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diff --git a/docs/about-deepak-sood/intros.md b/docs/about-deepak-sood/intros.md
@@ -1,6 +1,6 @@
 # Intros
 
-## Intro 1
+## Intro 1 - General
 
 **Deepak Sood: Data and AI Architect | Innovator in Intelligent Systems**
 
@@ -15,14 +15,18 @@ With over 8 years of experience in Data and AI, Deepak Sood specializes in desig
 
 Deepak’s journey is marked by his evolution from a passionate learner to an accomplished AI expert. His commitment to staying ahead in the fast-paced tech landscape is evident through constant learning and sharing insights via his blog, inspiring others to explore the transformative potential of AI.
 
-## Intro 2
+## Intro 2 - General
 
 Deepak Sood is a seasoned Data and AI Architect with over 8 years of experience in designing intelligent, scalable systems that address complex industry challenges. Currently at OpsTree Solutions, he leads the development of advanced AI-driven solutions, leveraging his deep expertise in data architecture and AI technologies. Previously, as an Engineering Lead at Stashfin, he played a key role in scaling the company’s loan portfolio from ₹5 Cr to ₹500 Cr per month and growing the tech team from 20 to 50 members. Deepak’s career journey reflects his evolution from a passionate learner to an AI expert, marked by continuous innovation, leadership, and knowledge-sharing through his blog, inspiring others to harness the transformative power of AI.
 
+- 116 words 751 characters
+
 ## Intro 1 - Education
 
 Deepak Sood is an accomplished AI, Data, and DevOps Architect with over 8 years of experience across diverse fields in the IT industry. Starting his career as a software developer and data engineer, he has evolved into a versatile professional, taking on roles like product manager and collaborating with business teams to achieve impactful objectives. A proud M.Tech graduate from IIIT Delhi, Deepak has worked across multiple industries, including Media, EdTech, IoT, Crypto, FinTech, and Service-based companies, gaining a broad perspective on technology’s role in solving real-world problems. Passionate about innovation and continuous learning, he inspires young minds to explore the exciting opportunities in AI, data, and software development, showcasing how diverse roles in IT contribute to shaping the future.
 
+- 120 words 819 characters
+
 ## Intro 2 - Education
 
 **Deepak Sood: AI, Data, and DevOps Architect | Exploring IT’s Endless Possibilities**
@@ -41,3 +45,11 @@ An CSE M.Tech graduate from IIIT Delhi, Deepak has combined his academic excelle
 **Career Highlights**
 
 From developing innovative software solutions to managing products and teams, Deepak has gained a 360-degree view of the IT landscape. His journey inspires students to explore the vast opportunities in technology, showing how roles in Software Development, Data Engineering, AI, and DevOps come together to shape the future of the industry.
+
+## Intro 1 - DevOps
+
+Deepak Sood is a seasoned AI, Data, and DevOps Architect with over 8 years of experience in designing, scaling, and optimizing complex systems across diverse industries, including Media, FinTech, EdTech, IoT, and Crypto. With a robust foundation in data engineering and software development, Deepak excels in implementing microservices architectures, leveraging cutting-edge technologies like Kubernetes, Elasticsearch, Kafka, Prometheus, and GraphQL to enhance scalability and efficiency.
+
+A proud M.Tech graduate from IIIT Delhi, he has successfully led and mentored multi-disciplinary teams, driving innovation and fostering collaboration across business and technical domains. Deepak's expertise in building resilient infrastructures and his passion for automation and optimization enable organizations to deliver agile, reliable, and impactful solutions. A natural mentor and communicator, he inspires professionals and young minds alike, emphasizing the transformative potential of technology in solving real-world challenges.
+
+- 133 words 1,030 characters
diff --git a/docs/about-deepak-sood/meetups-talks-sessions.md b/docs/about-deepak-sood/meetups-talks-sessions.md
@@ -1,5 +1,22 @@
 # Meetups / Talks / Sessions
 
