What is AI :)

Many times truth can be expressed in simple words :)

 Keep Thinking!!!

Why Startups progress well with GenAI Solutions

My perspectives

•  It’s not about solving the problem instantly but gaining better clarity and progressively moving closer to the solution.
• The difference between a big company and a startup is that in a startup, you tackle such problems multiple times, discuss them repeatedly, and prototype extensively.
• In my view, it’s not just about finding the solution; it’s about engaging in discussions, gaining perspectives, prototyping, and solving iteratively. This process leads to innovative solutions rather than adopting a binary stance of zero or one.
• There’s no single way to solve these challenges. It’s about the approach: Are we gaining perspective? Are we taking a step forward? Is the problem solvable? How do we pivot and consider different angles?
• This iterative process is the essence of learning—not just limiting ourselves to Boolean states of zero and one.
• "Innovation comes from persistent iteration, not instant perfection."

Happy Learning!!!

Experimentation, Ownership, and Continuous Learning in Tech

Only certain things we learn through experimentation will provide a clear perspective. At least you have an intuition—it may work, or it may not.

Work in databases, machine learning, or deep learning requires conducting a certain number of experiments to be sure of what works and what doesn't. Of course, learning in these areas is ongoing and continuous.

Ownership is not just about having something without having done it. Ownership is more about having built a certain number of things and gathered the experience so you are able to own it because you know how to do it. When it's not a process that governs things, it's the people with experience and perspective who build better things.

Sometimes, I feel that the term "ownership" is very overloaded. Being a full-stack developer or a jack-of-all-trades requires only a certain amount of fixed time, and you cannot be good at everything. For example, knowing how to fine-tune an index or a model, understanding different types of prompts, context latency, and conducting various experiments.

I think we need to be very clear about learning. Don't just rely on certifications, working on one project, or having some perspective. It's more than just keywords, jargon, and perspectives. Undertaking a good number of experiments, embracing failures, and gaining diverse perspectives will give you the intuition to deal with ambiguity when facing the next set of problems.

#TechLeadership #ContinuousLearning #Experimentation #MachineLearning #DeepLearning #Ownership #FullStackDeveloper #DataScience #TechGrowth #ProblemSolving #Intuition #CareerDevelopment #SoftwareEngineering #SkillBuilding

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My Learnings as an AI Strategist / AI Advisor / Solution Architect

• Finding a Job: Understand the difference between finding a job that pays well and aligns with your passion. Aim for the latter; it fuels long-term growth and fulfillment.
• Building Passion: In the early stages, it's okay to prioritize learning over earning. Build your passion—it's an investment in your future.
• Focus on Problem-Solving: Choose a direction that enables you to build impactful products and address core challenges comprehensively.
• Avoid Distractions: Stay focused and don't get sidetracked by every new opportunity or idea that comes your way.
• Maximize Peak Years: Your 'peak' years—when you have the best health, time, and energy—are limited. Make the most of them by working on meaningful goals.
• Leverage Your Perspective: Your unique perspective is your strength and grows more valuable over time. Share your authentic lessons to inspire and guide others.
• Spread Optimism: Life is short. Be a source of hope and positivity. Remember, technology isn't inherently complex—it's often how it's communicated or understood that makes it seem so.

"Focus on building your passion, solving meaningful problems, and sharing your unique perspective. In your peak years, embrace learning, spread optimism, and leave a legacy of impact and inspiration."

Keep Going!!!

Humans vs Ants

Sometimes we push our ideas vs working towards a common goal :)

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Transforming Setbacks into Strength: Lessons in AI Consulting and Trust

Introduction

I embarked on a journey to engage with my ex-colleague, who is currently a VP in a small industrial construction company, by providing AI advisory and learning sessions. Initially, it seemed like a promising exploration—teaching and guiding them through foundational concepts like LLMs, prompts, and RAG (Retrieval-Augmented Generation).

Early Teaching Phase

In the beginning, I had to explain the basics: what an LLM can do, what a prompt is, and how RAG techniques enhance information retrieval. After about a month, this ex-colleague returned, claiming there was negligible value and no tangible deliverables. This should have been a red flag, indicating that the cost and effort involved in teaching, training, and experimenting were not fully appreciated.

