The HTTP Archive Web Almanac has been tracking color contrast failures for years. The numbers have barely moved. After half a decade of design system tooling, accessibility linters, and entire JavaScript libraries dedicated to computing readable text colors, 70% of websites still fail basic WCAG contrast checks in 2025. The WebAIM Million paints an even grimmer picture — 83.9% of homepages flagged for low contrast text in 2026, up from 79.1% in 2025. The rate improves by maybe a few percentage points per year on one benchmark and actually gets worse on another. That’s not progress — that’s proof that relying on runtime JavaScript for something this fundamental doesn’t scale across the open web. We didn’t need better libraries. We’ve needed better CSS.

The contrast-color() function is that better CSS. One declaration. The browser runs the contrast math during style computation, before the page paints, and hands you the right text color. No library, no build step, no hydration flash.

Note: If you’ve seen it called color-contrast() in older articles and spec drafts — that name was changed, and the old syntax no longer works in any browser.

What It Does (And What It Doesn’t)

The Level 5 version is simple. You give it a color. It gives you back black or white, whichever has more contrast against your input.

.button {
  background-color: var(--brand-color);
  color: contrast-color(var(--brand-color));
}

Change --brand-color to neon green, text goes black. Change it to midnight navy, text goes white. Swap themes at runtime via JavaScript and the text adapts instantly — no event listeners, no recalculation.

A few things to know about the current version:

• It returns a <color>, not a number. You get an actual color value (black or white), you can use anywhere CSS accepts a color.
• Black or white only, for now. Candidate color lists and target ratios are planned for Level 6.
• No keywords. If you’ve seen max in older blog posts, that was stripped from the spec. Using it will silently break your declaration.
• As mentioned above, this function used to be called color-contrast() in early drafts. That name is dead — the CSSWG renamed it to follow the convention that CSS functions are named for what they return. color-mix() returns a color. contrast-color() returns a color. The old color-contrast() name sounded like it returned a contrast ratio (a number like 4.5), which was misleading. Any tutorial from 2021–2023 showing color-contrast() syntax won’t work in current browsers.

The Spec Split: Level 5 Versus Level 6

This function lives across two specifications. That’s unusual and worth understanding.

CSS Color Level 5 defines what browsers ship today. One color in, black or white out. The algorithm is deliberately marked “UA-defined”, meaning the browser decides what math to use internally. Right now, every engine uses WCAG 2.x relative luminance. But that “UA-defined” label isn’t accidental — it’s a planned escape hatch.

You’ll see APCA (Accessible Perceptual Contrast Algorithm) mentioned a lot in this context. APCA models how human eyes actually perceive contrast, factoring in font weight, spatial frequency, and ambient light — a genuine improvement over the WCAG 2.x formula. By not locking “use WCAG 2.x” into the Level 5 spec, browser vendors could swap to APCA later without breaking any existing code. If the spec had shipped with a wcag2() keyword as the default, every site using it would’ve been stuck on the old math permanently.

But APCA’s future is far less certain than the hype suggests. Adrian Roselli’s “WCAG3 Contrast as of April 2026” lays out the current situation clearly: APCA was pulled from the WCAG 3 working draft in mid-2023 after failing to gain enough Working Group support. The WCAG 3 spec currently says the contrast algorithm is “yet to be determined,” and the standard itself may not be finalized until 2030 or later. Roselli also filed a Chromium issue in May 2024 asking for the “Advanced Perceptual Contrast Algorithm” experiment flag to be removed from DevTools entirely, arguing that the implementation is outdated and risks misleading developers into thinking APCA is further along — or more official — than it actually is. That issue is still open.

None of this means APCA is dead. The research behind it is peer-reviewed and substantive, and its creator has noted that colors passing APCA guidelines greatly exceed WCAG 2 minimums in the vast majority of cases. But right now, there is no guarantee APCA will be the algorithm that replaces WCAG 2.x — and that uncertainty matters for contrast-color(). If a different algorithm wins out, or if WCAG 3 adopts something entirely new, the “UA-defined” label means browsers can adapt without breaking your code. It also means the Level 6 features — candidate color lists, target ratios, the tbd-fg/tbd-bg keywords — are all designed around an algorithm that may or may not materialize in its current form.

CSS Color Level 6 adds the extended syntax — candidate color lists and target contrast ratios:

/* Level 6 future syntax — not shipping yet */
color: contrast-color(var(--bg) tbd-bg wcag2(aa), #1a1a2e, #e2e8f0, #fbbf24);

The browser would evaluate each candidate left to right and pick the first that meets the 4.5:1 AA threshold. The tbd-fg and tbd-bg keywords indicate whether the base color is foreground or background, which matters for directional contrast models like APCA. This is all Working Draft territory — doubly so given APCA’s uncertain status. Use the Level 5 version for now.

Browser Support

This one’s in better shape than most new CSS features. All three major engines have shipped it in stable releases: Chrome 147 (April 2026), Firefox 146, and Safari 26.0. It reached Baseline Newly Available status in April 2026. Check caniuse for the full version matrix. All three engines pass the Web Platform Tests for contrast-color(), which means the edge cases (e.g., tie-breaking logic, color space conversion, syntax parsing) behave the same across browsers.

The raw global support percentage on caniuse looks low, but that mostly reflects enterprise browsers and people who never update. If you’re reading this, your browser almost certainly supports it already.

Progressive enhancement is straightforward using @supports:

.card {
  background: var(--bg);
  color: #fff;
  text-shadow: 0 0 4px rgb(0 0 0 / 0.8);
}

@supports (color: contrast-color(red)) {
  .card {
    color: contrast-color(var(--bg));
    text-shadow: none;
  }
}

Older browsers get white text with a dark shadow for legibility. Supporting browsers get the native calculation. Nobody sees broken text.

One thing to watch for: automated accessibility scanners (Lighthouse, Axe, etc.) can’t evaluate text-shadow. They only look at the computed color against background-color. So the fallback will still get flagged as a contrast failure in CI/CD pipelines, even if the shadow makes the text perfectly legible to human eyes. If your team runs automated a11y checks, you may need to allowlist that specific rule or add a comment explaining why the flag is a false positive.