+### Kong in Action: Simplifying API Management for Modern Applications - CNCG (18 January 2024)
+
+In today’s rapidly evolving digital landscape, efficient API management is the cornerstone of seamless application performance, scalability, and security. Join us for an insightful session on **"Kong in Action: Simplifying API Management for Modern Applications"**, where we unravel the power of Kong as a leading API gateway and management solution.
+
+This session introduces Kong’s key features, including load balancing, traffic control, authentication, observability, and its plugin-driven architecture. Learn why API management is critical for modern application ecosystems and how Kong empowers developers to streamline API lifecycle management effortlessly.
+
+**Key Takeaways**
+
+- Explore real-world use cases showcasing Kong’s transformative capabilities in diverse industries.
+- Understand the "why" behind API management and its role in enabling secure, scalable, and efficient integrations.
+- Dive into API lifecycle management with Kong, covering design, deployment, monitoring, and iteration.
+- Get actionable tips for getting started with Kong effectively, whether you're deploying on-premises or in the cloud.
+
+Whether you’re a developer, architect, or tech enthusiast, this session offers valuable insights into leveraging Kong to simplify API management, reduce operational overhead, and future-proof your applications. Let’s simplify API management together!
+
+[See API Kong-Versations:Gateway to 2025 at CNCF New Delhi](https://community.cncf.io/events/details/cncf-new-delhi-presents-api-kong-versationsgateway-to-2025/)
+
 ### Podcast - Stream Processing using Kafka and Flink (20 December 2024)
 
 - Transcript - [Podcast - Stream Processing using Kafka and Flink](about-deepak-sood/projects/43-podcast-stream-processing-using-kafka-and-flink.md)
@@ -24,7 +41,7 @@ Deepak Sood is a Senior AI, Data, and DevOps Architect with over 8 years of expe
 - [Simplifying API Management for modern Application , Sat, Dec 14, 2024, 11:00 AM | Meetup](https://www.meetup.com/kong-delhi/events/304930016/?slug=kong-delhi&eventId=304930016)
 - [Deepak Sood on LinkedIn: #kongmeetup #apimanagement #techtalks #networking #delhitechcommunity](https://www.linkedin.com/feed/update/urn:li:share:7272973093516582912/)
 - [Kong](devops/others/kong.md)
-- [\[Meetup\] Mastering APIOps:From spec to portal with kong's Tools & Best Practices, Sat, Dec 7, 2024, 10:30 AM | Meetup](https://www.meetup.com/kong-bengaluru/events/302975712/)
+- [Meetup - Mastering APIOps:From spec to portal with kong's Tools & Best Practices, Sat, Dec 7, 2024, 10:30 AM | Meetup](https://www.meetup.com/kong-bengaluru/events/302975712/)
 
 ### Neo4j Enablement Session at Opstree (12 December 2024)
 

diff --git a/docs/about-deepak-sood/social-links.md b/docs/about-deepak-sood/social-links.md
@@ -40,6 +40,7 @@
 - Instagram - https://instagram.com/deepaksood619/
 - Facebook - https://www.facebook.com/deepaksood619
 - Twitter - https://twitter.com/deepaksood619
+	- https://x.com/deepaksood619
 - Skype Username - deepaksood619@gmail.com
 - Github Personal - https://github.com/deepaksood619
 - Gitlab Personal - [Deepak Sood · GitLab](https://gitlab.com/deepaksood619)

