Code review is how professionals improve. You’re building professional skills.
✅ Better coding practices - Learn from others’ approaches ✅ Debugging skills - Find issues before they become problems ✅ Communication - Explain technical decisions clearly ✅ Humility - Accept that your code can always improve ✅ Confidence - Defend good choices, learn from mistakes
Companies use code review daily. Start practicing NOW.
Weekly Reviews (Required):
Project Reviews (Highly Recommended):
Ad-Hoc Reviews (Anytime):
Use this for: Quick questions, daily code snippets, brief feedback
Template:
📝 QUICK REVIEW REQUEST - Day X
Topic: [Brief description]
Language: Python | PyTorch | [Other]
Code: [GitHub link or snippet below]
Specific question: [What you want feedback on]
@username @username - Would love your eyes on this! 👀
Example:
📝 QUICK REVIEW REQUEST - Day 23
Topic: CNN architecture for image classification
Language: PyTorch
Code: https://github.com/yourname/100days/blob/main/day23_cnn.py
Specific question: Is my conv layer sizing correct? Getting dimension mismatch errors.
@alice @bob - Would love your eyes on this! 👀
Use this for: Weekly reviews, project reviews, comprehensive feedback
Template:
Title: [Week X Review] or [Day X Project Review] - [Topic]
---
## 📊 Context
**Day/Week:** X
**Topic:** [What you built]
**Time Spent:** X hours
**Difficulty Level:** [Easy/Medium/Hard]
## 🔗 Links
**GitHub Repo:** [Link]
**Specific File:** [Link to main file]
**Live Demo (if applicable):** [Link]
## 💻 What I Built
[2-3 sentence description of what the code does]
## 🎯 What I Want Feedback On
Please review:
1. [ ] Code structure and organization
2. [ ] Algorithm efficiency
3. [ ] Edge case handling
4. [ ] Documentation quality
5. [ ] PyTorch/NumPy best practices
6. [ ] Security issues
Specific concerns:
- [Concern 1]
- [Concern 2]
## 🤔 Specific Questions
1. [Question about approach]
2. [Question about alternative solution]
3. [Question about best practice]
## 📝 My Self-Assessment
**What I think I did well:**
- _______________
**What I'm unsure about:**
- _______________
**Known issues:**
- _______________
## 🙏 Looking for reviewers!
@username @username @username - Would appreciate your feedback!
Tag: #codereview #weekX #[topic]
Example:
Title: [Week 4 Review] - Image Classification CNN with PyTorch
---
## 📊 Context
**Week:** 4 (Days 22-28)
**Topic:** Convolutional Neural Networks for CIFAR-10
**Time Spent:** 15 hours
**Difficulty Level:** Hard
## 🔗 Links
**GitHub Repo:** https://github.com/john/100days-ai
**Specific File:** https://github.com/john/100days-ai/blob/main/day28_cnn_classifier.py
**Training Notebook:** [Colab link]
## 💻 What I Built
Built a CNN classifier for CIFAR-10 achieving 87% accuracy. Implemented custom architecture with 3 conv blocks, batch normalization, dropout, and data augmentation.
## 🎯 What I Want Feedback On
Please review:
1. [x] Code structure and organization
2. [x] Model architecture choices
3. [x] Training loop implementation
4. [x] Data augmentation strategy
5. [x] PyTorch best practices
Specific concerns:
- Is my learning rate schedule appropriate?
- Should I use more aggressive data augmentation?
## 🤔 Specific Questions
1. Is there a more efficient way to handle the data pipeline?
2. Should I use torch.nn.ModuleList for the conv blocks?
3. Any obvious bottlenecks in training speed?
## 📝 My Self-Assessment
**What I think I did well:**
- Clean separation of model, training, and evaluation code
- Good documentation with docstrings
- Proper use of GPU acceleration
**What I'm unsure about:**
- Whether my architecture is standard or overcomplicated
- If my regularization (dropout + weight decay) is too much
**Known issues:**
- Training takes 2 hours (seems slow?)