A note on PostCSS:

There’s a plugin (@csstools/postcss-contrast-color-function) that evaluates contrast-color() at build time. It works for static colors like contrast-color(#ff0000). But the moment you use a custom property — contrast-color(var(--bg)) — the plugin can’t help because it has no access to runtime values. If your theming is dynamic (which is the whole point of doing this), skip the polyfill and rely on @supports.

The Gotchas

It Doesn’t Guarantee Perceptual or AAA Compliance

This can trip people up: “I used the contrast function, so my site passes accessibility checks now, right?”

Mathematically? Usually yes. There is a persistent myth that for certain “mid-tone” backgrounds, both black and white fail the standard WCAG 4.5:1 AA ratio. That’s mathematically false. Under the WCAG 2.x relative luminance formula, there is absolutely no background color where both pure black and pure white fail AA. One (or both) will always pass.

Take #2277d3 (a medium blue). It sits right on a mathematical knife-edge where both black and white actually pass AA (both hit roughly 4.58:1). contrast-color() will hand you whichever has the slight mathematical edge.

But here is the actual gotcha: the WCAG 2.x math has known perceptual blind spots. That same #2277d3 with black text mathematically passes AA, but to human eyes, it can be incredibly difficult to read. contrast-color() gives you mathematical compliance, which is great for automated audits, but that doesn’t always equal perceptual accessibility. (This is exactly why APCA exists and why the spec was designed to let browsers swap algorithms later.)

Furthermore, if you’re aiming for the stricter WCAG AAA standard (7.0:1), a true dead zone does exist. For backgrounds with a luminance between roughly 10% and 30%, neither black nor white will hit 7:1. In those cases, contrast-color() can’t save you — it just hands you the “least bad” failing option.

Transitions Snap, Not Fade

If you’re animating a background from white to black on hover:

.btn {
  background-color: #fff;
  color: contrast-color(#fff); /* black */
  transition: background-color 1s, color 1s;
}
.btn:hover {
  background-color: #000;
  color: contrast-color(#000); /* white */
}

The background fades smoothly over one second. But because the Level 5 output is a discrete value (black or white), the text color can’t be interpolated. It snaps.

And here is the visual gotcha: the snap doesn’t happen halfway through. If you’ve been building themes for a while, you probably have muscle memory from the old Sass days, where we checked if lightness($bg) > 50%. That relied on HSL lightness, where 50% is the geometric midpoint.

But WCAG 2.x relative luminance is a non-linear scale. Under the WCAG formula, the mathematical tipping point — where black and white have identical contrast against the background — actually occurs at approximately 18% relative luminance (specifically ~17.9%).

Because of that, the visual behavior during a white-to-black fade is heavily skewed. The text doesn’t snap in the middle. It stays black for the vast majority of the animation, only snapping to white at the very tail-end of the transition when the background gets extremely dark. It’s a jarring, late hard cut.

You might assume transition-behavior: allow-discrete fixes this. It doesn’t. allow-discrete does not fix the jarring visual experience because it cannot interpolate a binary output; it only shifts the timing of the hard snap to the 50% mark of the animation duration. If you need smooth text color transitions, you’ll have to layer color-mix() or manage the crossfade yourself.

Tie Goes To White

If the background is a perfect middle gray where both black and white produce identical contrast ratios, the spec has a hardcoded tiebreaker: white wins. Not a big deal in practice, but worth knowing if you’re debugging gray palettes and the text isn’t doing what you expect.

Gradients And Images Are Out

The function takes a flat <color> value. You can’t pass it a gradient or a url(). contrast-color(linear-gradient(...)) is a parse error. If your background is a photo or a complex gradient, you still need JavaScript or manually color-pick for overlay text.

Transparent Colors Are Composited First

Pass a semi-transparent color, and the browser blends it against an assumed opaque canvas (usually white) before running the contrast math. It’s not ignoring your alpha channel — it’s compositing it. But the result might surprise you if you expected the function to “see through” to whatever’s actually behind the element.

Windows High Contrast Mode

If a user enables Windows High Contrast, the forced-colors: active media query kicks in and the browser aggressively overwrites author-defined colors. contrast-color() bows out — forced system colors like CanvasText take over completely. You don’t need to write manual media queries to undo your contrast logic; the browser handles the hierarchy.

Combining It With Other Color Functions

Black or white sounds limiting, but once you feed that output into other CSS color functions, you can build an entire component palette off a single custom property.

Brand-Tinted Contrast With Relative Color Syntax

Pure black text on a vibrant card looks fine. Pure white on a coral card can feel flat. What if the contrast text was a very dark or very light tint of the background color instead?

Kevin Hamer explored related territory in his CSS-Tricks piece “Approximating contrast-color() With Other CSS Features”, where he used OKLCH lightness and round() to approximate the black/white switch without contrast-color() — essentially oklch(from <color> round(1.21 - l) 0 0). That’s a polyfill strategy: get the binary light/dark decision working in browsers that don’t support the native function yet. What we’re doing here is different — we start with contrast-color()’s native output and then enrich it by injecting the background’s own hue:

.card {
  --bg-hue: 260; /* Indigo */
  --bg: oklch(0.6 0.1 var(--bg-hue));
  background: var(--bg);

  /* Pull L from the black/white contrast color,
     but inject subtle chroma and the background's hue */
  color: oklch(from contrast-color(var(--bg)) l 0.05 var(--bg-hue));
}

When contrast-color() returns white, l is 1 (full lightness). When it returns black, l is 0. By pulling the background’s hue back in and adding a touch of chroma, you get text that reads as a deep dark indigo or a pale icy indigo instead of generic black/white. Hamer’s approach gives you the black/white decision without browser support; this one takes the decision the browser already made and gives it personality.

Fair warning: By tweaking the lightness and chroma of the black/white output, you can push a borderline contrast ratio into failing territory. Always run your tinted output through an accessibility linter before shipping.