diff --git a/docs/ai/content-moderation.md b/docs/ai/content-moderation.md
@@ -0,0 +1,57 @@
+# Content Moderation
+
+Community Moderation / Profanity Detection (Profane) / Abusive / Toxicity Detection
+
+## Categories
+
+| **Top-Level Category** | **Second-Level Category**    |
+|------------------------|------------------------------|
+| Explicit Nudity        | Nudity                       |
+|                       | Graphic Male Nudity          |
+|                       | Graphic Female Nudity        |
+|                       | Sexual Activity              |
+|                       | Illustrated Explicit Nudity  |
+|                       | Adult Toys                   |
+| Suggestive             | Female Swimwear Or Underwear |
+|                       | Male Swimwear Or Underwear   |
+|                       | Partial Nudity               |
+|                       | Barechested Male             |
+|                       | Revealing Clothes            |
+|                       | Sexual Situations            |
+| Violence               | Graphic Violence Or Gore     |
+|                       | Physical Violence            |
+|                       | Weapon Violence              |
+|                       | Weapons                      |
+|                       | Self Injury                  |
+| Visually Disturbing    | Emaciated Bodies             |
+|                       | Corpses                      |
+|                       | Hanging                      |
+|                       | Air Crash                    |
+|                       | Explosions And Blasts        |
+| Rude Gestures          | Middle Finger                |
+| Drugs                  | Drug Products                |
+|                       | Drug Use                     |
+|                       | Pills                        |
+|                       | Drug Paraphernalia           |
+| Tobacco                | Tobacco Products             |
+|                       | Smoking                      |
+| Alcohol                | Drinking                     |
+|                       | Alcoholic Beverages          |
+| Gambling               | Gambling                     |
+| Hate Symbols           | Nazi Party                   |
+|                       | White Supremacy              |
+|                       | Extremist                    |
+
+[Moderating content - Amazon Rekognition](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html)
+
+## Toxic Speech Categories
+
+- **Profanity**: Speech that contains words, phrases, or acronyms that are impolite, vulgar, or offensive.
+- **Hate speech**: Speech that criticizes, insults, denounces, or dehumanizes a person or group on the basis of an identity (such as race, ethnicity, gender, religion, sexual orientation, ability, and national origin).
+- **Sexual**: Speech that indicates sexual interest, activity, or arousal using direct or indirect references to body parts, physical traits, or sex.
+- **Insults**: Speech that includes demeaning, humiliating, mocking, insulting, or belittling language. This type of language is also labeled as bullying.
+- **Violence or threat**: Speech that includes threats seeking to inflict pain, injury, or hostility toward a person or group.
+- **Graphic**: Speech that uses visually descriptive and unpleasantly vivid imagery. This type of language is often intentionally verbose to amplify a recipient's discomfort.
+- **Harassment or abusive**: Speech intended to affect the psychological well-being of the recipient, including demeaning and objectifying terms. This type of language is also labeled as harassment.
+
+[Detecting toxic speech - Amazon Transcribe](https://docs.aws.amazon.com/transcribe/latest/dg/toxicity.html)
diff --git a/docs/ai/libraries/mlops-model-deployment.md b/docs/ai/libraries/mlops-model-deployment.md
@@ -47,6 +47,16 @@ There are mainly two ways of deploying an ML model:
 
 [MLOps guide](https://huyenchip.com/mlops/)
 
+### MLOps Components
+
+- Version Control
+- CI/CD
+- Orchestration
+- Experiment Tracking & Model Registry
+- Data Lineage & Feature Stores
+- Model Training & Serving
+- Monitoring & Observability
+
 ## Tools
 
 ### KubeFlow
@@ -160,4 +170,3 @@ https://www.seldon.io
 - [End-to-End Machine Learning Project – AI, MLOps - YouTube](https://www.youtube.com/watch?v=o6vbe5G7xNo)
 	- ZenML
 	- MLflow
--
diff --git a/docs/ai/libraries/tools.md b/docs/ai/libraries/tools.md
@@ -194,6 +194,7 @@ https://www.cortex.dev
 	- SHAP (SHapley Additive exPlanations)
 	- FastAPI
 	- Docker
+- [GitHub - feast-dev/feast: The Open Source Feature Store for Machine Learning](https://github.com/feast-dev/feast)
 
 ## SAAS Tools
 

diff --git a/docs/ai/llm/limitations-problems.md b/docs/ai/llm/limitations-problems.md
@@ -17,10 +17,8 @@ First find relevant information, then answer the question based on the relevant
 - **Not Updated to the latest information:** Generative Al uses large language models to generate texts and these models have information only to date they are trained. If data is requested beyond that date, accuracy/output may be compromised.
 - **Hallucinations:** Hallucinations refer to the output which is factually incorrect or nonsensical. However, the output looks coherent and grammatically correct. This information could be misleading and could have a major impact on business decision-making.
 - **Domain-specific most accurate information:** LLMs' output lacks accurate information many times when specificity is more important than generalized output. For instance, organizational HR policies tailored to specific employees may not be accurately addressed by LLM-based Al due to its tendency towards generic responses.
-
 - **Source Citations:** In Generative Al responses, we don't know what source it is referring to generate a particular response. So citations become difficult and sometimes it is not ethically correct to not cite the source of information and give due credit.
 - **Updates take Long training time:** information is changing very frequently and if you think to re-train those models with new information it requires huge resources and long training time which is a computationally intensive task.
-
 - Presenting false information when it does not have the answer.
 - Non-deterministic - same request can give different response/solution/output
 - Confidence is low because of hallucination

