Express YourSelf !

https://aikosha.indiaai.gov.in/home It looks like huggingface website for India

Models https://aikosha.indiaai.gov.in/home/models/all

Datasets https://aikosha.indiaai.gov.in/home/datasets/all
https://aikosha.indiaai.gov.in/home/resources?from=resource-detail having some PDF books
https://aikosha.indiaai.gov.in/home/toolkit having a list of popular AI related tools.

Kannada Models

nomic-embed-text-v2-moe

snowflake-arctic-embed2

OpenAIs text-embedding-3-large

Vovage

Cohere

intfloat/multilingual-e5-large-instruct

paraphrase-multilingual

BGE-M3 is based on the XLM-RoBERTa

TinyML

one possible hardware https://store-usa.arduino.cc/products/arduino-tiny-machine-learning-kit

Topics: https://github.com/tinyMLx/courseware/tree/master/edX

Courses: https://tinyml.seas.harvard.edu/courses/

Workshop videos and slides: https://tinyml.seas.harvard.edu/SciTinyML-24/

Article: https://hdsr.mitpress.mit.edu/pub/0gbwdele/release/5

embedUR https://www.embedur.ai/

LLM on mobile

Google launches app to let anyone run AI models on their phone without internet

How to Install and Run AI Models Locally on Your iPhone

URL for AI/GenAI

LLM

Inside The Brain Of An LLM: What Makes AI So Powerful?

Landscape

https://landscape.lfai.foundation/

https://landscape.pytorch.org/

Models for coding

1. Qwen2.5 Coder

2. Granite Code

3. CodeGemma

4. Deep Seek Coder V2

5. StarCoder 2

6. Code llama

7. Codestral

Interviews

Pioneering Innovation in Cloud and AI Transformation Done By Chandrakanth Devarakadra Anantha

Innovation in Machine Learning & Engineering Leadership by Pratik Parekh

Amazing Innovation in Telecom Cloud: The Journey of Jayavelan Jayabalan

CAMARA - NaaS

Keywords

CAMARA APIs
Open GW
Network API
NaaS

AI impacts API development

Usecase

1. anti fraud

2. location API : book cab for people not having smart phone

3. voice activated AI transaction. book a cab

4. geo fencing. warn people when other people comes close to them. logistic when truck reaches store, offloading

5. future Quality in demand

Challenges

1. monetizing

2. standardizing

3. presenting to non-telco audience

4. focus on right APIs: There are 15 fraud APIs, customer only wants to know is it fraud or not?

5. scale and coverage: approach operator and help them coming to eco system

6. data privacy and consent: no need for customer to provide consent for each new API. it is bad experience

7. education and certification about API. Let developer make new business models and business case.

8. telco shall listen to industry need. how to solve challenges using advance connectivity and APIs. demand side focus.

https://www.youtube.com/watch?v=Rg-TKpBuiPI

Popular APIs

Messaging
authentication
Device location,
QoS,
fraud prevention,
identity verification
age verification

https://youtu.be/XYAwAEM2QQU

https://cpaasaa.com/post-mwc-aduna-vonage-and-the-future-of-network-apis/

Network API centralizes complexity and distribute simplicity

https://www.youtube.com/watch?v=4C9zrRNoxas

Vonage and Infobip : service aggregation

https://www.youtube.com/watch?v=Jh8iUuNHFYw

Network APIs, allow operators to virtualize parts of their networks and provide tailored data and features to developers

Network API v/s Usecase

1. Verify Location: Navigation, geotagging and location-aware notifications, personalized marketing

2. Device Status: Optimize resource usage based on device health and network condition. Identify issue and proactive customer support

3. SIM Density: Ensure optimal user experience during peak hours, SON

4. SIM swap: Fraud Prevention

5. QoD

6. Device identification, device location, and phone number verification

7. Identity and consent management

8. OTP validation

https://www.vonage.com/resources/articles/what-is-a-network-api/

Vonage Network Registry

CSP can find who developer uses

Developer can decide which CSP to choose.

