Mert

Comparing Traffic Data APIs: Road511 vs HERE vs TomTom vs INRIX

yazar

If you’re building an app that needs traffic data, you have a few options. The big names — HERE, TomTom, INRIX — dominate the market. But they all sell the same thing: traffic flow from probe data (GPS traces from phones and connected vehicles).

Road511 is different. It aggregates infrastructure data from government 511 systems that the big providers don’t touch.

What Each Provider Actually Offers

Data Type Road511 HERE TomTom INRIX
Traffic flow (speed/congestion) Limited (select states) Yes (core product) Yes (core product) Yes (core product)
Traffic events/incidents 57 jurisdictions Yes (own sources) Yes (own sources) Yes
Live cameras (10K+) Yes No No No
DMS sign messages Yes (30+ states) No No No
Bridge clearances (621K) Yes No No No
Truck routes (479K STAA segments) Yes No No No
Weight restrictions Yes (multi-state) No No No
Spring load limits Yes No No No
Road conditions (surface state) Yes (15+ states) No No Limited
RWIS weather stations Yes (20+ states) No No No
Rest areas & truck parking Yes (30+ states) No No No
EV charging (100K+ stations) Yes No No No
Fuel prices (state-level) Yes (EIA + StatCan) No No No
Event lifecycle history Yes No No No
GeoJSON native Yes No No No
Webhook notifications Yes Via HERE Platform No No

Pricing

Provider Entry Price What You Get
Road511 $0 (14-day trial) / $29/mo Full API access, 50K req/day
HERE ~$200/mo Traffic flow + incidents, limited calls
TomTom ~$150/mo Traffic flow + incidents
INRIX ~$50K/year Enterprise only, negotiated

Road511 at $29-99/mo is 10-100x cheaper than HERE/TomTom for similar request volumes, and has data categories they simply don’t offer.

When to Use Which

Use HERE/TomTom if you need:

  • Real-time traffic flow (speed, congestion, travel times)
  • Turn-by-turn routing with live traffic
  • Global coverage (Europe, Asia, etc.)

Use Road511 if you need:

  • Camera feeds, DMS messages, road conditions
  • Bridge clearances, truck routes, weight restrictions
  • Cross-state normalized 511 data
  • Event lifecycle analytics (clearance times, trends)
  • EV charging station data
  • Fuel prices by state

Use both if:

  • You’re building a fleet management platform — HERE/TomTom for routing + Road511 for truck restrictions, bridge clearances, fuel prices, and camera verification
  • You’re building a comprehensive navigation app — traffic flow from one, infrastructure data from the other

The Real Differentiator

HERE, TomTom, and INRIX compete with each other on the same data: traffic flow. They spend hundreds of millions on probe data collection.

Road511 provides the infrastructure data that nobody else aggregates. 10,000 cameras, 621,000 bridges, 479,000 truck route segments, real-time sign messages, road conditions — all normalized from 57 government sources into one API.

The data is complementary, not competitive.

Try It

What I Learned From Reading 50 Data Pipeline Postmortems

yazar

After analyzing 50 public postmortems from Uber, Netflix, Stripe, and others, four failure patterns emerge again and again. Most of them are preventable at the design stage.

The postmortem paradox

Every major tech company publishes them now. Stripe has a status page full of them. Netflix writes detailed engineering analyses. Uber, LinkedIn, GitHub, Cloudflare — they’ve all opened the curtain on what went wrong and why.

Here’s the paradox: the same failures keep happening. Not the same companies, not the same systems, but the same patterns. A team at DoorDash loses payment data the same way a team at Netflix lost viewing metrics three years earlier. An Uber pipeline breaks from schema drift in 2024 the same way a LinkedIn pipeline broke in 2021.

I spent the last few weeks reading through 50 public postmortems and incident reports from companies that have collectively processed trillions of events. The goal wasn’t to catalog every possible failure mode. It was to find the clusters — the root causes that show up often enough that they can’t be dismissed as one-off bad luck.