The Red Flags

In hindsight, I realize I should have caught the warning signs earlier. I kept insisting that experimentation was the key to understanding the capabilities and limitations of GenAI tools. Instead, this person seemed to push for more work in a shorter timeframe—a strategy to extract maximum value with minimal investment.

Shifting Roles and Promises

Later, I received an offer to join their team with a fixed pay and 5K shares, helping to architect solutions, pitch them to the market, and shape the product roadmap. The proposal seemed promising, aligning with my goal of taking on a more advisory and architectural role. Little did I know it was a tactic to consult, gain maximum value, and then part ways.

Building a Product and Architecture

As trust deepened—bolstered by a long-standing relationship spanning over a decade—we agreed on shares and informal terms. I invested significant effort: in training the team from scratch in LLM prompting, RAG, search customization, improving accuracy, data preprocessing, and system architecture. I demonstrated how to organize data effectively and leverage different approaches for better product outcomes. I also built a pitch deck, developed an API strategy, and created a technical feature roadmap.

The Unexpected Termination

Even as the product began to take shape, I was blindsided. Suddenly, he informed me they no longer required my services because they had found someone else to present the solution to the market. My requests for formal acknowledgments, like patents, were brushed aside. From the start, they had planned to offer low pay and shares, then terminate later. There was a clause stating that shares were invalid if I was no longer working for them—a clever strategy of betrayal. This was a person I had known for 14 years. It’s a stark reminder of what even people you know well can do.

Lessons Learned

This experience taught me that trust should be tempered with caution. Even long-standing relationships can falter when values, mindsets, and ethics come into play. Nonetheless, the knowledge I gained—developing product pitches, architectures, and end-to-end solutions—are useful for my current customers :), whether they need unstructured ETL solutions, industrial RAG systems, or tailored recommendation engines. Everything that broke you, builds you even stronger in the next epoch.

Always approach advisory roles with careful consideration and safeguards in place, no matter the length or depth of prior relationships. While losing out can hurt, the experience and skills you acquire will benefit you and your future clients.

My Advice

• → Don't be too open to "any opportunity."
• → Share your perspectives and answer questions with a clear, direct response.
• → Ask strategic, thoughtful questions to understand the big picture.
• → Show your value proposition and let your work speak for itself.

Keep Going!!!

Consistency, Latency, and Relevance in my AI Advisory Role :)

• Sometimes we need to move beyond the myth of waiting for #growth or #title to understand how well we can perform across roles, leveraging our strengths based on the situation.
• Sometimes you provide a tech overview, and that may translate into a PRD (Pseudo PM :).
• Sometimes you discuss latency again and again, slice and dice data, and there will be an “aha” moment (RAG specialist).
• Past experiments, current failures, and handling unknowns with a bit of intuition make you an optimistic solution architect.
• With GenAI, every role seems to blend multiple functions. Know the product, know the futuristic workflow, code, demo, and be hands-on whenever possible to make an impact.
• Be focused only on areas where you can scale. You cannot scale in every tech/role.
• GenAI in supply chain course preparation work made me revisit my Microsoft experience and reimagine the agentic supply chain.
• GenAI for the leadership project creation work helped me create usecases to evaluate balance time, money, and talent for 'C'-suite planning.
• Auditing the GenAI app made me realize that security and Ethical AI need more attention.
• Knowledge is collected through multiple highs, lows, failures, and moments in life. In the end, you can be a well-informed mentor who has tried technology to the best of your ability.

#HumanwrittenAIEdited #Perspectives #GenAI #Myworkperspectives

Best practices - Culture, Leadership, Talent, Skills ?

"Best practices" is a very common jargon, and we need to understand its meaning.

Understanding "best practices" is about recognizing its role in improving processes and outcomes across domains.

In my early career, when I used to write code, the review comments I would receive were, "This is not following best practices; go and check." So, there, you gain some awareness of IDE tools, language, and coding approaches.