We are moving from Transactional world to conversational world.

https://camaraproject.org/resources/

https://camaraproject.org/api-overview/

https://github.com/camaraproject

अष्टाध्यायी - 2

This article is my key take away points from PythonKrit workshop, at Samskrit Bharti Bangalore during March 2025

Dr. Amba Kulkarni explains how ASHTADHYAYI by sage PAANINI is similar to computer programming and compiler design

https://sanskrit.uohyd.ac.in/faculty/amba/ and https://www.sanskritstudiespodcast.com/1759898/episodes/12324157-16-amba-kulkarni-sanskrit-and-computers

ASHTADHYAYI is also Algorithm and Data structure. Class/Object has data and function. Paanini's DHTAATU list has name of DHAATU and "इत् प्रत्यय". Here "इत् प्रत्यय" indicates, which operation to be performed. We know the challenges with multiple inheritance in Object Oriented Programming. Prof. Ashvini Bhave shows how TADDHITTA indicates single inheritance.

Sage PAANINI introduced a new data structure SHIVA-SUTRA. He rearranged all character and did slicing then perform Boolean operation that input character belongs to given list or not, given input set of character is subset or not.

Meta language itself is part of ASHTADHYAYI .

Three types of rules

1. regular rules

2. context free rules

3. context sensitive rules

We use regular expression * for beginning AADI , UPAADHAA for set of characters in middle with [] and $ for end of line (ANTHA).

We know yacc and bison tools are for context free grammar. If we write all PAANINI rules as per syntax of yacc and bison then we can analyze the complexity of PAANINI's ASHTADHYAYI grammar. There are few non-formal aspects in ASHTADHYAYI, as it was written to understand by human brain, not by computer.

Sage PAANINI was about 1500 years ahead of time compare to today's computing power.

Rules are like event in programming. To understand grammar one of the rule shall be evaluated, it is like firing an event.

ANUVRUTI is similar to factorization in Maths.

Dr. Saroja Bhate explained (1) what is role of aakanksha in deciding anuvruti and

fundamentals of anuvruti

Compression in Sanskrit grammar. Sage PAANINI compressed LAGHU SIDDHANTA KAUMIDI with help of VRUTI to 1/3 size and wrote ASHTADHYAYI.

yathodasa is like Interpreter and karyakala is like compiler

abhidha, laxana and vyanjana are 3 powers of words. Not sure, when today's LLM will understand them.

MAHAA-BHAASHYA learning is better than learning ASHTADHYAYI.

Dr Srinivas Varkhedi has strong opinion that, Sanskrit scholar already knows computer science concepts and programming with knowledge of Sanskrit grammar. In future, we will not have trust our car. As it might be hacked by AI tools. So we will travel by bullock cart, because bullocks listen to us, car may not. AI tools cannot hack bullocks.

Today's LLMs are fed against SANAATANAA DHARMA. It is difficult to erase. They are biased against SANAATANAA DHARMA. We know that, it is difficult to re-train our kids who studied in school/college. Then retraining LLMs is even more difficult. The current generation will use AI and they may be against SANAATANAA DHARMA in future. So we need 1000 Sanskrit students who can say what is right and what is wrong about SANAATANAA DHARMA with authenticity.

Reference:

Slides: https://web.stanford.edu/~kiparsky/Papers/paris.pdf and Stream rtsp://stream-serv.inrialpes.fr/Roc/Symposiums_2007/Sanskrit291007B_Gillon.rm by Paul Kiparsky

https://ashtadhyayi.com/

The entire data that powers https://ashtadhyayi.com https://github.com/chaitanya-lakkundi/ashtadhyayi-com-data/

https://github.com/chaitanya-lakkundi/ashtadhyayi-commentaries/

https://drdhaval2785.github.io/siddhantakaumudi/

https://github.com/drdhaval2785/siddhantakaumudi

https://en.wikipedia.org/wiki/Mahabhashya

https://sanskrit.uohyd.ac.in/

Books

https://en.wikipedia.org/wiki/Algorithms_%2B_Data_Structures_%3D_Programs

https://www.sushmajee.com/reldictionary/literature/grammar/sanskrit-grammar.htm