AI Technology | Claude & ChatGPT Prompts
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Best AI and ML Resources on Telegram
Get free resources & trending updates for Artificial intelligence (AI) technology.
Admin: @mani3721”
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پستهای کانال
🚀 7 Real-World Python Projects You Can Build in 2026
1. AI Scam & Notice Checker
Detect scam SMS, phishing messages, and fake notices with AI.
📖 https://huggingface.co
2. Multi-Agent Research Assistant
Build AI agents that research the web and generate reports.
📖 https://machinelearningmastery.com
3. Breast Cancer Prediction API
Train an ML model and deploy it with FastAPI.
📖 https://machinelearningmastery.com
4. AI Market Research Dashboard
Automate market research and trend analysis using AI.
📖 https://www.olostep.com/blog/agentic-market-research-olostep
5. Recycling Data Analysis
Analyze recycling data and create insightful visualizations.
📖 https://towardsdatascience.com
6. AI Resume & Job Match Analyzer
Match resumes with jobs and identify skill gaps.
📖 https://www.datacamp.com/tutorial/kimi-k2-6-api-tutorial
7. AI Data Analysis Report Generator
Generate charts, insights, and reports from datasets with AI.
📖 https://www.datacamp.com/tutorial/gemini-3-api-tutorial
❤️ Follow AIJobs for more AI drops
| 2 | 7 Best Small Language Models Under 10B Parameters in 2026
1. IBM Granite 4.1 8B
With industry-leading coding performance and a massive context window, Granite 4.1 8B is built for enterprise applications, RAG systems, and tool-calling workflows.
🔗 Click Here: https://huggingface.co/ibm-granite/granite-4.1-8b-instruct
2. Qwen3.5-9B
One of the strongest reasoning models under 10B parameters, Qwen3.5-9B excels in multilingual tasks, science QA, and advanced problem-solving.
🔗 Click Here: https://huggingface.co/Qwen/Qwen3.5-9B-Instruct
3. Gemma 4 E4B
Google's Gemma 4 E4B is optimized for AI agents, tool calling, and edge deployment, delivering powerful performance with minimal hardware requirements.
🔗 Click Here: https://huggingface.co/google/gemma-4-e4b-it
4. Qwen3-8B
A proven favorite for developers, Qwen3-8B offers excellent code generation capabilities and supports more than 29 languages.
🔗 Click Here: https://huggingface.co/Qwen/Qwen3-8B
5. DeepSeek-R1-Distill-Qwen-7B
Built for mathematical reasoning and logical thinking, this compact model delivers exceptional step-by-step problem-solving performance.
🔗 Click Here: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
6. Phi-4-mini
Microsoft's Phi-4-mini packs impressive AI capabilities into just 3.8B parameters, making it ideal for laptops and low-resource environments.
🔗 Click Here: https://huggingface.co/microsoft/Phi-4-mini-instruct
7. Llama 3.1 8B Instruct
Llama 3.1 8B remains one of the most versatile open-source models, backed by a massive ecosystem of fine-tunes and community support.
🔗 Click Here: https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct
🔥 Which small LLM is your favorite in 2026?
❤️ Follow AIJobs for more AI drops | 3 054 |
| 3 | 📚 5 Must-Read Papers to Understand LLMs
1️⃣ Attention Is All You Need
📝 Description: Introduced the Transformer, the architecture behind every modern LLM. Replaced older recurrent/convolutional models for sequences.
🔑 Key Ideas: Self-attention • Multi-head attention • Positional encoding • Transformer block
🔗 Paper: https://arxiv.org/abs/1706.03762
━━━━━━━━━━━━━━━
2️⃣ Language Models Are Few-Shot Learners
📝 Description: The GPT-3 paper. One 175B model handles many tasks just by reading prompts — no retraining.
