I Help CEOs & Founders Deliver 5-Star Customer Experiences with AI Chatbots & Voice Responders — Bridging the Gap between Remote Teams & Global Customers in Italy, the EU, the US, and Brazil

This is the space where I’m supposed to talk about myself…

…but I’d rather start with you.

If you’re a business owner or leader who’s tired of:
	•	phone systems that “kind of” work
	•	Wi-Fi that drops at the worst moment
	•	IT decisions that feel like expensive guesses
	•	great expertise that never turns into LinkedIn opportunities

…we should talk.

I help in two simple ways:
	1.	Phone systems + IT networks (built to last)
I design and set up modern VoIP/phone systems and business networks that work the way you need them to work — reliably, securely, and ready for what’s coming next (including AI tools).
	2.	LinkedIn content + personal brand (that sounds like you)
I turn what you already know into clear, useful posts that build trust and make the right people think: “This is who we should hire.”

Why listen to me?

Because I’ve been on the “real world” side of this for 25+ years.

I started with electrical and phone installations with my father (back when Windows 95 was the new thing). I learned cabling the hard way — hands on, from copper to fiber.

In 2006, when open-source VoIP started spreading in Italy, I went deep into Linux, networking, internet access, and security. That grew into designing and managing VoIP systems, wireless networks, and Linux environments for B2B companies in Italy and across Europe.

Along the way, I built partnerships across video surveillance, pro audio-video, and IT solutions for SMEs and larger companies.

Then I noticed something else:

A lot of smart entrepreneurs can run a business…
…but they struggle to explain it simply.

So now I work at the intersection of three things:
	•	Reliable systems (so your business keeps running)
	•	A clear message (so it keeps growing)
	•	Smart automation (so it scales)

Lately, that includes AI voice agents — adding an “intelligent layer” to customer interactions so calls and requests get handled faster and better.

I’m based in Italy. I work in Italian, English, and Portuguese with clients across Europe, the US, and Brazil.

If you want solid IT/VoIP, better LinkedIn visibility, or you’re curious about AI voice agents…

Connect and DM me.

Or, if you just want to follow along and enjoy my posts, connect and set the bell to “All” so you get notified whenever I post.

And if you made it this far, connect and send me a DM anyway. That would make me truly happy!

Thanks for stopping by.

Manuel Veronesi

The most interesting trend I've seen this week is four prominent tech writers publicly switching from Windows to Linux between January 4 and January 10.

Nathan Edwards from The Verge, Sam Medley from Notebookcheck, and two other tech writers all documented their migrations to Linux distributions like CachyOS and Artix Linux.

Their main frustration?

Microsoft's aggressive push to embed AI agents throughout Windows 11, which the company admitted introduces novel security risks including potential data exfiltration and malware installation.

The timing makes sense. Windows 10 reached end-of-support in October 2025, and nearly 90% of Windows games now run on Linux according to ProtonDB data.

Edwards successfully played games using Valve's Proton, while another writer reported nearly 144fps in newer titles.

The transitions weren't perfect. Edwards had mouse compatibility issues, Medley spent 45 minutes troubleshooting WiFi drivers, and anti-cheat software still blocks popular multiplayer games like Fortnite and Call of Duty.

But all four expressed satisfaction with Linux's stability and freedom from forced updates and advertising.

Curious how you see this. 
👉 Windows or Linux, and why?

Attention is all you need on LinkedIn. (More in the About section.)

Italy just fined Cloudflare €14.2M for refusing to filter pirate sites through its 1 dot 1 dot 1 dot 1 DNS under Piracy Shield rules that demand blocks within 30 minutes.

Cloudflare is now threatening to pull its servers from the country citing the impact on 200 billion daily queries and collateral damage to legitimate sites.

I think there's something interesting happening here.

On one hand we've built a reputation as a privacy-first enforcer. 

We fined TikTok.
We banned ChatGPT early on. 
We challenged Google execs in court.

The message to US tech giants has been clear:

Comply or adapt.

But I'm not sure AGCOM fully understands how DNS filtering works at scale.

IP-level blocks catch innocent sites on shared hosting. There have been reports of over 500 legitimate sites already blocked.

The technical realities don't align well with 30-minute enforcement windows when you're processing queries at Cloudflare's volume.

Big Tech’s threat to exit the market follows a familiar pattern, one that tends to surface whenever regulation starts to impose real constraints.

But regulators also risk overreach when they ignore technical constraints.

I think Italy is genuinely trying to lead on protection but the question is whether we're accounting for how these systems actually function.

Getting the balance right matters because at the end of the day both privacy and innovation depend on it.

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I've spent enough time on LinkedIn to spot fake engagement pods, recycled content, and made-up success stories.

It's easy to become cynical about this platform when you see the same repeated advice and fabricated metrics every day.

But I've also connected with founders who genuinely share their failures and lessons learned.

I've had conversations with people who took time to help without expecting anything in return.

I've seen experts give away valuable knowledge just because they wanted others to succeed.

These real connections are what make LinkedIn worth it despite everything.

The fake stuff gets attention because it's designed to. But the genuine people are here too and they're building real relationships that lead to actual business and meaningful collaborations.

So yes there's a lot of garbage to filter through but that shouldn't make us forget about the good ones who make this platform valuable.

—-

PS: Posting late gets penalized by the algorithm, but it doesn’t change the quality of the connections you build here. And thankfully, it has no authority over real relationships.

PPS: Agree?

The most common question I get from tech founders is whether an AI writing its own code changes how we should build products.

My answer is always:

✔️ I don’t know.

And since at that point they usually look disappointed, I add the only thing consultants are legally allowed to say:
 
Well, it depends...

But since that's not useful I want to try to give a real answer based on what's happening at Anthropic. 

Dario Amodei has said that over 90% of Claude’s code is now written by Claude itself.

His March 2025 prediction came true by October. 

Their Claude Code tool even had 100% of December contributions written by AI.  

But here's what matters for you: 

👉 they still need the same number of engineers.  