Four patterns dominate. And here’s what surprised me: most of them are preventable at the design stage, not the operations stage.

How the 50 were selected

Before diving into the patterns, a quick note on methodology. I focused on public postmortems from companies running large-scale data infrastructure: Uber, Netflix, Stripe, LinkedIn, GitHub, Cloudflare, DoorDash, Airbnb, Spotify, and AWS. I skipped security breaches and pure infrastructure outages (like DNS failures) unless they directly affected data pipelines.

The selection wasn’t random. I prioritized postmortems that included:

  • Root cause analysis with technical depth
  • Timeline of failure and recovery
  • Explicit mention of data quality or pipeline impact
  • Lessons learned or process changes

Some companies publish frequently (Cloudflare, GitHub). Others rarely (Netflix). The 50 represent a cross-section of batch ETL, streaming, and hybrid architectures.

Pattern 1: Schema drift (38% of incidents)

The most common root cause was deceptively simple: the upstream system changed its data format, and the pipeline didn’t know.

In one well-documented incident, a data team discovered that a downstream warehouse had been loading corrupted records for eleven days. The source API had added a new field. The pipeline’s JSON parser treated it as an unexpected key and silently dropped the entire record batch. No alerts fired because the pipeline didn’t crash — it just produced fewer rows than expected, and the difference was within normal variance until it wasn’t.

This isn’t an edge case. It’s the default behavior of many data integration tools.

The postmortems reveal three variants of this pattern:

Additive drift

A new field, column, or event type appears. The pipeline ignores it or fails depending on how strict its schema validation is. Most postmortems noted that their pipelines were configured to be “permissive” because strict validation had caused false alarms in the past.

Type drift

An existing field changes its type. A string becomes a number. A timestamp loses its timezone. These are the hardest to catch because the data still looks valid. One postmortem described a revenue metric that silently doubled because a currency code field changed from ISO format to a numeric enum, and the pipeline interpreted the enum value as a multiplier.

Semantic drift

The format stays the same, but the meaning changes. A “user_id” field starts containing device IDs instead of account IDs. A “status” field gains a new state that the downstream logic treats as an error. The data passes all validation checks and is still wrong.

What’s striking is how rarely these incidents were caught by schema registries or data contracts. In most cases, the teams had a registry. It just wasn’t enforced at the pipeline boundary. The schema was documented somewhere, but the pipeline wasn’t required to validate against it.

Pattern 2: Backpressure and load spikes (24% of incidents)

The second cluster involves pipelines that work perfectly at normal load and collapse under unexpected volume. The trigger varies — a marketing campaign, a viral event, a quarterly reporting cycle, a misconfigured upstream job that suddenly emits 10x its usual rate.

The failure mode is almost always the same: the pipeline can’t shed load, so it drops it.

One postmortem from a streaming platform described a Kafka consumer that fell behind by six hours during a product launch. The consumer group auto-scaled, but the new instances hit a database connection pool limit that had never been tested at that scale. The pipeline didn’t crash. It just stopped processing new events while old ones aged out of retention. By the time the team noticed, the data was gone.

Another described a batch ETL job that ran fine for two years until Black Friday, when the source system emitted files 40x larger than usual. The job ran for 18 hours, exhausted temporary storage, and failed without cleaning up its partial outputs. The next scheduled run started on top of the corrupted data.

The common thread: these pipelines were designed for steady-state operation, not for boundary conditions. They had monitoring for whether they were running, but not for how close to their limits they were operating.

Several postmortems noted that load testing had been deprioritized because “we’ll just auto-scale.” Auto-scaling works for compute. It doesn’t work for connection pools, memory limits, disk I/O, or downstream API rate limits — the bottlenecks that actually break pipelines.

Pattern 3: Silent data loss (19% of incidents)

This is the pattern that keeps engineers up at night. The pipeline reports success. The dashboards show green. The SLA is met. But the data is incomplete, duplicated, or corrupted — and nobody knows until a business user asks why the numbers look wrong.