Early exposure to "best practices" builds foundational skills in tools, languages, and methodologies, shaping your problem-solving approach.

Now, working in a startup, best practices are constrained to a few things:

• The budget,
• The cloud of choice, and
• The talent you work with.

In startups, constraints like budget, cloud resources, and available talent redefine how "best practices" are applied.

The architecture needs to be cost-effective. Startups often run on a very tight budget, so you need to be frugal. You need to make things work, and for every backup or option, you need to pay. Until you reach a certain stage, most startups may rely on credits or cohorts from cloud providers, so it's always about leveraging all of this.

"Do what you can, with what you have, where you are." – Theodore Roosevelt

Cost-effective architecture demands frugality, creative problem-solving, and strategic leveraging of resources like cloud credits.

Additionally, you may not get top-class talent, and people don't stay for various reasons. It's not just about money. Of course, money is one part of it, but factors like learning opportunities, culture, and trust also matter. I've been working with many freshers, and there's often a knowledge gap between someone from a high-profile institution and someone from a tier 2 or tier 3 college. But it's an investment of time, effort, trust, and mentoring. Things don't happen magically, but by being there, guiding, troubleshooting, and taking it one step at a time, progress happens.

Building talent in startups requires investing in mentorship, bridging knowledge gaps, and fostering trust and a growth mindset.

"An investment in knowledge pays the best interest." – Benjamin Franklin

Sometimes, there are hard choices or debates with the founder: "Oh, we need to be frugal, we need to cut the budget, we need to be fast." These are good points, but every startup I've seen runs on a shoestring budget and faces a lot of trade-offs. Not everyone gets top-class talent. So, I think it has to be a blend of talent, knowing how to gauge and mentor them, and slowly helping them grow.

Balancing speed, frugality, and growth in startups involves tough trade-offs and a focus on developing talent progressively.

I wouldn't say it's easy, but think about it: building the culture, fostering a learning mindset, mentoring, whiteboarding problems, being candid about your strengths and weaknesses, and creating a comfort zone for them to ask questions instead of worrying, "What will they think if I ask?" It's not about just giving orders; it's about being there to troubleshoot with them. Many factors play a role, but the first principles and ground rules still remain the same.

Fostering a learning culture in startups hinges on openness, active mentoring, and emphasizing first principles to solve challenges.

This is a different perspective. You cannot be the master of all, but whatever you know, be willing to share and grow. It's not about whose opinion is right; it's more about frugal ways of building a product, trying to meet the customer's needs, and aligning with the product goals.

"Culture eats strategy for breakfast." – Peter Drucker

True success in startups comes from collaboration, customer focus, and building differentiated products through frugal innovation.

Keep Thinking!!!

Exploring the Tough Questions in GenAI Product Building

In all my GenAI product-building efforts, these questions consistently arise across various tasks: Data, ETL, Marketing, NER, Fashion, Design, and ESG.

• Choosing between vision models and text descriptions: When should you use vision models versus text descriptions? For the same task, OCR provides a certain level of accuracy, multimodal approaches yield different accuracy levels, and benchmarking takes time. Should a hybrid approach be considered?

• Improving model accuracy: How do you balance the use of low-cost models versus pursuing high accuracy? What are effective strategies for building products while minimizing costs?

• Catching critical hallucinations before production: How can you effectively address cases where a model misclassifies metrics in its interpretation?

• Maintaining transparent communication about AI limitations: How do you handle situations when founders ask, "Company X does this—why can't we?" especially when you lack insight into their models, architecture, or talent?

• Building trust through transparency: How can you reinforce that being open about AI limitations builds long-term trust? Developing production-grade applications requires considerable time and effort.

• Encouraging models to admit uncertainty: What are innovative ways to make models reason through their uncertainty, validate it, and improve reliability using multiple methods or ensemble approaches?

AI Advisory involves a combination of solution evaluation, in-depth research, continuous learning, hands-on coding, and assisting others in troubleshooting and resolving their issues.

Finding Meaning Beyond the Clock: Embrace Passion, Perspective, and Purpose in Work and Life

 There are different categories of people based on their work hours. 

