The Practical DeveloperModify

2026-01-21 04:20:52

Every day, your applications generate mountains of data. User clicks, API calls, error logs, transaction records. It all piles up. But raw data sitting in a database is like having a library where all the books are written in a language you don't speak. The real value lies in asking the right questions and knowing which analytical approach will provide the answers you need.

Let me walk you through the four types of data analytics that transform noise into insight, each answering a progressively more sophisticated question about your data.

Descriptive analytics: the rearview mirror

The question: What happened?

Descriptive analytics is your starting point. It takes historical data and organizes it into a format that humans can understand. Dashboards, reports, charts. Think of it as looking in the rearview mirror while driving. You're not trying to predict where you're going. You're simply getting a clear picture of where you've been.

Real-life example:

Imagine you're running an e-commerce platform. Descriptive analytics tells you that last month you processed 15,000 orders, your average order value was $67, and mobile traffic increased by 23% compared to the previous quarter. Is your monitoring dashboard showing API response times over the past 24 hours? That's descriptive analytics. Your weekly active users chart? Descriptive analytics.

These insights don't explain why mobile traffic jumped or what caused that spike in API latency at 3 AM last Tuesday. They just show you the facts. But those facts are crucial. You can't diagnose problems or predict the future without first understanding your baseline reality.

In practice:

Most developers encounter descriptive analytics daily through tools like Grafana, Datadog, or Google Analytics. Is that graph showing your deployment frequency? Is your error rate trending over time? Your database query performance metrics? All descriptive. It's the foundation everything else is built on.

Diagnostic analytics: the detective work

The question: Why did it happen?

Once you know what happened, the natural next question is why. Diagnostic analytics digs into your data to find causes, correlations, and connections. It's detective work. Looking for clues that explain the patterns you observed.

Real-life example:

Let's say your descriptive analytics revealed that user signups dropped 40% last week. That's concerning, but knowing it happened isn't enough. Diagnostic analytics helps you investigate. Did the drop coincide with a recent deployment? Was there a change in your ad spending? Did a particular browser start having compatibility issues with your registration form?

Maybe you drill down and discover that 95% of the drop came from mobile users on iOS, and it started exactly when you deployed version 2.4.1. You check the logs and find a JavaScript error that only triggers on Safari. Mystery solved.

In practice:

This is where you use techniques like drill-downs, filters, and correlation analysis. You might join your application logs with deployment timestamps, compare conversion rates across different user segments, or analyze A/B test results to understand why variant B performed better. Tools like your ELK stack, Splunk, or even SQL queries with GROUP BY and WHERE clauses become your investigative toolkit.

Diagnostic analytics doesn't just identify problems. It also helps you understand successes so you can replicate them.

Predictive analytics: the crystal ball

The question: What will happen?

Now we're getting into the future. Predictive analytics uses your historical data, statistical models, and often machine learning to forecast what's likely to happen next. It's not magic. It's pattern recognition at scale.

Real-life example:

Your SaaS application has been collecting user behavior data for two years. Predictive analytics can help you identify which users are at high risk of churning based on patterns like decreased login frequency, reduced feature usage, or specific sequences of actions that historically preceded cancellations.

Or consider capacity planning. Based on historical growth trends, seasonal patterns, and current trajectory, predictive models can estimate that you'll need to scale your database cluster by 30% before the holiday shopping season. This gives you time to prepare instead of scrambling when traffic spikes.

In practice:

This is where machine learning enters the chat. You might build regression models to forecast server load, classification models to predict user churn, or time series models to anticipate seasonal traffic patterns. Libraries like scikit-learn, TensorFlow, or even simpler statistical approaches can power these predictions.

The key insight: predictive analytics doesn't tell you what will happen with certainty. It tells you what's likely to happen based on what you know. It's probabilistic, not deterministic. But that probability can be incredibly valuable for planning and preparation.

Prescriptive analytics: the strategic advisor

The question: What should we do about it?

This is the most sophisticated level. Prescriptive analytics takes everything you've learned (what happened, why it happened, what's likely to happen) and recommends specific actions to achieve your goals.

Real-life example:

Your predictive model says you're likely to see a 50% traffic increase next month. That's useful information, but what should you actually do about it? Prescriptive analytics might recommend:

• Scaling your Kubernetes cluster to 15 pods (not 10, not 20, but exactly 15 based on cost performance optimization)
• Increasing your database connection pool size by 40%
• Shifting 30% more budget to your CDN
• Implementing rate limiting for endpoints that become bottlenecks under load

Or imagine a recommendation engine. It doesn't just predict that a user might like sci-fi books. It prescribes showing them specific titles in a specific order at specific times to maximize engagement and conversion.

In practice:

Prescriptive analytics often combines optimization algorithms, simulation, and business rules. It might use techniques like linear programming to optimize resource allocation, reinforcement learning to recommend actions in dynamic environments, or decision trees to map out the best course of action given multiple constraints.

This is where data science truly becomes a decision-making tool. You're not just observing or predicting. You're actively shaping outcomes.

Bringing it all together

These four types of analytics aren't separate silos. They build on each other in a natural progression:

1. Describe what's happening in your systems and user base
2. Diagnose why those patterns exist
3. Predict what's likely to happen next
4. Prescribe what actions will move you toward your goals

As developers, we often start with descriptive analytics (monitoring, logging, metrics) because it's the most accessible. But the real power comes from climbing this ladder. Each level requires more sophisticated tooling and techniques, but each also delivers exponentially more value.