Silent loss shows up in several forms across the postmortems:

The filter that was too aggressive

A data quality rule dropped records that matched a malformed pattern. The rule was intended to catch corrupted upstream data, but it also caught legitimate records with unusual but valid values. Over three weeks, 12% of legitimate transactions were filtered out.

The exactly-once that wasn’t

A pipeline claimed exactly-once semantics but used a non-idempotent sink. When a transient network error triggered a retry, some records were written twice. The deduplication logic existed in theory but not in the actual code path.

The retention gap

A streaming pipeline wrote to a message queue with a 24-hour retention window. When downstream processing fell behind due to a separate incident, the unprocessed data expired before recovery. The pipeline logs showed successful writes. The data just wasn’t there when someone tried to read it.

What makes silent loss so dangerous is that it’s invisible to traditional monitoring. Pipeline health metrics — runtime, throughput, error rate — don’t catch it. You need data quality metrics: row counts, cardinality checks, referential integrity, distribution tests. Most of the postmortems admitted these checks were added after the incident, not before.

Pattern 4: Cascade failures from shared state (14% of incidents)

The smallest cluster but often the most catastrophic. These are incidents where a failure in one pipeline corrupts or disables others through shared infrastructure.

One memorable postmortem described a “poison pill” event — a single malformed record that caused a parser to enter an infinite loop. The consumer thread hung, the partition rebalanced, and the new consumer thread also hung. Within minutes, an entire consumer group was offline. Because the pipeline shared a Kafka cluster with other services, the broker’s log compaction was affected, and unrelated pipelines began seeing increased latency.

Another described a metadata store used by multiple batch jobs. A schema migration for one job locked the metadata table for 90 seconds. Every other job that touched the same table failed or timed out. What should have been a single-team issue became a company-wide incident.

The lesson from these postmortems isn’t just “isolate your failures.” It’s that shared state is often invisible. Teams don’t realize they’re sharing infrastructure until it fails. The Kafka cluster, the metadata table, the shared NFS mount — these aren’t considered part of the pipeline’s design, but they are part of its failure domain.

What the remaining 5% looked like

The rest of the postmortems were genuinely one-off: a cosmic ray flipping a bit, a vendor API changing behavior without notice, a certificate expiring on a holiday weekend. These are the failures you can’t design away. The 95% above, you can.

The design checklist

After reading these 50 postmortems, I kept seeing the same gap. The failures didn’t happen because teams lacked talent, tooling, or awareness. They happened because specific design questions weren’t asked early enough.

Here are six questions that, if answered honestly during design review, would have prevented the majority of incidents I analyzed:

1. What happens when the schema changes without warning?

Not “do we have a schema registry?” — that’s a tooling question. The design question is: does the pipeline fail when the schema deviates from expectations, or does it silently adapt? Adaptive behavior feels safer until it produces wrong data. Default to failure. Make schema mismatches loud.

2. What’s the maximum load this pipeline has been tested at, and what breaks first when we exceed it?

Most teams test for correctness. Far fewer test for limits. Know your first bottleneck — memory, connections, disk, downstream rate limits — and have a graceful degradation plan for when you hit it.

3. How would we know if we were silently losing 10% of our data?

This is the most important question. If your only validation is “the job finished,” you’re flying blind. You need independent data quality checks that compare output volume, distribution, and key metrics against historical baselines.

4. Are our retries safe?

Any retry logic is a potential duplication mechanism unless the sink is strictly idempotent. Review every API call, every database write, every file append. If you can’t guarantee idempotency, guarantee at-most-once and accept the occasional loss over the guaranteed duplication.

5. What other systems fail if this one does?

Map your failure domain. If your pipeline hangs, does it block a shared queue? Does it exhaust a connection pool? Does it fill a disk that other jobs need? Design for blast radius containment, not just recovery.