Also worth noting: This example chains two very modern features — contrast-color() and oklch(from ...). If either one isn’t supported, the entire declaration fails silently. Your @supports block needs to test for both:

@supports (color: contrast-color(red)) and (color: oklch(from red l c h)) {
  /* Safe to use both */
}

Softened Contrast With color-mix()

Similar idea, simpler API. Mix the sharp black/white output back into the background to soften it:

.alert {
  --bg: var(--alert-color);
  background: var(--bg);

  /* 80% contrast, 20% background = softer but readable */
  color: color-mix(in oklch, contrast-color(var(--bg)) 80%, var(--bg));

  /* 40% contrast for a subtle border */
  border: 1px solid
    color-mix(in oklch, contrast-color(var(--bg)) 40%, var(--bg));
}

One custom property driving text, border, and potentially box-shadow or outline. Change --alert-color and the entire component recalculates.

This pattern also works well for ::placeholder text, which is a common pain point in dynamic theming. Placeholder text should be readable but visually softer than the input’s main text — color-mix() with contrast-color() gets you there:

input {
  --bg: var(--input-bg);
  background: var(--bg);
  color: contrast-color(var(--bg));
}

input::placeholder {
  color: color-mix(in oklch, contrast-color(var(--bg)) 50%, var(--bg));
}

50% mix gives you a muted but legible placeholder that adapts automatically to whatever background the input sits on.

Theme-Aware Contrast With light-dark()

For apps that support system light/dark mode:

:root {
  color-scheme: light dark;
  --surface: light-dark(#fff, #121212);
}

.component {
  background: var(--surface);
  color: contrast-color(var(--surface));
}

When the operating system switches to dark mode, --surface resolves to #121212, and contrast-color() returns white. No media queries, no JavaScript theme detection. The whole chain resolves natively.

What You Can Remove From Your Bundle

The practical payoff: every one of these libraries existed because CSS couldn’t do contrast math. If you’re only using them for readable-text-color selection, you can pull them out of your runtime entirely:

You might still need these for generating complex color scales, but the contrast-for-readability use case is now covered natively.

Beyond bundle size, there’s a performance angle that’s easy to overlook. Those JavaScript libraries don’t just cost you network bytes — they run on the main thread. Every time a theme changes or a component mounts with a dynamic background, your JS has to parse the color, compute luminance, decide black or white, and write the result back to the DOM. That’s main-thread work competing with layout, event handlers, and everything else your app is doing. contrast-color() moves all of that into the browser’s native style computation phase — heavily optimized C++ that runs before paint. For apps with lots of themed components, that’s a real difference in responsiveness.

There’s also a subtle bug that goes away: hydration flash. In React or Vue SSR apps, the server renders HTML without JavaScript. The client then hydrates, running JS to calculate contrast and inject the correct text color. For a brief window between initial paint and hydration, the text is either invisible or the wrong color. Moving contrast into CSS eliminates that entirely — the browser resolves the correct color during the initial paint, before JavaScript loads.

What We Used To Do

For context on what this replaces:

Sass era. You’d write a function that checked lightness($bg) > 50% and returned black or white at compile time. Worked for static themes. Completely useless for user-picked colors, CMS palettes, or dark mode, because the output was baked into the CSS file and could never change at runtime.

The variable toggle hack. When CSS custom properties shipped, people got creative. GitHub used a version of this for their issue label picker — splitting colors into --r, --g, --b channels, calculating Rec.709 luminance inside calc(), multiplying by negative infinity, and clamping to 0 or 1. It worked. It was also unreadable, unmaintainable, and would break silently if you got one parenthesis wrong. (Kevin Hamer’s OKLCH-based approximation is the most elegant version of this lineage — cleaner math, better perceptual alignment — but it’s still a workaround for a function that now ships natively.)

contrast-color() replaces all of these approaches with a single function call. And because the spec lets browsers upgrade the underlying algorithm, your code won’t need to change if and when a successor to WCAG 2.x contrast math lands — whether that’s APCA or something else entirely.

That 70% failure rate was never about developers refusing to care about contrast. It was about the distance between caring and shipping — the library, the build step, the runtime calculation, the hydration flash, the one component someone forgot to wire up. Every gap in that chain was a spot where accessibility quietly dropped out.

contrast-color() doesn’t make developers care more. It makes caring cost nothing.

ORA-00031: Session Marked for Kill — What It Means and How to Fix It

ORA-00031 occurs when a DBA issues ALTER SYSTEM KILL SESSION but Oracle cannot terminate the target session immediately. Instead, Oracle marks the session as “KILLED” and waits for it to reach a safe termination point — typically after completing a rollback or releasing OS-level resources. This is less of a hard error and more of a transitional state that every Oracle DBA will eventually encounter.

Top 3 Causes

1. Large Transaction Rollback in Progress

When you kill a session mid-transaction, Oracle must roll back all uncommitted changes to preserve data integrity. The larger the transaction, the longer the session stays in KILLED status.

-- Check rollback progress for KILLED sessions
SELECT s.sid,
       s.serial#,
       s.username,
       t.used_ublk AS undo_blocks,
       t.used_urec AS undo_records
FROM   v$session s
JOIN   v$transaction t ON s.taddr = t.addr
WHERE  s.status = 'KILLED';

2. Unresponsive or Disconnected Client

If the client network connection is broken or the client process has hung, Oracle cannot deliver the kill signal. The session lingers in KILLED state until the OS-level connection finally times out.

-- Find the OS process ID (SPID) for stuck KILLED sessions
SELECT s.sid,
       s.serial#,
       s.username,
       s.status,
       p.spid AS os_pid,
       s.machine,
       s.program
FROM   v$session s
JOIN   v$process p ON s.paddr = p.addr
WHERE  s.status = 'KILLED';

3. OS-Level I/O or Resource Wait

Sessions blocked at the OS level (disk I/O stall, memory pressure, storage issues) cannot respond to Oracle’s internal kill signal. In these cases, only an OS-level process termination will resolve the problem.

