love to hear what worked (or didn't) for you.

DEV Community: Charles Snow

How to Prepare Images and Videos for Social Media Faster in the Browser

Why uploads still look wrong

Crop vs Resize vs Aspect Ratio — what's the difference?

Crop

Resize

Aspect Ratio

Recommended sizes for major platforms

A practical browser-based workflow

When to use ImageVideoFit

Key facts and service limits

When you need more than resizing

Final checklist

Why I Built a Multilingual SaaS Frontend Without React, Next.js, or a Build Step

The architecture in one sentence

Why I did not use a framework

Clean URLs without a frontend router

Edge middleware became the SEO control plane

A custom i18n system was enough

Shared business logic still matters, even on a static site

What I like about this approach

1. The HTML is predictable

2. Page-level ownership is easy

3. Performance stays naturally restrained

4. Deployment is simple

What I do not like about this approach

1. Duplication is real

2. Type safety is basically social, not technical

3. Cross-page refactors are more manual

4. You need to think harder about boundaries

Where I think this model still works in 2026

Final thought

Why PDF to Word Conversion Is Harder Than You Think (And How to Do It Right)

1) What a PDF really is (and why that matters)

PDF stores how to draw a page, not how to understand it

Why PDF copy-paste produces weird text

Scanned PDF vs “native” PDF (the most important distinction)

2) Comparing common PDF→Word conversion approaches

Option A: LibreOffice CLI (free, fast, rough edges)

Option B: pdf2docx (Python library)

Option C: Adobe Acrobat (expensive, but often the most reliable)

Option D: Online converters (convenient vs privacy risk)

What I built (and the engineering tradeoffs)

3) OCR: the lifesaver for scanned PDFs

Tesseract vs commercial OCR

When you need OCR (simple rule)

How to improve OCR accuracy (3 practical tips)

4) Three tricks to keep formatting sane

Trick #1: Table-heavy PDFs → consider Excel first

Trick #2: Mixed text + images → choose higher fidelity over smaller output

Trick #3: Pure text documents → TXT can be cleaner than DOCX

5) Batch processing: where PDF conversion becomes a real workflow problem

Scenario 1: HR receives 200 PDF resumes

Scenario 2: Legal teams archiving contracts

Privacy note (server processing)

Need to convert PDFs today?

I Replaced My Meeting Notes With AI Transcription — Here’s What Actually Works (and What Didn’t)

1) Why built-in speech recognition wasn’t enough

Web Speech API: great demo, fragile workflow

2) The three approaches I tested (and why I chose Whisper API)

Option A: Google Cloud Speech-to-Text

Option B: Self-hosted Whisper

Option C: Whisper API (what I shipped)

3) What my accuracy tests taught me (real audio, real pain)

Test 1: Quiet meeting room (best case)

Test 2: Café / background noise (realistic case)

Test 3: Accents + fast speech (hard case)

The 5 things that improved accuracy the most

4) Subtitles were cool… until I realized I didn’t need them

Export formats (TXT / SRT / VTT)

5) My weekly meeting workflow (the one that actually sticks)

6) Under the Hood: How I Built the Upload → Transcribe → Delete Pipeline

The pipeline

What mattered most (practical lessons)

Why retention matters

Privacy note (server processing)

Try it (no setup)

I Built a File Converter That Never Uploads Your Images — Here's How

The Architecture: What Runs Where

Client‑Side Image Conversion with Canvas API

Option B: `pdf2docx` (Python library)