6. Can someone who’s never seen this pipeline debug it at 3 AM?

The postmortems with the fastest recovery times all had one thing in common: observability that didn’t require institutional knowledge. Logs that explain decisions, not just state changes. Metrics that show data health, not just system health. Alerts that point to root cause, not just symptoms.

The uncomfortable truth

Reading 50 postmortems doesn’t make you immune to failure. But it does make the patterns obvious. And the patterns are, for the most part, boring. Schema drift. Load limits. Missing validation. Shared state. These aren’t exotic distributed systems problems. They’re design hygiene.

The teams that published these postmortems are among the best in the world at building data infrastructure. If they’re still hitting these patterns, everyone else is too. The difference is whether you catch them in design review or at 3 AM.

SEO Mistakes That Are Silently Killing Your Dev Projects

yazar

I spent 3 hours debugging why Google couldn’t crawl my React app. Lighthouse scores were green. The app loaded fine in the browser. But Googlebot was seeing a blank <div id="root"></div> and walking away confused. The fix was 4 lines of meta tags and a prerender config I’d been skipping for months.

If you’ve ever launched a side project and wondered why it sits at position 94 forever this one’s for you. These are the SEO mistakes I see constantly in developer-built projects, with the actual code to fix each one.

Mistake #1: Your <head> Is Almost Empty

This is the most common one. You scaffold a Vite or CRA project, get lost in the fun part (building features), and ship with a <title> tag that just says “React App.”

Google uses your <head> to understand what your page is about. If it’s empty or generic, you’re asking the algorithm to guess and it usually guesses wrong.

Here’s what a proper meta setup looks like: 

<!-- index.html or your _document.tsx in Next.js -->
<head>
  <meta charset="UTF-8" />
  <meta name="viewport" content="width=device-width, initial-scale=1.0" />

  <!-- The basics Google actually reads -->
  <title>SyncBoard – Real-Time Collaborative Whiteboards for Remote Teams</title>
  <meta name="description" content="Create, share, and collaborate on visual boards in real time. No signup needed. Works in any browser." />

  <!-- Open Graph for social sharing (also used by some search engines) -->
  <meta property="og:title" content="SyncBoard – Real-Time Collaborative Whiteboards" />
  <meta property="og:description" content="Visual collaboration for remote teams. No signup needed." />
  <meta property="og:image" content="https://syncboard.app/og-image.png" />
  <meta property="og:url" content="https://syncboard.app" />
  <meta property="og:type" content="website" />

  <!-- Twitter Card -->
  <meta name="twitter:card" content="summary_large_image" />
  <meta name="twitter:title" content="SyncBoard – Real-Time Collaborative Whiteboards" />
  <meta name="twitter:description" content="Visual collaboration for remote teams." />
  <meta name="twitter:image" content="https://syncboard.app/og-image.png" />
</head>

Result: Google now has a clear signal for what to rank your page for. Your Twitter/LinkedIn shares show a proper preview card instead of a broken link. That alone increases click-through rates.

For multi-page apps, each route needs its own unique title and description. In React, use react-helmet-async. In Next.js, use the built-in <Head> component or the newer metadata export.

Mistake #2: Client-Side Rendering Is Invisible to Crawlers (Sometimes)

Googlebot can execute JavaScript but it does so in a low-priority queue that can take days or weeks. For a brand-new site, that means your content might not get indexed for a long time, if ever.

The safest fix: generate your HTML on the server, or at build time. 

// Next.js generateStaticParams for static generation
// This creates actual HTML files at build time. Zero JS required for indexing.

export async function generateStaticParams() {
  const posts = await fetch('https://your-api.com/posts').then(r => r.json());
  return posts.map(post => ({ slug: post.slug }));
}

export default async function BlogPost({ params }) {
  const post = await fetch(`https://your-api.com/posts/${params.slug}`)
    .then(r => r.json());

  return (
    <article>
      <h1>{post.title}</h1>
      <p>{post.excerpt}</p>
      <div dangerouslySetInnerHTML={{ __html: post.content }} />
    </article>
  );
}

If you’re not on Next.js and can’t do SSR/SSG, consider prerendering. Tools like prerender.io or vite-plugin-ssr can serve static HTML snapshots to crawlers while still delivering the full SPA experience to real users.

