DEV Community: Anas Rhimi

eBPF for Cloud-Native Security and Observability

Anas Rhimi — Tue, 11 Aug 2026 17:14:09 +0000

The Problem with Traditional Cloud Security

Securing a Kubernetes cluster has historically meant injecting sidecar containers (like Envoy) into every single pod, or loading risky kernel modules to intercept network traffic. Sidecars consume massive amounts of CPU and memory, and kernel modules can crash your entire node if they fail.

This architectural flaw has finally been solved by eBPF (Extended Berkeley Packet Filter).

What is eBPF?

eBPF is a revolutionary technology that allows you to run sandboxed programs directly within the Linux kernel, without having to change kernel source code or load kernel modules. It provides absolute visibility into everything happening on a machine.

- Zero-Overhead Observability: Because eBPF runs at the kernel level, it can see every network packet, file access, and system call across the entire node without requiring a sidecar in every pod.



Unprecedented Security: Tools like Cilium use eBPF to enforce network policies and drop malicious packets before they even reach the Kubernetes networking stack.

Performance Profiling: eBPF can pinpoint exactly which function in your application is causing a CPU spike in production, with less than 1% overhead.

Cilium: The New Standard for Kubernetes CNI

If you are deploying a new Kubernetes cluster today (whether on Proxmox, AWS, or Civo), using Cilium as your Container Network Interface (CNI) is practically mandatory. It leverages eBPF to replace kube-proxy, resulting in dramatically faster network throughput and lower latency for microservices.

Conclusion

eBPF has fundamentally changed how we build cloud-native infrastructure. It is the engine powering the next generation of networking, security, and observability tools. Mastering eBPF concepts is now a critical skill for any Senior Cloud Architect.

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The Death of Traditional DevOps: Platform Engineering & IDPs

Anas Rhimi — Tue, 11 Aug 2026 17:14:03 +0000

The Failure of "You Build It, You Run It"

The original promise of DevOps was to break down the silos between developers and operations. The idea was simple: "You build it, you run it." Developers would write the code, write the Dockerfile, write the Terraform, and manage the Kubernetes deployments.

The result? Developers drowned in cognitive overload. Instead of shipping features, senior software engineers were spending 40% of their time debugging Helm charts and fighting IAM permissions. The DevOps model, as originally conceived, has failed at scale.

Enter Platform Engineering

Platform Engineering is the evolution of DevOps. Instead of forcing developers to become infrastructure experts, Platform Engineers treat developers as their customers. They build an Internal Developer Platform (IDP).

- Golden Paths: Platform engineers define secure, highly-available infrastructure templates (Golden Paths).



Self-Service: Developers use a portal (like Backstage or Port) to request a new microservice. The IDP automatically provisions the repo, CI/CD pipeline, database, and Kubernetes namespace in minutes, with zero ticketing required.

Abstraction: Developers write code and push to main. The platform handles everything else. They don't need to know how the Ingress controller works.

The Tools of the Trade

Building an IDP requires stitching together best-in-class tools into a cohesive product. This usually involves Backstage (for the UI), ArgoCD (for GitOps deployment), Crossplane (for provisioning cloud resources via Kubernetes), and strict OPA Gatekeeper policies (for security guardrails).

Conclusion

DevOps is not a job title; it is a philosophy. Platform Engineering is the practical implementation of that philosophy at scale. By abstracting infrastructure complexity into a self-service platform, companies are finally achieving the deployment velocity they were promised a decade ago.

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Data Sovereignty in 2026: Production-Grade Local LLMs

Anas Rhimi — Tue, 11 Aug 2026 17:13:56 +0000

The API Trap

Over the last few years, thousands of startups have built their entire business logic around API calls to closed models like OpenAI's GPT-4 or Anthropic's Claude. While convenient, this architecture has critical flaws: vendor lock-in, skyrocketing API costs at scale, and catastrophic privacy risks when handling sensitive user or healthcare data.

In 2026, the era of "API-first" AI is ending. Enterprise teams are taking their intelligence in-house to guarantee Data Sovereignty.