The next time you're looking at a dashboard or debugging an issue, ask yourself: Am I just describing what I see, or am I working toward prescribing what I should do? Understanding which question you're trying to answer will guide you to the right analytical approach and the right insights.

What level of analytics does your team currently use? The data is already there, waiting to tell you not just what happened, but what you should do next.

2026-01-21 04:18:46

Memorizing vs. Understanding

During my studies transitioning to data engineering – in SQL language, from the beginning it intrigued me to know how everything worked, why it worked, why "this" has to be "here" and "that" has to be "there".

So when I was studying the basics, this thought came to my mind. I believe that normally when someone is starting, the first thing the person wants is to memorize the order to write a query, because it is common to get confused, like using ORDER BY before GROUP BY, or sometimes getting psyched and writing WHERE before FROM.

When we memorize that a query is written in the order:

1. SELECT / DISTINCT
2. FROM / JOIN
3. WHERE
4. GROUP BY
5. HAVING
6. ORDER BY
7. LIMIT

We begin to ask: "Why this order? Is the code executed in this same order?".

Write order ≠ Execution order

And it is not quite like that, the execution order is completely different from the write order, and understanding this made me more assertive when writing a query, no matter how basic it is and especially the more complex ones. I particularly start writing a query by the execution order, because it is easier to maintain the reasoning when writing a more complex query, for example:

1. FROM / JOIN
2. WHERE
3. GROUP BY
4. HAVING
5. SELECT / DISTINCT
6. ORDER BY
7. LIMIT.

First PostgreSQL reads the table data and loads it into memory (buffer pool), then the row, then groups everything to be able to filter, then calculates and selects what was filtered, after that eliminates duplicate rows, puts the table reading in order and limits the rows that will be displayed to show the query.

I learned in practice that we should always limit the amount of rows to be displayed in a query with a large database, the amount of memory used to process all this, depending on the number of records, is very large and limiting this saves you time.

How this changes everything

It happens that, the write order will be compiled by PostgreSQL and transformed into an execution plan. Next, this plan will be executed to process the data according to the query logic, and the table data will be read into the memory of the server on which the database is running (we can visualize this plan using the EXPLAIN ANALYZE command). Finally, it will start processing the data.

Therefore, when it comes to execution, it will start with the FROM clause, the data will be read from the table into memory and then it will apply the conditions of the WHERE clause.

If the table has 1,000 rows and PostgreSQL performs a sequential scan (no indexes), all 1,000 rows will be read into memory. After that, the data will be filtered and the intermediate results will be stored in memory again. It does not matter the amount of records: if we are querying 3 records in the WHERE clause, these 3 records will be placed in memory; if the same condition returns 100 out of 1,000 records from the table, these 100 records will be stored in memory again.

However, if there are indexes on the WHERE columns, PostgreSQL may use an index scan to read only the necessary rows directly, which is much more efficient.

So, all filtered data will be made available to us in memory after the WHERE clause, which will execute everything inside GROUP BY.

Normally as part of GROUP BY, we specify the keys (PK or FK) on which the data should be grouped in the SELECT clause, we can also have aggregation functions in the SELECT clause in addition to executing the logic in a GROUP BY that will also execute what is in the SELECT clause.

As there will often be dependencies between the two, PostgreSQL will process in an integrated way: GROUP BY prepares the groups, and SELECT calculates the aggregations (COUNT, SUM, AVG, etc) on top of these groups, from there, PostgreSQL executes the additional filters (HAVING) or goes straight to the ordering of the results (ORDER BY), depending on the query we are doing.

It sorts the data again after grouping by the key to perform the aggregation, so we have ORDER BY classifying the output and returning the results according to the logic.

It is good to remember that filters also have an order, the WHERE clause filters the rows before grouping, the HAVING expression filters after grouping. Understanding this gives us a processing advantage, because filtering the rows (WHERE -> FILTER -> GROUP BY) is faster than filtering a grouping (GROUP BY -> AGGREGATE -> HAVING: filters groups).

Full Mind Map

For an interactive and detailed view of the entire SQL execution order, check out the complete mind map:

Click on the image to open the interactive map in MindMeister

Practical example

Suppose we want to see the count of orders from a store by date, whose order status are "COMPLETE" and "CLOSED":

SELECT order_date, count(*) order_count -- Using aliases is not mandatory
FROM orders
WHERE order_status IN ('COMPLETE', 'CLOSED')
GROUP BY 1 -- Column (order_date)
ORDER BY 2 DESC LIMIT 10; -- Column (order_count)

Or orders are "COMPLETE" and "CLOSED" above a certain value:

SELECT order_date, count(*) order_count
FROM orders
WHERE order_status IN ('COMPLETE', 'CLOSED')
GROUP BY 1
HAVING count(*) >= 120 -- Filter for values above 120 (example)
ORDER BY 2 DESC LIMIT 10;

To alias a column, you do not necessarily need to use the AS keyword (count). To summarize the query above:

1. we filter the count of orders from the orders table (FROM orders);
2. we identify the necessary rows, which filters only the records that exactly meet the criteria defined in parentheses, before performing the count (WHERE status IN ('COMPLETE', 'CLOSED'));
3. we group this filter according to the first column of SELECT (GROUP BY 1);
4. we order the results according to the second column of SELECT in descending order (ORDER BY 2 DESC;); and
5. we select the base column(s) of our query and temporarily create a new column that will show the result, with an alias (SELECT date, count(*) count).