The key diagnostic: open your browser’s DevTools, disable JavaScript, and reload your page. If it’s blank so is Googlebot’s view of your site.

Mistake #3: You Have No Structured Data

Structured data (JSON-LD) is how you communicate with search engines in their native language. It’s what powers rich results star ratings in search, FAQ dropdowns, article publishing dates, product prices. You’re leaving real estate on the results page unclaimed.

This one is frustratingly easy to add and almost nobody does it: 

<!-- Add this to your <head> or just before </body> -->
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "SEO Mistakes That Are Silently Killing Your Dev Projects",
  "description": "The most common SEO errors in developer-built projects, with working code fixes.",
  "datePublished": "2026-05-19",
  "dateModified": "2026-05-19",
  "author": {
    "@type": "Person",
    "name": "Your Name",
    "url": "https://yoursite.dev"
  },
  "publisher": {
    "@type": "Organization",
    "name": "YourBlog",
    "logo": {
      "@type": "ImageObject",
      "url": "https://yoursite.dev/logo.png"
    }
  },
  "image": "https://yoursite.dev/article-image.png",
  "url": "https://yoursite.dev/articles/seo-mistakes"
}
</script>

For e-commerce, add Product schema. For tools and SaaS, SoftwareApplication schema. For FAQ pages, FAQPage schema. The Schema.org validator will tell you immediately if you’ve got it right.

Mistake #4: Your sitemap.xml Is Missing or Stale

Google finds new pages by crawling links but a sitemap is the express lane. Without one, internal pages (especially anything not linked from your homepage) might never get discovered.

Here’s a minimal sitemap generator you can run at build time: 

// scripts/generate-sitemap.js
import fs from 'fs';

const BASE_URL = 'https://yoursite.dev';

// Pull your routes from wherever they live a CMS, a file system, a DB
const routes = [
  { path: '/', changefreq: 'weekly', priority: 1.0 },
  { path: '/blog', changefreq: 'daily', priority: 0.9 },
  { path: '/projects', changefreq: 'monthly', priority: 0.7 },
  { path: '/about', changefreq: 'yearly', priority: 0.5 },
];

const sitemap = `<?xml version="1.0" encoding="UTF-8"?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
${routes.map(route => `
  <url>
    <loc>${BASE_URL}${route.path}</loc>
    <changefreq>${route.changefreq}</changefreq>
    <priority>${route.priority}</priority>
    <lastmod>${new Date().toISOString().split('T')[0]}</lastmod>
  </url>`).join('')}
</urlset>`;

fs.writeFileSync('./public/sitemap.xml', sitemap.trim());
console.log(`✓ Sitemap generated with ${routes.length} URLs`);

Add node scripts/generate-sitemap.js to your build script, then submit https://yoursite.dev/sitemap.xml in Google Search Console. You’ll start seeing crawl coverage improve within a few days.

For projects where you want all of this meta tags, OG images, JSON-LD, sitemaps managed in one place, I recently came across @power-seo, an npm package that handles the boilerplate of setting these up consistently across a site. It’s worth a look if you’re tired of copy-pasting meta tag templates between projects.

What I Actually Learned

  • The blank <head> problem is epidemic in dev projects. It takes 20 minutes to fix and has an outsized impact on whether anyone discovers your work.
  • Test with JS disabled. It’s the fastest way to see exactly what Googlebot sees. If your content disappears, you need SSR or prerendering.
  • JSON-LD is low-effort, high-reward. One <script> tag can unlock rich results. Most devs skip it; that’s your competitive edge.
  • Sitemaps aren’t just for enterprise sites. Even a 5-page portfolio benefits from one. Generate it at build time and forget about it.

