Hey, long time no see

🚀 Excited to share my latest article: "Brain-based Management: The Power of Checkbox" now published on Medium!

🧠 Delve into how cognitive psychology principles can revolutionize project management. This piece isn't just theoretical – it's a practical guide to mastering the art of handling multiple projects simultaneously with efficiency and precision.

📋 I discuss the transformative power of integrating simple yet robust tools like checkboxes and formulas into your management process, offering a unique perspective on tackling project complexities.

🔗 Read the full article
https://medium.com/@artemantonenko/brain-based-management-the-power-of-checkbox-3964bcf77919
and discover how to elevate your project management strategies to the next level.

Hey guys!

🚀 Excited to unveil "Brain-Based Management: Meaningful Retrospective, or Where's Your Issue Log?" now live on Medium!

🧠 Dive deep into the agile world with a neuroscientific twist! Explore how understanding memory and cognition can make your retrospectives more effective and meaningful.

📚 Learn about the game-changing role of issue logs in capturing the essence of iterative processes, ensuring every cycle is a step towards excellence.

🔄 Discover techniques to turn routine meetings into powerhouse sessions of learning and innovation, enhancing team dynamics and project success.

🔗 Ready to transform your approach? Read the full article
https://medium.com/@artemantonenko/brain-based-management-meaningful-retrospective-or-wheres-your-issue-log-38ecd2c8dfdf
and elevate your agile practices to new heights!

🌟 Introducing My New Article: "Why Team Metrics Fail: The Unseen Power of Individual Contributions in Tech"

Have you ever pondered why your team seems to meet all its goals, yet something crucial appears to be missing? Why does it seem that despite a sea of positive metrics, the spark of innovation and team spirit isn't quite as bright?
In my latest exploration, I tackle a nuanced yet pivotal topic that often goes unspoken in the corridors of tech companies: the limitations inherent in team-based metrics and the vital role of individual contributions.

This article serves as a gentle nudge for managers, team leaders, and tech team members to reconsider how success is measured and celebrated. It’s an invitation to reflect on the balance between collective achievements and the individual efforts that propel them.

https://medium.com/@artemantonenko/why-team-metrics-fail-the-unseen-power-of-individual-contributions-in-tech-1b3932596913

🚀 Coding with AI: How Agentic AI is Changing the Game 🤖

AI is rapidly becoming an integral part of our lives, and while some engineers have been disappointed by the limitations of generative AI, agentic AI has arrived to push the boundaries. We are already seeing reports of engineers achieving up to 5x productivity boosts with AI-assisted workflows. However, we must recognize that interacting with AI is not the same as working with another human—the thinking process is fundamentally different.

Pair programming with AI, especially with agentic AI like transformers, introduces a unique dynamic compared to traditional human pair programming. While humans use abstraction and modular architecture to zoom in and out of context, AI transforms these concepts into concrete steps. Unlike generative AI, which primarily predicts the next line of code based on statistical probabilities, agentic AI can plan, reason, and break down complex tasks into structured steps. This changes how we collaborate: instead of treating AI as an auto-complete tool, we should consider an AI-driven development approach—breaking tasks into smaller steps, starting with tests, and then coding while keeping the big picture in mind.

This structured process aligns with how agentic AI works best, making it easier to refine and iterate toward better solutions. One of the most promising aspects of agentic AI is its ability to store memory and learn from past interactions—allowing it to build on previous successful patterns rather than starting from scratch each time. For example, an AI agent assisting in refactoring a large codebase can remember how similar problems were solved earlier, reducing redundancy and improving efficiency. However, challenges remain, such as ensuring that AI doesn’t carry over incorrect assumptions or introduce hidden biases from past iterations.

