Quema

Quema We help companies of all sizes to create and support a scalable/secure IT infrastructure to launch and operate their products at the highest level.

In our insanely fast time, many companies are faced with the task of building or maintaining complex, fast-growing IT infrastructures, with a bunch of dependencies and technologies, staff search and compliance with modern PCI DSS, GDPR, HIPAA security standards and the best practices of world leaders. Often people do not know how to find the right solution to this issue, they are faced with a cons

tant drop in services, untimely support, unqualified personnel, and a constant headache of related processes. We already have ready-made experience in solving such problems. The best, certified and well-trained DevOps and architects provide a turnkey solution, best practices and Scrum processes debugged over the years, eliminate pain points in the shortest possible time and gradually solve all your worries. Working with us, you just have to forget about all these worries and calmly focus on business processes. Just write to us and you will receive absolutely free of charge non-binding advice on specific tasks that are bothering you at the moment. Reviews:
- GoodFirms:
https://www.goodfirms.co/company/quema-oue

- Clutch:
https://clutch.co/profile/quema-o%C3%BC

- Upwork:
https://www.upwork.com/ag/quema/

Quema
Your DevOps provider
[email protected]

Hackers aren't targeting your code anymore. They are targeting your AI's hallucinations. 🦠🤖We all know the danger of Sup...
25/05/2026

Hackers aren't targeting your code anymore. They are targeting your AI's hallucinations. 🦠🤖

We all know the danger of Supply Chain attacks. But in 2026, a terrifying new vector has emerged for engineering teams: AI Hallucination Squatting.

Here is how the attack lifecycle works:

1️⃣ A developer asks an LLM (like ChatGPT or Claude) to solve a complex coding problem.
2️⃣ The AI hallucinates and suggests importing a highly plausible, but completely fake open-source library (e.g., fast-api-auth-utils).
3️⃣ The developer copies the code and runs pip install fast-api-auth-utils.

The catch? Hackers are constantly prompting LLMs to map out these common hallucinations. They then proactively register these exact fake package names on npm, PyPI, and RubyGems, packing them with malicious payloads and backdoors.

Your developer didn't make a typo. They just trusted an AI that pointed them directly to a trap. 🪤

You cannot stop your engineers from using AI to write code. But you can stop relying on public package registries directly.

If your DevSecOps strategy doesn't include:
- Strict private artifact registries
- Automated Software Composition Analysis (SCA)
- Zero-trust CI/CD pipelines
..then you are leaving your production environment wide open to an AI's imagination.

Secure your supply chain before a hallucination becomes a data breach. 🛡️

04/05/2026

We are trying to communicate with supercomputers through a plastic straw. Welcome to "Thought Engineering." 🧠⚡

Everyone is obsessed with "Prompt Engineering," learning the exact phrases to make AI generate code or cloud architecture.

But Prompt Engineering is just a temporary patch for a massive hardware bottleneck: the keyboard.

We have AI capable of designing complex microservices in milliseconds, yet we communicate with it at 60 words per minute using our fingers. The friction isn't in the AI; the friction is in our physical input.

Advancements in Brain-Computer Interfaces (BCIs) are about to eliminate this bottleneck. We are approaching the era of Thought Engineering.

Imagine merging an enterprise LLM directly with a BCI. You won't type a prompt to deploy a cluster or debug a pipeline. You will visualize the architectural state, and the AI will instantly translate that cognitive intent into code.

In this near future, the skill of the engineer won't be writing syntax, but achieving absolute clarity of thought.

Forget Prompting. The IDE of the future isn't autocompleting your text. It's autocompleting your INTENT.

That face your CISO makes when you ship code a little too fast. 🛑🔓It's the face of "Shadow AI." And it's becoming a seri...
10/04/2026

That face your CISO makes when you ship code a little too fast. 🛑🔓

It's the face of "Shadow AI." And it's becoming a serious problem in 2026.

Years ago, "Shadow IT" meant a developer spinning up an unapproved AWS server on their personal credit card to bypass slow IT processes.

Today, the threat has evolved.

If your corporate AI coding assistant is heavily restricted, heavily monitored, or just... not smart enough, your engineers don't stop using AI. They just stop telling you about it. 🥷

Developers are copy-pasting proprietary algorithms, architectural designs, and sometimes even API keys into unapproved, public LLMs. While your IP is safe in your Git repositories, it's simultaneously being used as context in public chat windows. 💻🔥

Firewall bans are a band-aid, not a strategy.