The Rise of Production-Grade Local LLMs

Thanks to incredible advancements in model quantization and open-weight models (like Llama 3.1, Mistral, and Qwen), it is now possible to run GPT-4 level intelligence on commodity hardware.

- vLLM for High-Throughput: If you are serving thousands of requests per second, vLLM provides state-of-the-art PagedAttention, maximizing GPU utilization and serving inferences at lightning speed.



Ollama for Developers: For internal tooling, CI/CD integrations, and local development, Ollama makes pulling and running an LLM as easy as running a Docker container.

Architecting a Sovereign AI Stack

A modern sovereign AI stack runs entirely within your Virtual Private Cloud (VPC). You deploy vLLM on a GPU-enabled Kubernetes node, expose it internally via an OpenAI-compatible API endpoint, and point your applications (or n8n automation workflows) to that internal IP.

Your data never leaves your network. You pay a flat rate for the compute instance, rather than a variable tax on every token generated. You own your intelligence.

Conclusion

Relying on a third-party API for your core product intelligence is a liability. By mastering local LLM deployment with vLLM and Ollama, DevOps engineers are empowering their companies to scale AI securely, privately, and cost-effectively.

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WebAssembly (WASM) in Kubernetes: The Micro-Edge Revolution

Anas Rhimi — Tue, 11 Aug 2026 17:13:49 +0000

Containers are Heavy. Meet WebAssembly.

Docker revolutionized software delivery by packaging applications with their dependencies. But containers, at their core, still require an entire Linux OS filesystem, resulting in images that are hundreds of megabytes in size and take seconds to start up.

As we push compute closer to the edge (Edge AI, Serverless functions, IoT), seconds of cold-start latency and heavy memory footprints are no longer acceptable. Enter WebAssembly (WASM) on Kubernetes.

Why WASM is the Future of Kubernetes

WebAssembly was originally designed to run high-performance code in the browser. But with WASI (WebAssembly System Interface), it can now run natively on servers, completely sandboxed, at near-native speeds.

- Instant Cold Starts: WASM modules start in milliseconds, not seconds. They are perfect for event-driven architectures and serverless workloads.



Tiny Footprint: A typical WASM module is a few megabytes. You can pack thousands of WASM modules onto a single Kubernetes worker node that would normally only hold a few dozen Docker containers.

Language Agnostic: You can write your microservice in Rust, Go, Python, or C++, compile it to WASM, and it will run exactly the same on an x86 server or an ARM64 edge device without recompiling.

Deploying WASM on K8s with Spin

Projects like SpinKube and Kwasm have made it trivial to run WASM workloads alongside traditional containers in your existing Kubernetes clusters.

apiVersion: core.oam.dev/v1beta1
kind: Application
metadata:
  name: wasm-hello-world
spec:
  components:
    - name: wasm-hello
      type: wasm-worker
      properties:
        image: ghcr.io/fermyon/hello-world:latest

Conclusion

WASM isn't going to replace Docker for your massive, legacy monoliths. But for microservices, AI inference at the edge, and highly concurrent workloads, WASM is the new standard. If you are building a modern cloud-native architecture in 2026, WASM must be in your toolkit.

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The Rise of Agentic DevOps: How Autonomous AI is Replacing Static CI/CD

Anas Rhimi — Tue, 11 Aug 2026 17:13:41 +0000

The Era of Static CI/CD is Over

For the last decade, CI/CD pipelines have been defined by static, rule-based YAML files. We hardcode steps: checkout code, run tests, build image, push to registry, deploy. When a step fails, the pipeline halts, a slack notification is sent, and a human engineer has to drop everything to investigate the logs.

In 2026, the sheer velocity of AI-assisted coding means developers are committing code 10x faster than before. Static pipelines have become a massive bottleneck. The solution is Agentic DevOps.

What is Agentic DevOps?

Agentic DevOps introduces autonomous AI agents into the software delivery lifecycle. Instead of merely executing a predefined script, an agent possesses context, reasoning capabilities, and the authority to take corrective action.

- Self-Healing Pipelines: If a unit test fails due to a missing dependency, the agent reads the error log, modifies the <code>package.json</code>, runs the test again, and if it passes, commits the fix to a new branch for review.