The difference is engineers now focus on the hardest 10% and supervising AI models instead of writing everything end to end.

My concern though is: if AI does all the junior work how will we train the next generation of senior engineers?

If today's seniors use Claude instead of hiring juniors we risk having no seniors in five to ten years because nobody learned the fundamentals.

Or maybe I’m overthinking it.
 
What do you think?

Nvidia CEO Jensen Huang wants engineers to stop writing code and let tools like Cursor write 100% of it.

It’s a powerful vision. And at Nvidia it can work. They have the processes, talent, and standards to move fast without losing control.

Huang’s logic is clear: coding is a *task*, not the *purpose* of an engineer. The real job is to discover and solve new problems, and AI should handle as much of the syntax as possible so humans can focus on higher‑value work.

The problem is when the same idea gets copied outside that context.

I spent over 25 years in IT infrastructure and I can tell you this path can lead to technical debt that keeps compounding if we’re not careful.

We are already seeing “vibe coding” where developers accept AI output just because *it looks right.*

According to Uplevel’s study, experienced devs using AI are actually 19% slower when debugging complex logic.

It’s like when doing the job yourself takes less time than fixing the mistakes made by someone who was supposed to help.

And there is the “black box” problem. If your team stops writing code, they eventually stop understanding it.

Sure, you don’t always need to understand how something works when it works.

But over time that lack of practice turns into atrophy. And atrophy changes how we build. Then it changes what we can control.

Eventually you get outputs you can ship, but no real ability to predict them, explain them, or trust them under pressure.

Imagine an AI coder introduces a security vulnerability and nobody catches it because nobody understands the code well enough anymore.

We’ll probably get AI with real hallucination control one day, but for now it’s smart to move cautiously.

Without clear ownership and hard verification we risk automating mistakes and scaling irresponsibility.

So yes, letting AI write more code is good advice. But writing 100% of the code with AI, if you’re not Nvidia, is a very different bet.

Because if nobody understands the code anymore, the only option left is blind trust in whatever the machines spit out.

Curious how you see this. 
👉 Where do you draw the line between AI acceleration and human accountability?

Don't assume China's AI lag will last forever because the gap is closing faster than most people think.

Google DeepMind's CEO just said Chinese AI models are only months behind Western ones, not years like many believed.

That's a massive shift in the competitive landscape that should change how you think about your AI strategy.

The catch is that catching up isn't the same as leading. China has shown they can replicate what exists, but they haven't demonstrated breakthrough innovation yet - like inventing something fundamentally new beyond current frontiers.

Still, with companies like DeepSeek building competitive models on less advanced chips, and conflicting signals on semiconductor access, the dynamics are shifting fast.

My take is that if you're building in AI, you need to focus on what truly differentiates your product now, not rely on a technology gap that might disappear in months.

Now I’m curious: 

would you bet on a better product or better distribution, and why?

Breaking news triggers instincts.
Reflection reveals principles.
 
With fresh reports of a U.S. strike in Venezuela, it’s easy to slip into “team mode”: 

pick a side fast, simplify the story, and turn the volume up.
 
When the news cycle speeds up, I try a different move: 

pause for two minutes and ask a better question.
 
What values are driving my reaction, and what trade-offs am I accepting (consciously or not)?
 
I put together a short, non-partisan political self-reflection exercise you can run with whatever chatbot you prefer, or do on your own. 

It’s designed to help you:
 
1. Name 5 guiding values before you debate policy
2. Clarify your red lines vs. acceptable compromises
3. Separate goals (security, fairness, freedom, stability) from tools (sanctions, taxation, regulation, welfare design, civil-liberty limits)
4. Disagree without turning it into an identity fight
 
Full prompt is in the document below and in the first comment.
 
If you’d like, post your single most important core value in the comments. 👇

Political Self-Reflection Coach

The main thing I’ve learned in 20 years on LinkedIn is that nobody knows anything.

Nobody knows if your post is good or bad.

You don’t know if it’s good or bad. You need to publish it, test it, let it collide with the algorithm and actual humans.

That’s the only way to learn.

—

Personal take inspired by Marc Randolph

graphical user interface, text, application, website

Apple evaluated OpenAI and Anthropic and still chose Google paying roughly $1B a year for a custom 1.2 trillion-parameter model.

Why?
 
Because Google offered stronger capabilities on better terms.
 
The new Siri launches with iOS 26.4 in March or April and introduces contextual understanding and multi-step task execution across apps.
 
Apple structured the deal to run entirely on their Private Cloud Compute servers so no user data ever gets shared with Google.
 
They still plan to build their own trillion-parameter model over time but admitted this effort takes longer than expected.
 
The takeaway feels clear. Even the world’s most valuable companies can’t push the AI frontier alone anymore.
 
If you’re building a company right now this deserves a pause.
 
Knowing when to work with partners while building internal strength helps companies move faster without losing control.
 
My advice to founders watching this move is to focus on progress over pride.

Now, the question you’re probably waiting for.

Which one of these do you hope you’ll never hear again from the new Siri? Be honest.

1. “Here’s something I found on the web!”
2. “I’m sorry, I can’t help you with that”
3. “I’m not sure I understand”

I Help Freshers & Professionals from Non-IT & Engineering backgrounds break into the Data Analytics | Podcaster - The Starlight Podium | BI&A Associate @ Eli Lilly and Company

With nearly 4 years in healthcare and pharmaceutical analytics, I design data-driven strategies, scalable products, and actionable insights that drive business impact. As a Consultant at Eli Lilly in Product Analytics, I deliver HCP and patient analytics solutions that boost efficiency and deliver measurable outcomes across multiple brands.

My career has spanned HCP/HCO targeting, next-best-action (NBA) strategy, patient journey and leakage analysis, global market insights, and analytics product development. I combine technical expertise with product thinking and cross-functional collaboration to create solutions that are both impactful and scalable.