-- Identify what the session was waiting on before being killed
SELECT sid,
       serial#,
       status,
       event,
       wait_class,
       seconds_in_wait
FROM   v$session
WHERE  status = 'KILLED';

Quick Fix Solutions

Option 1 — Use the IMMEDIATE keyword (recommended first step)

-- Standard kill (asynchronous)
ALTER SYSTEM KILL SESSION '123,456';

-- Immediate kill (forces faster termination)
ALTER SYSTEM KILL SESSION '123,456' IMMEDIATE;

Option 2 — OS-level kill (last resort)

-- Get the SPID first
SELECT p.spid
FROM   v$session s
JOIN   v$process p ON s.paddr = p.addr
WHERE  s.sid     = 123
AND    s.serial# = 456;

# Linux/Unix: hard kill using SPID from above query
kill -9 <spid>

⚠️ Warning: Use OS-level kill -9 only after confirming no active rollback is in progress. Interrupting a rollback at the OS level can lead to block corruption.

Prevention Tips

1. Set IDLE_TIME in user profiles to automatically disconnect sessions that have been inactive too long — reducing the need for manual kills in the first place.

-- Create a profile that disconnects idle sessions after 30 minutes
CREATE PROFILE app_profile LIMIT
    IDLE_TIME    30
    CONNECT_TIME 480;

ALTER USER app_user PROFILE app_profile;

2. Use batch commits for large DML operations to minimize rollback size, so that if a session must be killed, the rollback completes quickly and ORA-00031 resolves faster.

-- Batch delete with intermediate commits
BEGIN
    LOOP
        DELETE FROM large_table
        WHERE  status = 'EXPIRED'
        AND    ROWNUM <= 5000;

        EXIT WHEN SQL%ROWCOUNT = 0;
        COMMIT;
    END LOOP;
END;
/

Related Errors

Pro Tip: Before reaching for kill -9, always check v$transaction to see if a rollback is actively running. Patience is often the safest fix — let Oracle finish the rollback cleanly rather than risk data block corruption with a forced OS kill.

Here are two scenes. They look unrelated. They’re not.

Scene 1

Two people at a café, talking about a restaurant they want to try. A stranger walking past stops: “That place closed six months ago. The one on the corner is better.” A brief nod, and they walk on.

The two people exchange a glance, taken aback. Why did that person stop? What did they want?

A few steps away, the stranger is also confused. They had useful information. They shared it. Why did these people react so strangely?

Scene 2

A colleague is visibly stressed, describing a difficult situation at work. One friend pulls their chair closer, puts a hand on their arm: “That sounds really hard.” Another opens their laptop: “I found something that might help — HR has a process for exactly this, I’ll send you the link.”

The colleague leans into the first. Glances uncertainly at the second.

The second person doesn’t understand why sitting close and saying “that sounds hard” counts as helping. You haven’t solved anything. The first doesn’t understand why anyone would respond to distress with links.

Both scenes end the same way: people on both sides convinced they did the right thing, confused by the other’s reaction. The mismatch is mutual and invisible from the inside.

Two survival instincts, two empathy systems

For many autistic people, information is a survival mechanism. Uncertainty is threat, missing information is a vulnerability, and the drive to correct and share runs below conscious awareness. Empathy, expressed through that system, looks like giving someone what keeps you safe: accurate information, solutions, resources. The social preamble before sharing — announcing yourself, softening the approach — doesn’t arise as a concept. Why would useful information require an introduction?

For many neurotypical people, social safety is a survival mechanism. Group cohesion and reading others accurately are what keep people safe. Empathy, expressed through that system, looks like presence: mirroring distress, making someone feel held, maintaining the social fabric. An uninvited approach from a stranger bypasses the protocol that signals safe intent — and that protocol isn’t a nicety, it’s the unlock code. Without it, the content can’t land regardless of how useful it is.

The social preamble is as foreign a concept to the autistic person as the direct approach is unsettling to the neurotypical person. The information response is as opaque to the neurotypical person as emotional attunement is to the autistic person. Neither protocol is natural to the other system. The incomprehension runs in both directions, with equal depth.

Milton’s double empathy problem

In 2012, autistic researcher Damian Milton described what he called the double empathy problem: cross-neurotype communication difficulties aren’t a deficit on one side, they’re a mismatch between two coherent systems that are mutually opaque to each other. Historically, the autistic side has been asked to compensate, the neurotypical system treated as the default rather than as one particular survival logic among two.

What these two scenes show is that both sides are trying to care for the other, each in the only language their system knows, and neither is being received as care.

That’s not a deficit. That’s two survival systems, built for different threats, each expressing empathy in the only currency it has.

For a while, my starter kits didn’t include any Docker configuration. The foundation was solid with auth, roles, MFA, Horizon, Logs Viewer, but the deployment side was left to whoever cloned the project.

That was a deliberate choice at first. Docker setups vary a lot depending on the infrastructure: some people use a reverse proxy, others have Cloudflare in front, some run on bare metal, others on managed platforms. I didn’t want to ship something that would need to be ripped out immediately.

But over time I changed my mind. Here’s why and what the process taught me.

The problem with “just configure it yourself”

Leaving deployment out of a starter kit sounds reasonable. In practice, it means every project starts with the same 4-6 hours of Docker work that never really changes.

Multi-stage Dockerfile. PHP-FPM config. Nginx with HTTPS. PostgreSQL and Redis wired up. Horizon and the scheduler running as proper services. Healthchecks everywhere so Docker knows when things are actually ready.

None of it is so complicated. But it’s time-consuming, easy to get subtly wrong, and almost identical from one project to the next.

Once I admitted that, the question wasn’t whether to include Docker, it was how to do it in a way that’s actually useful without being too opinionated about production infrastructure.

What I ended up building

The setup I settled on covers the full local development stack:

• A multi-stage Dockerfile : separate stages for Composer dependencies, Node assets, and the final PHP-FPM image. Keeps the production image lean.
• Nginx with HTTP-to-HTTPS redirect and a self-signed certificate for local dev, already included, no setup needed.
• PostgreSQL and Redis as services with proper healthchecks.
• Horizon and the scheduler as dedicated services, not crammed into the main app container.
• A bootstrap service that runs php artisan migrate --force before the app starts.

The Dockerfile uses three stages to keep the final image as lean as possible:

FROM php:8.4-fpm-alpine AS composer_builder
# Install extensions, run composer install
# ...