- Validation accuracy plateaus around epoch 40
## 🙏 Looking for reviewers!
@alice @bob @charlie - Would appreciate your feedback!
Tag: #codereview #week4 #cnn #pytorch
Review code the way you’d want YOUR code reviewed:
## 👀 Code Review - @username's [Week X / Day X Project]
**Reviewed by:** @yourname
**Date:** [Date]
---
### ✅ What You Did Well
1. **[Specific thing]:** [Why it's good]
2. **[Specific thing]:** [Why it's good]
3. **[Specific thing]:** [Why it's good]
---
### 🔧 Suggestions for Improvement
#### **1. [Issue/Area]**
**Current approach:**
```python
# Their code snippet
Suggested improvement:
# Your suggested code
Why: [Explanation of benefit]
Observation: [What you noticed]
Suggestion: [What to change]
Resources: [Link to relevant doc/article if applicable]
[Question about their approach]: Have you considered [alternative]? What was your reasoning for [choice]?
[Discussion point]: Interesting approach to [thing]. How does it perform compared to [standard approach]?
Must fix:
Should fix:
Nice to have:
Based on your code, you might find these helpful:
Code Quality: [Beginner / Intermediate / Advanced] Readiness: [Needs work / Good for learning / Production-ready]
Summary: [1-2 sentences of overall feedback]
Keep it up! 💪
---
### **Example Review:**
Reviewed by: @alice Date: 2025-01-15
Clean code organization: Your separation of concerns (model.py, train.py, utils.py) is excellent. This is production-level structure.
Proper GPU handling: You correctly check for CUDA availability and move tensors to device. Many beginners forget this.
Good documentation: Docstrings are clear and explain parameters well.
Current approach:
dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
Suggested improvement:
dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True)
dataloader = DataLoader(
dataset,
batch_size=32,
shuffle=True,
num_workers=4, # Add this
pin_memory=True # Add this for GPU
)
Why: num_workers=4 uses multiple processes for data loading (faster training). pin_memory=True speeds up CPU-to-GPU transfer. Should cut your training time in half.
Observation: You’re using a fixed learning rate of 0.001 for all epochs.
Suggestion: Try torch.optim.lr_scheduler.ReduceLROnPlateau - it automatically reduces LR when validation loss plateaus. This often breaks through accuracy plateaus.
Resources: https://pytorch.org/docs/stable/optim.html#torch.optim.lr_scheduler.ReduceLROnPlateau
Architecture choice: Why 3 conv blocks specifically? Have you experimented with 4 or 5? Curious about your thought process.
Data augmentation: You’re using RandomHorizontalFlip and RandomCrop - good! Have you tried adding ColorJitter? CIFAR-10 often benefits from color augmentation.
Must fix:
Should fix:
Nice to have:
Based on your code, you might find these helpful:
Code Quality: Intermediate (moving toward advanced!) Readiness: Good for learning, close to production-ready with suggested fixes
Summary: This is solid work. Your code structure and PyTorch usage are both strong. Main improvements are in training efficiency and learning rate management. You’re on the right track!
Keep it up! 💪
---
## 🎓 How to Receive Code Review
### **The Right Mindset:**
❌ **Wrong:** "They're criticizing my code. They think I'm bad at this."
✅ **Right:** "They're helping me improve. This is free mentorship."
### **How to Respond:**
**1. Always Thank the Reviewer**
Thanks @reviewer for the detailed feedback! Really appreciate you taking the time. 🙏
**2. Ask Clarifying Questions**
On point #2 about learning rate scheduling - should I implement this from scratch or use the PyTorch scheduler? Any specific scheduler you’d recommend for CNNs?
**3. Report Back After Implementing**
UPDATE: Implemented @alice’s suggestions!
✅ Added num_workers=4 to DataLoader ✅ Added ReduceLROnPlateau scheduler ✅ Added more data augmentation
Results:
Thanks again @alice - those changes made a huge difference!