The reason I used "1" and "2" in GROUP BY and ORDER BY is to simplify for me, normally we write the column names for greater clarity. "1" represents the first column of SELECT (date) and "2" the second column (count).

Understanding the concepts behind all code writing allows us to create ways to read, interpret, simplify, and if we can simplify something, it means we understand, and if we understand, we will not forget.

Detailed practical example (EXPLAIN ANALYZE)

Summarizing the EXPLAIN ANALYZE command (image):

Seq Scan: PostgreSQL read the entire table (38428 rows)
Filter: Applied WHERE, removed unwanted rows
HashAggregate: Grouped by order_date
Filter (HAVING): Removed 342 groups with count >= 120
Total time: ~21ms

2026-01-21 04:16:51

When your AI agent fails, it can be a trap to think you just need to fix the prompt. It's reflexive, and a bit like when you're working with a new intern. If they don't understand your instructions the first time, it's normal to go back and try to figure out a better way to explain what you want them to do before putting them through a full on new training plan. Refining the way you are 'prompting' also gives you lots of quick wins early on, so EVERYONE involved feels more confident, but you can get to a point of diminishing returns after a while.

This is the moment where re-framing the task instructions stops being 'enough'. With prompting for AI, you've probably used a prompt generator by this point, added some examples (and counter examples), added some rules and constraints, and maybe even threatened firing the agent in the prompt if you're really feeling spicy. But even after all this, it's totally possible that the agent will still get stuck on a little calendar widget, then lose its context an sanity halfway through a booking flow. Once there's a bit of momentum at play, its so easy to just take the front-end dropdowns at face value when they look like they've been updated, even though the backend state hasn't been updated. This is when it's time for some practice drills.

The Gap Between Proof of Concept and Production

I was a mathematics major, so one of my little joys is reading the Amazon Science blog and keeping up with the research coming out of Amazo research teams. Today when I was absolutely not procrastinating from doing a reporting task, I was reading the blog and saw a post from Amazon's AGI Lab that caught my eye called "The unseen work of building reliable AI agents". In the post, the researchers describe "normcore agents" which are systems who excel at monotonous interactions that are very boring but vrey crucial for reliable software.

Reading this blog changed from being procrastination to 'research' when I got to this line:

"Before an AI can plan a vacation, it must learn to scroll. Literally. It must learn how to scroll … and click … and tab … and select a date that's hidden behind a pop-up … and recover when a form silently resets."

because this feels so relevant to what I'm seeing developers struggle with when they're working on building their own agentic systems.

Asking an agent to "book my summer vacation" is a simple request that leads to a wild workflow with hundreds of itty bitty steps. The agent needs to wrangle airline reservtation systems that were built when I was still in primary school, then it needs to deal with hotel systems which are consistently inconsistent, then there's payments (with currency conversions and regional taxes), and loyalty programs, and all sorts of compliance checks. A lot has go right for you to be able to book, and it needs to be right every time.

"She'll be right, mate" is one of my favourite Australian-isms, and I say it to myself every time I'm going from dev to prod. Every time, I think it's going to be easy pasy, and everything SHOULD be ok, but then life happens. It always feels like getting an acne breakout before a big event when things go wrong, even though you think you've done everything right. A big misconception (which the Amazon Science blog did a great job of unpacking for me) was that it's not always enough to fix the entire thing with a new or updated prompt.

The Gym Metaphor

Amazon's AGI Lab builds what they call "RL gyms" which are reinforcement learning environments where agents practice atomic behaviours:

"Just as an athlete builds core stability by repeating fundamental movements under controlled conditions, an agent develops reliability by practicing the smallest units of interaction in repeatable, instrumented scenarios."

This is the difference between bodybuilders with big 'show muscles' and people who are functionally fit. (I am in neither group, congrats if you're in one of them.) You can get puffed up with impressive muscles that look great in a demo on stage, but when it's time to actually help your friend move their fridge, it comes down to core strength and flexibility with movement that you can only really get by practicing, or walking the walk.

So a gym, in this cotext, is where you can 'isolate a skill, vary it, stress it, and measure it.' And this all results in what the Amazon Science researchers called an "agentic substrate" which is a foundation layer of basic skills and capabilities that agents can build on to use for domain-specific tasks.

The Three Workouts

The Amazon Science blog describes 3 workouts that show what agents actually need to practice, so they have the foundational skills they need.

Workout 1: Calendars

Calendars and booking systems are hard because they seem so simple in theory - we use them all the time. But once you get into recurring systems that do bookings every 3rd Monday of the month, and then that Monday happens to be a public holiday, things get really annoying really quickly. There's also time zone changes, daylight savings start/end dates (which always trip me up, because I didn't ever have daylight savings growing up!), and different holidays observed in different locations. Automating meeting booking for a team spread across time zones can turn into a nightmare super easily.