Highlights of my journey include:

- Led and evolved multi-semester HCP targeting and NBA programs for launch and in-line brands, cutting delivery timelines by 50% and enabling consistent stakeholder alignment.
- Designed and implemented dynamic targeting frameworks that drove a 4% revenue uplift for immunology brands.
- Built and deployed analytical products in Python, Flask, SQL, and Tableau/Power BI, standardizing processes and saving 1,000+ hours across teams.
- Developed OTGS dashboards tracking patient journeys and leakage trends across US and 10+ global markets, guiding retention strategies and reducing drop-offs.
- Mentored and upskilled peers on analytics tools and methodologies, embedding best practices across teams.

Currently pursuing a Master’s in Data Science (AI/ML specialization) from Deakin University, with the goal of transitioning into a data science role by mid-2026 and applying advanced machine learning to solve high-impact problems in healthcare.

My professional philosophy centers on building scalable solutions that merge analytics with business strategy, enabling teams to make faster, clearer, and more confident decisions.

Varun Sagar Theegala

Many meaningful opportunities at work & life don’t come from waiting at a desk. They come from putting yourself out in the world.


𝗕𝗮𝗰𝗸 𝗶𝗻 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿 𝟮𝟬𝟮𝟯, I started a podcast to share learnings from industry professionals.

There were no expectations attached to it — just curiosity and a willingness to start.

A year later, 𝗜 𝘄𝗮𝘀 𝗶𝗻𝘃𝗶𝘁𝗲𝗱 𝗯𝘆 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 to be part of a LinkedIn Video Experience, as one of several consistent video creators.

𝗜𝗻 𝗙𝗲𝗯𝗿𝘂𝗮𝗿𝘆 𝟮𝟬𝟮𝟱, I began my Master’s in Data Science with a specialization in AI.

Alongside work, I shared my learnings on LinkedIn, participated in trainings, and explored small, practical AI initiatives whenever time allowed.

Today, I’ve been given the opportunity to 𝗹𝗲𝗮𝗱 𝘁𝘄𝗼 𝗺𝗮𝗷𝗼𝗿 𝗔𝗜 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲𝘀 within my organization.

None of this happened overnight, and none of it happened by chance.

It happened because I took initiative and made that work visible.

When you consistently 𝗽𝘂𝘁 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝗼𝘂𝘁 𝗶𝗻 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱, you open yourself up to 𝗮𝘁𝘁𝗿𝗮𝗰𝘁𝗶𝗻𝗴 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 you never imagined existed.

𝗪𝗵𝗮𝘁 𝗵𝗲𝗹𝗽𝗲𝗱 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁𝗹𝘆:

 • Talking to others about what I was exploring and learning
 • Sharing insights and progress with peers, supervisors, and the wider community
 • Letting people know what I was working on, instead of assuming they’d notice
 • Engaging with others doing similar work and offering help where possible

Through steady, consistent effort — and a willingness to step outside comfort zones — new doors gradually opened.

Putting yourself out there can feel uncomfortable at times.
But visibility creates connection, and connection creates opportunity.

You don’t need a perfect plan.
You just need to start somewhere to eventually reach somewhere.

———

Follow me Varun Sagar Theegala for stories and tips from my 4+ years in analytics, studies, building in public and office life!

Don’t care.
Not in an arrogant way — but in an intentional one.


When you start putting yourself out there — building something, sharing your work, learnings, experiences, or creating in public — most people don’t know you, your story, or the road you’ve walked to get here.

And early on, that shows.

You may see:

 • Little to no support
 • Little to no traction
 • Comments questioning your ability
 • Assumptions that you’re trying to boast or seek attention

That’s not a reflection of your intent.
It’s a reflection of unfamiliarity.

People react to what they see in a moment, not the context behind it.

If your intentions are honest and your commitment is real, progress will come — quietly at first, then consistently.
And with that progress, external noise gradually loses its power.

You don’t need universal approval to move forward.
You just need clarity on why you started.

Stay focused. Keep building.
Let the work do the explaining over time.

I see a lot of people fear AI because they see it as a threat to their jobs. That fear isn’t irrational — but it may be misplaced.


Recently, I attended a personality and leadership workshop where I was grouped in a breakout room with professionals across industries — banking, manufacturing, healthcare, IT — ranging from freshers to highly tenured leaders.

Different roles. Different sectors.
One common theme.

AI.

Across conversations, AI came up not just as a tool, but as a 𝘀𝗼𝘂𝗿𝗰𝗲 𝗼𝗳 𝗮𝗻𝘅𝗶𝗲𝘁𝘆.

Many shared concerns around layoffs, automation, and the possibility of being replaced by AI bots or agents.

While downsizing is one path many organizations take, there’s another reality many of us discussed that’s equally likely — and already happening.

Many organizations are also choosing to 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗼𝘂𝘁𝗽𝘂𝘁 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴 𝗵𝗲𝗮𝗱𝗰𝗼𝘂𝗻𝘁, using AI as a force multiplier.

Either way, the implication for individuals is the same.

The question isn’t whether AI will change how work is done. It already has.

The real question is whether you use 𝗔𝗜 𝘁𝗼 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗶𝗺𝗽𝗮𝗰𝘁 — or stay static while the system evolves around you.

𝗧𝗵𝗲 𝗶𝗻𝗱𝗶𝘃𝗶𝗱𝘂𝗮𝗹𝘀 𝘄𝗵𝗼 𝗿𝗲𝗺𝗮𝗶𝗻 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲 𝘁𝗲𝗻𝗱 𝘁𝗼:

 • Get comfortable with AI tools for variety of tasks
 • Use AI to accelerate thinking, problem-solving, and execution
 • Learn to train, deploy, and oversee AI agents in real workflows
 • Apply AI to reduce manual work & improve decision quality

No matter how advanced AI becomes, certain things remain constant.