Successful outcomes should be stored in the AI’s memory, enhancing future collaboration and results. This iterative approach can significantly improve coding efficiency and creativity, paving the way for a more intelligent and adaptive development process.

https://www.linkedin.com/posts/artem-antonenko-al_coding-with-ai-how-agentic-ai-is-changing-activity-7302350218761515008-Si3H?utm_source=share&utm_medium=member_ios&rcm=ACoAABAzki0B0PGcHHP4HeqQUsFGe8Psph19QZI

📊 AI Deep Research: from days of work to hours of insights

ChatGPT and Perplexity can already deliver structured reports, comparisons, and market analysis much faster than manual search.

And one thing is clear now: the way you write the prompt changes everything.
Prompt and context are the king and queen of great results — they define how accurate, structured, and useful the output will be.

I’ve shared some of my own discoveries from running research tasks — practical tips that can help you get better results.

👉 Full details + a ready-to-use template are in my LinkedIn post: https://www.linkedin.com/posts/artem-antonenko-al_ai-deepresearch-productivity-share-7379812114062028800-KlW1?utm_source=share&utm_medium=member_desktop&rcm=ACoAABAzki0B0PGcHHP4HeqQUsFGe8Psph19QZI

🚀 AI Transformation Insights: from pilots to real ROI

Most organisations today use AI — but very few transform with it.
At our recent roundtable on AI-Ready Organisations, we explored what truly separates the leaders from the rest.

💡 The key insight: AI success is not about adoption — it’s about adaptation.
The most advanced companies move beyond chat tools and pilots to build systems that learn, integrate, and deliver measurable ROI.

I’ve summarised the main findings and practical takeaways in my latest LinkedIn post — from the “GenAI Divide” to what actually makes an organisation AI-ready.

👉 Read more here: https://www.linkedin.com/posts/artem-antonenko-al_ai-digitaltransformation-aiready-activity-7381626466955964416-a7PE?utm_source=share&utm_medium=member_desktop&rcm=ACoAABAzki0B0PGcHHP4HeqQUsFGe8Psph19QZI

🧠 Hey everyone! Quick poll for all devs and AI enthusiasts 👇

I’m curious — what AI-powered coding tools are you currently using or experimenting with the most? ⚙️💻
These agentic assistants are changing how we write, debug, and ship code — so let’s see what’s hot in our community 👇

💬 Thinking about an open discussion on AI and its real-world applications

AI is transforming how we work, learn, and create. Yet many of us are still figuring out how to apply it meaningfully in our daily lives.

I’ve just shared some reflections from my recent session “How AI Is Changing the World” with the Luxembourg–Ukraine Chamber of Commerce (LUCC).

If you’re interested in joining an online discussion about AI applications, please add a “👏” under the post. If at least 10 people join, we can organize it together.

🔗 Read the full post here: https://www.linkedin.com/posts/artem-antonenko-al_ai-artificialintelligence-futureofwork-activity-7386394002746396672-n1P4?utm_source=share&utm_medium=member_desktop&rcm=ACoAABAzki0B0PGcHHP4HeqQUsFGe8Psph19QZI

🚀 Big news from DeepLearning.AI!

The platform now offers official certifications, and I just completed one of the first, the Agentic AI course by Andrew Ng.

In my new LinkedIn post, I shared key insights about first course.

🔗 Read the full post here: https://www.linkedin.com/posts/artem-antonenko-al_deeplearningai-course-certificate-for-agentic-activity-7388537116470964225-j68I?utm_source=share&utm_medium=member_desktop&rcm=ACoAABAzki0B0PGcHHP4HeqQUsFGe8Psph19QZI

🚀 AI is no longer a question. It’s the foundation.

Just watched Jensen Huang’s keynote at NVIDIA GTC 2025 and it felt like peeking into the next decade of technology.
Not hype. Not theory. A real shift in how we think about work, computing, and intelligence itself.

Here are my reflections 👇
🔗 Read on LinkedIn

💣 AI won’t steal your code. But fear might steal your speed.

Still hear people saying “we can’t use AI for coding. Our IP won’t be safe”?
That’s one of the biggest myths slowing down innovation today.

In my new post, I break down what’s actually risky (and what’s not) — from code context to compliance, and how agentic AI can safely power 80% of your codebase.

