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!!!

Creativity vs Copyright

 

Keep Thinking!!!

ChatGPT - Timely Assistance

 

ChatGPT saved my life, and I’m still freaking out about it
byu/sinebiryan inChatGPT

Keep Thinking!!! 

 

🚀 Navigating the Complex Landscape of AI Adoption in Business 🚀

In the rapidly evolving world of artificial intelligence, businesses face a multifaceted challenge when it comes to AI adoption. The decision to build or buy, to hire directly or outsource, and to choose the right use cases are critical and can significantly impact the success of AI integration within any organization.

🔍 Key Considerations:

1. Cloud Partnerships: Aligning with a cloud provider can dictate the models and technologies available to you. It's essential to leverage these partnerships effectively to maximize your AI capabilities.   

2. Use Case and Data Availability: Choosing the right use case is just the beginning. The availability and adequacy of data for model training or fine-tuning are paramount. Without sufficient data, even the most promising AI projects can falter.

3. Model Development Timeline: Whether it's benchmarking, extended testing cycles, or A/B testing, understanding the time required to develop and refine AI models is crucial for planning and execution.

4. Costs and Talent: The infrastructure and talent costs can often lead businesses to outsource AI and machine learning tasks. However, this brings its own set of challenges and dependencies.

5. Accuracy and Maintenance: Developing AI models that not only perform well initially but also maintain high accuracy over time requires continuous updates and skilled personnel.

6. Ethical AI: Adopting AI responsibly ensures that the technology not only serves the business goals but also aligns with broader ethical standards.

🌟 Solution Spotlight:

Innovative solutions like vector search, keyword search, semantic search, or rule-based search can address specific needs, but success fundamentally depends on the right blend of talent, technology, and timing.

As we continue to embrace AI, let's discuss how we can overcome these challenges through innovative strategies and collaborative efforts. How is your organization navigating these complexities in AI adoption? 

Share your insights! 

#AI #BusinessStrategy #Innovation #DataScience #CloudComputing #EthicalAI

Course Launch: Generative AI and Cybersecurity – Frameworks and Best Practices 2024

• This blog is close to 15 years old. Every small learning/perspectives build on top of it.
• Consulting / Product Development / Teaching provides our own perspectives.
• Summing up AI Adventures + GenAI Lessons we have our course

Keep Learning!!!

Prompt Versioning Tools

Prompts Can Be as Valuable as Code

Well-crafted prompts are just as important as writing clean code, especially when versioning them. A good prompt is optimized for token usage, model compatibility, chunk sizes, and temperature settings, ensuring efficiency and performance. These parameters may need to be adjusted based on the type of document, text, or context being handled, making prompt versioning a critical practice.

Key Tools and Features for Prompt Management

Langfuse

• Widely adopted by companies like Khan Academy, Merck Group, Twilio, and more.
• Supports comprehensive compliance with GDPR, Single Sign-On (SSO), and offers unlimited members, projects, and data access.

Prompthub

• Ensures double encryption for prompt security.
• Displays prompts directly on the homepage for easy access.
• Provides users with direct access to the founders for personalized support.

Proper prompt versioning, coupled with tools like Langfuse and Prompthub, ensures optimal performance and adaptability across use cases

Keep Exploring!!

Old Ad vs GenAI Ad

Coco Cola Old Ad

Back in 1995, Coca-Cola launched their famous “Holidays Are Coming” ad.

It showed a convoy of Christmas-lit trucks arriving in a snowy town, spreading joy.

It became a classic and made Coca-Cola a big part of holiday traditions. pic.twitter.com/S5KAi5SqgM

— Salma (@Salmaaboukarr) November 20, 2024

Coco Cola GenAI Ad

What is Generative AI?

It’s advanced tech that creates text, images, audio, and video.

Coca-Cola used it to:

• Capture the nostalgic essence of the 1995 ad
• Reimagine it with modern visuals
• Create something that couldn’t have been done before pic.twitter.com/tDvmGElFUM

— Salma (@Salmaaboukarr) November 20, 2024

Keep Thinking!!!

🚀 *Rethinking Convenience: The Hidden Costs of Online Food Delivery* 🚀

In our fast-paced world, the allure of convenience often overshadows the hidden costs associated with it, particularly in the realm of online food delivery services like Instamart and others. While these services offer quick solutions to our daily needs, it's crucial to pause and consider the broader implications of their use.

🔍 *Quality and Health Concerns:*

Many of these platforms may lack stringent quality checks, especially for perishable items that endure various stages of the supply chain. The absence of transparency about food sources, shelf life, and kitchen standards raises significant health concerns. The convenience of having food delivered to your doorstep might seem appealing, but it could lead to health issues if the food's quality and handling are compromised.

🌍 *Environmental and Social Impact:*

The rise in quick deliveries contributes to increased pollution and traffic congestion. Moreover, the shift towards consumer convenience overlooks the potential for physical activity, such as walking to a nearby store, which can be beneficial for both health and the environment.

💸 *Economic Considerations:*

Opting for nearby eateries or cooking at home not only ensures a better understanding of what you consume but can also be more economical in the long run. The costs associated with frequent use of delivery apps add up, and the perceived convenience might not justify the expense.

🤖 *Technological Implications:*

While technology drives innovation in delivery methods, including potential shifts to drone deliveries, it's essential to question whether these advancements contribute to meaningful knowledge growth or merely support a consumerist mindset focused on profit.

👨‍🍳 *A Call to Action:*

Let's advocate for more transparency and responsibility in the food delivery industry. By choosing more sustainable and health-conscious options, we can drive change that benefits not just individual consumers but also the broader community.

