Kulonuwun (Hi)

A Journey into AI-Powered Conversations and Document Understanding It all started with a simple question: How can we make AI interactions more accessible, intuitive, and useful for everyday users? (me) In an era where AI models like GPT-4 and Claude are reshaping how we work, learn, and communicate, there’s still a gap between raw AI capabilities and how people actually interact with them. Many AI-powered tools feel complex, buried under APIs and technical jargon. This is where Gadageni was born—a vision to create a seamless, user-friendly way for anyone to engage with AI, whether for casual conversation, document analysis, or summarizing the web. ...

To help me stay sane, i have compiled a list of the latest AI news and in AI research. this is just a note for me https://paperswithcode.com/ https://paperswithcode.com/latest https://www.theneurondaily.com/ https://thesummary.ai/ (some paid) thats it thanks

Introduction In today’s rapidly evolving digital age, data has become an invaluable asset for businesses of all sizes. The term “Big Data” refers to the large volumes of data that organizations generate and collect daily. Leveraging this data effectively can transform companies, offering strategic benefits that translate into a competitive edge in the marketplace. In this blog post, we will explore how businesses can utilize Big Data, the competitive advantages it provides, and steps to implement it effectively. ...

Intro In the rapidly evolving world of technology, Natural Language Processing (NLP) plays a pivotal role in reshaping how machines understand and generate human language. Among its many applications, NLP significantly enhances multilingual text-to-speech (TTS) technology and voice assistants, making them more robust and user-friendly. This blog post will explore the transformative impact of NLP on TTS systems, focusing on its ability to improve language understanding, pronunciation accuracy, and overall user experience—especially for non-native speakers. ...

Introduction In an era where the digital landscape is constantly evolving, the role of Site Reliability Engineers (SREs) has become even more critical. Enter Cleric, an AI-powered SRE that leverages cutting-edge technology to enhance its capabilities in identifying and resolving production issues. This blog post delves into how Cleric has transformed its operational efficiency by utilizing LangSmith’s continuous learning framework, ultimately showcasing the necessity of evolving AI tools to meet the demands of modern operations. ...

Introduction As organizations increasingly invest in artificial intelligence (AI), understanding the return on investment (ROI) of these initiatives becomes crucial. Measuring the ROI of AI is not a straightforward endeavor; it requires a nuanced approach that integrates both quantitative metrics and qualitative assessments. This blog post will explore effective strategies and key metrics for measuring the ROI of AI, ensuring organizations can harness the full potential of their AI investments. ...

Candidate discovery demo In this post will be litle bit technical. continuing what i already created that talk with pdf (RAG). combining this nextjs from bolt.new chat with ollama and langchain for talking with pdf files we will slightly re-architec the app user flow design. before, we use FAISS to store our vector. i am afraid it cannot scale (do not know how to scale). so in search of vector databases. i encounter with bunch of option there is pgai (that really take my interest) still in beta, but also qdrant that already have their enterpise option. ...

is the internet still could be trusted ? Its bothering me in this last couple of month i am seeing or feel seeing and reading AI generated content. it could be text, images or video. and i wonder if articles or video was took a day to research and write or create, now, could take a couple of minutes? second? Content Creation Before AI Before the adoption of AI, content creation relied heavily on human. The years between 2000 and 2020 (maybe) saw a steady rise in content production, driven by the internet Boom and Social Media Growth, platforms like Facebook, Twitter, and Instagram introduced new opportunities for user-generated content. and TikTok of course. ...

1. Code Refactoring Prompt: “Refactor this code to make it cleaner and more Pythonic: {code}” Prompt with Example: “Refactor this code: for i in range(len(arr)): print(arr[i])” What for: To improve code readability, performance, or adhere to best practices. Example: for item in arr: print(item) 2. Bug Fixing Prompt: “Fix the bug in this code: {code} and explain what went wrong.” Prompt with Example: “Fix the bug in this code: x = int('123a')” What for: To debug and resolve issues in the code. Example: try: x = int('123a') except ValueError: print("Invalid input, cannot convert to integer.") 3. Code Generation Prompt: “Write a function to {task} in {language}.” Prompt with Example: “Write a function to reverse a string in Python.” What for: To quickly generate functions or reusable code snippets. Example: def reverse_string(s): return s[::-1] print(reverse_string("hello")) # Output: "olleh" 4. Write Unit Tests Prompt: “Write unit tests for the following function: {code}.” Prompt with Example: “Write unit tests for this function: def add(x, y): return x + y” What for: To ensure code behaves as expected through automated tests. Example: import unittest def add(x, y): return x + y class TestAddFunction(unittest.TestCase): def test_add(self): self.assertEqual(add(2, 3), 5) self.assertEqual(add(-1, 1), 0) unittest.main() 5. SQL Query Generation Prompt: “Write an SQL query to {task}.” Prompt with Example: “Write an SQL query to find all users older than 30.” What for: To create or optimize SQL queries for databases. Example: SELECT * FROM users WHERE age > 30; 6. Explain Code Prompt: “Explain what the following code does: {code}.” Prompt with Example: “Explain this code: for i in range(3): print(i)” What for: To clarify or document code for better understanding. Example: “The loop iterates over values 0, 1, and 2, printing each value.” 7. Code Conversion Prompt: “Convert this {source_language} code to {target_language}: {code}.” Prompt with Example: “Convert this Python code to JavaScript: print('Hello')” What for: To translate code between programming languages. Example: console.log('Hello'); 8. Regex Creation Prompt: “Write a regex to {task}.” Prompt with Example: “Write a regex to validate email addresses.” What for: To validate or extract patterns from strings using regular expressions. Example: ^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$ 9. Generate API Request Code Prompt: “Write code to send a {request_type} request to {url} using {language}.” Prompt with Example: “Write code to send a GET request to https://api.example.com/data using Python.” What for: To create API integration code. Example: import requests response = requests.get("https://api.example.com/data") print(response.json()) 10. System Design Suggestions Prompt: “Design a system for {use_case} and describe its architecture.” Prompt with Example: “Design a system for an image upload service.” What for: To outline a scalable system design or architecture. Example: “Use the following components: Storage: AWS S3 for image storage. API: Load-balanced servers to handle upload requests. Processing: Background worker to resize or process images. Delivery: Use a CDN for fast delivery of images.”* To be honnest this is created by AI, with my ‘great’ prompt 😃 . prompting is the key of learning anything now. when it used to be a google search technique. If content in Internet now somehow could easyly created by AI is the prompt is actually more important to kmow than the content it self? just wondering. ...

Recently i am creating a blog that code and create RAG pipeline. Since Langchain (the library that I use) wraps all the functions behind the curtain, some users, like myself, may misunderstand its workings. I want to discuss this part of the code: def build_rag(vectorstore): retriever = vectorstore.as_retriever() llm = OllamaLLM(model="mistral") # Specify Mistral model qa_chain = RetrievalQA.from_chain_type(llm, retriever=retriever) return qa_chain In understanding this code I assume that when qa_chain = RetrievalQA.from_chain_type(llm, retriever=retriever) retreiver is vectorstore right?. i assume again that llm is actually read the vector. ...