Here’s the first episode of AI Coding MythBusters 👇
🔗 Read on LinkedIn

💡 New title. New role. New reality.

Everyone’s been asking how AI will change the job market.
Now we’re seeing real answers.

In my latest LinkedIn post, I unpack the rise of the FDE (Forward-Deployed Engineer) a role that’s already being hired by OpenAI, Anthropic, and others.

Why does it matter?
Because this might be the blueprint for how engineers and architects evolve in an AI-driven world.

It’s not just a trend — it’s a signal.
👉 Hybrid skills.
👉 Direct impact.
👉 Strategic integration of AI in real business environments.

🔗 Read on LinkedIn

⚡️ *“China will win the AI race.” – Jensen Huang, NVIDIA*

When the CEO of NVIDIA says it, it’s not noise — it’s a signal.
A signal that Europe might be moving too cautiously in the global AI race.

In my new LinkedIn reflection, I explore what’s behind this moment —
🇪🇺 pressure on the EU Commission to delay the AI Act,
🇺🇸 U.S. export controls reshaping strategy,
📊 and why only 13.5% of European companies are using AI today.

Is this just politics… or a warning that we’re falling behind in competitiveness?
Maybe both.

💡 Read my new post and join the discussion —
Should Europe speed up its AI adoption before it’s too late?

🔗 Read on LinkedIn

“RAG is delivering more real ROI today than any other AI architecture.”

That’s the core message of my new LinkedIn post.
While everyone talks about training custom LLMs, the companies getting real results are using something far simpler — retrieval-augmented generation.

From nuclear maintenance to financial advisory, RAG is quietly becoming the production layer of enterprise AI.

In the next post, I’ll break down the actual architecture:
vector DB → retriever → orchestrator → LLM → guardrails.

Stay tuned — tech deep dive coming.
🔗 Read the LinkedIn post

“Backend engineers don’t need to learn AI.”
I kept hearing this in 2024 and it’s already one of the worst predictions of the decade.

That’s the core message of my new LinkedIn post.
Because 2025 made something very clear: the backend is shifting from execution → governance, and agents are taking over the workflow layer.

With MCP enabling LLMs to call real tools and services, the architecture has fundamentally changed:
user → server → LLM → MCP → tools/backends.

Backend engineers who understand agentic systems will lead this transition.
Those who don’t will be maintaining yesterday’s architectures.

In the next post, I’ll break down what backend teams actually need to learn from tool surfaces to evaluators to PEFT.

🔗 Read the LinkedIn post

A lot of engineers think they understand RAG… until they see it in production.
That’s exactly why I published a new deep-dive: a practical walkthrough of what a real production-grade RAG system actually looks like. 🏭

If you’ve ever wondered why clean notebook examples fall apart with real users and real data — this is for you.

I break down the key pieces that matter in practice: ingestion, hybrid retrieval, prompt assembly, guardrails, observability, and freshness.
And yes — there’s an interactive visualization you can explore and play with. 👇

🔗 LinkedIn post
🔗 Visualization

Short read, big mental-model upgrades. Let me know what you think.

“Agentic AI is still too early for production.”
This was the most common excuse I heard from companies in 2024.

After AWS re:Invent 2025, that argument is officially dead.

AWS didn’t just announce new models, they unveiled a complete, production-ready ecosystem for agentic systems: Strands, Agent Core, Nova Act, RFT, episodic memory, neurosymbolic safety… and dozens of real companies already running agents at scale.

Blue Origin uses 2,700+ agents.
Cox Automotive cut multi-day workflows to 30 minutes.
PGA Tour reduced content costs by 95%.

The message is clear:
Agentic AI has moved from “experiment” to “enterprise infrastructure.”

And the real question companies should be asking in 2025 is simple:

👉 “Are we already running agentic AI in production or are we falling behind?”

In my new LinkedIn article, I break down the announcements, the use cases, and why this moment changes the calculus for AI investment entirely.

🔗 Read the LinkedIn post