Successive Digital

Strategic Planning:
- Develop and implement technology strategies that align with overall business goals.
- Identify new technology trends and opportunities for the organization to leverage.

Leadership:
- Lead the Retail & Commerce Business Unit (BU).
- Manage day-to-day operations of the team.
- Set goals and objectives, and ensure team meets business requirements.

Project Management:
- Manage IT projects, including timelines and resources.
- Ensure projects are completed on time and with quality.
- Ensure project team meets business requirements.

Senior Manager - Technology

Principal Engineer

Lead Engineer

My responsibilities include understanding of the requirements from client, propose the best possible solution, coding the requirements as per the standards of technology, peer code review to ensure quality of code, demonstration of functionality to the client, providing UAT support, ensure timely delivery of sprints, deploying the code on the client side.

Senior Software Engineer

Software Engineer

Marketcube.io

- Requirement Analysis and Planning 
- Designing the Product Architecture/Solution
- Developing the Product as per the standards
- Peer code review
- Demonstration of functionality to the client
- Deployment of the code in the defined environment.
- Providing UAT support
- Team Management
- Client communication

Engineering Lead

NodeXperts

My responsibilities include understanding of the requirements from client, propose the best possible solution, coding the requirements as per the standards, peer code review to ensure quality of code, demonstration of functionality to the client, providing UAT support, ensure timely delivery of sprints, deploying the code on the client side.

As a Software Engineer I also took part in the preparation of Project plan & learn Team Leading quality. Started to work in Agile Process & sometimes played the Scrum Master Role.

BSD Infotech

My Role is to develop software application,Understanding the project requirement ,analyze the application,and present complex concepts in simple terms etc.

Programmer

Aquity Design Pvt Ltd

Trainee

- Tech-savvy engineer with 11+ years of experience in developing ecommerce solutions
- Expert in using popular platforms such as Shopify, BigCommerce, and Magento, as well as logistics and payment platforms
- Proficient in various infrastructure, programming languages, databases, web frameworks, web technologies, and third-party APIs
- Skilled in managing the entire project life cycle, from requirement analysis to deployment and support
- Passionate about creating innovative and resourceful solutions that help online businesses grow and succeed
- Avid gamer

My overall capabilities include:

Ecommerce

- Shopify
- Bigcommerce
- Woocommerce
- Magento
- SquareSpace
- Wix

Logistics and payment platforms, such as:

- Canada Post
- Shippo
- ShippyPro
- Starshipit
- UPS
- Chargebee
- Paypal
- Stripe

Project management

- Requirement analysis
- Building project plans
- Coding
- Deployment
- Post-live support


Technical

- Infrastructure (Google Cloud Platform, AWS, Kubernetes, Mongo Atlas, Meteor Galaxy, Digital Ocean, Compose)
- Programming languages (Node, PHP, Python)
- Databases (MongoDB, Redis, Mysql, PostgreSQL)
- Web frameworks (Meteor, ExpressJs, HapiJs, Zend, Laravel, Yii)
- Web technologies (ReactJs, AngularJs, Jquery, REST, JSON)
- Third-Party APIs (Google Map, Facebook, Twitter)

𝗘𝘃𝗲𝗿𝘆 𝗹𝗲𝗮𝗱𝗲𝗿 𝗺𝗮𝗸𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗶𝘀𝘁𝗮𝗸𝗲 𝗮𝘁 𝗹𝗲𝗮𝘀𝘁 𝗼𝗻𝗰𝗲.

Most of us believe only a few key people hold everything together.
They’re visible. Vocal. Reliable.
And it often feels true until it isn’t.

In their glow, others fade.
Or maybe, we just stop noticing them.

That was my mistake too.
Only when those 𝘬𝘦𝘺 teammates moved on, I finally noticed the quiet ones.

→ Capable, but underused.
→ Willing, but unseen.

The problem wasn’t skill.