**4. If You Disagree, Discuss Respectfully**
Thanks for the suggestion on [X]. I considered that approach but went with [Y] because [reasoning].
Do you think [Y] is problematic? Or is it just a preference thing?
Always open to learning if there’s something I’m missing!
---
## 🤝 Finding Review Partners
### **How to Build Your Review Network:**
**Week 1: Find 3 Accountability Partners**
Post in #100daysofaiengineer:
🤝 LOOKING FOR REVIEW PARTNERS
I’m on Day X of the challenge.
Looking for 2-3 people around the same level to:
My focus areas: [Python/PyTorch/CNNs/etc] My timezone: [Timezone] My commitment: Will review your code within 24 hours
Reply or DM if interested! 🙏
**Create a Review Squad:**
- Group of 3-5 people
- Private Discord thread or separate channel
- Commit to reviewing each other's code weekly
- Rotating schedule (everyone reviews at least one person per week)
---
## 📊 Review Metrics
### **Track Your Review Activity:**
In your GitHub repo, create `REVIEW_LOG.md`:
```markdown
# Code Review Log
## Reviews I've Given: X
| Date | Reviewer | Day/Week | Topic | Link |
|------|----------|----------|-------|------|
| 2025-01-10 | @alice | Week 2 | Pandas | [Link] |
| 2025-01-17 | @bob | Day 23 | CNN | [Link] |
## Reviews I've Received: X
| Date | Reviewer | Day/Week | Topic | Key Takeaway | Link |
|------|----------|----------|-------|--------------|------|
| 2025-01-08 | @charlie | Week 1 | NumPy | Use vectorization instead of loops | [Link] |
| 2025-01-15 | @alice | Week 2 | ML Pipeline | Add cross-validation | [Link] |
## Impact of Reviews
**Code improvements made from reviews:**
1. [Specific improvement from review]
2. [Specific improvement from review]
**Concepts I learned from reviewing others:**
1. [Concept learned]
2. [Concept learned]
✅ Focus on the code, not the person
✅ Be specific with examples
✅ Explain reasoning
✅ Acknowledge good work
✅ Ask questions
❌ Nitpick style preferences
❌ Rewrite their entire code
❌ Be condescending
❌ Only point out negatives
❌ Review when you don’t understand the topic
Friday/Saturday: Post your week’s code for review Saturday/Sunday: Review 2-3 others’ code Monday: Implement feedback and report back
This creates a rhythm:
📝 Week 1 Review Request
I'm brand new to NumPy. This is my first time using arrays seriously.
Code: [link to basic NumPy operations]
Please review:
- Am I using NumPy correctly or fighting against it?
- Are there obvious beginner mistakes I'm making?
Be gentle, but honest! I want to learn the right way. 🙏
📝 Week 7 Review Request
Built my first CNN from scratch (no transfer learning yet).
Code: [link to CNN implementation]
Please review:
- Is my architecture reasonable?
- Am I following PyTorch best practices?
- Any obvious inefficiencies?
I understand the concepts but want to make sure implementation is solid.
📝 Week 12 Review Request - Transfer Learning Project
Implemented fine-tuning pipeline with EfficientNet for custom dataset.
Code: [link to full project]
Please review:
- Model architecture decisions (unfrozen layers strategy)
- Training loop edge cases and error handling
- Code organization for production readiness
Looking for advanced feedback - be ruthless! 🔥
Track your review contribution:
Announce milestones in Discord:
🎉 Just hit 15 code reviews given! 🥈
I've learned SO MUCH by reviewing others' code. Seeing different approaches has improved my own coding significantly.
If you haven't requested a review yet - do it! And volunteer to review others. You learn by teaching.
#codereview #100daysofaiengineer
Code review is NOT optional.
It’s how professionals work. It’s how you’ll work in your AI Engineering job.
Start practicing now.
Request your first review this week. 💪
Give your first review to someone else. 🤝
Learn faster together. 🚀
Now go find someone’s code to review. Or post yours for review. 🔥