𝗔𝗜 𝘀𝘁𝗶𝗹𝗹 𝗻𝗲𝗲𝗱𝘀 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗵𝗼 𝗰𝗮𝗻:

 • Train it
 • Validate its outputs
 • Provide context and domain knowledge
 • Apply judgment based on real business conditions

𝗧𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝗻𝗼 𝗴𝘂𝗮𝗿𝗮𝗻𝘁𝗲𝗲𝘀 𝗶𝗻 𝗮𝗻𝘆 𝗺𝗮𝗿𝗸𝗲𝘁. Upskilling doesn’t make anyone immune to macroeconomic forces.

But choosing to work with AI gives you the 𝗯𝗲𝘀𝘁 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲 𝗼𝗱𝗱𝘀.

In uncertain times, the most practical strategy is simple:

Do your best. Keep learning. Increase your leverage. Stay hopeful.

#AI #WaysOfWorking

People who believe in their own abilities don’t compete with others. They enable their colleagues and multiply impact through collaboration & guidance

One of the quieter signs of leadership I’ve noticed over time is restraint.

It’s knowing the details of the work, yet allowing your fellow team members or project collaborators to take the lead in explaining it.

Not because you lack understanding but because you trust your team enough to let them own it.

You listen.
You step in only when it truly adds value.

That silence isn’t absence.

It’s support, confidence, and trust, clearly felt by those around you.

From an analyst’s perspective, this creates space for ownership, better thinking, and stronger collaboration.

Often, the strongest presence in the room is the one that doesn’t need to speak first.

We Think: “Focus on one task at a time.”
In Reality: You’re juggling multiple analyses, projects & deadlines, all at once!


When I joined the workforce (and even four years in), I genuinely believed I could plan my day neatly.

One task after another. Finish on time. Leave peacefully.

Some days, that worked
Most of the days, it doesn’t.

In analytics consulting especially, work moves at the pace of business decisions.

Stakeholders aren’t asking one question — they’re asking several, often at the same time, and they need answers quickly.

As an analyst, you’re balancing:

 • Long-term projects that require depth and context
 • Recurring work like dashboards and reporting
 • Short-turnaround analyses that suddenly become high priority

And somewhere in between, the perfectly structured day disappears.

You switch contexts.
You work across multiple screens.
You reprioritize in real time.

That’s also why “ability to multitask” shows up so often in job descriptions.

Not as a buzzword — but as a reflection of how work actually gets done.

A few practical realities worth learning early:

 • You’ll often juggle more than one task at a time
 • Multiple screens and parallel analyses become normal
 • Caffeine helps 😅 , but clarity helps more
 • Regular check-ins with your manager help you prioritize, even if you can’t drop work entirely
 • Delegation matters, when it’s an option

Multitasking isn’t about doing everything at once.

It’s about learning how to switch context thoughtfully, prioritize under pressure, and still deliver quality.

That skill only becomes more valuable as responsibilities grow.

———

Follow me Varun Sagar Theegala for stories and tips from my 4+ years in analytics, studies, building in public and office life!

P.S My reality 👇

My colleague lost 3 valuable hours debugging a python program to automate an SQL script run. The Reason - Nulls Not Handled!

Few days back, I sat with a colleague trying to help fix a seemingly invisible bug in a SQL script automation program written in python.

This python program had been previously used to successfully execute several SQL codes. 

After few hours of Copilot, ChatGPT & cancelling out potential reasons for the error, we finally arrived at a particular SQL code snippet for a summary.

This summary was calculating averages on a numerical column. Didn't really look like something alarming.

BUT IT WAS.

This numeric fields had nulls which weren't handled. In SQL, when you perform arithmetic operations on fields with Nulls, its gets automatically handled. 

In Python, however, anything apart from numeric values in field used for arithmetic operations is bound to return errors like -

- TypeError: unsupported operand type(s) for +: 'NoneType' and 'float'
- ValueError: The truth value of a Series is ambiguous

This was a clear reminder from real work — writing production-grade SQL isn’t just about getting correct outputs in isolation.

It’s about ensuring your code behaves safely when it interacts with downstream systems.

Many of us compromise on such practices to save time.

But the cost often shows up later — as lost hours, broken pipelines, incorrect results, or production issues.

Small defensive practices in SQL may feel optional early on.

In real systems, they’re what prevent avoidable failures.

If you’re learning SQL, revisiting fundamentals, or helping others improve their coding practices, these details matter far more than they seem — and they’re often the difference between code that runs and code that lasts.

———

I'm Varun Sagar Theegala & I share experiences from my 4 years in analytics, studies, building in public and office life!

#SQL #DataAnalytics

10 Non-Negotiable SQL Practices

One of the more practical Agentic AI frameworks I learned during my Master’s — and have since applied in real initiatives at work — is the 𝗥𝗲𝗔𝗰𝘁 (𝗥𝗲𝗮𝘀𝗼𝗻 + 𝗔𝗰𝘁) approach.

At a high level, ReAct is a simple but powerful idea:

Instead of responding directly to a user query, an agent is guided to 𝘁𝗵𝗶𝗻𝗸 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺, 𝗱𝗲𝗰𝗶𝗱𝗲 𝘄𝗵𝗶𝗰𝗵 𝘁𝗼𝗼𝗹 𝘁𝗼 𝘂𝘀𝗲, 𝘁𝗮𝗸𝗲 𝗮𝗻 𝗮𝗰𝘁𝗶𝗼𝗻, 𝗼𝗯𝘀𝗲𝗿𝘃𝗲 𝘁𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁, and then 𝗱𝗲𝗰𝗶𝗱𝗲 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗶𝘀 𝘀𝗼𝗹𝘃𝗲𝗱 𝗼𝗿 𝗻𝗲𝗲𝗱𝘀 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝘀𝘁𝗲𝗽.

In my Advanced Generative AI for NLP module, I applied this framework while building a 𝗡𝗲𝘄𝘀𝗙𝗶𝗻𝗱𝗲𝗿 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 — an agent designed to analyze and summarize news using multiple tools.