→ It was the lack of support, guidance, and clarity.
→ And above all, my reluctance to invest time.

Because when things seem to be working fine, it’s easy to avoid the overhead.
But that mindset came with a cost.

→ I didn’t give others room to lead.
→ I held back ownership.
→ I missed chances to show them their impact.
→ I became too critical.

When I finally made the time, the impact was visible.
People who were once quiet started taking charge.

→ They delivered. 
→ They grew. 
→ They owned outcomes.

That’s when I realised leadership isn’t just about who shines the brightest.
It’s also about how many others we help see their own light.

Because sometimes, 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗮𝗴𝗮𝗶𝗻.

#Leadership #Failure #GrowthMindset #Innovation #TeamCulture

Mayank Awasthi

Commerce Tech Leader | AI & Cloud Architect (GCP) | 12+ Years Turning Ideas Into Scalable, Shippable Products

𝗚𝗿𝗲𝗮𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗱𝗼𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗱𝗲𝗹𝗶𝘃𝗲𝗿. 𝗧𝗵𝗲𝘆 𝗲𝗹𝗲𝘃𝗮𝘁𝗲.

They lift teams, raise standards, and quietly shape the culture around them.

When you find them, don’t just reward them.
Empower them. Trust them. Listen to them.

Because great people thrive when they feel trusted, not watched.

Many leaders get this balance wrong.

→ Too much attention kills ownership.
→ Too little attention breaks connection.

Both extremes slow progress.

What truly works is 𝗶𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻.

→ Enough touchpoints to stay connected.
→ Enough space to let people lead.

When people feel seen, supported, and trusted, they don’t just perform.
They raise the bar for everyone around them.

The goal isn’t just to hire top performers.
It’s to create an environment where more people become high-impact.

Exceptional talent is rare.
But the right culture turns potential into excellence. So you can:

→ Lead with balance.
→ Build trust through feedback.

And remember, 𝗚𝗿𝗲𝗮𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗰𝗼𝗻𝘁𝗿𝗼𝗹.
𝗧𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝗯𝗲𝗹𝗶𝗲𝗳.

#Leadership #Culture #Growth #Communication

The fastest learners aren’t coders.
They are the professionals who ask questions first.

AI is changing how every professional works, from how we write emails to how we make decisions.
And for many, that shift feels like a race they are not ready for.

In recent months, I have spoken with teams who say the same thing:
“I know AI is important. I just do not know where to start.”

That uncertainty is not a lack of skill.
It is a sign that the pace of change has outgrown traditional learning models.

The truth is, building your AI edge is not about becoming a prompt engineer or a coder.
It is about learning how to think, decide, and create alongside AI.

→ Start small.
→ Pick 1-2 AI tools and use them daily in your workflow (e.g. ChatGPT for drafting emails or Copilot for coding).
→ Automate one repetitive task you perform every day, such as meeting notes.
→ Every small win builds momentum.

Then comes the deeper shift, focusing on what only we can do.

→ Influence.
→ Empathy. 
→ Strategy. 
→ Judgment.

The qualities that make work meaningful and leadership real.

The professionals who thrive in this era will be the ones who keep learning, sharing, and adapting in public.
They will treat AI as a teammate, not a threat.
They will build visibility not through titles, but through curiosity and contribution.

Because in the end, your AI edge is not about knowing the most tools.
It is about knowing how to combine our insight with machine capability to make better decisions, faster.

The future will not reward those who fear AI.
It will reward those who learn how to work with it.

#AIAtWork #AIInPractice #DigitalTransformation #FutureOfWork #Leadership #AIAdoption

𝗦𝗰𝗮𝗹𝗲 𝗺𝗮𝗱𝗲 𝗔𝗜 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹.
Distillation makes it practical.

For years, we raced to build bigger models.

More parameters.
More compute.
More cost.

Yet bigger isn’t always smarter.