The only way to defeat Shadow AI is to securely enable developer velocity. Build a "Golden Path"—a secure Internal Developer Platform (IDP) with embedded enterprise-grade AI models, where data privacy is guaranteed. When you provide a faster, smarter, and secure way, engineers won't need to bypass security.

At Quema, we help teams build these secure platforms, turning Shadow AI risks into competitive advantages.

Is your company fighting Shadow AI with bans, or are you building better internal platforms? 👇

The Enterprise AI crown is quietly changing hands. 👑🤖For the last three years, OpenAI was the default choice for every t...
31/03/2026

The Enterprise AI crown is quietly changing hands. 👑🤖

For the last three years, OpenAI was the default choice for every tech company. But the data from March 2026 shows a massive architectural and financial shift.

Anthropic’s share of enterprise AI spending has climbed to 40%, while OpenAI’s share fell to 27%. When it comes to new business deals, Anthropic is now winning approximately 70% of head-to-head matchups against OpenAI.

Why is Anthropic winning the enterprise war? It comes down to trust, security, and the developer ecosystem:

🔹 Strict Security Principles: Recently, Anthropic walked away from a potential deal with the Pentagon. They refused to allow their technology to be used for mass surveillance of US citizens or autonomous weapons. 🔹 Market Backlash: The government labeled Anthropic a "supply chain risk". However, following OpenAI's $200 million deal with the DoD, uninstalls of the ChatGPT app jumped 295% overnight.

🔹 Developer Dominance: Claude Code now holds between 42% and 54% of the code generation market. In contrast, OpenAI sits at 21% in that specific market. Engineers are also reportedly eight times more likely to leave OpenAI for Anthropic than the reverse.

🔹 Financial Stability: OpenAI is projected to lose $14 billion in 2026. Meanwhile, Anthropic is projecting positive cash flow by 2027.

In Platform Engineering and DevOps, predictability and security trump hype every single time. Enterprises don't want the most "creative" model; they want the safest and most reliable one.

Which AI models are you integrating into your internal developer platforms today? Let's discuss! 👇

23/02/2026

Is Terraform dead? No. But writing HCL from scratch probably is. 💡
The way we interact with cloud infrastructure is shifting. Look at the evolution:
❌ Level 0: ClickOps (Clicking around the AWS console)
📜 Level 1: Scripts (Bash & AWS CLI)
🏗️ Level 2: IaC (Writing declarative Terraform/Ansible)
🤖 Level 3: IaP (Infrastructure as Prompts)

With the rise of Agentic AI, we are moving to IaP. You define the intent in plain English: "Deploy a highly available EKS cluster under $500/mo." The AI generates the Terraform modules, sets up variables, and opens a PR.

Does this replace DevOps engineers? No. It transforms them from "syntax typists" into Architects and Senior Reviewers.

Are you already using AI to generate your infrastructure code? Let me know below! 👇

16/02/2026

Would you give an AI agent sudo access? 🔐🤖

Google’s latest Gemini 3 update just made "Computer Use" mainstream. It’s no longer just about generating code snippets; the model can now navigate UIs, use a browser, and yes — interact with a terminal.

With the new Deep Think reasoning capabilities hitting ~85% on complex benchmarks, the gap between "Copilot" and "Autopilot" is vanishing.

We are entering a weird phase in DevOps:
- The AI is smart enough to diagnose the issue.
- The AI has the tools (Computer Use) to fix the issue.
- The only bottleneck is… our trust.

I’m curious: If an AI agent could prove it knows how to fix a production incident, would you let it execute the command? Or are we strictly "Human-in-the-loop" forever?

Let the debate begin. 👇

Will AI replace DevOps engineers? No. But engineers using AI will replace those who don't.The "AI-Driven DevOps" approac...
05/02/2026

Will AI replace DevOps engineers? No. But engineers using AI will replace those who don't.

The "AI-Driven DevOps" approach is gaining massive traction. We see it streamlining workflows and catching bugs that humans miss.

We’ve summarized the key benefits and strategies in our new article: 🔗 [https://quema.co/news/ai-driven-devops/]

A question for my network: What is the biggest challenge you see in adopting AI for infrastructure management today? Trust? Cost? Or complexity?

Let's discuss in the comments! 👇

Your Startup Doesn't Need GPT-4. How Small Language Models (SLMs) Are Changing the Game.For the last few years, the AI i...
13/01/2026

Your Startup Doesn't Need GPT-4. How Small Language Models (SLMs) Are Changing the Game.

For the last few years, the AI industry has chanted a single mantra: "Bigger is Better."