diff --git a/docs/ai/llm/models.md b/docs/ai/llm/models.md
@@ -46,6 +46,7 @@
     - [Llama 3 cost more than $720 million to train : r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/comments/1cyxdgc/llama_3_cost_more_than_720_million_to_train/)
     - [Llama 3.1 launched and it is gooooood! - by Bugra Akyildiz](https://mlops.substack.com/p/llama-31-launched-and-it-is-gooooood)
 - [SQLCoder-2–7b: How to Reliably Query Data in Natural Language, on Consumer Hardware | by Sjoerd Tiemensma | Use AI | Medium](https://medium.com/use-ai/sqlcoder-2-7b-how-to-reliably-query-data-in-natural-language-on-consumer-hardware-cb352a3cf3ab)
+- [DeepSeek](https://www.deepseek.com/)
 
 | Model              | Parameters | Size  |
 | ------------------ | ---------- | ----- |
@@ -103,7 +104,7 @@ Emotional prompting example - You are Dolphin, an uncensored and unbiased Al ass
 - [Hugging Face - The AI community building the future.](https://huggingface.co/)
 - [sentence-transformers/all-MiniLM-L6-v2 · Hugging Face](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
 
-## Evaluation
+## Model Evaluation
 
 - [LMSYS Chatbot Arena (Multimodal): Benchmarking LLMs and VLMs in the Wild](https://lmarena.ai/)
 - [Hugging Face Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)

diff --git a/docs/ai/ml-algorithms/feature-engineering.md b/docs/ai/ml-algorithms/feature-engineering.md
@@ -1,11 +1,25 @@
 # Feature Engineering
 
+## Intro
+
 - Scale to large datasets
 - Find good features
     - Synthetic features
 - Preprocess with Cloud MLE
 - Hyperparameter tuning
 
+## Tools
+
+[GitHub - feast-dev/feast: The Open Source Feature Store for Machine Learning](https://github.com/feast-dev/feast)
+
+Feast (**Fea**ture **St**ore) is an open source feature store for machine learning. Feast is the fastest path to manage existing infrastructure to productionize analytic data for model training and online inference.
+
+Feast allows ML platform teams to:
+
+- **Make features consistently available for training and serving** by managing an _offline store_ (to process historical data for scale-out batch scoring or model training), a low-latency _online store_ (to power real-time prediction)_,_ and a battle-tested _feature server_ (to serve pre-computed features online).
+- **Avoid data leakage** by generating point-in-time correct feature sets so data scientists can focus on feature engineering rather than debugging error-prone dataset joining logic. This ensure that future feature values do not leak to models during training.
+- **Decouple ML from data infrastructure** by providing a single data access layer that abstracts feature storage from feature retrieval, ensuring models remain portable as you move from training models to serving models, from batch models to realtime models, and from one data infra system to another.
+
 ## Good vs Bad features
 
 - **Good Feature**

diff --git a/docs/ai/nlp/chatbot-saas.md b/docs/ai/nlp/chatbot-saas.md
@@ -100,6 +100,8 @@ Turn your unstructured chatbot data into immediate action. Identify unhandled an
 
 https://simplify360.com/
 
+[Conversational AI Chatbot Software for Your Digital Assets | Engati](https://www.engati.com/)
+
 ## Examples
 
 https://goldenpi.com

diff --git a/docs/ai/readme.md b/docs/ai/readme.md
@@ -19,6 +19,7 @@
 - [Others / Resources / Interview / Learning](ai/others-resources-interview-learning-courses.md)
 - [Hackathons](ai/hackathons.md)
 - [Solutions](ai/solutions.md)
+    - [Content Moderation](ai/content-moderation.md)
     - [Social Media Analytics Solution](ai/social-media-analytics-solution.md)
 
 ## Data & AI Landscape