FROM node:24-alpine AS node_builder
# Install npm dependencies, build Vite assets
# ...

FROM php:8.4-fpm-alpine AS php_fpm
# Final image, only what's needed to run
# Copy vendor/ from composer_builder
# Copy public/build/ from node_builder
# ...

Each stage does one thing. The final image never contains Composer, Node, or dev dependencies.

The full Dockerfile architecture with extensions, non-root user, Xdebug for local dev, is covered here: Production-Ready Docker Setup for Laravel Filament.

The bootstrap service

Running migrations on deploy is one of those things that sounds simple until you’ve had a deployment fail because the app started before the database was ready.

The pattern I use is a dedicated bootstrap service that exits when migrations succeed. The app service depends on it, so the app simply doesn’t start until migrations are done.

bootstrap:
  image: ${APP_IMAGE}:${APP_VERSION}
  command: php artisan migrate --force
  depends_on:
    db:
      condition: service_healthy
    # ...

app:
  image: ${APP_IMAGE}:${APP_VERSION}
  depends_on:
    bootstrap:
      condition: service_completed_successfully
    # ...

horizon:
  image: ${APP_IMAGE}:${APP_VERSION}
  command: php artisan horizon
  depends_on:
    bootstrap:
      condition: service_completed_successfully
    # ...

scheduler:
  image: ${APP_IMAGE}:${APP_VERSION}
  command: php artisan schedule:work
  # ...

No SSH. No manual commands. No “did someone run the migrations?” before going live.

A word of honesty on this pattern: it works well for single-instance deployments, one VPS, one app container. If you’re running multiple replicas or need strict zero-downtime guarantees, this approach has limits. Multiple bootstrap services running simultaneously can conflict, and the app will be briefly unavailable during migration. In those cases, migrations should be handled at the CI/CD pipeline level, before containers are deployed. That’s a topic worth a dedicated article, and it’s on the roadmap.

The full compose setup, volumes, healthchecks, network config, restart policies, is covered in detail here: Production-Ready Docker Compose for Laravel Filament.

What I deliberately left out

I didn’t include a production-ready Nginx config. Not because it’s hard to write, but because production environments vary too much.

Some projects sit behind Traefik. Others use Cloudflare in front. Some have real Let’s Encrypt certificates managed externally, others use internal PKI. Shipping a “production” Nginx config that works for one setup and silently breaks another isn’t helpful.

What I do ship is a docker-compose.example.yaml, clearly labeled as a starting point, not a drop-in solution. The dev config is complete and ready to use. The production side is documented, commented, and deliberately left for the developer to adapt.

I think that’s the right balance for a starter kit. Give people enough to be productive immediately, without making decisions that belong to them.

What this changes for the starters

Both the Backend Starter and the Multipanel Starter now include the full Docker setup.

Copy the repo, copy .env.example, define your app key, rundocker compose up -d --build, then php artisan backend:setup, and you have a running panel with auth, roles, MFA, Horizon, and Logs Viewer, all in one go.

It’s a meaningful improvement over “configure Docker yourself.” Not because Docker is complicated, but because those 4-6 hours are better spent on the actual project.

Both starters are available with the Filament Mastery membership.

As always, if something doesn’t work the way you’d expect or you’d approach it differently, let me know in the comments.

After careful consideration, we have made the difficult decision to sunset DataSpell as a standalone product.

DataSpell was created to focus on the needs of data science and analytics professionals within the JetBrains ecosystem. It allowed us to build and refine a dedicated experience for working with Jupyter notebooks, data exploration, and analytical workflows. These improvements have been successfully integrated into PyCharm, where they can benefit a much broader audience. 

Today, we’ve reached a point where maintaining a separate product is no longer the most effective path forward. This choice is part of JetBrains’ broader strategy to build a more cohesive ecosystem around the workflows users rely on most. By consolidating these capabilities in PyCharm, we can move faster, simplify the overall experience, and deliver more value in one place.

What’s next for DataSpell users?

Starting from September 1, 2026, we will help existing DataSpell users transition to PyCharm, where data science and analytics workflows will continue to be actively developed and improved.

To make this transition as smooth as possible, eligible DataSpell customers will be able to transition to PyCharm at no additional cost. Current DataSpell users also maintain access through a fallback license.

The transition will happen in stages:

• May 28, 2026: DataSpell will be deprecated as a standalone product, meaning it will no longer be possible to purchase new DataSpell subscriptions. Existing licenses will continue to work.
• September 1, 2026: Eligible DataSpell subscriptions will be converted to PyCharm Pro subscriptions. Eligible customers will receive JetBrains AI Credits matching the value of their remaining DataSpell subscription period.
  • Personal customers who already have licenses for both PyCharm Pro license and DataSpell when the conversion happens on September 1 will not experience any changes. Their DataSpell licenses will remain valid until the original expiration date.

Please contact us if you need any assistance as you prepare for the transition on September 1.

For organizational commercial customers

We plan to convert organizations’ DataSpell licenses to PyCharm ones on September 1, 2026.

Since DataSpell subscriptions include bundled AI Credits, in addition to the license conversion, eligible organizational customers will receive JetBrains AI Credits matching the value of the remaining DataSpell subscription period. 

Organizational customers will also retain access to DataSpell through a fallback license, giving their teams more time to complete the transition to PyCharm.

For personal commercial customers

On September 1, 2026, your DataSpell license will automatically be converted to a PyCharm Pro license for the remaining subscription period, and eligible customers will receive JetBrains AI Credits matching the value of the remaining DataSpell subscription period.

You will also continue to have access to DataSpell through a fallback license, so you’ll have time to complete your transition to PyCharm.

If you already have PyCharm Pro and DataSpell subscriptions, your DataSpell license will remain unchanged and will not be converted.

All Products Pack holders will retain access to their DataSpell licenses.

For free license holders

If you are using DataSpell with a free license, you can continue using DataSpell through your current license terms. But we recommend moving to PyCharm for your data science workflows. Eligible users can apply for a free PyCharm Pro license through the existing JetBrains programs for students, teachers, open-source contributors, and other supported groups.