Dynamic Orchestration: Agents analyze the git diff and intelligently determine which tests actually need to run, drastically reducing build times without sacrificing safety.

FinOps Integration: Agents constantly monitor staging environments. If a costly GPU instance is left idling after an integration test, the agent automatically tears it down.

Building the Agentic Pipeline

Transitioning to an agentic model doesn't mean throwing away GitHub Actions or Jenkins. It means wrapping them in an intelligence layer. Tools like n8n combined with local LLMs (like Llama 3.1 or Mistral) are the perfect orchestrators for this.

You can create an n8n webhook that listens for failed GitHub Actions, pipes the failing log to a local LLM, asks it for the bash command to fix the issue, and then executes that command in an ephemeral container to test the hypothesis before alerting a human.

Conclusion

The role of the DevOps engineer is shifting from "Pipeline Builder" to "Agent Supervisor." By embracing agentic workflows, we stop babysitting builds and start focusing on architecture, security, and velocity.

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The "Works on My Machine" Syndrome (And How to Cure It)

Anas Rhimi — Tue, 11 Aug 2026 03:45:23 +0000

"I don't understand, it literally works perfectly on my machine!"

Every developer has uttered this phrase. It usually happens right after a deployment brings down the staging environment, the QA team is angry, and you're sweating profusely trying to figure out why a TypeError is happening in the cloud when it ran flawlessly on your MacBook.

The "Works on My Machine" syndrome is a symptom of Configuration Drift. Here is how modern DevOps practices cure it permanently.

The Root Cause

Why does it work on your machine?
Because your machine has Python 3.10 installed globally. Because you manually tweaked a config file 6 months ago and forgot. Because your local PostgreSQL database has a different timezone setting than AWS RDS.

Your local machine is a unique, handcrafted artisanal artifact. Production is a cold, standardized reality.

Step 1: Embrace the Container (Docker)

If you aren't using Docker for local development in 2026, you are playing Russian Roulette with your deployments.

Docker allows you to package your application, its dependencies, and the exact operating system it runs on into a single immutable image.
When you run docker-compose up, you aren't running code on your Mac; you're running it in a pristine Linux environment that perfectly mirrors production.

If it works in the container locally, it will work in the container in the cloud. End of story.

Step 2: Infrastructure as Code (IaC)

Clicking around the AWS console to provision a database or set up an S3 bucket is a recipe for disaster. You will forget to check a box. You will mess up an IAM policy.

Use Terraform or AWS CDK. Define your infrastructure in code. Code can be reviewed, version-controlled, and audited. When you need a staging environment, you don't spend 3 days clicking buttons; you run terraform apply -var="environment=staging" and it spins up an exact replica of production in 5 minutes.

Step 3: CI/CD Parity

Your CI/CD pipeline should be the only entity allowed to deploy code.
If developers are manually SSH'ing into servers to pull code or restart PM2 processes, you do not have a deployment pipeline; you have a ticking time bomb.

Automate everything. When code is merged to main, GitHub Actions or GitLab CI should build the Docker image, run the automated tests against that specific image, and push it to the registry.

Conclusion

The phrase "Works on My Machine" is an admission of failure in your deployment pipeline.

By utilizing Docker, Terraform, and strict CI/CD pipelines, we shift from "hoping" deployments work to "knowing" they will. Cure the syndrome, and get your weekends back.

Debugging Fatigue: How to Stop Chasing Ghosts in Your Code

Anas Rhimi — Tue, 11 Aug 2026 03:44:42 +0000

It's 11:30 PM. Your coffee is cold. You've been staring at the same stack trace for four hours. You've added console.log("here"), console.log("here 2"), and console.log("WTF") across twelve different files.

You are experiencing Debugging Fatigue.

Debugging fatigue happens when you abandon systematic problem-solving and devolve into frantic, trial-and-error code changes, hoping something—anything—will magically fix the bug.

Here is a systematic framework to stop chasing ghosts and fix bugs like a senior engineer.

1. Stop Guessing. Start Isolating.

When a bug surfaces, the amateur instinct is to look at the code and guess where the error is. This is a trap.