🔑 Key Ideas: In-context learning • Few-shot prompting • Autoregressive next-token prediction
🔗 Paper: https://arxiv.org/abs/2005.14165
━━━━━━━━━━━━━━━
3️⃣ Scaling Laws for Neural Language Models
📝 Description: Showed model performance improves predictably as parameters, data & compute grow. The logic behind going big.
🔑 Key Ideas: Scaling laws • Compute-optimal training • Data vs. model size tradeoffs
🔗 Paper: https://arxiv.org/abs/2001.08361
━━━━━━━━━━━━━━━
4️⃣ Training LMs to Follow Instructions with Human Feedback
📝 Description: The InstructGPT paper. Turns a raw text predictor into a helpful, instruction-following assistant.
🔑 Key Ideas: RLHF • Supervised fine-tuning • Reward model • Human preference ranking
🔗 Paper: https://arxiv.org/abs/2203.02155
━━━━━━━━━━━━━━━
5️⃣ Retrieval-Augmented Generation (RAG)
📝 Description: LLMs fetch external documents instead of relying only on stored memory — great for facts that change over time.
🔑 Key Ideas: Dense retrieval • Document index • Grounded generation • Knowledge-intensive QA
🔗 Paper: https://arxiv.org/abs/2005.11401
━━━━━━━━━━━━━━━
❤️ Follow AIJobs for more AI drops | 3 517 |
| 4 | 5 Must-Know Python Concepts for AI Engineers
1. 🔥 Tensors & Autograd
Stop writing backprop by hand. requires_grad=True tracks every operation → .backward() applies the chain rule automatically.
import torch
x = torch.tensor(2.0)
y = torch.tensor(5.0)
w = torch.tensor(0.5, requires_grad=True)
b = torch.tensor(0.1, requires_grad=True)
pred = w * x + b
loss = (pred - y) ** 2
loss.backward()
print(w.grad.item(), b.grad.item())
✅ Exact gradients, zero math errors.
2. ⚙️ The __call__ Method
Why model(x) works, not model.forward(x). call runs hooks before forward.
class LinearLayer:
def __init__(self, w, b):
self.w, self.b = w, b
self._hooks = []
def __call__(self, x):
for hook in self._hooks:
hook(x)
return self.forward(x)
def forward(self, x):
return x * self.w + self.b
⚠️ Always call model(x) — .forward() skips hooks → silent bugs.
3. 💾 Pickle vs ONNX
pickle = Python-locked + code execution risk 🚨. ONNX = static, language-agnostic graph.
import torch
model.eval()
dummy_input = torch.randn(1, 10)
torch.onnx.export(
model, dummy_input, "model.onnx",
export_params=True,
opset_version=15,
input_names=["input"],
output_names=["output"],
dynamic_axes={"input": {0: "batch_size"}}
)
✅ Portable, fast, decoupled from training code.
4. 🧱 Abstract Base Classes
@abstractmethod forces subclasses to implement methods. Miss one → fails at startup, not mid-request.
from abc import ABC, abstractmethod
class ModelInterface(ABC):
@abstractmethod
def predict(self, x: list) -> list: ...
@abstractmethod
def get_metadata(self) -> dict: ...
✅ Fail fast, fail safe.
5. 🔐 Env Variables & Secrets
Never hardcode keys. Store in .env, gitignore it, load with python-dotenv.
import os
from dotenv import load_dotenv
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
raise ValueError("OPENAI_API_KEY is not set!")
✅ Same code locally + Docker/Lambda. Zero leaks.
❤️ Follow AIJobs for more AI drops | 2 622 |
| 5 | 7 Real World AI Projects to Build in 2026
🤖 Build an AI Job Search Assistant
Searching for jobs is repetitive — JobFit AI reads your CV, searches live postings, and generates a ranked job-fit report automatically.
📖 Guide: Kimi K2.6 API Tutorial
🐙 GitHub: kingabzpro/JobFit-AI
🔬 Build a Multi-Agent Research Assistant
Most research workflows involve several steps — this multi-agent system handles web search, source filtering, and report writing all in one pipeline.