If you want to see how I pulled all of this together on a recent project, here’s the writeup: https://ccbd.dev/blog/seo-mistakes-fixed-using-the-power-seo-toolkit

What’s Your Biggest SEO Headache?

I’m curious which of these bit you hardest? Did you ship a React app that sat invisible in search for months? Are you on a team where “we’ll add SEO later” is a running joke? Drop it in the comments. I read every one, and honestly the war stories are the best part of this community.

I Needed a Faster Way to Make VTT Tokens for DnD, So I Built a Local-First Token Maker

yazar

I built a simple token maker for my DnD and VTT workflow
Tags: dnd, webdev, gamedev, tooling
Making tokens for a tabletop campaign should not take that long.
But if you run DnD games, you probably know the small routine:
• find character art
• open an image editor
• crop the face or body
• add a border
• resize it
• export a PNG
• repeat it for every NPC, monster, boss, and player character
Doing this once is fine. Doing it for a full encounter is annoying.
That is why I built a small browser tool for this workflow:
Token Maker: https://www.tokenmaker.one/
It is a free VTT token maker for DnD, Roll20, Foundry VTT, and Owlbear.

The problem I wanted to solve

Most image tools are too broad for this job.
They can make tokens, of course, but they are not built around the actual tabletop workflow.
When I am preparing a session, I usually do not want a full design tool. I just want to:

  1. upload character art
  2. place it inside a frame
  3. adjust the crop
  4. export a transparent PNG
  5. move on to the next token
    That repeated flow is the real problem.
    The tool does not need to be complicated. It just needs to remove the boring steps.
    ## What the tool does
    Token Maker is focused on VTT token creation.
    The current workflow is simple:
  6. Drop an image into the editor
  7. Choose a border or style
  8. Adjust the image scale and position
  9. Pick an export size
  10. Download the PNG token
    It supports different token shapes and borders, including circular, square, and polygon-style tokens.
    The export is a transparent PNG, which works well for virtual tabletop platforms.
    ## Why I made it local-first
    For this kind of tool, I wanted the workflow to feel quick.
    You should be able to open the page, drop in an image, make the token, and export it.
    • No heavy setup.
    • No long design process.
    • No need to turn a small token task into a full graphics project.
    That is also why the editor is built around one main workspace instead of spreading the workflow across several steps.
    ## Where this helps
    I think this is most useful for:
    • DnD character tokens
    • NPC tokens
    • monster tokens
    • boss encounter tokens
    • quick campaign prep
    • Roll20 and Foundry VTT games
    • token pack preparation
    It is especially helpful when you need several tokens at once.
    One token is not a big deal. Twenty tokens is where the small time savings start to matter.
    ## A few details I cared about
    ### Fast preview
    When making a token, you need to quickly see if the face, weapon, or character pose fits inside the frame. A slow preview makes the whole process feel worse.
    ### Different border styles
    A plain ring works for many tokens, but not every token should look the same. A rogue, a mage, an undead creature, and a dragon boss usually need different visual treatment.
    ### Transparent PNG export
    This matters for VTT use. Transparent PNG tokens are easier to place on different maps and backgrounds.
    ### Simple controls
    The tool should not feel like Photoshop. For most token work, scale, position, border, style, and export size are enough.
    ## What I learned while building it
    Small tools are often useful because they are specific.
    This is not trying to replace a design app. It is just trying to make one repeated tabletop task faster.
    That is the kind of product I enjoy building: not huge, not over-designed, just focused on one workflow that people actually repeat.
    If you make tokens for DnD, Roll20, Foundry VTT, or Owlbear, you can try it here:Making tokens for a tabletop campaign should not take that long.
    But if you run DnD games, you probably know the small routine:
    find character art
    open an image editor
    crop the face or body
    add a border
    resize it
    export a PNG
    repeat it for every NPC, monster, boss, and player character
    Doing this once is fine. Doing it for a full encounter is annoying.
    That is why I built a small browser tool for this workflow:
    Token Maker | https://www.tokenmaker.one/
    It is a free VTT token maker for DnD, Roll20, Foundry VTT, and Owlbear.
    The problem I wanted to solve
    Most image tools are too broad for this job.
    They can make tokens, of course, but they are not built around the actual tabletop workflow.
    When I am preparing a session, I usually do not want a full design tool. I just want to:
    1.upload character art
    2.place it inside a frame
    3.adjust the crop
    4.export a transparent PNG
    5.move on to the next token
    That repeated flow is the real problem.
    The tool does not need to be complicated. It just needs to remove the boring steps.
    What the tool does
    Token Maker is focused on VTT token creation.
    The current workflow is simple:
    6.Drop an image into the editor
    7.Choose a border or style
    8.Adjust the image scale and position
    9.Pick an export size
    10.Download the PNG token
    It supports different token shapes and borders, including circular, square, and polygon-style tokens.
    The export is a transparent PNG, which works well for virtual tabletop platforms.
    Why I made it local-first
    For this kind of tool, I wanted the workflow to feel quick.
    You should be able to open the page, drop in an image, make the token, and export it.
    No heavy setup.
    No long design process.
    No need to turn a small token task into a full graphics project.
    That is also why the editor is built around one main workspace instead of spreading the workflow across several steps.
    Where this helps
    I think this is most useful for:
    DnD character tokens
    NPC tokens
    monster tokens
    boss encounter tokens
    quick campaign prep
    Roll20 and Foundry VTT games
    token pack preparation
    It is especially helpful when you need several tokens at once.
    One token is not a big deal. Twenty tokens is where the small time savings start to matter.
    A few details I cared about
    Fast preview
    When making a token, you need to quickly see if the face, weapon, or character pose fits inside the frame. A slow preview makes the whole process feel worse.
    Different border styles
    A plain ring works for many tokens, but not every token should look the same. A rogue, a mage, an undead creature, and a dragon boss usually need different visual treatment.
    Transparent PNG export
    This matters for VTT use. Transparent PNG tokens are easier to place on different maps and backgrounds.
    Simple controls
    The tool should not feel like Photoshop. For most token work, scale, position, border, style, and export size are enough.
    What I learned while building it
    Small tools are often useful because they are specific.
    This is not trying to replace a design app. It is just trying to make one repeated tabletop task faster.
    That is the kind of product I enjoy building: not huge, not over-designed, just focused on one workflow that people actually repeat.
    If you make tokens for DnD, Roll20, Foundry VTT, or Owlbear, you can try it here:
    Token Maker – Free VTT Token Maker | https://www.tokenmaker.one/
    I am still improving the border styles, token templates, and batch workflow.
    If you run VTT games and have suggestions, I would be interested to hear what would make token prep faster for you.

Publishing checklist
Paste the title into DEV title field.
Paste the article body into the editor.
Use tags: dnd, webdev, gamedev, tooling.
Keep only the two natural Token Maker links already included.
Preview once before publishing.
Token Maker – Free VTT Token Maker: https://www.tokenmaker.one/
I am still improving the border styles, token templates, and batch workflow.
If you run VTT games and have suggestions, I would be interested to hear what would make token prep faster for you.

Sosyal medyada 15 yaş sınırı ne zaman uygulanacak? Bakan Göktaş açıkladı

yazar
Aile ve Sosyal Hizmetler Bakanı Mahinur Özdemir Göktaş, 15 yaş altı çocukların sosyal medya kullanımını düzenlemek amacıyla bir çalışma grubu kurulduğunu ve ilgili yönetmeliğin 6 ay içinde yürürlüğe gireceğini açıkladı. Düzenleme kapsamında güvenli dijital alanlar oluşturulması, kontrollü kullanımın teşvik edilmesi ve yaş doğrulama sistemlerinin e-Devlet gibi yöntemlerle hayata geçirilmesi hedefleniyor.