Some will work 100 hours, others will work 8 hours, and some will find a balance. Some work more when they find the work interesting. It's hard to say whether working 100 hours equals productivity or working 8 hours means mediocrity.

I suggest spending more time on activities you enjoy and less on those you don't. Remember, life is about choices and how you live each day. Personally, I need to read the same subject multiple times to understand it. This doesn't mean I do it all within 100 hours. My learning involves repeating experiments and gaining new perspectives each time.

Understanding a subject, connecting with it, and seeing it from different perspectives are unique learning moments. These cannot be measured simply by the hours spent. Instead of counting hours, focus on the new ideas you discover and how engaging they are. Ask yourself if you are connecting with your work and if your experiments are satisfying.

It's not just about money. Everything in life is finite. Evaluate whether you are productive and if your techniques are effective. 

Thank you.!!!

Working as AI Advisor

• Close proximity to decision-makers
• Tech will be commodity but Domain + Tech is true knowledge
• Loyalty works in long term, talent gets visibility in short term
• Take time and provide accuracte inputs, Optimism + Clear perspectives matter
• Building relationships, Experimenting on meaningful projects, Novelty to build solutions / integrate past experience + latest tech to match quality matters
• Balancing business vs tech roadmap, Being able to spot features that differentiate the rest
• In the end everything may fail but there is a satisfaction in the Journey

Keep Exploring!!!

Career Blues and perspectives

My selected list from below bookmarked articles

• Try to become a new person every 3-6 years.
• Try to solve big problems as fast as possible. 
• Adding a little bit of extra productivity to every day is great advice. The challenge can be finding the time, which means you need to subtract time from some other activities.
• I write only when inspiration strikes. Fortunately it strikes every morning at nine o'clock sharp — W. Somerset Maugham
• Focus more on my own future rather than on what others think.
• Set clear short-term and long-term career goals.
• Enjoying programming and having the time outside of work. When it's a passion it all becomes a lot easier.
• Effort and consistency trumps all
• I personally believe anybody can find success if they just focus on the journey and the joy of coding, make sure they show up and participate, be present, stay curious and playful, and don't expect any rewards for their efforts. The real reward is having fun and feeling fulfilled while you're creating something.

Bookmarks

• Measuring personal growth
• The unreasonable effectiveness of just showing up everyday
• Outstanding Programmers
• My 25-Year Engineering Career Retrospective

Keep Exploring!!!

Different stages of ML / DL Learning

I want to learn ML -> Take a course 
I know the basics from the course -> Try the code examples 
I tried but I don't know what's next -> Find a use case 
I found a use case -> Collect data 
I collected the data -> Model the ML problem 
I built an ML model -> Create an API to consume it 
I built an API -> Dockerize it 
Is the API scalable? -> Check options such as serverless functions, Endpoint providers like Anyscale / SageMaker Endpoints, GCP, Azure Inferencing 
I deployed the model -> Version your models using MLFlow 
When to update -> Audit / Track data 
What tools to learn -> Align with what your organization uses and cloud vendors

Learn to walk before you try to fly. Everything is incremental learning. Keep going!!!

Keep Exploring!!!

Good vs Bad Leaders

From my 2 decade observations,

• Title <> Experience 
• Title <> Knowledge 
• Good failures can be Good knowledge experiences but people without Titles

I have worked with all ages 20's, and 30's. My peers.

• With my juniors, I give them context, references, and How I would do in their situations.
• With my seniors, I get only what to be done, I don't mind, work comes to me what they can't do
• With my peers, When asked for input, I share the same
• You meet people with Ego without knowledge. Not knowing and pretending is more dangerous.

Be an Empathetic Leader and Manager. Be accountable for your work

Keep Exploring!!!

Product Data Scientist vs AI Consultant

 

Keep Exploring!!!

Which #GenAI use case will succeed ?

In 2006, I was part of a Reverse Logistics team at Microsoft working on the launch of a new product, the Zune—a competitor to the iPod. The Zune was somewhat boxy and heavy. At first glance, it was clear that the iPod and Zune were worlds apart. Despite tight deadlines for setting up the supply chain and tracking every aspect, we all know the Zune's life ended by 2012.