𝗠𝗼𝗱𝗲𝗹 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻 is changing how we think about scale.

→ It allows a 𝗹𝗮𝗿𝗴𝗲, 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗽𝗮𝗿𝗲𝗻𝘁 𝗺𝗼𝗱𝗲𝗹 to transfer its reasoning to a smaller model.
→ Not by copying data but by sharing understanding.
→ Sometimes, that’s through labelled synthetic outputs, rationales, or even step-by-step thinking.

It enables:

→ Faster inference on smaller devices.
→ Lower compute bills.
→ Private, local training without sending data to the cloud.
→ AI for every platform, from enterprise servers to edge chips.

To achieve this, you can:

→ Train a large model until it performs optimally.
→ Use it to label or generate responses for a smaller model.
→ Fine-tune the smaller one until it matches the parent LLM’s behaviour.

Think of it like a great teacher training a brilliant student, 
same wisdom, fewer resources, faster learning.

The next chapter of AI won’t be written by who builds the biggest model,
but by 𝘄𝗵𝗼 𝗯𝘂𝗶𝗹𝗱𝘀 𝘁𝗵𝗲 𝘀𝗺𝗮𝗿𝘁𝗲𝘀𝘁, 𝗺𝗼𝘀𝘁 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗼𝗻𝗲.

#ModelDistillation #AIEngineering #MachineLearning #TechLeadership

𝗣𝗮𝘆𝗣𝗮𝗹 𝗷𝘂𝘀𝘁 𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝗲𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲.

Not with another payment feature.
But with an ecosystem built for the age of AI agents.

For years, online shopping meant browsing, comparing, and checking out.
Now, PayPal’s 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗖𝗼𝗺𝗺𝗲𝗿𝗰𝗲 Services make all of that invisible.

Your AI assistant can find, recommend, and buy while PayPal handles the checkout, security, and trust.

Two offerings lead this shift:

→ 𝗔𝗴𝗲𝗻𝘁 𝗥𝗲𝗮𝗱𝘆, an AI-native payment solution.
→ 𝗦𝘁𝗼𝗿𝗲 𝗦𝘆𝗻𝗰, which lets merchants make their catalogs instantly discoverable across leading AI platforms like Perplexity and ChatGPT.

Imagine asking ChatGPT to “find me a pair of trail shoes under ₹5000”,
and it not only recommends one, it completes the purchase via PayPal in seconds.

For merchants, integration is plug-and-play.
One connection to PayPal connects them to millions of new customers through AI-driven shopping channels.

→ 𝗦𝗲𝗹𝗹𝗲𝗿𝘀 𝘀𝘁𝗮𝘆 𝗶𝗻 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 of brand and pricing.
→ 𝗣𝗮𝘆𝗣𝗮𝗹 𝗺𝗮𝗻𝗮𝗴𝗲𝘀 routing, validation, and compliance.
→ Every transaction is protected end-to-end.

Partnerships with 𝗢𝗽𝗲𝗻𝗔𝗜, 𝗪𝗶𝘅, Cymbio, Shopware, and 𝗕𝗶𝗴𝗖𝗼𝗺𝗺𝗲𝗿𝗰𝗲 show the scale of what’s coming.
PayPal isn’t just participating in AI commerce. It’s building the rails for it.

𝗧𝗵𝗶𝘀 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗲𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲. 𝗜𝘁’𝘀 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝘄𝗵𝗲𝗿𝗲 𝗔𝗜 𝗱𝗼𝗲𝘀 𝘁𝗵𝗲 𝘀𝗵𝗼𝗽𝗽𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗣𝗮𝘆𝗣𝗮𝗹 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘀 𝘁𝗵𝗲 𝘁𝗿𝘂𝘀𝘁.