On top of all that, you're dealing with widgets that can go haywire under zoom or hide behind other UI layers. Elements re-render mid-click. And don't get me started on how differently calendar components behave across browsers.

So what does the agent really need to learn? It needs to recognize a widget's current state, and recover when it drifts, then commit the correct date exactly once, and remember to verify that the backend registered the change.

Workout 2: Dropdowns

"A dropdown menu might appear to have been updated before the backend has actually processed the change."

Once you start looking for this mismatch, you'll never unsee it. I've spotted it in enterprise apps, government systems, even slick consumer sites that should know better based on how lush their UI branding is. The agent can see a dropdown update and think "my work here is done" but the backend might still be processing, and the UI just lied to them.

Things can look fine on the surface - social media has taught us all this! - but the actual system state under the shiny UI tells us what's REALLY happening. Before you take the slick interface at face value, it's time to get a healthy amount of trust issues and dig in to check that the action was registered in the backend properly.

Workout 3: Async Endurance

Long workflows are brutal. When I'm doing a big spec task with Kiro, I get tired of watching the agent work and clicking 'next task' but then I always remember how much more tired I'd be if I was actually routing the requests or doing the work myself. Once you've got some async steps chained together - things like search, filter, validate, maybe refresh a few times - and then each one can have its own timing quirks. Text fields start to fight with autosuggest dropdowns that haven't finished loading. Sometimes the backend just... fails. And then the page looks loaded, but half the data is missing.

This is where agents "hit the wall." I know I would hit a wall too. They hit the context window limit - which for AI agents can happen when you have to look through a lot of large code files over an enormous repo, or researching lots of entries in a sprawling knowledge base. The agent just runs out of room to remember what it was doing in the first place, let alone figure out how to do that well.

The hard part is staying aligned with the true state of the system across dozens or hundreds of steps.

When Near Enough Isn't Good Enough

The Amazon Science blog mentions that some of their engineers come from autonomous vehicles. In that world, "almost right" is the same as "unsafe." You don't get points for being close, and the stakes are high.

"Agents don't just produce outputs; they take actions inside live systems. They touch databases, initiate transactions, and modify system states. And when the output of a model is a real change in the world, reliability becomes non-negotiable."

If your agent is booking flights or modifying customer records, "works most of the time" isn't good enough. You wouldn't accept that from a human employee. Why accept it from an agent?

What Does "Success" Actually Mean?

The research talks about "formal verifiers" - basically, specifications that define exactly what successful completion looks like. The button got clicked? Cool, but did the thing actually happen?

"A workflow like 'send an e-mail,' for example, isn't declared successful just because a button appears to have been clicked; it's declared successful because exactly one new e-mail record exists in the database, and no unrelated records have been created, modified, or deleted."

That's the bar, and agents have to clear it "not once but thousands of times, under shifting timing, network, and UI conditions."

What Can You Actually Build Today?

The Amazon Science blog describes internal research. In this case, it's RL gyms that Amazon's AGI Lab uses to train agents. So what can you use right now?

Amazon Bedrock AgentCore Browser solves the "I need my agent to browse the web but I don't want to become a browser infrastructure company" part. If you try to do this yourself, it will probably look like:

• Week 1: "I'll just use Selenium!" (seems easy)
• Week 2: Fighting ChromeDriver version mismatches
• Week 4: Debugging memory leaks and zombie processes
• Week 6: You're now managing browser pools, IP rotation, security hardening, and scaling infrastructure, and you have a job you never wanted

AgentCore Browser handles all the gross parts, so you can manage your own context window (in addition to the model's!). You are in charge of writing the agent logic, then AWS runs the browser for you. Session recording and replay let you debug exactly the kinds of calendar/widget failures the research describes.

Amazon Bedrock AgentCore Evaluations is the "formal verifiers" part of this, but for production. Remember how the research said success isn't "the UI looked right" but "the system state matches the specification"? You can go in and define what success actually looks like for your workflows, and then keep checking that your agent passes those tests. There are 13 built-in evaluators for things like tool selection accuracy and goal success rate, and you can build your own.

Amazon Bedrock AgentCore Memory helps with the "running out of room to remember" problem. Short-term memory is "what did we just talk about 3 messages ago?" and it keeps track within a single session so users don't have to repeat themselves. Long-term memory is "this user prefers morning meetings and hates Mondays" where it extracts insights across sessions, not just raw logs. This is how agents stay coherent across long workflows without hitting context window limits. I also dig into this concept in a recent blog called "Why AI Agents Need Context Graphs (And How to Build One with AWS)"

Amazon Bedrock AgentCore Observability shows you what REALLY happened. It gives you sessions, traces, and then spans let you see exactly what the agent attempted, Vs what the backend actually did. Instead of guessing based on what the UI showed, this is where you can see the real story.

Your Workout Plan

The takehome tip: your agent needs practice.

Prompts are instructions, but practice is repetition under varied conditions until the behaviour becomes reliable. The Amazon Science research shows us that agents need to satisfy verifiers "thousands of times" before they're production-ready.

The gym metaphor is a good memory device - so use it next time you need to explain this to the rest of your dev team. Your agents need isolated practice environments where failure is safe, varied conditions that stress-test edge cases, formal verification that defines what success actually means, and repetition until reliability becomes automatic.

This works for any agent framework, and also for humans. So, before your agent books a vacation, teach it to scroll.