Continuing with your data workflows in JetBrains IDEs

Although DataSpell is being retired as a standalone product, its key workflows will remain available within other JetBrains tools.

PyCharm Pro provides built-in support for:

• Jupyter notebooks
• SQL and databases 
• Python data analysis workflows

You can continue working with notebooks, data visualization, and Python-based data processing within the JetBrains ecosystem without losing the capabilities you rely on today. You can learn more about PyCharm’s support for data science and data analysis here. 

Thank you

We want to express our deepest gratitude to the DataSpell community – your feedback, support, and engagement have been invaluable.

While this marks the end of DataSpell as a standalone product, it also represents a new chapter where data workflows continue to evolve within the broader JetBrains ecosystem.

The JetBrains team

FAQ

What will be the last DataSpell release?
The final release of DataSpell will be 2026.1. There will be no further regular updates beyond potential vulnerability fixes, and support will gradually be phased out.

What alternatives are available for DataSpell users?
Most DataSpell workflows are available in PyCharm Pro, which supports Jupyter notebooks, Python data analysis, and interactive development workflows. If you mainly work with databases, you may want to consider using DataGrip.

What should I do if I have questions or reservations about the automatic license migration?
If you have questions or reservations about automatic conversion, it’s important that you contact a JetBrains representative by August 31, 2026, at the latest.

What should I do if I recently purchased a DataSpell license?
You will also be automatically converted to PyCharm Pro licenses. Also our standard refund policy applies.

What can I do if I need more information?
Please contact JetBrains Sales Support or your existing JetBrains Sales representative for additional help. You can also get more information about licenses and terms in JetBrains legal information portal. 

If you’ve ever built or set up a subscription experience on Shopify for a coffee brand, you’ve probably run into the same problem most merchants face:
The signup flow works great. The first order goes out. And then subscribers start quietly disappearing before the third delivery.
This isn’t a coffee problem. It’s a subscription infrastructure problem — and it’s almost always caused by the same handful of missing pieces in the system underneath the storefront.
In this guide I’ll walk through the practical setup decisions that actually move the needle on retention for coffee subscription businesses on Shopify — from choosing the right model and pricing structure to the fulfillment calendar, dunning logic, and cancellation flow that most setups skip entirely.

Why Coffee Works So Well as a Subscription Product

Before getting into setup, it’s worth understanding why coffee is genuinely one of the better products to build a subscription around — when the infrastructure supports it.
Predictable consumption cycle. A 12-oz bag of whole beans lasts roughly two to three weeks for a single drinker. That natural rhythm makes it easy to design a billing and delivery schedule that matches actual usage patterns.
Daily habit. Roughly two-thirds of American adults drink coffee every day. A subscription removes the friction of reordering, and that convenience compounds into real retention over time.
Freshness as a retention argument. Coffee quality degrades noticeably after roasting. Subscribers who care about quality genuinely prefer a recurring shipment over buying retail — which means freshness becomes a built-in reason to stay subscribed that most product categories simply don’t have.
The global coffee subscription market reached $808.8 million in 2024 and is projected to surpass $2.2 billion by 2033. The infrastructure opportunity for developers and merchants building on Shopify is real and still early.

Step 1 — Choose the Right Subscription Model Before You Build

The model choice affects everything downstream — your Shopify app configuration, your fulfillment complexity, and ultimately your churn rate.
Fixed Product Repeat
The customer selects a product and receives it on the same schedule every cycle. Lowest operational complexity, easiest to configure, best starting point for any coffee brand launching subscriptions for the first time. One selling plan group, one or two products, a few billing interval options.
Curated or Roaster’s Choice
You select a different blend each cycle. Requires a process — manual or automated — to update the product variant on each active contract before the billing cycle runs. More engaging for subscribers, more operational overhead for the merchant.
Build Your Own
Customers configure their own subscription — beans, grind type, bag size, delivery frequency. Most complex to implement correctly. Multiple variant combinations, more inventory forecasting challenges, higher potential for fulfillment errors. Save this model for merchants with 100 or more active subscribers and a solid fulfillment operation already running.
Prepaid or Gift Subscriptions
Customers pay upfront for a fixed number of cycles — 3, 6, or 12 months. Strong during holiday seasons, great for cash flow. Requires setting a maximum cycle limit on the billing policy so the contract expires correctly after the final cycle. Converting prepaid subscribers to ongoing monthly plans at the end of their term should be automated, not manual.
Practical recommendation: Start with fixed product repeat. Get the first 50 subscribers, watch the churn data, and let that tell you what to build next.

Step 2 — Shopify Subscription Architecture: What You’re Actually Working With

Shopify’s recurring billing infrastructure runs through the Subscription API, built on the Purchase Options API. Here’s the practical architecture you’re working with:
SellingPlanGroup — defines the subscription options that appear on the product page. Each selling plan within the group specifies a billing interval, delivery frequency, and pricing adjustment. For a coffee subscription, you’d typically create plans for every 2 weeks, every 3 weeks, and every 4 weeks, each with a 10 to 15% subscribe-and-save discount applied.
SubscriptionContract — created when a customer completes checkout with a subscription product. This is the central object representing the subscriber relationship. It stores the billing policy, delivery policy, contract lines (products), customer details, delivery address, and current status. Every subscriber-facing action — skip, pause, swap, cancel — is an operation on this object.
SubscriptionBillingAttempt — triggered on each billing date against the active contract. A successful attempt generates a new order. A failed attempt fires a webhook your dunning system should be handling.
Understanding this structure matters because every retention decision maps directly to a contract operation:

Skip next delivery → update nextBillingDate on the contract
Pause → update contract status to PAUSED
Resume → update contract status to ACTIVE
Change frequency → update billingPolicy interval count
Swap product → update variant ID on the contract line
Cancel → update contract status to CANCELLED (after cancellation flow)

Step 3 — Pricing Configuration That Protects Margins

A subscribe-and-save discount of 10 to 15% is the range that works for coffee subscriptions.
That range is enough to feel meaningful without destroying margins. Going above 15% tends to attract price-sensitive subscribers who churn the moment a cheaper option appears. More than 38% of potential subscribers have delayed or cancelled a coffee subscription due to cost concerns — but the brands with the highest retention compete on freshness and experience, not discount depth.
On shipping:

Bake shipping into the subscription price and advertise free shipping as a subscriber perk, or
Set a minimum order threshold for free shipping to encourage larger orders

Either approach works. What doesn’t work is surprising subscribers with unexpected shipping charges at renewal — that’s one of the fastest paths to cancellation.