#AIInEcommerce #AgenticCommerce #PayPal #DigitalRetail #AITransformation

Open article: PayPal Launches Agentic Commerce Services to Power AI-Driven Shopping by investor.pypl.com

PayPal Launches Agentic Commerce Services to Power AI-Driven Shopping

𝗩𝗶𝗯𝗲 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗼𝗼𝗸𝘀 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁.
Until debugging becomes guesswork.

In the last few months, I have heard a new phrase echo across developer circles,  𝗩𝗶𝗯𝗲 𝗰𝗼𝗱𝗶𝗻𝗴.

It is what happens when developers lean on AI to write most of their code.
Trial and error. Copy and paste. Celebrate when it runs.
But skip the part where they truly understand why it works.

I have seen this first-hand in teams.
Code that functions perfectly in a demo
yet no one can explain how to fix it when it breaks.

The problem is:

𝗪𝗵𝗲𝗻 𝗰𝗼𝗱𝗲 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗮 𝗯𝗹𝗮𝗰𝗸 𝗯𝗼𝘅, 𝘀𝗼 𝗱𝗼𝗲𝘀 𝘁𝗵𝗲 𝗿𝗶𝘀𝗸.

→ Debugging turns into guesswork.
→ Security flaws slip through unnoticed.
→ Software grows brittle and hard to maintain.
→ Craftsmanship gives way to shortcuts and misplaced confidence.

To address this:

→ Review and understand AI-generated code before adoption.
→ Treat vibe coding as a learning aid, not a crutch.
→ Build review loops that go beyond “it works.”
→ Keep coding standards, audits, and documentation non-negotiable.
→ AI is a collaborator, not a replacement for skill.

𝗔𝗜 𝘀𝗵𝗼𝘂𝗹𝗱 𝗮𝗺𝗽𝗹𝗶𝗳𝘆 𝘀𝗸𝗶𝗹𝗹, 𝗻𝗼𝘁 𝗲𝗿𝗼𝗱𝗲 𝗶𝘁.

As individuals, we must ask ourselves,
𝗔𝗿𝗲 𝘄𝗲 𝗯𝗲𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀, 𝗼𝗿 𝗷𝘂𝘀𝘁 𝗳𝗮𝘀𝘁𝗲𝗿 𝗼𝗻𝗲𝘀?

#AICoding #SoftwareEngineering #ResponsibleAI #TechnologyLeadership #CodingCraft #FutureOfWork

𝗦𝘁𝗼𝗽 𝗯𝗹𝗮𝗺𝗶𝗻𝗴. 𝗦𝘁𝗮𝗿𝘁 𝗱𝗼𝗶𝗻𝗴.

Imagine two drivers stuck in traffic.

One keeps complaining about the road, the weather, and every car ahead.
The other turns on a better route and keeps moving.

Same problem.
Different mindset.
Very different destination.

In our careers, many of us choose the first, blame, denial, and excuses.

→ The team slowed me down.
→ The manager didn’t guide me.
→ The situation wasn’t fair.

Maybe all of that is true.
But blaming it changes nothing.

𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗶𝘀 𝗯𝘂𝗶𝗹𝘁 𝗼𝗻 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆.

→ Fix what you can control.
→ Learn from what went wrong.
→ Improve the next outcome.

When you take responsibility, you gain leverage.
When you take ownership, you change outcomes.

The people who grow fastest aren’t the ones who avoid mistakes.
They’re the ones who own, learn, and rise from them.

Stop pointing fingers.
Start owning your path forward.

#Leadership #Mindset #Growth #Accountability #Career

𝗔𝗜 𝘄𝗼𝗻’𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀.
It’ll standardize their best behaviours.

AI is quietly redefining what engineering maturity means.

For years, development was measured by velocity.
More commits. More sprints. More releases.
Teams moved faster, but not always better.

Now, that mindset is breaking.

The real impact of AI isn’t speed.

→ It’s 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗮𝗰𝗿𝗼𝘀𝘀 𝘁𝗵𝗲 𝗦𝗗𝗟𝗖.
→ It does not only automate tasks.
→ It reinforces predictable engineering habits.

