Tuesday, March 10, 2026

Infrastructure as Code through AI workloads

March 10, 2026 0



A Kubernetes operator is a specialized controller designed to extend Kubernetes API, enabling the management of complex application through declarative configurations.
Kubernetes Operator operate within continuous reconciliation loop. This cycle begins when user create a resource, It prompting controller to monitoring a change and take a necessary action to ensure a desire state. Operator allow user to defined a desired state of application in custom resources, while operator
controller continue reconcile the actual state with this desire state, embodying the operational expertise of human site reliability engineer.
KubeFlow : It is a primary of orchestration tool for MI workflow. It is focus on training aspect of models.

Most using of Kubernetes resources in the real time.
APIService
ClusterRole
ClusterRoldBinding
ConfigMap
CronJob
CSIDriver
CSINode
DaemonSet
Deployment
EphemeralContainers
HorizontalPodAutoscalar
Ingress
IngressClass
Job
Namespace
Node
PersistVolume
Pod
PodDisruptionBudget
PodTemplate
ReplicatSet
ResourceQuota
Role
RoleBinding
Secret
Service
ServiceAccount
StatefulSet
StorageClass
VolumeAttachment
Binding
CertificateSigningRequest
ComponentStatus
ControllerRevision
CustomResourceDef
Endpoints
EndpointSlice
LeaseReplicationController
LimitRange
LocalSubjectAccess
MutatingWebhookConfiguration
NetworkPolicy
PodSecurityPolicy
PriorityClass
RuntimeClass
SelfSubjectAccess
SelfSubjectRules
SubjectAccessReview
TokenReview
ValidatingWebhook



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Monday, March 2, 2026

TLS 1.3 Cipher Suite

March 02, 2026 0

 



TLS1.3 is released in August 2018 (RFC8446).  It is a latest version of Transport Layer Protocol. It will remove a weaker algorithms and improve a speed of authentication. 

TLS 1.2 Cipher suit diagram:

TLS_DHE_RSA_WITH_AES_256_CBC_SHA
Key Exchange[DHE], Authentication [RSA], Encryption [AES_256_CBC] and Hashing [SHA]

TLS1.3 will support 5 Cipher suites compare to TLS.2 will support of multiple Cipher suites.

TLS1.3 including 5 Cipher Suites:
  • TLS_AES_128_GCM_SHA256 [Must Implement]
  • TLS_AES_256_GCM_SHA384 [Should be Implement]
  • TLS_CHACHA2-_POLY305_SHA256 [Should be implement]
  • TLS_AES_128_CCM_SHA256 [Can implement]
  • TLS_AES_128_CCM_8_SHA256 [Can implement]
It will follow up with forward secrecy [Once Encrypted always encrypted]
TLS1.3 will remove a custom DH Groups and support a standard based group only, because it will lead may insecure groups being used and breach a security.
DH means Diffi-Hellman starts with agreeing upon some values.
Approved DH groups are designated via various standards.
* Traditional DH groups : RFC 2409 & RFC 3526
* Elliptic Curve Groups : RFC 5639, FIPS 186-4

Handshake method of TLS 1.2 Vs 1.3


TLS1.2 is using 2routing method for handshake a request, but TLS1.3 is using 1 routing method for handshake method. It will improve a quick response compare to TLS1.2.
TLS1.2 is created 4 keys while handshake connection.
  • Client encryption
  • Client HMAC
  • Server Encryption
  • Server HMAC

 TLS1.3 will create a 11 keys while handshakes connection request.

TLS workflow:
* TLS/SSL will send a highest support version of client Hello and Sever Hello for handshake.
Middlebox or Load balancer will drop a request if mismatch of version upto TLS1.2.
TLS1.3 will create a header with TLS1.0 , Client hello version with TLS 1.2 and Client hello extension with TLS1.3, Hence the request will not drop off in between Middlebox.
* TLS is providing forward end-to-end handshake encryption. Handshake will create a session keys which project an application data. Session keys will be derived from RSA & DS.
DS - It will share public key store and private key will delete after SEED establishment. It will support of forward Secrecy.

Client Hello is carrying on information about Version, Session ID and Cipher suites.
Below diagram will give a details about 11 keys of TLS1.3.



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Saturday, November 8, 2025

MCP - Model Context Protocol

November 08, 2025 0

 

MCP - Model Context Protocol:

MCP defined a LLM to access an external data, tools and context in a a structure way. MCP (Model Context Protocol) is an open-source standard for connecting AI applications to external systems and data.