Choosing the right use case is crucial; Zune vs. iPod is a classic example. It's not only about applying AI but also about selecting the right use cases that bring tangible benefits and align with our business strategy.

Courses may teach us the fundamentals, like LEGO blocks, but the same challenges persist today in terms of supply chain visibility and customer experience. Choose the right use case, invest time in understanding your data and domain. Out of the several applicable AI use cases within domain context, focus on those that hold relevance to your business needs, domain, data, to derive real value.

Copycat use cases will not work unless they are relevant and meaningful for your business.

Stay observant of industry trends, and align your AI initiatives with your strategic goals. 

#AI #Strategy #Innovation #TechHistory #Business #Data #ProductDevelopment #Microsoft #Zune #iPod #DataScience #GenAI

Keep Exploring!!!

AI News, Tools, Observations

Good discussion on observations and experiments with #CreativeAI, Summary, and adding my perspectives

1. AI-generated visuals: Frustration over production inconsistency despite enjoying creative collaboration. 

2. #CreativeExploration by embracing the unpredictability in current ideation tools broadening horizons and seeking inspiration. 

3. Lack of emotional connection; you don't draw anymore. 

4. Things are advancing rapidly, reminiscent of the text AI scene pre-#ChatGPT. Rapid progress is observed, yet we will need to do more. 

5. #Startups is never ending in Creative Exploration Image-space - Midjourney, RunwayML, alpacaml, clad.ai, dreamlook.ai, photoroom, neurallove, letsenhance, topazlabs. 

6. The latest one today morning is #OpenAI Sora - capable of generating a minute of high-fidelity video. #GenAI #Startups Currently inspiration oriented, seems #OpenAI will consolidate this space with #DALLE5/6 :). Sometimes we need to patiently wait to pick a winner and move forward. #GenAI #CreativeAI #DigitalTransformation #generativeai #midjourney #dalle3 #imagegeneration 

AI is now a CIO boardroom Topic

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2023 - Lessons - AI - GenAI

After every year learning extends Data, AI, Products, and Domain. 2023 had a blend of experiences. Still figuring out answers for every dimension #2023 #Learnings

→ How you've adapted to industry shifts, and GenAI's meaningful adoption. Possible use cases vs relevant, meaningful production-ready use cases. Example - Newly launched section in Amazon reviews, What customers say. 

→ How you've overcome engineering challenges balancing business goals. New ways to solve old problems with Foundation models. Time vs building a production-grade solution. Example - Moving away from custom NER vs Leveraging LLM Embeddings, Blend of both custom embedding + RAG, New ways of solving.

→ How your skills align with the company's vision, Learning to predict the future. New approaches and papers evolve faster than certifications. A blend of tech + and domain is key. Segment Anything model, Visual QnA, Intructpix2pix have made more vision use cases feasible Tryon, etc..

→ How you bridge the gap between tech and business, Fast yet impactful use cases, Get the basics right. Demos / New offerings vs making it to production need a careful selection of use cases / applying past experiences to get things right in the first iteration. Balance the tradeoff between creativity vs innovation vs build a product strategy vs solve a real need vs fancy demos. #learning #perspectives #solutions #datascience #MachineLearning #AI #DeepLearning

Keep Exploring!!!

Why Legacy companies Fail to Innovate :)

 

Well summarized - Vision needs implementation strategy

#Tesla - build one #product with a #vision

Legacy companies - Modules developed by component providers without integration/vision

Lesson - Write Software yourself for 2.0 products

Keep Exploring!!!

The guilt in me

I have been always running for work. I hardly remember when I took a week last time. I feel guilty for not being a good son. Last week dengue made me feel so weak and lost. I was left with nothing except pain and guilt, I have not taken care of my mom in all these years.

There is life outside work, We need to take care of people around us. I don't know how I can get over this guilt. Every time I struggled in Life the person whom I looked up to for support and guidance was just my mom. 

At least going forward I have to take care of people around me.