The agent was equipped with tools such as:

 • Web search (DuckDuckGo, YahooSearch) for retrieving recent news
 • A Python REPL for parsing, cleaning, and structuring results
 • The LLM itself for reasoning and synthesis

What ReAct changed was 𝗵𝗼𝘄 𝘁𝗵𝗲 𝗮𝗴𝗲𝗻𝘁 𝘂𝘀𝗲𝗱 𝘁𝗵𝗲𝘀𝗲 𝘁𝗼𝗼𝗹𝘀.

Instead of hard-coding tool usage, the agent followed a structured loop:

 • 𝗥𝗲𝗮𝘀𝗼𝗻: Interpret the user’s query and break it into steps
 • 𝗔𝗰𝘁: Select the most appropriate tool for the current step
 • 𝗢𝗯𝘀𝗲𝗿𝘃𝗲: Evaluate the tool’s output
 • 𝗗𝗲𝗰𝗶𝗱𝗲: Check whether the result answers the question or if another action is needed

For example, when asked to find and summarize recent news on a topic, the agent would:

 • First reason that fresh information was required
 • Invoke a search tool to retrieve articles
 • Observe whether the results were relevant and sufficient
 • Use Python or the LLM to clean, filter, and summarize
 • Repeat the loop only if gaps remained

𝗧𝗵𝗶𝘀 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗺𝗮𝗱𝗲 𝘁𝗵𝗲 𝗮𝗴𝗲𝗻𝘁:

 • More deliberate in tool usage
 • Easier to debug and reason about
 • Less likely to hallucinate or overuse tools
 • Closer to how a human analyst would approach the task

In practice, ReAct acts as a lightweight control mechanism — not restricting the model, but guiding it to behave more like a problem-solver than a text generator.

If you’re designing agentic workflows, ReAct is a strong foundation to start with, especially when multiple tools and real-world uncertainty are involved. 


———

Follow me Varun Sagar Theegala for stories and tips from my 4+ years in analytics, studies, building in public and office life!

A must know for individuals learning or practicing data visualisation!

Data Scientist | 3+ years in Predictive Analytics, Experimentation & Business Impact | 200K+ Data Community | Featured on Times Square, Fox, NBC

View Venkata Naga Sai Kumar Bysani’s profile

Perfect analysis. Beautiful dashboard. Zero impact. Sound familiar?

Not because the data is wrong. Because the visualization quietly misleads, overwhelms, or confuses.

Here are 6 data visualization mistakes that make stakeholders ignore your insights, even when the numbers are correct 👇

𝟏. 𝐒𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐛𝐚𝐫 𝐜𝐡𝐚𝐫𝐭𝐬 𝐚𝐰𝐚𝐲 𝐟𝐫𝐨𝐦 𝐳𝐞𝐫𝐨 Makes small changes look dramatic and distorts perception.

𝟐. 𝐔𝐬𝐢𝐧𝐠 𝐩𝐢𝐞 𝐜𝐡𝐚𝐫𝐭𝐬 𝐟𝐨𝐫 𝐭𝐨𝐨 𝐦𝐚𝐧𝐲 𝐜𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐞𝐬 Human eyes can't accurately compare angles and areas.

𝟑. 𝐎𝐯𝐞𝐫𝐮𝐬𝐢𝐧𝐠 𝐜𝐨𝐥𝐨𝐫𝐬 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐦𝐞𝐚𝐧𝐢𝐧𝐠 Every extra color adds cognitive noise instead of clarity.

𝟒. 𝐃𝐮𝐚𝐥 𝐚𝐱𝐞𝐬 𝐭𝐡𝐚𝐭 𝐢𝐦𝐩𝐥𝐲 𝐟𝐚𝐥𝐬𝐞 𝐜𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬 Scale manipulation can manufacture relationships that don't exist.

𝟓. 𝐂𝐡𝐚𝐫𝐭 𝐭𝐢𝐭𝐥𝐞𝐬 𝐭𝐡𝐚𝐭 𝐝𝐞𝐬𝐜𝐫𝐢𝐛𝐞, 𝐧𝐨𝐭 𝐜𝐨𝐧𝐜𝐥𝐮𝐝𝐞 If the title doesn't state the insight, readers must guess.

𝟔. 𝐒𝐡𝐨𝐰𝐢𝐧𝐠 𝐚𝐥𝐥 𝐝𝐚𝐭𝐚 𝐢𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐭𝐡𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭 Executives want conclusions, not raw tables.

The common thread? Data doesn't speak for itself. Design decisions decide what people believe.

Good visualization isn't about being fancy. It's about being honest, clear, and decisive.

If your charts don't guide attention, they invite misinterpretation.

Which one are you guilty of? 
♻️ Repost if someone in your network builds dashboards

—
📚 Get 150+ real interview questions (with solutions & frameworks) in our Data Analyst Interview Prep Book: https://lnkd.in/dyzXwfVp

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 20,000+ readers here → https://lnkd.in/dUfe4Ac6

Data Visualization Mistakes to Avoid

Progress doesn’t come from silence or complaints — it comes from someone stepping in to steer things back, even if it makes others uncomfortable.


Large forums and group discussions in organizations are often created with the right intent — to align people, surface challenges, and move toward shared solutions.

In practice, these settings can be some of the hardest to navigate.

What I’ve observed repeatedly is a familiar pattern.

A few individuals try to set context and get the discussion started.
But soon after:

 • The same points are repeated in different words, sending the conversation in circles
 • The discussion drifts without direction or closure

Over time, another group emerges.
People who are engaged, thoughtful, and capable of contributing — but choose to stay quiet.

Not because they don’t care,
but because the discussion feels chaotic, unproductive, or unlikely to change.

The result is predictable.
Momentum is lost. Time is spent. Objectives remain unmet.

From recent experiences, I’ve come to believe that while repetition and lack of structure make conversations harder, silence in the face of disorder doesn’t help either.

In moments like these, progress depends on individuals who are willing to:

 • Re-anchor the discussion to the original objective
 • Bring focus back when conversations drift
 • Moderate respectfully, even if it feels uncomfortable

Yes, this can make some people feel interrupted, overlooked, or uneasy.
But the purpose of the forum — and the time invested by everyone present — matters.