🌟 *Your Health, Your Choice:*

Next time you're about to order from a food app, consider the potential long-term benefits of alternative options like a simple home-cooked meal or a visit to a local restaurant. It's not just about saving time; it's about investing in your health and our planet.

#FoodIndustry #HealthAndWellness #SustainableLiving #TechnologyImpact #ConsumerAwareness

Language Models - Reasoning / Learning Abilities / Self Learning Way Forward

• AI systems teaching themselves
• Models developing unexpected capabilities
• Programs showing signs of emergent behavior

But the scariest part? pic.twitter.com/AzeU7xA2bt

— Baptiste (@BaptisteVicini) November 19, 2024

 

• Many copies of Network
• Look up data
• Share the Learning
• Apply the Logic

Very much focused agents on each Topic can make magic if trained well :)

Keep Exploring!!!

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.!!!

AI in Movie Making - Pros vs Cons

• Easy Experimentation
• AI = Best Craftsmen
• AI cross-pollinates things that exist
• Lack of Consistency vs Quality at this point
• Background, Color changes, and designs can happen with AI

Keep Exploring!!!

Good vs Great Solutions

 Solutions can be built with different levels of accuracy/scalability based on Talent, Time, and Money

• Product built with 20$ Upwork contractor
• Product built with 400$ up-work expert
• Product built with 40K FTE
• Product built with 100K FTE Experienced Research / Dev

How do you differentiate each one based on architecture, performance, and benchmark against the best? 

Steve Jobs: The difference between good people and great people in software is 50-to-1

“I’ve always considered part of my job was to keep the quality level of people in the organizations I work with very high. I mean that’s what I consider one of the few things I can contribute… pic.twitter.com/NBlk3UoWSC

— Startup Archive (@StartupArchive_) November 17, 2024

Keep Learning!!!

Reasoning vs Next word Prediction vs LLM

 

Keep Exploring!!!

Proplens - GenAI Powered Real Estate Solution :)

 

We are in news !!!  Link

Happy Responsible, Accurate, Consistency and Low Latency Adoption :)

Knowledge vs Perspective vs Learning

• Focus on understanding over copy-pasting
• Code can be replicated, but understanding cannot
• Embrace experimentation and learning from failures
• Build deep knowledge of system behavior:
• Identify potential failure points
• Understand edge cases
• Document limitations
• Quality is everyone's responsibility

Keep Experimenting!!!

Vision Use case

How to Implement the Use Case Correctly

• Field of View
• Stable Infrastructure
• Minimal Occlusion
• No Manual Calibration
• With a good setup, half of the complexity and noise can be eliminated.

Keep Exploring!!!

Prediction - 🔍 Anticipating AI's Big Shift in 2025: OpenAI’s Focus on Domain-Centric Solutions

Prediction:

OpenAI is set to shift towards domain-centric solutions, making 2025 a transformative year for AI. This transition is based on the data collected and learned from APIs serving different domains, focusing on context window improvements, reasoning patterns, and cross-modal integration. This will significantly enhance decision-making in critical sectors like FinTech and healthcare. By tackling technical challenges and integrating user feedback, these advancements will result in more powerful, tailored AI applications that will reshape entire industries.

Expanding Beyond Language Models

Today, OpenAI is primarily recognized as a leading provider of large language models, but its true capabilities extend much further. Its question-answering abilities, for instance, are exceptionally powerful and evolving rapidly. As clients integrate this technology into critical sectors like FinTech and healthcare, they will unlock new levels of context window improvements, and cross-modal integration and reasoning by adopting techniques like tree of thought, chain of thought, and graph-based approaches, enabling AI to think and deduce more effectively. Feedback from users will be pivotal in this journey, guiding organizations on how best to structure information flows and assess when to fine-tune models, use Retrieval-Augmented Generation (RAG), or determine the optimal use of short-term and long-term memory. This constant feedback loop will allow AI to achieve unprecedented levels of contextual understanding and adaptive reasoning, creating models that align more closely with complex real-world needs. 

"OpenAI's journey is no longer just about language—it's about thought and contextual adaptation."

Building Resilient and Adaptive Systems

These advancements will likely lead to the development of more resilient and adaptable systems. Future systems will not only enhance decision-making but also push reasoning capabilities into new territories, setting the stage for increasingly sophisticated agents and refined RAG architectures. These improved architectures are expected to reduce hallucinations, boost accuracy, and lead to products that are more responsive to real-world challenges. Overcoming issues like catastrophic forgetting, hallucinations, and knowledge manipulation will be critical, positioning these systems as robust, reliable solutions across industries. 

"Resilient, adaptive AI systems will transform decision-making and redefine industry standards."

Addressing Technical Challenges

Currently, accuracy challenges remain in areas such as domain-relevant embedding, balancing retrieval techniques against accuracy and latency, chunking methods based on usage or query types, contextualization, and routing or re-ranking processes. Yet, these elements are essential for advancing the capabilities of AI models. Despite these ambiguities, ongoing data processing and analysis are paving the way for more focused, domain-specific AI products. Within the next six to eight months, we’re likely to see a new wave of AI-driven applications, from highly specialized agents to RAG applications and APIs crafted for specific industries.

 "Technical hurdles are simply steps toward the next wave of AI-driven, domain-specific innovation."

The Transformative Potential of 2025

The year 2025 is set to be a pivotal moment in AI, marking the dawn of domain-centric solutions that will reshape how AI interacts with our world. As more industry-specific applications emerge, OpenAI’s technologies will bring powerful, tailored solutions closer to reality. 

"2025: The year AI becomes truly domain-centric, reshaping industries with precision, customized models, and highly accurate agents and RAG systems."

Keep Exploring!!!

#AI #OpenAI #DomainSpecificAI #Innovation #MachineLearning #FinTech #Healthcare #FutureOfAI