Overview of MCP:

AI application such as Claude or chatGPT can connect to data sources, tools [search engine] and workflow [prompts] through MCP and perform a tasks.

MCP like an interface which communicated to MCP client and discover their requirement and offer available services for their requirement. 
MCP Framework:
  • MCP SDK - It is a foundation for all the MCP development. It will use for Production and standard projects. It can be integrate into any tools or transport (STDIO, SSE)
  • FASTMCP 1.0 - It became a legacy support and integrated into MCP python SDK.
  • FASTMCP 2.0 - This is a latest and modern feature tools kits for advanced MCP workflows.
  • Others Frameworks - Java SDK and third party libs in other languages.
Agent workflows inside of Memory:


RAG - Retrieval Augmented Generation
It converts a data into numerical representation where each piece of data has information about how it relates to others.
Retrieval - when user ask a question or search, RAG turns question or search into own numerical representation (Embedding) and find a data which is similar meanings.
Augmentation - The top search result are then added into prompt and send to back to LLM
Generation - The search results give the LLM some local context and consider as response.
Embedding:
Embedding represent text as set of numerical data along with tensors (different dimensions)
Each dimension will store some information about text meaning or syntactical meaning.
Each words or sentence with similar meaning are stored near by vector space.
Models will learn to place a similar words or sentences close  together in the embedded space.
Common pre-trained models such as BERT and RoBERTs are  used for generating an embedding inside of vector space.
We can able to use an embedded for NLP tasks like semantic search, text classification and sentimental analysis.
Agentic RAG:
It is integrate an AI agents to enhance the RAG approach. It will breakdown from complex queries into manageable parts and using API tools where need to augment processing and better result.


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Implementation of AI agent

November 08, 2025 0


                                 

Installation of Ollama:
Ollama is an open source tool which will helps us to run a NLP [Natural Language Processing] through locally.
Step1) Downloading the Ollawa tool for your suitable operating system and installed it.





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Friday, July 25, 2025

Data Science - Basics

July 25, 2025 0




Data science:

Data has classified as three types:
Unstructured data: the data is collected in a random way such as social media data, Phone calls, Weather.
Structured data: It is collected a data in a structural and specific data. 
DB and APP data: Customer and invoice data.
ML has a four life cycles:
Build, Deploy, Train, Manage
Jupyter Lab:
* It is a webbased interface for notebook sessions
* Ability to work with integrate documents and activity including:
* Jupyter Notebook
* Text editors
* Terminals
Features of Jupyter Lab:
Menu Bar : Top level menu that expose actions available in JupyterLab
Launcher: Provide easy access to your notbooks, console, text editor and environment explorer
Left Sidebar: File browser and command Palette
Conda environment:
It is an Open source and environment management system.
Features of Conda Environment:
* Install and updated their dependencies
* Maintain a different software group
* Change over between environments
* Develop a netbook and deploy a modules
Data Science use of Conda environment:
Provide a below specific framework and build a module:
*PyTorch
*TensorFlow
General Machine learning Algorithm:
use case:
* Data manipulation
* Supervised machine learning 
* AutoML functionality
* Machine learning explainability
Top Libraries used for GML:
* category encoders
* lightgbm
* scikit-learn
* TesorFlow
Natural Language Process [NLP]
Use case:
* Text extraction
* Part of speech tagging
* Key pharse extraction
Top Libraries of NLP:
* nltk
* transformers
* eli5
* Lime
ONNX:
Opensource software for the Data science.
Use cases:
* Portability and interoperability between ML frameworks
* ONNX runtime liberary allows us to run a module on different platforms.
Top Libraries of ONNX:
* onnx
* onnxconverter-common
* onnxmltools
* onnxruntime
PyTorch:
It is an opensource of machine learning framework. It is used mulitple of deep learning algorithm. 
Use cases:
* Computer vision, NLP and general machine learning
* Deep neural networks and algorithms for deep learning.
Top Libraries of PyTorch:
* Panda
* daal4py
TensorFlow:
Use cases:
* Machine learning
* Deep neural networks
* Flexible architecture run on CPUs, GPUs and TPUs
Top Libraries of TensorFlow:
* Panda
* scikit-learn
* Tensorboard
* TensorFlow
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Sunday, July 20, 2025

AI Basics

July 20, 2025 0

 


