DEV Community: anesmeftah

RIP Fine-Tuning — The Rise of Self-Tuned AI

anesmeftah — Fri, 10 Oct 2025 12:09:54 +0000

Stanford just dropped a paper called Agentic Context Engineering (ACE) — and it might just end fine-tuning as we know it.

No retraining. No weights touched.
The model literally rewrites and evolves its own prompt, learning from every mistake and success.

🔥 The results:

🚀 +10.6% better than GPT-4 agents

💰 +8.6% on finance reasoning

⚡ −86.9% cost & latency

🧠 No labels. Just feedback

Everyone’s been chasing short, clean prompts.
ACE flips that idea completely.

Turns out, LLMs don’t want simplicity — they want context density.
They perform better when surrounded by rich, evolving information.

So yeah, the next wave of AI won’t be fine-tuned.
It’ll be self-tuned.

Welcome to the era of living prompts.

Python Tip: Use else with Loops Like a Pro

anesmeftah — Mon, 06 Oct 2025 17:19:31 +0000

Most people think else is only for if. But in Python, loops can have else too!

for i in range(5): if i == 10: print("Found it!") break else: print("Not found 😅")
Output: Not found 😅

Why it’s powerful:

Runs only if the loop didn’t break
Perfect for search algorithms
Cleaner than flags or extra variables

Use it to write more Pythonic, elegant code your loops just got smarter.

5 Free Books for Data Science & Machine Learning

anesmeftah — Sat, 04 Oct 2025 20:32:45 +0000

Looking to learn Data Science or Machine Learning for free? Here are 5 excellent books you can access online — covering Python, ML, Deep Learning, Bayesian methods, and Statistics.

1. Python Data Science Handbook

What it covers: NumPy, Pandas, Matplotlib, and introductory Machine Learning
Author: Jake VanderPlas
Link: jakevdp.github.io/PythonDataScienceHandbook A must-read for anyone starting with Python for data analysis.

2. Deep Learning

What it covers: Full deep learning theory and practice
Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville
Link: deeplearningbook.org This is the definitive deep learning book, used in many university courses.

3. Probabilistic Programming & Bayesian Methods for Hackers

What it covers: Bayesian thinking with Python and PyMC
Author: Cameron Davidson-Pilon
Link: GitHub A fun, practical guide to understanding Bayesian statistics through Python.

4. Dive into Deep Learning

What it covers: Hands-on deep learning with PyTorch and MXNet
Authors: Aston Zhang, Zachary C. Lipton, Mu Li, Alex Smola
Link: d2l.ai Perfect for learners who want practical coding examples alongside theory.

5. Think Stats

What it covers: Introduction to probability and statistics using Python
Author: Allen B. Downey
Link: greenteapress.com/wp/think-stats-2e/ Great for beginners to understand statistical thinking with Python examples.

💡 Tip: Bookmark these books and follow along with the exercises — learning by doing is the fastest way to become proficient.

Vision Transformer (ViT) from Scratch in PyTorch

anesmeftah — Thu, 02 Oct 2025 11:32:56 +0000

For years, Convolutional Neural Networks (CNNs) ruled computer vision. But since the paper “An Image is Worth 16x16 Words”, the Vision Transformer (ViT) has challenged CNNs by treating an image as a sequence of patches—similar to how words form a sentence.

In this post, we’ll walk through a PyTorch implementation of ViT, trained on a small food classification dataset (pizza, steak, sushi).

Core Idea

Split an image into fixed-size patches (e.g., 16×16).
Flatten patches into vectors → feed them as tokens.
Add:
- [CLS] Token → represents the entire image for classification.
- Positional Embeddings → retain spatial info.
Process the sequence with a Transformer Encoder.

ViT-Base Config

Image size: 224×224
Patch size: 16×16 → 196 tokens
Embedding dim: 768
Layers: 12
Attention heads: 12
Params: ~85.8M

Dataset

We used a 3-class dataset:

🍕 Pizza
🥩 Steak
🍣 Sushi

All images resized to 224×224.

Training Setup

Parameter	Value
Optimizer	Adam
Loss	CrossEntropyLoss
LR	0.001
Batch Size	32
Epochs	10
Device	GPU (CUDA)

Results

Training Loss → decreases fast (ViT is very powerful).
Validation Loss → may plateau or rise (overfitting risk).
Accuracy → Training near 100%, validation reflects true performance.

ViTs are large models. On small datasets, they overfit quickly. For real use, try pretrained ViTs + fine-tuning.

Takeaways

ViT proves attention works for vision, not just text.
Even a scratch implementation highlights the shift from pixels → patches → tokens.
Next steps:
- Try on larger datasets (CIFAR-100, ImageNet subset).
- Use pretrained weights (HuggingFace, timm).
- Experiment with augmentations (Mixup, CutMix).

Python Tip: Quickly Comment or Uncomment Multiple Lines

anesmeftah — Tue, 30 Sep 2025 14:52:23 +0000

Tired of typing # on every line in Python? Most editors (VS Code, PyCharm, Jupyter) have a shortcut that makes this instant:

Windows/Linux: Ctrl + /

Mac: Cmd + /

*How it works:
*

Highlight the lines you want to comment or uncomment.
Press the shortcut.
Done! Press again to remove the comments.

x = 10 y = 20 print(x + y)

Highlight and hit Ctrl + / → all lines get commented out in one go. Press again → they’re back!

A tiny shortcut, but a huge time-saver when debugging or experimenting.

Struggling to figure out what to learn next in tech?

anesmeftah — Mon, 29 Sep 2025 22:05:09 +0000

Check out roadmap.sh
Visual roadmaps for any role, from Frontend to AI Engineer.
Plan your path, track your progress, and never get lost in tutorials again!