Instead of guessing, use the Binary Search Method for debugging.

Is the bug in the frontend or backend? Check the network tab.
It's the backend. Is it the controller or the database? Log the payload right before the DB call.
It's the controller. Is it the validation logic or the business logic?

Halve the search space with every check. Don't read the code; follow the data.

2. Replicate the State

If you can't replicate it, you can't fix it.
Never try to patch a bug in production blindly. Spend the time required to write a failing unit test that reproduces the exact scenario.

A failing test gives you a tight, controlled sandbox. When the test turns green, you know the ghost is dead. No more "I think I fixed it, let's deploy and see."

3. The 45-Minute Rule

Your brain is a muscle, and when it's fatigued, your IQ effectively drops by 20 points.

Implement the 45-Minute Rule: If you have been stuck on a bug for 45 minutes without making measurable progress toward isolating it, you must walk away.

Go for a walk. Take a shower. Play a video game.
I guarantee you, the solution will hit you like a ton of bricks the moment you stop staring at the IDE. Your subconscious background processing is vastly superior to your stressed, hyper-focused conscious mind.

4. Rubber Ducking (With AI)

We all know the concept of explaining your code to a rubber duck. But today, we have AI.
Paste your function into ChatGPT or Claude and say: "Here is what this function is supposed to do. Here is the output I'm getting. What assumptions am I making that are incorrect?"

Often, the AI won't give you the exact answer, but it will ask a question that breaks your tunnel vision.

Debugging is not a test of intelligence; it's a test of methodology. Stay systematic, take breaks, and stop chasing ghosts.

The Silent Killer of Engineering Teams: Scope Creep (And How to Defeat It)

Anas Rhimi — Tue, 11 Aug 2026 03:44:00 +0000

You start with a simple Jira ticket: "Add a 'Download PDF' button to the invoice page." It's estimated at 3 story points. You think you'll knock it out before lunch.

Three weeks later, you're deep in the trenches integrating a third-party headless Chromium microservice, rewriting the entire authentication middleware to support role-based access for PDF generation, and migrating the database to handle the new pdf_metadata JSONB columns.

What happened? Scope creep happened.

Scope creep is the silent killer of engineering teams. It drains morale, destroys velocity, and turns clean codebases into spaghetti architectures hastily patched together to meet evolving "requirements."

Here is how you, as a developer or team lead, can defeat it.

1. The "Phase 2" Bucket

When a stakeholder says, "While you're in there, can you also make the PDF customizable with a drag-and-drop editor?" your answer should never be a flat "No" (which builds resentment) or a blind "Yes" (which ruins your sprint).

Your answer should be: "That's an excellent idea. Let's put that in the Phase 2 bucket so we can ship the core value today."

The "Phase 2" bucket is a psychological safety net. It acknowledges the stakeholder's idea without derailing the current execution. (Spoiler alert: 90% of Phase 2 features are never requested again once Phase 1 goes live).

2. Write Ironclad Technical Specs

Never start writing code based on a one-sentence Slack message.

If a feature takes more than a day to build, it needs a technical spec. It doesn't need to be a 10-page document. A simple markdown file outlining the Goal, Non-Goals, and Implementation Steps is enough.

Non-Goals are crucial. Explicitly stating what you are not building sets the boundary. When scope creep tries to sneak in, you simply point to the agreed-upon Non-Goals.

3. Visualize the Butterfly Effect

Stakeholders often don't understand that a "small UI tweak" might require a massive backend refactor.

When new scope is introduced, visualize the impact. Say, "We can absolutely add real-time WebSocket updates to the PDF generation, but that will add 2 weeks to the timeline and delay the payment gateway release. Should we prioritize the WebSockets?"

Force the business to make the tradeoff decision. You are the engineer; you provide the cost. They provide the priority.

Stop Being a Code Monkey

Your job isn't just to write code. Your job is to deliver value predictably. By mastering the art of managing scope, you elevate yourself from a junior programmer to a senior engineer who dictates the pace of execution.

Protect your sprint. Protect your sanity.

Have you ever been burned by massive scope creep? How did you handle it? Let's discuss in the comments.