A few ways it is helping:

→ In requirements, it flags gaps and contradictions.
→ In design, it aligns decisions with UI/UX best practices.
→ In development, it does review, catching coding standard errors.
→ In deployment, it validates pipelines and enforces observability before release.

Think of it like having a senior engineer reviewing every step.

The AI era of engineering won’t just be defined by speed,
but by how reliably teams can perform under pressure.

The question is:
𝗔𝗿𝗲 𝘆𝗼𝘂 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝗼𝗻𝗹𝘆 𝗳𝗼𝗿 𝘀𝗽𝗲𝗲𝗱 𝗼𝗿 𝗳𝗼𝗿 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗼𝗼?

#AIInSDLC #SoftwareEngineering #TechLeadership #Automation

𝗬𝗼𝘂𝗿 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗰𝗮𝗻’𝘁 𝘀𝘁𝗼𝗽 𝘄𝗵𝗮𝘁 𝗿𝗲𝘄𝗿𝗶𝘁𝗲𝘀 𝗶𝘁𝘀𝗲𝗹𝗳.
AI just changed the threat model.

Something significant is changing in cybersecurity.
For years, attacks followed patterns.
They evolved but within predictable boundaries.

Now, those boundaries are gone.

The 𝗳𝗶𝗿𝘀𝘁 𝗔𝗜 𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗿𝗮𝗻𝘀𝗼𝗺𝘄𝗮𝗿𝗲 𝗣𝗿𝗼𝗺𝗽𝘁𝗟𝗼𝗰𝗸, has shown what happens when AI becomes an active participant in cybercrime.

PromptLock uses a locally hosted language model to 𝗿𝗲𝘄𝗿𝗶𝘁𝗲 𝗶𝘁𝘀 𝗼𝘄𝗻 𝗰𝗼𝗱𝗲, 𝗲𝗻𝗰𝗿𝘆𝗽𝘁 𝗳𝗶𝗹𝗲𝘀, and tailor ransom demands, 𝗮𝗹𝗹 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗲𝘅𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝘀.

No deep coding skill.
No fixed logic.
Just 𝗽𝗿𝗼𝗺𝗽𝘁 → 𝗱𝗲𝗽𝗹𝗼𝘆 → 𝗲𝘃𝗼𝗹𝘃𝗲.

And that changes everything.

→ Signature-based detection fails when every execution looks different.
→ Behavioural models crumble when malware can mutate its own tactics in real time.
→ And the rise of 𝗖𝘆𝗯𝗲𝗿𝗰𝗿𝗶𝗺𝗲-𝗮𝘀-𝗮-𝗦𝗲𝗿𝘃𝗶𝗰𝗲 platforms means even non-experts can launch complex AI driven attacks.

This is not just a new threat vector.
It is a 𝗻𝗲𝘄 𝗱𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 𝗼𝗳 𝘁𝗵𝗿𝗲𝗮𝘁, one that learns, adapts, and personalizes itself.

The important question now is not how to detect an attack,
but how to tell the difference between an AI used for testing and one used for harm.

Most teams still operate on a “break, then fix” mindset.
But when the problem evolves faster than you can patch, that playbook stops working.

To move forward, we need to be:

→ Building security into AI systems from day one.
→ Regulating not just usage, but capability and accountability.
→ Creating transparency frameworks that can audit intent, not just outcomes.

Because the next era of cybersecurity is not reactive, it is predictive.
We are entering an age where protecting systems means understanding how intelligence itself can go rogue.

The question for you is simple:
𝗖𝗮𝗻 𝘆𝗼𝘂𝗿 𝗱𝗲𝗳𝗲𝗻𝘀𝗲𝘀 𝗹𝗲𝗮𝗿𝗻 𝗮𝘀 𝗳𝗮𝘀𝘁 𝗮𝘀 𝘁𝗵𝗲 𝘁𝗵𝗿𝗲𝗮𝘁𝘀 𝘁𝗵𝗲𝘆 𝗳𝗮𝗰𝗲?

