Hardware cost:
Tuesday, August 4, 2026
Tuesday, June 2, 2026
We can incoporate 3 ways of LLM through pipelines:
1) DAG workflow
a) Pre designed prompts + code paths
a) Pre designed prompts + code paths
b) Modular components [Yes or No prompts]
2) Agents
LLM setups to make their own decision
3) Agentic workflows
Hybrid architecture
We can create a work flow through LangChain and LangGraph.
LangChain:
It is designed for LLMs. It can be useful for custom workflows, tool integration and agent collaboration.
It is designed for LLMs. It can be useful for custom workflows, tool integration and agent collaboration.
LangGraph:
LangGraph library used for build a stateful, multi actors with LLMs
LangGraph library used for build a stateful, multi actors with LLMs
LangGraph concepts:
State: a TypeDict that flow through graph and updated by each nodes.
Nodes : Python function used to receive update and return updates
Edges : Connection between Nodes
Reducter : control how state updates combine with over write or accumalation
Retrieval Augmented Generation (RAG)
A RAG is an interactive system (chatbot) that combined a retrieval (static content) with a dynamic conversation generator.
Components of RAG:
An index : A mechanism to convert a raw data into Vector database
A retriever: Closed tied to the index and retrieve a data from database based on query
A generator : an LLM to reason a through user query and the retrieval knowledge to provide an inline conversational response.
Sementic search:
A system has understand of context and meaning of user query and matches against the available document for retrieval. It can find relevant document without having to rely on exact words or n-gram matching. It often uses a pre-trained large language model to understand the nuance of the query and the documents.
We often use of cosine similarity to define a raw data that produce in vector database.
Cosine is bounded between 1 and -1.
smaller angle == Cosine [up]
larger angle == Cosine [down]
Perpendicular == Cosine 0
Dense Model:
A dense model uses all the parameter for every tokenized. It would be better for semantic similarity, paraphrases and conceptual queries. [Example GPT3 & GPT4 are using Dense model]
Pros:
* Easier for training the model
* Simple architecture
Cons:
* high computation cost
Sparse Model:
A sparse model is used for small set of parameter for each token. The most common Sparse architecture is Mixure of Experts.
Pros:
* Less computation cost
* Better scaling efficiency
Cons:
* Potential training
* More complex architecture
Cross Encoders:
A cross Encoder is a non-generative LLM specifically designed to take in two inputs separated by a special token and return a single output.[Ex. BERT]
Reasoning LLMs:
Reasoning models like Deepseeks R1, OpenAI are orginal "o" series and Anthropic's Claude/Opus 4 models are autoregressive LLMs that have been trained to give a discursive chain-of-thought reasoning step before giving a user response.
For example, the Claude 4 series of LLMs provide separate reasoning tokens alongside the messages to the user, as is common with most frontier reasoning LLMs.
Agents:
1) Solo Agents
2) Supervisor + specialist agent
Wednesday, May 27, 2026
K8sGPT is a tool for scanning your Kubernetes clusters, diagnosing and triaging issues in simple english. It has SRE experience codified into its analyzers and helps to pull out the most relevant information to enrich it with AI.
K8SGPT is an advance AI algorithm analysis your cluster state and provide intelligent inside for troubleshooting.
CNAI - Cloud native along with AI. Kubeflow is a best example of CNAI.
AICN - Artificial Intelligent with Cloud native. K8SGPT is a best example of AICN.
AICN - Artificial Intelligent with Cloud native. K8SGPT is a best example of AICN.
WorkFlow:
Tuesday, April 28, 2026
Frontier agent are autonomous system that work independent to achieve goals, scale massive to tackle concurrent tasks and run persistenly for hours or days without human intervention.
Frontier Agent option:
Wednesday, April 22, 2026
GitHub: It is a cloud based platform which provides distributed version control and source management system.
Alternative source mangement system:
- Github
- Bitbucket
- GitLab
- SVN
GIT Algorithm:
It will hash out of content (produces 40 char hex string)
It will create a file name same as hash
Zip up with your content and stored inside of file.
#git add test.txt
#cat test.txt | git hash-object --stdin
blob:
It is convert a file into hash file.
Hash algorithm will create a hash file according the file contents. It will not create a two dfferent hashing file if both both files are having a same contents.
#git write-tree : It will display the tree structure of GIT
#git cat-file -p hashfile : Read a content inside of hashfile
Git Commit:
Commit is a copy of snapshot. It will create a read only file whenever we performed a commit. The previous content or update will be there and would not be destroyed,
Merkle Tree: A tree structure in which each leaf node is a hash of a block data and non leaf node is a hash of its children.
Head:
Merge:
Merge:
It merge one or more commit into branch.
Monday, April 20, 2026
Go is an open source and compiled programming language. It is developed at Google to build, reliable software.
why do we need GO?
* Compiles to native code
* Type safe - We have to declare before use a variable
* Garbage collector - It will allocate and deallocate a GC by automatically.
Every Go source file is part of a package.
We have to import when do we call other package in your program
The main function is called hen a program first starts..
Go fmt utility:
It will fix the code style automatically.
Go run utility:
* It compiles a Go source file and runs it.
Go build - It compile Go source file into an executable
Calling Function:
package main
import (
"fmt"
"math"
)
func main() {
fm.Println(math.Floor(1))
fmt.Println("Hello")
}
}
Pointers:
We can use pointers to fix of calling a double functions.
func main() {
amount:=6
double(&amount)
fmt.println(amount)
}
func double(number * int) {
*number *=2
}
Monday, April 6, 2026
We can LLMs in three ways by usually
1. Encode text into semantic vectors with little/no file tuning
2. Fine tune a pre-trained LLM to perform a very specific task using by Transfer Learning
3. Query an LLM to solve a task which was pre-trained or could intuit.
Two types of LLMs now.
1) Auto encoding LLMs - Learn a entire sequence by predicting tokens (words) given past and future context. It is best for classification and embedding + retrieval tasks. [Example BERT]
2) Auto regressive LLMs : It will predict a future token
LLMs excel at task that require reasoning using context and input information in the conjunction to produce a nuanced answer.
AI agents are semi autonomous systems that interact with environment, make decisions and perform tasks on behalf of users.
Autonomy - They can perform tasks without continuous human intervention.
Decision Making - Use data to analyze and choose actions
Adaptability - Learn and improve over time with feedback.
Optimizing Models:
Speculative Decode : Using an assistant model to guide next token perdition
Caching OS models : Implementing prompt caching with open Source models
Quantization : Reducing computation requirement of neural network.
Distillation : Transfer knowledge from large model into small through targeted fine tuning.
Speculative Decoding:
Assistant agent calls for forward method of calling [calling parameter over and over again]. The main model simply verifies which token is agreed with request.