We chased trillions of parameters, infinite context windows, and data centers that consume the energy of small nations. But while everyone was staring at the clouds, a quiet revolution started right on our desks.

Welcome to the era of Small Language Models (SLM) and Edge AI.

Why is the focus shifting from massive LLMs (like GPT-4 or Claude) to compact models that can run on a standard laptop or even a smartphone?

I see three key drivers shifting the Enterprise and Consumer IT markets right now:

1. Privacy is the New Luxury 🔒 Businesses are tired of fearing leaks. Sending sensitive financial reports or medical data through an API into a third-party corporation's "black box" is a massive risk. A local model (like Llama or Mistral) running inside your perimeter (on-premise) or directly on an employee’s device guarantees data sovereignty. "What happens on your device, stays on your device."

2. Token Economics vs. Hardware 💸 Paying for every API call gets expensive, especially at scale. SLMs allow us to offload the computational burden to the user (Edge). With the rise of powerful NPUs in modern processors (Apple Silicon, Intel Core Ultra, Snapdragon X Elite), we can perform inference for "free" using hardware we already own, rather than renting expensive cloud compute.

3. Latency and Autonomy ⚡ The internet isn't perfect everywhere, and a two-second latency kills real-time UX. Local AI works instantly and without a network connection. This is critical for coding assistants, autonomous agents, and smart manufacturing.

What does this mean for us? We are moving from a model of "One Giant Brain for Everything" to a "Swarm of Specialized Agents." Instead of asking GPT-4 to write an email, check code, and summarize a meeting, we will use three different micro-models, each weighing just a few gigabytes and perfectly fine-tuned for its specific task.

Developers need to get comfortable with quantization, ONNX, and local inference. And businesses need to rethink their cloud budgets.

The future of AI isn't just in massive server farms. It’s in your pocket.

👇 Colleagues, have you tried running local models (Llama 3, Phi, Gemma) yet? Or do you still trust everything to the API?

Sustainability is not charity. It’s smart engineering. 📉🌱We used to think of "Green IT" as a nice-to-have PR move. But i...
15/12/2025

Sustainability is not charity. It’s smart engineering. 📉🌱

We used to think of "Green IT" as a nice-to-have PR move. But in 2025, Sustainable Infrastructure is actually about Efficiency.

Every idle server, every unoptimized container, and every bloated pipeline isn't just increasing your carbon footprint. It is burning your cloud budget.

At Quema, we see a direct correlation: The cleaner your infrastructure, the higher your margins.

In our latest article, we dive deep into: ✅ Why the industry is shifting towards GreenOps. ✅ How sustainable architecture reduces TCO. ✅ The real impact of data centers on global energy consumption.

Stop paying for resources you don't use.

👇 Read the full breakdown here: https://quema.co/news/why-do-we-talk-about-sustainable-it-infrastructure-in-industry/

12/12/2025

Stop Building Chatbots. Start Building Employees.

2023 was the year of "Chatting with AI." 2024 was the year of RAG (searching documents). 2025 is the year of Agentic AI.

Let’s be honest: The enterprise is tired of "smart talkers." Passive Q&A bots are cool, but they don't move the needle. You ask, it answers, you do the work.

The market is shifting to the Bleeding Edge: from text generation to Action Ex*****on.

What is the fundamental shift?

🔴 LLM (The Chatbot Era): — You: "How do I migrate this legacy .NET 4.8 code to Core?" — AI: Outputs a text tutorial. (You still have to do it manually).

🟢 Agentic AI (The Workforce Era): — You: "Migrate this repo to .NET Core." — AI Agent:

- Scans the codebase.
- Spins up a Docker container.
- Refactors dependencies (removing IIS/COM ties).
- Runs tests.
- Self-corrects errors.
- Submits a Pull Request.

See the difference in ROI?

We are moving from a Copilot paradigm (helper) to an Autopilot paradigm (worker).

Where should CTOs and Architects look right now?

- Multi-Agent Orchestration: One LLM is good; a "virtual department" is better. Tools like CrewAI allow you to assign roles: a PM Agent creates the plan, a Coder Agent writes it, and a QA Agent critiques it.
- Stateful Graphs (LangGraph): Forget chaotic prompt chains. We are building deterministic logic graphs where the AI makes decisions at specific nodes. This brings back control and predictability.
- Tool Use: The ability to escape the chat window—execute SQL, call APIs, manage infrastructure.

Prediction: The UI of the future isn't a chat window. It’s a dashboard where you monitor AI agents performing asynchronous work, only stepping in for critical decisions.

Are you still using AI as a better Google, or have you started delegating real actions? 👇

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