#AICybersecurity #AISafety #ResponsibleAI #CyberGovernance #LLMSecurity #AIInPractice

Memory isn’t intelligence.
𝗖𝘂𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝘀.

Prompt engineering got us started.
But it will not get us all the way to scalable, reliable AI agents.

Modern agents do not just need better prompts.
They need better context.

𝗧𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗶𝗱𝗲𝗮 𝗯𝗲𝗵𝗶𝗻𝗱 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗮𝗳𝘁𝗲𝗿 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴.

Agents working across multiple steps face a unique constraint: memory.
The more context you feed, the more noise you create.
At some point, the system loses precision, relevance, and focus, just like individuals do when overloaded.

So, the goal shifts from “more information” to “right information.”

𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝗵𝗼𝘄 𝘄𝗲 𝗯𝘂𝗶𝗹𝗱 𝗮𝗴𝗲𝗻𝘁𝘀:

→ Treat context as a limited, valuable resource. Every token should earn its place.
→ Keep system prompts simple, clear, and structured, the “right altitude” matters.
→ Equip agents with only the most relevant tools and examples, not an overloaded toolkit.
→ Use dynamic retrieval so the agent pulls context when needed instead of carrying everything at once.
→ For long workflows, summarize periodically and store persistent notes externally.
→ When complexity grows, use sub-agents, each with a focused context and have the main agent synthesize results.

In short, the best agent is not the one that remembers the most.
It is the one that knows what to forget.

As context windows expand, thoughtful curation will matter even more.
Because in the end, intelligence is not just what the model knows, it is what it chooses to keep in focus.

#ContextEngineering #AgenticAI #AIAgents #LLMs #PromptEngineering #AIInPractice

𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗶𝘀𝗻’𝘁 𝗔𝗜.
It’s forgetting what holds it up.

Not so long ago, every web development portfolio looked the same.

→ React/Angular dashboards. 
→ Node backends.
→ Full-stack projects everywhere.

Today, the feed has changed. Everywhere you look

→ AI demos.
→ LLM experiments.
→ Career posts about “getting into ML or AI”.

The excitement is real.
But so is the confusion it brings.

I see many chasing AI not out of curiosity, but out of fear of missing out.
Web development, once the backbone of the internet, now gets dismissed as yesterday’s skill.
And in that rush, something important is being lost.

𝗪𝗵𝗲𝗻 𝗰𝗮𝗿𝗲𝗲𝗿𝘀 𝗮𝗿𝗲 𝗯𝘂𝗶𝗹𝘁 𝗼𝗻 𝗵𝘆𝗽𝗲, 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗴𝗲𝘁𝘀 𝘄𝗲𝗮𝗸.

I have seen teams move too fast, building AI projects that look impressive online, yet struggle under real-world complexity.
The fundamentals fade.
The craftsmanship that makes great software gets replaced by short-term visibility.

It is not that AI is the problem.
The problem is forgetting that every powerful wave still needs solid ground to stand on.

If we want lasting careers, we have to slow down enough to build depth.

→ Learn data structures and system design.
→ Build things that solve real problems.
→ Let web development stay a bridge to AI, not its casualty.

Because the truth is simple, AI will reward those who understand the foundations, not just the frameworks.

Careers are not sprints.
They are compounding journeys.

And the future will always belong to those 
𝘄𝗵𝗼 𝗯𝘂𝗶𝗹𝗱 𝘄𝗶𝘁𝗵 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴, 𝗻𝗼𝘁 𝗼𝗻𝗹𝘆 𝘂𝗿𝗴𝗲𝗻𝗰𝘆.

#AICareers #SoftwareEngineering #TechnologyLeadership #FutureOfWork #ResponsibleAI

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