The research referenced in this article comes from "The unseen work of building reliable AI agents" by Jason Laster, published on Amazon Science in January 2026. The AWS service descriptions are based on Amazon Bedrock AgentCore documentation.

2026-01-21 04:05:08

I’ve published a new OSF deposit introducing DELTA-X.

DELTA-X is an invariant reasoning and decision framework,
designed to be auditable, reproducible, and structurally opposable.

It explicitly avoids:

• narratives
• authority-based validation
• belief systems
• adaptive or opaque heuristics

Instead, it relies on:

• vector-based reasoning
• explicit thresholds
• closed operational sequences
• formal stop conditions
• traceable sealing mechanisms

The framework is applicable to:

• human cognition
• AI systems
• hybrid human–AI decision processes

This is not a philosophy, not an opinion, and not a model.
It either works structurally — or it fails.

OSF deposit (public):
https://osf.io/ub5f4

Formal review and technical critique are welcome.

2026-01-21 04:04:20

Looking for the best way to deploy DocuSeal, the open-source document signing platform? Whether you need a quick setup for production, want full control over your infrastructure, or just need to test locally: there's an option for you.

DocuSeal is a powerful alternative to expensive SaaS solutions like DocuSign and HelloSign. The best part? You have multiple deployment options depending on your needs, budget, and technical expertise.

In this guide, I'll walk you through 5 different ways to deploy DocuSeal, from the simplest one-click deployment to enterprise-grade Kubernetes setups. Want the quick summary? Skip to the comparison table at the end.

Let's dive into each option!

1. Sliplane — The Easy Way

If you want the simplest and most cost-effective way to self-host DocuSeal in production, Sliplane is your answer. Deploy DocuSeal in under 30 seconds with just a few clicks. No server management, no Docker knowledge required.

Here's how fast it is:

Sliplane is a Platform-as-a-Service (PaaS) built on top of Hetzner's infrastructure, giving you the best of both worlds: ease of use and affordable pricing.

How it works:

1. Sign up at sliplane.io (free GitHub login)
2. Create a server (or use the free 48-hour trial server)
3. Click "Deploy Service" → Select the DocuSeal preset
4. Done! Your DocuSeal instance is live with automatic HTTPS

Pros:

• ✅ One-click deployment: No Docker or server knowledge needed
• ✅ Fixed pricing: €9/month flat, no surprise bills
• ✅ Automatic HTTPS: SSL certificates handled for you
• ✅ Easy updates: One-click redeploy to update DocuSeal

Cons:

• ❌ No perpetual free tier: 48-hour free trial only

Best for: Developers, startups, and small businesses who want DocuSeal running in production without the DevOps overhead.

2. Self-Hosted on a VPS — The DIY Way

For developers who love full control and want the absolute lowest cost, self-hosting DocuSeal on a Virtual Private Server (VPS) is the way to go. Providers like Hetzner, DigitalOcean, or Linode offer affordable servers where you can run DocuSeal with Docker.

How it works:

1. Rent a VPS (e.g., Hetzner CX23 for ~€3/month)
2. Install Docker and Docker Compose
3. Set up a reverse proxy (Caddy or Nginx) for HTTPS
4. Deploy DocuSeal using Docker Compose
5. Configure DNS and SSL certificates

We have a complete step-by-step guide with screenshots: Self-hosting DocuSeal on a Hetzner Ubuntu Server

Pros:

• ✅ Cheapest option: As low as €3-5/month on Hetzner
• ✅ Full control: SSH access, custom configurations, any OS
• ✅ No vendor lock-in: Move to any provider anytime
• ✅ Learn valuable skills: Great for understanding DevOps

Cons:

• ❌ Manual setup required: Docker, reverse proxy, SSL, firewall
• ❌ You handle everything: Updates, backups, security patches
• ❌ Time investment: Initial setup takes 30-60 minutes
• ❌ Debugging is on you: No support if something breaks

Best for: Developers with Linux/Docker experience who want maximum cost savings and enjoy managing their own infrastructure.

3. Local Development — The Testing Way

Need to test DocuSeal or develop integrations? Running it locally on your laptop is the fastest way to get started. No server needed, no costs involved.

How it works:

# Using Docker (one command!)
docker run -d -p 3000:3000 -v docuseal_data:/data docuseal/docuseal:2.2.9

# Open http://localhost:3000

Or with Docker Compose, create a compose.yml:

services:
  docuseal:
    image: docuseal/docuseal:2.2.9
    ports:
      - "3000:3000"
    volumes:
      - docuseal_data:/data

volumes:
  docuseal_data:

Then run:

docker compose up -d

Pros:

• ✅ Completely free: No hosting costs
• ✅ Instant setup: One command to start
• ✅ Great for testing: Experiment without consequences
• ✅ Development friendly: Test API integrations locally

Cons:

• ❌ Not for production: Your laptop isn't a server
• ❌ No public access: Others can't access your instance
• ❌ Data loss risk: If you forget to persist volumes
• ❌ Resource usage: Uses your laptop's CPU/RAM

Best for: Developers who want to test DocuSeal, develop integrations, or evaluate features before committing to a hosting solution.

4. Kubernetes — The Enterprise Way

For organizations with existing Kubernetes infrastructure or those needing enterprise-grade scalability and high availability, deploying DocuSeal on K8s is an option.