📖 Guide: Multi-Agent Research Assistant in Python
🐙 GitHub: Multi-Agent-Research-Assistant
📈 Automate Investment Research with Olostep and n8n
Investment research means checking news, financials, and public sources — this workflow automates the entire process and delivers AI-generated reports.
📖 Guide: How to Automate Investment Research Using Olostep and n8n
🐙 GitHub: kingabzpro/olostep-n8n-investment-agent
📊 Build an Agentic Market Research and Trend Analysis App
Manually collecting competitor updates and trend reports takes hours — this agentic pipeline handles research, extraction, and brief writing automatically.
📖 Guide: Agentic Market Research & Trend Analysis with Olostep
🐙 GitHub: kingabzpro/agentic-market-research-olostep
🧾 Build an AI Invoice Processing Pipeline
Invoice processing combines document understanding and structured extraction — this pipeline uses vision AI to pull useful fields and output clean structured data.
📖 Guide: Qwen 3.6 Plus API Tutorial
🐙 GitHub: BexTuychiev/qwen-invoice-pipeline-tutorial
📉 Build a Chart Digitizer with Claude Opus 4.7
Visual data trapped inside static charts and PDFs is now extractable — this tool reads chart images and saves the data points into a clean CSV or DataFrame.
📖 Guide: Building a Chart Digitizer
🏋️ Build an Exercise Trainer with Persistent Memory
Most AI agents forget everything after a session — this exercise trainer remembers your workout history and suggests personalized sessions every time you run it.
📖 Guide: Add Persistent Memory to AI Agents
❤️ Follow AIJobs for more AI drops | 2 865 |
| 6 | 10 Python Libraries for Building LLM Applications
🔹 1. Transformers
Core library for loading, fine-tuning, and running LLMs with ease.
👉 Learn more: https://huggingface.co/docs/transformers
🔹 2. LangChain
Connect prompts, tools, APIs, and models into powerful workflows.
👉 Learn more: https://docs.langchain.com
🔹 3. LlamaIndex
Bring your own data into LLMs for smarter, grounded responses (RAG).
👉 Learn more: https://docs.llamaindex.ai
🔹 4. vLLM
High-performance LLM serving with faster inference and better scaling.
👉 Learn more: https://docs.vllm.ai
🔹 5. Unsloth
Efficient fine-tuning with LoRA & QLoRA — even on limited hardware.
👉 Learn more: https://github.com/unslothai/unsloth
🔹 6. CrewAI
Build multi-agent systems where AI agents collaborate on tasks.
👉 Learn more: https://docs.crewai.com
🔹 7. AutoGPT
Create goal-driven autonomous agents with step-by-step execution.
👉 Learn more: https://github.com/Significant-Gravitas/AutoGPT
🔹 8. LangGraph
Design advanced, stateful workflows with branching logic.
👉 Learn more: https://docs.langchain.com/langgraph
🔹 9. DeepEval
Test and evaluate LLM outputs for accuracy and reliability.
👉 Learn more: https://github.com/confident-ai/deepeval
🔹 10. OpenAI Python SDK
Quickly integrate powerful AI features without managing infrastructure.
👉 Learn more: https://platform.openai.com/docs
❤️ Follow AIJobs for more AI drops | 2 476 |
| 7 | Learn AI for free directly from top companies
1. Anthropic:
anthropic.skilljar.com
2 - Google:
grow.google/ai
3 - Meta:
ai.meta.com/resources/
4 - NVIDIA:
developer.nvidia.com/cuda
5 - Microsoft:
learn.microsoft.com/en-us/training/
6 - OpenAI:
academy.openai.com
7 - IBM:
skillsbuild.org
AWS:
skillbuilder.aws
9 - DeepLearning.AI:
deeplearning.ai
10 - Hugging Face:
huggingface.co/lear | 2 741 |