Re-architecting AWS to Proxmox LXC: Saving 70% on Cloud Costs

Anas Rhimi — Mon, 10 Aug 2026 23:30:33 +0000

The VMware Exodus

With Broadcom's acquisition of VMware causing massive licensing fee hikes, enterprise companies are scrambling for alternatives. The default reaction is often a lift-and-shift to AWS or Azure. However, this often replaces licensing bloat with compute bloat.

In this tear-down, I dissect how I re-architected a high-traffic SaaS from a heavy AWS EC2/EKS environment to a bare-metal Proxmox LXC cluster, resulting in a 70% reduction in monthly cloud costs while increasing I/O performance.

The Problem: AWS Compute Bloat

The client was running a microservices architecture on AWS. They utilized EKS (Elastic Kubernetes Service) for orchestration, RDS for their PostgreSQL databases, and Elasticache for Redis. The monthly bill was nearing $12,000, primarily driven by compute instances (EC2), NAT Gateway data transfer, and managed service premiums.

The Solution: Proxmox Virtual Environment

We pivoted to a collocated bare-metal approach using Proxmox VE. Proxmox allows for both KVM-based virtual machines and lightweight LXC (Linux Containers).

1. Replacing EKS with LXC and Docker Swarm

Instead of heavy KVM virtual machines, we deployed stateful services directly onto Proxmox LXC containers. LXC shares the host kernel, providing bare-metal performance with zero virtualization overhead. For stateless microservices, we utilized a lightweight Docker Swarm cluster running across 3 VM nodes.

2. The Storage Layer: ZFS

AWS EBS volumes are expensive and charge for IOPS. We deployed NVMe drives in a ZFS RAID10 configuration on the bare-metal servers. ZFS provides native compression (lz4), snapshots, and incredible read/write speeds that dwarf standard EBS volumes, all without the per-IOPS cost.

3. Networking and Ingress

We replaced AWS ALB with a highly available HAProxy + Keepalived setup across two Proxmox nodes. This eliminated the hourly load balancer fees and provided deeper control over SSL termination and request routing.

The Outcome

By moving away from managed public cloud services and leveraging the raw power of bare-metal with Proxmox, the infrastructure cost dropped from $12,000/mo to approximately $3,500/mo (including colocation and bandwidth). Latency decreased due to the elimination of the AWS virtualization layer.

Looking to escape cloud vendor lock-in? I specialize in Proxmox architecture and Kubernetes migrations. Contact me to discuss your infrastructure.

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Building a Zero-Trust Kubernetes Cluster from Scratch

Anas Rhimi — Mon, 10 Aug 2026 23:30:26 +0000

The Illusion of Internal Security

Many organizations treat their Kubernetes cluster like a medieval castle: heavy defenses at the perimeter (ingress/WAF), but completely unprotected on the inside. If an attacker compromises a single container, they often have unfettered lateral access to the entire cluster network.

In this post, I break down how to architect a Zero-Trust Kubernetes Cluster from the ground up, ensuring that every microservice explicitly authenticates and authorizes its peers.

1. Network Policies: The Foundation

By default, Kubernetes pods can communicate with any other pod in the cluster. This is a massive security risk. The first step in Zero-Trust is implementing default-deny Network Policies.

We utilize Cilium as our CNI (Container Network Interface). Cilium uses eBPF for highly efficient networking and security. We implement a global CiliumClusterwideNetworkPolicy that denies all cross-namespace traffic by default, requiring developers to explicitly whitelist necessary communication paths.

2. mTLS with Istio Service Mesh

Network policies restrict who can talk, but they don't encrypt the traffic or cryptographically verify identity. For this, we deploy the Istio Service Mesh.

We enforce STRICT mTLS (Mutual TLS) across the entire cluster. Istio automatically provisions and rotates cryptographic certificates for every pod. When Service A talks to Service B, the connection is encrypted, and Service B cryptographically verifies that the request actually came from Service A.

3. Identity-Aware Proxy (IAP) & OIDC

For internal dashboards (like Grafana, Kibana, or ArgoCD), we do not expose them directly or rely on basic auth. We integrate an Identity-Aware Proxy (like Pomerium or oauth2-proxy) with the company's SSO (Okta/Google Workspace).

