Aakash Gupta

Members violating the group protocols are sent a warning message and their violating messages are deleted.

You create a WhatsApp group, it even gains a lot of engagement. But then you struggle with moderation. Users simply dont want to follow the WhatsApp group rules.

We vibe coded a WhatsApp agent which moderates messages across groups.

you can test the voice cloning capacity at:

mimiclabs.thinkevolvelabs.com

I have created a microsite where you can do the same. The Best part is that its completely local and the opensource package can run on a cpu instance. So no need to run any complex workloads on a GPU cloud, but a voice can be cloned and replicated on your laptop.

youtu.be/XTSp0Q-90bA

YouTube video by Think Evolve Consultancy Gemini 270m Fine tuning with MIRIAD dataset

🚀 New video: Fine-tuning a <150MB LLM on 5.8M+ medical Q&A samples.
Runs on mobile or laptop — no GPU required!
Watch here 👉
youtu.be/GOQRKzrM3gA

LLMs are Language models. The latest version of ChatGPT5 appears to be experiencing a hallucination issue when asked a simple query.

The solution is to encourage them to think more critically and provide logical steps to their response.

A query on Sam Altman's investment gets stalled on Gemini. What could be the reason?

I often feel socially anxious while speaking, so I keep notes—but glancing down can feel awkward. Inspired by pro teleprompters, I built one for video calls. "Smooth Teleprompter" is a free Chrome extension we made with Replit, using our playful “vibe coding” approach to dev.

Rendering Blade Runner 2049 final scene

And in that final breath,
A machine finds something almost human.
Snow drifts through the poisoned sky, silent and slow,
a quiet witness to grace where none was expected.
It falls on steel and sorrow, soft as forgiveness...

Interestingly, as I write this, the landscape is rapidly evolving—a startup focused on MCP technology just received its first seed funding from HubSpot's CEO. The messaging middleware space continues to develop quickly in today's fast-paced tech environment. (3/3)

Model Context Protocol (MCP) is like a USB-C port for applications. It allows LLMs to connect with different apps on your local machine. This can be superbly useful for automation of intelligent tasks. MCP has been designed by Anthropic (2/3)

YouTube video by Think Evolve Consultancy WhatsAPP MCP Windows Installation

I decided to experiment with setting up WhatsApp Model Context Protocol (MCP) on a Windows system. Though Windows isn't ideally supported by the Model Context protocol, I wanted to create a comprehensive guide to help others navigate this process. (1/3)

www.youtube.com/watch?v=-B5x...

YouTube video by Think Evolve Consultancy DeepSeek R1 Prevent Output in Chinese

Agentic Systems exhibit autonomy, decision-making, and adaptability in achieving goals. They can analyze data, take actions, and refine their approach based on feedback, often functioning with minimal human intervention.

#DeepSeek #PersonalAssistant #AIforall

youtu.be/JaZvkpgnXck

The seemingly "simple" problem statements, their clarity masking decades of complexity. Unraveled, layer by layer, with each genuine interaction.

In summary, fine-tuning bridges the gap between general-purpose capabilities and task-specific excellence, enabling LLMs to deliver tailored, efficient, and high-performance solutions across diverse industries. 🚀
(10/10)

7️⃣ Faster Convergence

Starting with pre-trained language patterns accelerates training and ensures quicker convergence to optimal performance.

- Example: Fine-tuning a base model for academic paper summarization to assist researchers.

(9/n)

6️⃣ Domain-Specific Performance
Customizes the model to the unique characteristics and language of a specific domain, ensuring accuracy and relevance.

- Example: Fine-tuning for financial risk analysis using historical market data and reports.
(8/n)

5️⃣ Task Adaptability

Enables broad versatility by adapting a single model to a range of tasks without requiring additional architectures.

- Example: Using the same base model for text summarization and sentiment analysis by fine-tuning it separately for each task.
(7/n)

4️⃣ Efficient Deployment

Fine-tuned models are optimized for specific applications, ensuring faster and more accurate results in production environments.

- Example: Deploying a fine-tuned model for e-commerce product recommendations, tailored to user behavior.
(6/n)

3️⃣ Improved Generalization

Enhances the model’s ability to perform well on specialized tasks by refining its understanding of nuanced requirements.

- Example: Training a model to excel in legal document review for compliance purposes.
(5/n)

2️⃣ Reduced Data Needs

Instead of requiring massive datasets, fine-tuning focuses on smaller, task-specific datasets, making it practical even for resource-constrained scenarios.

- Example: Fine-tuning a model for medical diagnosis using a curated set of clinical notes and case studies.
(4/n)

Example : Adapting GPT models for use in customer support chatbots, leverages their broad language understanding to quickly specialize in handling product-specific inquiries.

(3/n)

1️⃣ Transfer Learning Efficiency

Fine-tuning builds upon the foundational knowledge of pre-trained models, significantly reducing computational and time requirements compared to training from scratch.
(2/n)

The Importance of Fine-Tuning Large Language Models (LLMs)

Fine-tuning is a crucial step that unlocks the full potential of pre-trained models by adapting them to specific tasks and domains. Here’s why it matters, along with some practical examples:

(1/n)

Iteration 0

Iteration 2

Built a thread simulation in Replit in 30 minutes, despite no physics or coding background. Results are basic but eerily impressive. The last iteration to the first

Iteration 3

These two stages work hand-in-hand to create AI models that are both broad in understanding and precise in application. 🚀 (7/7)

- Examples: Fine-tuning LLaMA 3 specifically for summarization tasks.
In short:
- Pre-Training builds a model's general knowledge of language.
- Fine-Tuning tailors that knowledge to specific tasks or industries.
(6/n)

- Scope: Adjusts only the last layers of the pre-trained model to specialize it for new tasks, while retaining its foundational knowledge.
- Output: A customized model optimized for tasks like summarization, medical diagnosis, or legal analysis.
(5/n)