Step 4 — Delivery Frequency and Flexibility Configuration

The number one reason coffee subscribers cancel is not product quality. It’s delivery timing — too much coffee arriving faster than they can drink it.
Frequency options to configure:

Every 2 weeks — best for daily drinkers buying smaller bags
Every 3 weeks — most popular once subscribers know their consumption rate
Every 4 weeks — suits larger quantities or subscribers who supplement with local purchases

Avoid weekly deliveries for individual consumers. For most households, weekly is too much coffee, and they’ll cancel within two months.
Portal actions that must be one-click:
65% of consumers say flexibility to pause or cancel is the number one feature they look for in a recurring service. If a subscriber has to submit a support ticket to skip a delivery, you’ll lose them before you reply.
The customer portal needs to support these actions without any support interaction:

Skip next delivery
Pause subscription
Resume subscription
Change delivery frequency
Swap product variant
Update payment method
Cancel subscription (via cancellation flow — not direct)

Every one of these should resolve in one or two clicks. The easier these actions are, the longer subscribers stay.

Step 5 — Dunning Setup for Failed Payments

Failed payments account for 20 to 40% of total subscription cancellations. Most of these are involuntary — the subscriber didn’t intend to cancel, they just had an expired card or a temporary payment issue.
Your application should be registered to receive the billing failure webhook and respond with a structured retry sequence:
Day 0 — Billing attempt fails. Send immediate email notifying the subscriber with a direct link to update their payment method. Keep the tone helpful, not alarming.
Day 3 — Retry the billing attempt. If it fails again, send a follow-up email with slightly more urgency. Direct link to payment update in every email — not a generic login prompt.
Day 7 — Final retry attempt. Send a final notice informing the subscriber their subscription will be paused unless payment is updated.
Day 14 — If no payment recovered, update contract status to paused or cancelled based on your preference. Pausing keeps the door open for reactivation.
A healthy failed payment recovery rate is above 60%. Below that, your dunning sequence needs more retry attempts or better email timing and copy.

Step 6 — The Cancellation Flow

The cancellation flow is the last thing most developers build and the first thing that matters for retention.
When a subscriber initiates cancellation, your flow should intercept that action and present alternatives before the contract status is updated.
A practical cancellation flow:
Collect the cancellation reason first. Give subscribers a short list — too much coffee, too expensive, going on holiday, found another brand, quality issue. This data improves your retention logic over time and tells you what to fix in the product experience.
Present a contextual save offer based on the reason:

Too much coffee → offer a frequency change to every 4 weeks, or a one-time skip
Too expensive → offer a one-time discount on the next order
Going on holiday → offer a 4-week pause
Found another brand → offer a product swap to a different blend
Quality issue → route to customer support before proceeding

If the subscriber accepts the save offer — execute the relevant contract update and log the save event with offer type and original cancellation reason.
If the subscriber declines all offers — update the contract status to CANCELLED and send a confirmation with a reactivation link.
Tracking save rates per offer type gives you the data to optimise your retention logic over time.

Step 7 — Fulfillment: The Operational Layer That Makes or Breaks the Product Promise

The technical system only works if the operational process behind it is solid. Coffee freshness is the core product promise — and that promise lives or dies in the fulfillment calendar.
A roast-to-ship schedule that works:
Monday — Cutoff day. Subscription changes lock in. Skips, swaps, and address updates made after this point apply to the following cycle.
Tuesday or Wednesday — Roast day. Roast only the volume required for confirmed orders in this cycle.
Thursday — Pack and ship. Target shipping within 24 to 48 hours of roasting.
Every subscriber receives beans roasted within 48 hours of shipment. That freshness standard is a tangible quality difference that retail cannot match.
Inventory allocation: Reserve stock for active subscription contracts before making inventory available to one-time buyers. Treat confirmed subscription demand as committed volume. Overselling to one-time buyers and substituting subscriber orders breaks trust quickly.

Step 8 — Webhook Events to Register

For a complete coffee subscription system, register handlers for these topics:
subscriptions/create — new contract created. Trigger onboarding email sequence.
subscription_billing_attempts/success — payment succeeds. Trigger order fulfillment and pre-shipment notification.
subscription_billing_attempts/failure — payment fails. Start dunning retry sequence.
subscription_contracts/update — contract status or details change. Sync state to your database, trigger relevant communications.
subscriptions/update — line items or delivery details modified. Confirm swaps or address changes to the subscriber.

The 90-Day Retention Window: What to Do and When

44% of cancellations happen within the first 90 days. Here’s the communication and retention logic that matters most in that window:
Days 1 to 7: Onboarding sequence. Welcome email with portal link, delivery timeline, and clear instructions on how to skip or pause. First impressions are formed here.
Days 8 to 30: Post first delivery follow-up. Ask for feedback. Surface the swap option. Show subscribers the portal controls they haven’t used yet.
Days 31 to 60: Engagement monitoring. Two consecutive skipped deliveries is a churn signal worth acting on proactively. A targeted offer before they reach the cancellation page is significantly more effective than a win-back campaign after they’ve already left.
Days 61 to 90: Loyalty acknowledgement. A small gesture at the 90-day mark — a sample of a new roast, a loyalty discount — has a measurable impact on long-term retention for coffee brands specifically.

7 Common Mistakes That Kill Retention Early

1. Too many options at launch. Five tiers, three grind sizes, four frequencies. Customers freeze and leave. Start simple.
2. Rigid delivery schedules. No skip or pause = cancellation instead of a break.
3. No dunning automation. Silent payment failures are one of the largest sources of involuntary churn.
4. No cancellation flow. A one-click cancel is a one-click revenue loss.
5. Set and forget mentality. Subscriptions need active attention every month. Static experiences go stale.
6. Aggressive discounting. Over 15% attracts price shoppers who leave for the next deal.
7. Wrong metrics. Subscriber count alone tells you nothing. Track churn rate, LTV, MRR, and payment recovery rate from day one.