4. Ephemeral Secrets with HashiCorp Vault

Hardcoded database credentials in Kubernetes Secrets are a ticking time bomb. We integrate the cluster with HashiCorp Vault. Applications do not receive static passwords; instead, they authenticate with Vault using their Kubernetes Service Account JWT and receive dynamic, short-lived credentials that expire after 1 hour.

Conclusion

A Zero-Trust Kubernetes architecture assumes breach. By combining Cilium eBPF, Istio mTLS, and dynamic secrets, we isolate compromises and prevent lateral movement.

Need a security audit or a DevSecOps pipeline built? Let's talk.

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Automating Enterprise Workflows with n8n and Local LLMs (Ollama)

Anas Rhimi — Mon, 10 Aug 2026 23:30:17 +0000

The Privacy Dilemma in AI Automation

Companies want the power of AI to automate document parsing, lead scoring, and internal workflows. However, sending sensitive financial data, legal contracts, or customer PII to OpenAI's public API is often a massive compliance violation.

The solution? Self-hosted, programmatic AI workflows. In this tear-down, I demonstrate how to build a fully automated, privacy-first lead generation and processing engine using n8n and Local LLMs (Ollama).

The Architecture

1. The Orchestrator: n8n (Fair-code)

Instead of Zapier (expensive, cloud-only), we deploy n8n via Docker. n8n is a powerful workflow automation tool that can be entirely self-hosted. It connects to our local database, CRM, and email server without any data leaving our VPC.

2. The Brain: Ollama + Llama 3

We provision a GPU-enabled instance (or pass through a GPU in Proxmox) and run Ollama. Ollama serves open-source LLMs like Meta's Llama 3 or Mistral directly via a local API. This means our AI processing happens entirely on-premise.

The Workflow Breakdown

Here is a practical example of a workflow we automated for a B2B client:

Trigger: A new email arrives in the sales inbox with an attached PDF contract.
Extraction: n8n catches the webhook, downloads the PDF, and uses a local OCR container to extract the raw text.
AI Processing: n8n sends the raw text to our local Ollama instance (e.g., http://ollama:11434/api/generate) with a prompt to extract key entities: "Extract the Company Name, Deal Value, and Key Clauses from this text and return it as strict JSON."
CRM Update: The LLM responds with perfectly formatted JSON. n8n parses this JSON and automatically updates the PostgreSQL database and creates a new deal in the self-hosted CRM (e.g., Odoo or ERPNext).
Alerting: Finally, n8n sends a message to the internal Slack channel notifying the sales team of the new processed lead.

The Impact

This system processes thousands of documents per month at zero marginal cost (no API fees) while maintaining 100% data privacy and GDPR/HIPAA compliance.

Want to automate your business processes without compromising security? I build custom n8n and local AI pipelines. Contact me today.

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The Silent Killer of Engineering Teams: Scope Creep (And How to Defeat It)

Anas Rhimi — Sun, 09 Aug 2026 13:20:45 +0000

You start with a simple Jira ticket: "Add a 'Download PDF' button to the invoice page." It's estimated at 3 story points. You think you'll knock it out before lunch.

What happened? Scope creep happened.

Here is how you, as a developer or team lead, can defeat it.

1. The "Phase 2" Bucket

Your answer should be: "That's an excellent idea. Let's put that in the Phase 2 bucket so we can ship the core value today."

2. Write Ironclad Technical Specs

Never start writing code based on a one-sentence Slack message.

Non-Goals are crucial. Explicitly stating what you are not building sets the boundary. When scope creep tries to sneak in, you simply point to the agreed-upon Non-Goals.

3. Visualize the Butterfly Effect

Stakeholders often don't understand that a "small UI tweak" might require a massive backend refactor.

Force the business to make the tradeoff decision. You are the engineer; you provide the cost. They provide the priority.

Stop Being a Code Monkey

Protect your sprint. Protect your sanity.

Have you ever been burned by massive scope creep? How did you handle it? Let's discuss in the comments.