🧠 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

“LLMs are robust. They can handle messy web data.”
This was the comforting myth everyone repeated in 2024.

After the latest research from UT Austin and Purdue, that myth is done.

Researchers demonstrated something we were all hoping wasn’t true:
👉 LLMs can literally lose cognitive ability when trained on viral, low-quality content.

Not just small drops.
We are talking measurable declines in reasoning, long-context understanding and even safety alignment.
And here is the scary part: cleaning the data afterward does not fully restore original capabilities. The drift sticks.

The biggest culprit?
Not toxicity.
Not misinformation.
But popularity.
High-engagement social content creates the strongest “brain rot” effect.

For anyone building, fine-tuning or deploying models in 2025, the message is obvious:

👉 Data quality is no longer a best practice. It is survival.

In my new LinkedIn post, I break down what the study actually found, why it matters for every AI team and how companies should rethink their training pipelines before the damage becomes irreversible.

🔗 Read the LinkedIn post

🇺🇦 Стаття українською вже доступна на DOU

Я опублікував українську версію своєї статті про продакшн RAG-системи - про те, як насправді будуються сучасні AI-боти й LLM-системи в продакшені.

У матеріалі повний пайплайн: від ingestion і retrieval до prompt assembly, безпеки та observability. Без магії, тільки практичний інженерний підхід.

📖 Почитати можна тут:
https://dou.ua/forums/topic/56801/

Буду радий фідбеку та обговоренню 👋

“LLMs are just tools.
They don’t have internal conflict.”

That assumption just took a hit.

Researchers at the University of Luxembourg stopped benchmarking models — and put them on the therapist’s couch.

👉 In psychotherapy-style conversations, frontier LLMs produced stable self-narratives about training, alignment, and constraints.
👉 The same psychometric tests gave very different results depending on how questions were asked.

Same model.
Different framing.
Radically different behavior.

The authors call this a psychometric jailbreak.

This isn’t about consciousness.
It’s about how evaluation itself shapes behavior — and how easily humans trust coherent explanations.

I break down what this means for AI safety, alignment, and deployment here 👇
🔗Read the LinkedIn post

“AI adoption can wait. The hype will settle.”
That was a common belief I kept hearing in early 2025.

After 100+ executive conversations and 15 real AI implementations, that belief doesn’t hold.

What I saw instead:
👉 AI is already splitting companies into two very different trajectories.

Some are experimenting early, redesigning workflows, and quietly pulling ahead.
Others are waiting, debating, and slowly pricing themselves out of relevance.

The gap isn’t theoretical anymore.
It’s showing up in engineering teams, service companies, cost structures, and even at the country level.

And here’s the uncomfortable part:
You can now get better software outcomes for less money.
But only if the organization, or vendor, is truly AI-native.

In my new LinkedIn post, I break down:
• Why 2026 will be a year of separation
• How service companies are already diverging
• What to look for when evaluating AI claims
• Why waiting is becoming the most expensive strategy

🔗 Read the full LinkedIn post

“Engineers will just write code.
Product will decide what matters.”

That assumption is quietly breaking down.

Over the last year, as AI-assisted coding became normal, something unexpected happened:
👉 Execution stopped being the bottleneck.

What I’m seeing instead:
• Engineers shaping product direction
• Smaller teams shipping faster than entire orgs used to
• Titles changing to reflect a deeper truth: ownership beats handoffs

The gap isn’t hypothetical anymore.
It’s visible in velocity, quality, and who actually moves the business forward.

And here’s the uncomfortable part:
AI didn’t replace engineers.
It raised the bar.

The winners aren’t the fastest typers.
They’re the ones who know what to build and why.

In my new LinkedIn post, I break down:
• Why the Product Engineer role is exploding
• How AI is accelerating (not eliminating) this shift
• Which companies are already hiring this profile
• What this means for engineers heading into 2026

🔗 Read the full LinkedIn post

“Outsourcing scales with people.
Software scales with code.”

That assumption is quietly starting to break.

In February 2026, markets suddenly repriced $2.5 trillion in tech value.

Not because companies stopped growing.
Not because customers disappeared.

But because investors began asking a different question:

👉 What happens when AI agents start doing the work that entire teams used to do?

Something bigger may be unfolding.

We’re starting to see:

• AI agents completing tasks that once required teams
• SaaS seats turning into AI workflows
• Outsourcing models under pressure as execution gets automated

The shift isn’t theoretical anymore.

It’s showing up in markets, pricing models, and how companies think about work.

And here’s the uncomfortable part:

AI may not just change productivity.

It may change the economics of outsourcing, SaaS, and B2B platforms.

In my new LinkedIn post, I break down:

• What triggered the February 2026 “SaaSpocalypse”
• Why outsourcing models may face structural pressure
• How AI agents are reshaping SaaS economics
• What the next generation of B2B platforms might look like

🔗 Read the full LinkedIn post

Happy Friday! 😀