Allowing discussions to spiral unchecked rarely serves anyone.
And staying silent, only to voice frustration later, doesn’t move things forward either.

Progress often requires someone to step in — not to dominate, but to guide. Not to silence opinions, but to help them land where they matter.

In group settings, change doesn’t come from watching things go off track. It comes from those willing to help steer them back — thoughtfully, respectfully, with eye towards the goal.

That’s where real contribution often begins.

A wonderful and intuitive data science project roadmap for anyone looking to build their project portfolio 👇


Thanks for creating and sharing Penelope Lafeuille

Helping data scientists show their expertise on LinkedIn. 💪🏻 Data Career Tips 📈 From burnt-out data scientist to $180K+ and promoted.

How a data science project actually moves from idea to production 👇

Most data scientists think it starts with code....

It doesn't.

I’ve been working as a data scientists for 4 years and once I understood the real flow, I gain SO much clarity in my work.

Here's how it actually works:

1️⃣ Business Understanding 

Someone has a question. 
• "Why are we losing customers?" 
• "Can we predict churn?" 
Your job isn't to open a notebook yet. It's to listen. Ask. 
And turn a messy human problem into something data can actually answer. 

This is step one in CRISP-DM, the industry standard framework for data science projects, and it's the one most tutorials completely skip.

2️⃣ Data Understanding Now you go looking. 
• Which tables exist? 
• Which sources? 
• What does the data actually contain? 

You're not cleaning anything yet. You're just getting to know what you're working with. 

And sometimes you realize here that the data can't even answer the original question.

3️⃣ Data Preparation 

This is where the real work happens.

 Cleaning, transforming, handling missing values, engineering features. 

The unglamorous middle of every project.

 Fun fact: industry experts estimate that 50-80% of total project effort lives right here. If you rush this step, everything after it falls apart.

4️⃣ Modeling 
Yes — the part everyone romanticizes. 

You're not chasing a perfect model. 
You're building something good enough to test against the original business question. Perfect is the enemy of shipped.

5️⃣ Evaluation 

This is the step that separates beginners from seniors. 
You're not just checking accuracy metrics. 
You're asking: 
• does this model actually solve the problem from step 1? 
• Did we miss anything?

If the answer is no → you loop back. That's not failure. That's the process.

6️⃣ Deployment + Monitoring 

The model ships. But it doesn't end there. Data drifts. Behavior changes. Models degrade silently if no one's watching. 

Monitoring is what turns a one-time project into a living system. And then? The whole cycle starts again.

The biggest myth in data science education is that this is a straight line.

It's not. It's a loop. 

And understanding that loop is one of the most underrated skills you can build.

Still learning, so if I missed something, let me know in the comments 👇

Early in our careers, many of us are told “your work speaks for itself.” That belief isn’t wrong but it’s incomplete.

In real teams, good work doesn’t always stand out on its own.
Not because it lacks impact, but because 𝗺𝗮𝗻𝘆 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝘀𝘁𝗿𝗼𝗻𝗴 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗮𝘁 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝘁𝗶𝗺𝗲.

Over time, I’ve learned that the work that gets recognized is often the work that’s:

 • Clearly documented
 • Thoughtfully communicated
 • Connected back to business impact

This isn’t about over-sharing or self-promotion.
It’s about 𝗺𝗮𝗸𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗮𝗯𝗹𝗲 𝗮𝗻𝗱 𝘃𝗶𝘀𝗶𝗯𝗹𝗲.

This becomes especially clear:

 • During 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗿𝗲𝘃𝗶𝗲𝘄𝘀, impact that isn’t documented or communicated is often overlooked.
 • When 𝘁𝗲𝗮𝗺𝘀 𝗰𝗵𝗮𝗻𝗴𝗲 𝗼𝗿 𝗺𝗮𝗻𝗮𝗴𝗲𝗿𝘀 𝗿𝗼𝘁𝗮𝘁𝗲, undocumented work quickly loses visibility.
 • In 𝗽𝗿𝗼𝗺𝗼𝘁𝗶𝗼𝗻𝘀 𝗼𝗿 𝗿𝗼𝗹𝗲 𝘁𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻𝘀, people remember clearly articulated impact more than silent effort.
 • When 𝘀𝘄𝗶𝘁𝗰𝗵𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀, your documented work becomes the evidence you carry forward, not just your title or tenure.

𝗔 𝗳𝗲𝘄 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗵𝗮𝗯𝗶𝘁𝘀 𝘁𝗵𝗮𝘁 𝗵𝗲𝗹𝗽, especially as your experience grows:

 • Document decisions, not just deliverables
 • Capture the problem you solved and why it mattered
 • Ask what changed because your work existed
 • Share learnings and trade-offs, not just final outcomes
 • Periodically summarize your projects in simple terms

These habits compound quietly.

They help others trust your work, remember your impact, and involve you in more meaningful problems.

Do solid work. But also take ownership of documenting the work & ensuring its communicated & understood clearly

I’ve come to realise that people who truly understand a problem don’t rush to speak. They listen, connect the dots, and then add value.

Great success comes from good decisions. Good decisions come from experience. Experience comes from making decisions (sometimes bad ones). 



Many people give up the opportunity or depend on others to aid them with decisions to make around a project, a initiative or a communication - sometimes even for smaller ones for which they have sufficient experience and expertise to make the call.

This happens because of the fear of failure, looking bad or being judged.

No matter how smart, well-intentioned, meticulous you are - some of your decisions will go very well and many would not.

Be assured that no matter the outcome, the experience from those decisions will impart you the wisdom and courage to take bolder, calculated and impactful decisions down the road.

And such decisions will lead to you to bigger successes and breakthroughs.

Don’t be afraid to take your chances when the opportunity arises.