Technical Launch Checklist

Before going live:

• Selling plan group created and attached to subscription-eligible products
• Webhook handlers registered for all five subscription topics
• Dunning retry sequence active before first billing cycle
• Customer portal live with skip, pause, swap, frequency change, and cancel flows
• Cancellation flow with save offers built and tested
• Pre-renewal email firing 3 days before each billing date
• Inventory allocation separating subscriber demand from one-time stock
• MRR, churn rate, LTV, and payment recovery rate tracked from day one

If you’re building this on Shopify and want the full operational guide including pricing frameworks and fulfillment calendars, the complete version is on the Driftcharge blog.

Have you built a subscription experience on Shopify before? What was the trickiest part of the setup — the billing logic, the customer portal, or the retention workflows? Drop it in the comments — would love to hear what others have run into.

Every line of your smart contract costs something.

Some lines cost more than others.

In this part of our gas saving series, we’ll explore how to write smarter Solidity code that keeps your contract lean and efficient.

Here are six simple and practical ways to reduce gas costs while writing Solidity smart contracts.

1. Use payable Only When Needed, But Know It Saves Gas

In Solidity, a function marked payable can actually use slightly less gas than a non-payable one.

Even if you’re not sending ETH, the EVM skips some internal checks when the function is marked payable.

See this example:

function hello() external payable {}    // 21,137 gas

function hello2() external {}           // 21,161 gas

That tiny difference may not seem like much, but across thousands of calls, it adds up.

Only use payable when your function is actually meant to accept ETH

2. Use unchecked for Safe Arithmetic When You’re Sure

Since Solidity 0.8.0, all arithmetic operations automatically check for overflows and underflows. While this makes contracts safer, it also uses extra gas. When you’re certain that overflow won’t occur, you can use the unchecked keyword to skip these safety checks.

uint256 public myNumber = 0;

function increment() external {

unchecked {

myNumber++;

}

}

Gas used: 24,347 (much cheaper than using safe math)

Warning: Use unchecked carefully. Only when you’re confident there’s no risk of overflow.

3. Turn On the Solidity Optimizer

The Solidity Optimizer is like a smart helper that cleans up and tightens your compiled bytecode.

It does not change how your contract works, but it removes waste and makes it cheaper to run.

If you’re using tools like Hardhat or Remix, always enable the Optimizer before deploying to mainnet.

4. Use uint256 Instead of Smaller Integers (Most of the Time)

Smaller types like uint8 or uint16 might look more efficient, but they can cost more gas during execution.

That’s because the EVM automatically converts them to uint256 behind the scenes.

So, if you’re not tightly packing them in a struct for storage savings, just use uint256.

Use smaller types only in structs when trying to save storage space.

5. Understand Storage Costs: Read + Write Costs Almost Same as Write

Storage operations are expensive. But here’s something surprising:

Reading a storage variable before writing to it doesn’t cost much more than writing directly.

Example:

Read + Write = 2,100 (read) + 20,100 (write) = 22,200 gas
Write only = 22,100 gas

That means if your code needs to read before writing, it’s okay, you are not losing much.

Plan your storage usage wisely. Reuse variables and avoid unnecessary writes.

Reference: https://ethereum.github.io/yellowpaper/paper.pdf

6. Use < Instead of <= in Comparisons

When comparing numbers, use < instead of <=.

Why? Because:

< needs just one check

<= takes two checks (a comparison and an extra step to flip the result)

Fewer steps mean lower gas usage.

Example:

for (uint i = 0; i < limit; i++) {

...

}

This small change saves gas on every loop iteration and comparison operation.

Conclusion

Gas optimization is about understanding how the EVM works and making informed decisions about your code structure. Each of these techniques might seem to save small amounts of gas individually, but combined and applied across a large contract, they can result in significant cost savings.

Remember: Always test your optimizations thoroughly. Sometimes gas savings come at the cost of code readability or safety. Strike the right balance for your specific use case.

Key Takeaways:

• Use payable functions when appropriate
• Apply unchecked carefully for safe arithmetic
• Enable the Solidity optimizer
• Prefer uint256 for most variables
• Understand storage access patterns
• Choose efficient comparison operators

By implementing these strategies, you’ll create contracts that not only work well but also cost less to deploy and interact with, making them more accessible to users and more profitable for developers.

dotMemory Unit has long served as a unit testing framework for detecting memory issues in .NET code. We are grateful to everyone who has used it as part of their development and testing workflows.

After careful consideration, we have decided to retire dotMemory Unit. The project will no longer receive active maintenance, compatibility updates, or security fixes, and we recommend that users plan to discontinue its use.

Why we’re making this change

This decision is based on several technical, security, and product considerations.

dotMemory Unit has not been actively developed for some time and does not support the latest .NET versions. Bringing it up to modern compatibility, reliability, and security standards would require a substantial architectural redesign.

In addition, dotMemory Unit generates workspaces in a legacy format that is incompatible with recent versions of dotMemory. This creates friction for users and prevents seamless integration with the latest JetBrains profiling tools.

Finally, some of the project’s dependencies are outdated and include known security vulnerabilities. Because dotMemory Unit is no longer actively maintained, we cannot reliably update these dependencies without risking compatibility issues or undertaking a full rebuild. Continuing to distribute or support tooling with unpatched vulnerabilities would not meet our security standards.

What this means for you

If you currently use dotMemory Unit, we strongly recommend that you stop using it, especially in security-sensitive environments.

We understand that dotMemory Unit has been valuable for in-test memory profiling, and we recognize that its deprecation may create a gap in some workflows. At this time, we do not have a direct replacement.

Timeline

[20.05.2026]: dotMemory Unit marked as deprecated on NuGet.org. No further updates, patches, or security fixes will be released. 

[28.05.2026]: Official deprecation notice posted across different channels. Documentation will remain online for some time, but will be updated to reflect the current state of the project and potential risks.

Thank you

We are sincerely grateful to everyone who has trusted dotMemory Unit over the years.