But before you go down this path: Read our article on why Kubernetes probably isn't for you. For most teams, it's massive overkill. Also check out Docker vs Kubernetes to understand the key differences.

How it works:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: docuseal
spec:
  replicas: 2
  selector:
    matchLabels:
      app: docuseal
  template:
    metadata:
      labels:
        app: docuseal
    spec:
      containers:
      - name: docuseal
        image: docuseal/docuseal:2.2.9
        ports:
        - containerPort: 3000
        volumeMounts:
        - name: docuseal-data
          mountPath: /data
      volumes:
      - name: docuseal-data
        persistentVolumeClaim:
          claimName: docuseal-pvc
---
apiVersion: v1
kind: Service
metadata:
  name: docuseal
spec:
  selector:
    app: docuseal
  ports:
  - port: 80
    targetPort: 3000
  type: ClusterIP

You'll also need:

• Persistent Volume Claims for data storage
• Ingress controller for HTTPS
• Optionally: PostgreSQL deployed separately for scalability

Pros:

• ✅ High availability: Multiple replicas, auto-healing
• ✅ Enterprise-ready: Fits into existing K8s workflows
• ✅ Infrastructure as code: GitOps-friendly deployments

Cons:

• ❌ Massive overkill for most — You probably don't need this
• ❌ Complex setup: Requires K8s expertise
• ❌ Expensive: Managed K8s clusters aren't cheap
• ❌ Operational overhead: More moving parts = more things to break

Best for: Large enterprises with dedicated DevOps teams who already run Kubernetes and need to integrate DocuSeal into their existing infrastructure.

5. DocuSeal SaaS — The Official Cloud

Don't want to self-host at all? DocuSeal.com offers their own managed cloud service. They handle everything: hosting, updates, backups, and support.

How it works:

1. Sign up at docuseal.com
2. Choose a plan
3. Start creating and sending documents

Pros:

• ✅ Zero maintenance: DocuSeal team handles everything
• ✅ Official support: Direct help from the creators
• ✅ Always up-to-date: Automatic updates with latest features
• ✅ Free tier available: Limited free plan for testing

Cons:

• ❌ Most expensive option: Paid plans start at $13/month per user
• ❌ Per-user pricing: Costs scale with team size
• ❌ Data on their servers: Less control over data residency
• ❌ Usage limits: Document limits on lower tiers

Best for: Teams who don't want any technical responsibility and are okay paying premium prices for a fully managed experience with official support.

Comparison Table

Here's a quick summary to help you choose:

Which Should You Choose?

Let me make it simple:

• Want the best balance of ease and cost? → Sliplane: One-click deploy, €9/month, no DevOps needed
• Want the absolute cheapest option? → VPS Self-Hosting: €3-5/month, but you manage everything
• Just want to test it out? → Local Docker: Free, instant, no commitment
• Running a large enterprise with K8s? → Kubernetes: Only if you already have the expertise
• Don't want to touch any infrastructure? → DocuSeal SaaS: Pay premium for zero maintenance

For 90% of users, Sliplane offers the best value: production-ready deployment in 30 seconds, predictable pricing, and zero server management. You get the cost benefits of self-hosting without the headaches.

Cheers,
Atakan

2026-01-21 04:02:07

Developing a WordPress plugin can be a rewarding experience, and adhering to stringent WordPress Plugin Best Practices from the outset is crucial for creating robust, secure, and widely usable software. This isn't just about functionality; it's about security, internationalization, performance, and overall code quality.

For beginners looking to build high-quality plugins, understanding these core principles is essential. In this in-depth analysis, we'll break down the critical refinements needed to elevate your plugin from functional to truly professional, drawing directly from common development standards and crucial feedback points.

Why Robust WordPress Plugin Best Practices Matter

Before diving into the 'how,' let's understand the 'why.' A plugin that meets high standards isn't just easier to maintain; it's more reliable, secure, and user-friendly. Adhering to these WordPress Plugin Best Practices is crucial for protecting your users and ensuring your plugin is a valuable, sustainable asset for the entire WordPress ecosystem.

Internationalization (i18n): Speaking Every Language

One of WordPress's greatest strengths is its global reach. Your plugin should be accessible to users worldwide, which means all text strings must be translatable. This is a fundamental WordPress Plugin Best Practice for broad adoption.

Implementing Basic Translation

Wrap all user-facing strings in __() for echoing or _e() for direct output. Remember to specify your unique text domain, often matching your plugin's slug (e.g., 'aica-plugin').

// For strings that need to be returned
$text = __( 'Hello World', 'aica-plugin' );

// For strings that need to be echoed directly
_e( 'Welcome to my plugin!', 'aica-plugin' );

Advanced Translation with Placeholders and HTML

When you need to include dynamic data or HTML tags within a translatable string, printf() is your best friend. It allows translators to reorder phrases if necessary, without breaking your HTML.

<?php
/* translators: 1: format list, 2: strong tag start, 3: strong tag end */
printf(
    esc_html__( 'JPG, PNG, or SVG, up to 1MB. Ideal size: %2$s535 x 535 pixels%3$s.', 'aica-plugin' ),
    'JPG, PNG, SVG',
    '<strong>',
    '</strong>'
);
?>

Pluralization for Dynamic Messages

For messages that change based on quantity (e.g., "1 minute ago" vs. "5 minutes ago"), use _n() to handle pluralization correctly.