As AI is increasingly becoming part of our daily workflows, essential to have understanding of crucial AI terms & concepts 👇

A very informative share by Denis Panjuta! Thank you

Helping founders grow their LinkedIn presence with consistent content and strong positioning. Trusted by 160k+ followers | Strategy, content, and growth handled

AI Vocabulary For Teams !

Here’s a quick AI Vocabulary every team must understand :

1. Artificial Intelligence (AI): Tech that enables machines to think and act like humans.
2. Machine Learning (ML): AI that learns from data and improves automatically.
3. Deep Learning: Advanced ML using neural networks for complex tasks.
4. Neural Network: Systems modeled after the human brain to recognize patterns.
5. Generative AI: AI that creates new content like text, images, or videos.
6. Large Language Model (LLM): Models like ChatGPT or Claude that understand and generate language.
7. AI Agent: Autonomous programs that perform tasks and make decisions.
8. Agentic AI: Self-directed AI that plans, reasons, and executes tasks independently.
9. Chatbot: AI that interacts with users in natural language.
10. Prompt: The input you give to an AI to generate a response.

…and many more terms like Fine-Tuning, Dataset, NLP, Computer Vision, and AI Ethics that define how AI works today.

Understanding these terms isn’t just for tech teams, it’s for every business that wants to stay ahead in the AI era.

If you want help refining YOUR LinkedIn identity - so your content attracts clients, not confusion - DM me #GrowMyLinkedIn. 

I’ll tell you exactly how to position yourself for maximum visibility and inbound leads. 

Let’s make your presence impossible to ignore.
Promised.

Even in 2026, the core skillset for analysts hasn’t changed. What has changed is how these skills are amplified with AI (10 practical ways how )


At a foundational level, this is how these skills show up at work:

 • SQL is used to retrieve, join, and shape data directly from databases.
 • Excel is used for quick validation, ad-hoc analysis, and lightweight reporting.
 • Power BI or Tableau is used to communicate insights through dashboards and reports.
 • Python is used for deeper analysis, automation, and scalable data workflows.
 • Statistics helps interpret patterns, variability, and significance in data.
 • Critical thinking helps question assumptions and interpret results responsibly.
 • Problem solving turns vague questions into structured analytical approaches.
 • Communication and storytelling turn analysis into decisions and action.

𝗧𝗵𝗲𝘀𝗲 𝗿𝗲𝗺𝗮𝗶𝗻 𝗻𝗼𝗻-𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲.

What has evolved is how effectively analysts are 𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝘁𝗼 𝗰𝗼𝗺𝗯𝗶𝗻𝗲 𝘁𝗵𝗲𝘀𝗲 𝘀𝗸𝗶𝗹𝗹𝘀 𝘄𝗶𝘁𝗵 𝗔𝗜.

In practice, 𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝟭𝟬 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗮𝘆𝘀 𝗵𝗼𝘄:

 1) Using AI with SQL to draft queries, debug logic, and document intent — while validating joins, filters, and assumptions yourself.

 2) Using AI with Python to build programs, debug errors, and optimize workflows, while owning the final logic and outcomes.

 3) Using AI with Excel to speed up validation, formulas, and quick data checks during ad-hoc analysis.

 4) Using AI with Power BI or Tableau to draft dashboard layouts, metrics, and calculations, then refining them with business context.

 5) Using AI to brainstorm analytics requirements before writing queries or code.

 6) Using AI to explore multiple analytical approaches before committing to one.

 7) Using AI to clarify statistical methods and sanity-check assumptions, not replace understanding.

 8) Using AI to summarize insights and findings, while ensuring accuracy and relevance.

 9) Using AI to tailor stories and presentations for different audiences and situations.

 10) Using AI to automate repetitive work, not analytical judgment or decision-making.

For anyone entering analytics, or already working in it, the message is simple:

𝗕𝘂𝗶𝗹𝗱 𝘀𝘁𝗿𝗼𝗻𝗴 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀, 𝘁𝗵𝗲𝗻 𝗹𝗲𝗮𝗿𝗻 𝗵𝗼𝘄 𝘁𝗼 𝘄𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝗔𝗜 𝘁𝗼 𝗿𝗮𝗶𝘀𝗲 𝘁𝗵𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗶𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗼𝘂𝘁𝗽𝘂𝘁.

The tools will keep evolving.
Clear thinking and sound fundamentals will always matter.

#AI #DataAnalytics #SQL #Python #Dashboards #Excel

The most valuable AI education today is freely available.
The real gap in 2026 won’t be access — it will be application.


This year, the divide won’t be who knows AI.

It will be who can use it to drive progress, while others drown in activity with no impact.

Based on my studies, article reads as well hands-on work across AI transformation initiatives, here are the AI capabilities will meaningfully move careers and organizations ahead:

1. Prompt engineering
Clear context, constraints, and examples drive consistent, usable outputs.

2. AI workflow automation
Automating low-visibility work frees time and focus for higher-value thinking.

3. AI agents
Agents shift effort from task execution to outcome delivery, acting more like collaborators than tools.

4. Retrieval-augmented generation (RAG)
Most answers already exist — the advantage lies in surfacing the right knowledge at the right time.

5. Multimodal AI
Combining text, visuals, and voice improves context and reduces misinterpretation.

6. Domain-specific assistants
General intelligence helps, but systems tuned to business context deliver real value.

7. Voice AI and avatars
Knowledge shared once can be reused at scale across training and enablement.

8. AI tool stacking
No single tool solves everything; thoughtful integration removes friction.

9. AI video generation
Faster iteration enables quicker feedback, learning, and improvement.

10. LLM management
As usage scales, managing cost, latency, and reliability becomes critical.

A useful perspective: don’t chase tools — design systems that compound impact over time.

AI’s real value isn’t intelligence alone.
It’s leverage.

Which of these are you already applying in your work?


#ArtificialIntelligence #GenerativeAI #AIAgents #MachineLearning #AIEngineering

For anyone interested in building web applications for their Analytics/data science projects but has no prior background in web development - this is a great course from Andrew Ng that teaches you how to think of and build apps with AI

Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of LandingAI

If you’ve never written code before, this is for you. I’ve just launched a course that shows you, in less than 30 minutes, how to describe an idea for an app and build it with AI.