Supervised Learning:
It ill provides an output if we give an input into the applications called supervised Learning.
Machine Learning Vs Data Science:
Machine Learning:
Field of study that's gives computers the ability to learn without explicitly programs.
Ex. YouTube Advertisement and online shopping. It will generate an advertisement and shopping related notification for the user interest.
Data Science:
It will extract the knowledge and insights from data.
Ex. Share market data. It will analysis an insight of data and decided a probability of output.
Deep Learning:
It will take a multiple input and decided output like human brain by using a Artificial Neural Network.
Open-source frameworks for Machine Learning tool:
  • PyTorch
  • TensorFlow
  • Hugging Face
  • PaddlePaddle
  • Scikit-learn
  • R
How is Alexa works?
Steps to process the command:
1) Trigger word detection to activate the device [ Hi Alexa]
2) Speech recognition - "what is the weather in Delhi today" [convert audio file into text]
3) Intent recognition - purpose of the user "weather in Delhi"
4) Execute weather query and given output to user.
Generative AI:
AI system that can produce a high-quality content like text, image and audio. It used the machine learning model and learned a data and generate an output content.




 
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Monday, June 23, 2025

Ansible Modules

June 23, 2025 0

 

inlinefile module:

Adding /Modify or delete a line inside of file.
Main parameter:
path - full path of the file
line - text
insertbefore / insertafter - EOF/regular expression
validate - Validation of command
state - Present/absent
mode/owner/group - permission
setype/seuser/selevel - SElinux setup
Ping Module:
It is validated the host reachability of remote host.
ansible.builtin.ping is a module name.
Reboot Module:
We can reboot a remote host through reboot module.
Main parameter of this reboot module as below:
reboot_timeout - 600
msg - text of reboot notification
reboot_command - define a reboot command depends up on OS
pre_reboot_delay - 0
Post_reboot_delay - 0
test_command - 'whoami'
boot_time_command - "cat /proc/sys/kernel/boot_id"

Copy Module:
Copy a file from one location to other location.
Main Parameters:
dest - Remote file path
src - local file path
fail_on_missing - yes / no
validate_checksum - yes /no
flat - yes/no
Service Module:
We can enable and disable the system services through this module.
ansible.builtin.service_facts
Main parameters:
name : Service name
state :  started, stopped, restarted, reloaded
enabled: yes/no
arguments/args : extra args
Package installation module:
Main parameters:
name - Name of the package
state - present/installed/absent/removed/latest
Create a file:
Main parameter:
path - file path
state - absent/directory/hard/link/touch
Module for a file permission change:
Main parameters:
Path - file path
owner - user
group - group
mode - rwx mode
state - file state [absent/directory/hard/link/touch]
setype/seuser/selevel - SElinux
Module for Download a file through Internet
Main parameters:
url - download URL
dest - destionation path
force - no/yes
checksum - checksum:URL
force_basic_auth/url_username/url_password/use_gssapi - HTTP basic auth/GSSAPI kerberos
headers - custom HTTP headers
http_agent - ansible-http-get
owner/group/mode - permission
setype/seuser/selevel - SElinux

Example:
---
 - name: Download an ansible package
   hosts: all
   become: false
   gather_facts: false
   vars: 
     myurl: "https://releases.ansible.com/ansible/ansible-2.9.25.tar.gz"
mycrc: "sha256:https://releases.ansible.com/ansible/ansible-2.9.25.tar.gz"
mydest: "/home/test/ansible-2.9.25.tar.gz"
   tasks:
     - name: downloading an ansible file
   ansible.builtin.get_url:
     url: "{{ myurl }}" 
desk: "{{ mydest }}"
checksum: "{{ mycrc }}"
mode: '0644'
owner: devops
group: wheel
Module for backing up the file
Main Parameters:
src - source path
dest - destionation path
archive - mirrors the rsync archive flag, enables recursive, links, perms, times, owner, group, flags 
rsync_opts - no/yes

Changed the line inside of file:
---
- name: search demo
  hosts: all
  vars:
    myfile: "/etc/ssh/sshd_config"
    myline: 'PasswordAuthentication no'
  become: true
  tasks:
    - name: string found
      ansible.builtin.lineinfile:
        name: "{{ myfile }]"
        line: "{{ myline }}"
        state: present
      check_mode: true
      register: conf
      failed_when:(conf is changed) or (conf is failed)

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