<?php
$count = 5;
printf(
    _n( '%s minute ago', '%s minutes ago', $count, 'aica-plugin' ),
    number_format_i18n( $count )
);
?>

Localizing JavaScript Strings

JavaScript strings also need to be translatable. wp_localize_script() is the standard way to pass translated strings (and other dynamic data) from PHP to your JavaScript files.

// In your PHP file (e.g., during script enqueue)
wp_localize(
    $this->plugin_slug . '-admin',
    'aica_admin_data',
    array(
        'ajax_url'            => admin_url('admin-ajax.php'),
        'nonce'               => wp_create_nonce('aica_admin_nonce'),
        'i18n_visitor'        => __( 'Visitor', 'aica-plugin' ),
        'i18n_ai'             => __( 'AI Agent', 'aica-plugin' ),
        'i18n_deleting'       => __( 'Deleting...', 'aica-plugin' ),
        'i18n_saving'         => __( 'Saving...', 'aica-plugin' ),
        'i18n_summary_title'  => __( 'AI Intelligence Summary', 'aica-plugin' ),
        'i18n_confirm_delete' => __( 'Delete this conversation forever?', 'aica-plugin' ),
    )
);

// In your JavaScript file (e.g., admin.js)
console.log(aica_admin_data.i18n_deleting);

Security & Escaping: Protecting Against Malice

Security is paramount in WordPress development. Every piece of data displayed to the user or processed from user input must be properly escaped and sanitized. This is a non-negotiable WordPress Plugin Best Practice.

Escaping Output: Don't Trust Anything!

Always escape data before outputting it to the browser. This prevents Cross-Site Scripting (XSS) vulnerabilities.

• esc_html(): For general HTML content.
• esc_attr(): For HTML attribute values.
• esc_url(): For URLs.
• checked(): Helps securely mark radio buttons/checkboxes.

When generating links or other HTML elements with dynamic PHP content, use functions like add_query_arg for URL building and esc_url for final output. Similarly, for HTML attributes like input values or checked states, esc_attr and checked are essential.

<?php
// Example: Building a secure URL with add_query_arg() and escaping it
$page_url = admin_url( 'admin.php?page=my-plugin' );
$active_tab = 'content'; // Example value

$link_href = esc_url( add_query_arg( 'tab', 'content', $page_url ) );
$tab_class = $active_tab === 'content' ? 'nav-tab-active' : '';

// Translatable link text:
_e( 'Content', 'aica-plugin' );

// Example: Safely outputting an HTML attribute and checked state
$value = 'dark'; // Example value
$current_theme = 'dark'; // Example value

// The value attribute:
echo esc_attr($value); 

// The 'checked' attribute conditionally:
checked($current_theme, $value);
?>

Sanitizing Input: Clean Data is Safe Data

Before saving or using user-provided data, sanitize it to ensure it conforms to expected formats and doesn't contain malicious code.

• sanitize_key(): For sanitizing keys/slugs.
• intval(): For ensuring integers.
• wp_strip_all_tags(): For stripping HTML from strings (e.g., email subjects).
• wp_kses_post(): For sanitizing HTML content, allowing only safe tags (useful for rich text in emails).

<?php
// Example: Sanitizing a session ID
$session_id = sanitize_key( $_POST['session_id'] );

// Example: Sanitizing an integer ID
$item_id = intval( $_GET['item_id'] );

// Example: Sanitizing feedback before passing to an LLM
$user_query = esc_html( $_POST['user_query'] ); // Use esc_html for display, or more robust sanitization for LLM input

// Example: Sanitizing email subject and HTML content
$subject = wp_strip_all_tags( $_POST['email_subject'] );
$summary_html = wp_kses_post( $_POST['email_body'] );
?>

Preventing XSS in JavaScript

When dynamically inserting content into the DOM using JavaScript, be extremely careful. Direct use of .html() or template literals without proper escaping can introduce XSS vulnerabilities. Always prefer .text() for plain text or ensure content is sanitized on the server-side if it's expected to contain safe HTML.

// ❌ DANGEROUS: Potential XSS vulnerability
// $('.chat-content').html(msg.content);
// const username = response.data;
// $('#user-display').text(`Welcome, ${username}!`);

// ✅ SAFER: Use .text() for plain text or sanitize before using .html()
// For displaying plain text
$('.user-message').text(msg.user_input);

// If msg.content is *expected* to contain safe HTML, it must be sanitized on the server-side
// before being passed to the client. If it's pure text, use .text()

// For dynamic user names, always use .text() or equivalent for plain text insertion
const username = aica_admin_data.i18n_visitor; // Use localized string
$('#user-display').text(username);

Nonce Verification for AJAX Calls

Always verify nonces for AJAX requests to prevent CSRF (Cross-Site Request Forgery) attacks.

<?php
// In your AJAX handler
if ( ! isset( $_POST['nonce'] ) || ! wp_verify_nonce( $_POST['nonce'], 'aica_admin_nonce' ) ) {
    wp_send_json_error( array( 'message' => __( 'Security check failed.', 'aica-plugin' ) ) );
}
// Proceed with AJAX logic
?>

Database Interaction & SQL Security

Interacting with the database is common, but it's also a prime target for SQL Injection. Adhering to wpdb best practices is a must.