In this course, you'll build a working web application - a funny interactive birthday message generator that runs in your browser and can be shared with friends. You'll customize it by telling AI how you want it changed, and tweak it until it works the way you want. By the end, you'll have a repeatable process you can apply to build a wide variety of applications.

If you want to try vibe coding, this will be the best place to start! Further, you'll be able to use these techniques with whatever tool you're most comfortable with (like ChatGPT, Gemini, Claude, or others) -- we're vendor neutral.

Skills you'll gain:
- How to build web apps with AI - zero coding skills needed
- How to fix and improve your creations by chatting with AI
- A simple process you can use to build other things you can dream up

Building with AI is one of the most fun things in the world. Please join me and take your first step! I think you will be surprised at what you can build. And if you're already an experienced engineer, please share this with someone in your life who's been curious about building with AI.

Come build with me! https://lnkd.in/gEA5npAC

AI makes it easier to do more as a data analyst. But it also makes having strong fundamentals even more important.


AI tools are everywhere.
But here’s what often gets overlooked:

AI is only as good as the fundamentals behind it.

Those fundamentals help you:

 • Ask the right questions to AI
 • Review outputs instead of blindly trusting them
 • Debug issues when something doesn’t feel right

The core data analyst skillset that still matters most:

𝗦𝗤𝗟
 • Focus on understanding joins, filters, group by logic, and how queries change results.
 • This helps you spot wrong joins, incorrect aggregations, and misleading numbers in AI-generated SQL.

𝗣𝘆𝘁𝗵𝗼𝗻
 • Focus on data manipulation, reading code line by line, and understanding how transformations work.
 • This lets you review AI-written scripts, catch logic errors, and debug instead of guessing.

𝗘𝘅𝗰𝗲𝗹 𝗼𝗿 𝘀𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁𝘀
 • Focus on validation, cross-checks, and quick calculations.
 • This is where you sanity-check AI outputs before trusting dashboards or models.

𝗧𝗮𝗯𝗹𝗲𝗮𝘂 𝗼𝗿 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜
 • Focus on choosing the right chart, filters, and level of detail.
 • This helps you catch visual misrepresentations even if the chart was created by AI.

𝗕𝗮𝘀𝗶𝗰 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀
 • Focus on understanding distributions, trends, and comparisons.
 • This allows you to question whether AI-generated insights actually make sense.

𝗦𝗼 𝘄𝗵𝗲𝗿𝗲 𝗱𝗼𝗲𝘀 𝗔𝗜 𝗳𝗶𝘁 𝗶𝗻?

Tools like ChatGPT and Copilot are great for brainstorming analytical approaches, speeding up coding and execution, and helping debug logic or scripts.

But they work best when you can 𝗴𝘂𝗶𝗱𝗲 𝘁𝗵𝗲𝗺, 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝘁𝗵𝗲𝗺, and 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝘁𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀.

AI accelerates your workflow.
Fundamentals protect your judgment.

Learn to think like an analyst first.
Then use AI to move faster — not blindly.

Kickstart your 2026 with learning the most in-demand skills that is resharing our industries for FREE

A wonderful set of free gen AI resources shared by José Siles

Don't pay to learn GenAI. You can learn from FREE!

Here are the 15 courses with links to the repos:

𝟭) 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗻𝗱 𝗟𝗟𝗠𝘀
↳ Understanding what Generative AI is and how Large Language Models (LLMs) work.
🔗https://lnkd.in/e_XS43cz

𝟮) 𝗘𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗮𝗿𝗶𝗻𝗴 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗟𝗟𝗠𝘀
↳ How to select the right model for your use case. 
🔗https://lnkd.in/e-XWS5Dg

𝟯) 𝗨𝘀𝗶𝗻𝗴 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝘆
↳ How to build Generative AI Applications responsibly
🔗https://lnkd.in/evyBJnh9

𝟰) 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
↳ Hands-on Prompt Engineering Best Practices. 
🔗https://lnkd.in/eJZyR339

𝟱) 𝗖𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗣𝗿𝗼𝗺𝗽𝘁𝘀
↳ How to apply prompt engineering techniques that improve the outcome of your prompts.
🔗https://lnkd.in/erJjJ3Qu

𝟲) 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗧𝗲𝘅𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀
↳ A text generation app using Azure OpenAI / OpenAI API
🔗https://lnkd.in/eYCbXz7U

𝟳) 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗖𝗵𝗮𝘁 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀
↳ Techniques for efficiently building and integrating chat applications. 
🔗https://lnkd.in/eRSVyTBS 

𝟴) 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗦𝗲𝗮𝗿𝗰𝗵 𝗔𝗽𝗽𝘀 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ A search application that uses Embeddings to search for data.
🔗https://lnkd.in/eW6iQ5UU

𝟵) 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗜𝗺𝗮𝗴𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀
↳ An image generation application
🔗https://lnkd.in/eFpEJZ3J

𝟭𝟬) 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗟𝗼𝘄 𝗖𝗼𝗱𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀
↳ A Generative AI application using Low Code tools
🔗https://lnkd.in/eiF3mWpu

𝟭𝟭) 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀
↳ An application using an AI Agent Framework
🔗https://lnkd.in/eGExb_2q

𝟭𝟮) 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
↳ An application using a RAG Framework to retrieve embeddings from a Vector Databases.
🔗https://lnkd.in/eWpsEw8R

𝟭𝟯) 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝗟𝗟𝗠𝘀
↳ The what, why and how of fine-tuning LLMs
🔗https://lnkd.in/eRiRKEu3

𝟭𝟰) 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗠𝗲𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝘀
↳ The features and differences of the Meta Family Models
🔗https://lnkd.in/eXvc72hR

𝟭𝟱) 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 𝗮𝗻𝗱 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲
↳ An application using open source models available on Hugging Face
🔗https://lnkd.in/eVczYK9Z

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