Prepared Queries with $wpdb->prepare()

This is the golden rule for database security. Never concatenate user input directly into SQL queries. Always use $wpdb->prepare().

<?php
global $wpdb;
$table_name = $wpdb->prefix . 'my_custom_table';
$data_id = intval( $_GET['data_id'] );

// ✅ Secure way to fetch data
$result = $wpdb->get_row(
    $wpdb->prepare(
        "SELECT * FROM %i WHERE id = %d",
        $table_name,
        $data_id
    )
);

// ✅ Secure way to insert data
$wpdb->insert(
    $table_name,
    array(
        'column1' => sanitize_text_field( $_POST['input1'] ),
        'column2' => intval( $_POST['input2'] )
    ),
    array( '%s', '%d' ) // Format specifiers
);
?>

Indexing for Performance

Proper indexing improves database query speeds. When creating tables, ensure that VARCHAR(255) columns used for indexing are limited to 191 characters to ensure compatibility with older MySQL/MariaDB versions using UTF8mb4.

CREATE TABLE `wp_my_custom_table` (
    `id` BIGINT(20) UNSIGNED NOT NULL AUTO_INCREMENT,
    `session_key` VARCHAR(191) NOT NULL,
    `created_at` DATETIME NOT NULL,
    PRIMARY KEY (`id`),
    KEY `session_key_idx` (`session_key`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_520_ci;

Existence Checks for Robustness

Add IF NOT EXISTS logic to your CREATE TABLE statements to prevent errors if the table already exists, making your plugin more robust during updates or re-installations.

<?php
global $wpdb;
$table_name = $wpdb->prefix . 'my_custom_table';
$charset_collate = $wpdb->get_charset_collate();

$sql = "CREATE TABLE IF NOT EXISTS `{$table_name}` (
    id BIGINT(20) UNSIGNED NOT NULL AUTO_INCREMENT,
    session_key VARCHAR(191) NOT NULL,
    created_at DATETIME NOT NULL,
    PRIMARY KEY  (id),
    KEY session_key_idx (session_key)
) {$charset_collate};";

require_once( ABSPATH . 'wp-admin/includes/upgrade.php' );
dbDelta( $sql );
?>

Accessibility & User Experience

Good plugins consider all users. Accessibility features and a smooth user experience are critical WordPress Plugin Best Practices.

Image Alt Tags

Always provide meaningful alt attributes for images, especially in the admin area, to assist users with screen readers.

<?php
// Example: Safely outputting an image's URL and translatable alt text
$avatar_url = 'path/to/avatar.png';

// For the 'src' attribute:
echo esc_url( $avatar_url ); 

// For the 'alt' attribute, which is also translatable:
esc_attr_e( 'AI Agent Avatar', 'aica-plugin' ); 
?>

Localized Dates

Use date_i18n() instead of date() to display dates and times according to the user's WordPress locale settings.

<?php
// Display current date in localized format
echo date_i18n( get_option( 'date_format' ) );

// Display a specific timestamp in localized format
$timestamp = strtotime( '-5 minutes' );
echo date_i18n( 'F j, Y, g:i a', $timestamp );
?>

Robustness & Error Prevention

Preventing fatal errors and ensuring your plugin degrades gracefully is a sign of a well-engineered product.

class_exists for Sub-Models

If your plugin relies on dynamically loaded or optional classes, use class_exists() before instantiating them. This prevents fatal errors if a file is missing or a dependency isn't met.

<?php
if ( class_exists( 'My_Grok_Model' ) ) {
    $grok_instance = new My_Grok_Model();
} else {
    // Fallback or log error
    error_log( 'My_Grok_Model class not found.' );
}
?>

Header Security

When sending emails, ensure your headers are secure to prevent injection attacks.

<?php
function aica_filter_from_header( $from_email ) {
    $site_name = get_option( 'blogname' );
    // Strip angle brackets and double quotes from site name to prevent header injection
    $safe_site_name = str_replace( array( '<', '>', '"' ), '', $site_name );
    return $safe_site_name . ' <' . $from_email . '>';
}
add_filter( 'wp_mail_from_name', 'aica_filter_from_header' );
?>

Key Takeaways for Mastering WordPress Plugin Best Practices

• Internationalization is mandatory: Translate all strings using __, _e, _n, printf, and wp_localize_script.
• Security first: Escape all output (esc_html, esc_attr, esc_url) and sanitize all input (sanitize_key, intval, wp_strip_all_tags, wp_kses_post). Use add_query_arg for URL building and wp_verify_nonce for AJAX.
• SQL Safety: Always use $wpdb->prepare() for database queries to prevent SQL injection.
• Performance: Implement proper indexing (VARCHAR(191)) and use IF NOT EXISTS for robust table creation.
• Accessibility: Provide alt tags for images and use date_i18n() for localized dates.
• Error Prevention: Use class_exists checks and secure email headers.

By meticulously applying these WordPress Plugin Best Practices, you'll create a superior plugin that is robust, secure, and user-friendly. These principles are the bedrock of professional WordPress development.

What are your go-to best practices for WordPress plugin development? Share your insights and experiences in the comments below!