How to write program in the age of AI

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# Lecture 2b: Programming in the Age of AI 🤖

@luk036 👨‍💻 · 2026 📅

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![The Earth Is Flat?](figs/the-earth-is-flat.png)

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### Essential Coding Tips 💡

-   **Test rigorously** 🧪
-   **Write clean, maintainable code** ✨
-   **Refactor repetitive code** 🔄
-   **Use object-oriented principles** 🧩
-   **Apply generic programming** 🧠
-   **Leverage design patterns** 🏗️
-   **Utilize coroutines** 🔄
-   **Study high-quality code** 📚
-   **Search online as a last resort** 🔍

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![AI and Programming](figs/programming-in-the-age-of-ai.svg)

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![Test, Test, Test!](figs/testtest.png)

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### AI-Powered Code Generation Tools 🤖

-   **AWS CodeWhisperer**
  -   Automated test generation
-   **Cody AI** (by Sourcegraph)
-   **Gemini CLI** (by Google)

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### 🤖 AI-Assisted Programming Workflow

.mermaid[
<pre>
graph TD
    A[Write Code] --> B[Run Tests]
    B -->|Pass| C[Refactor Code]
    B -->|Fail| D[Debug with AI Tools]
    D --> A
    C --> E[Code Review]
    E --> F[Merge & Deploy]
    style A fill:#e3f2fd,stroke:#1565c0,stroke-width:3px
    style B fill:#fff3e0,stroke:#e65100,stroke-width:3px
    style C fill:#c8e6c9,stroke:#2e7d32,stroke-width:3px
    style D fill:#f3e5f5,stroke:#7b1fa2,stroke-width:3px
    style E fill:#e3f2fd,stroke:#1565c0,stroke-width:3px
    style F fill:#c8e6c9,stroke:#2e7d32,stroke-width:3px
</pre>
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### ✏️ Naming Best Practices

-   **Avoid single-letter names** ❌
  Example: `a, i, p, n`
-   **Use meaningful names**:
  `x: unknown, x_axis`
-   **Standard coordinate variables**:
  `x, y, z`

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### Improved Naming Conventions 📛

-   **p**: point, polygon, polynomial, previous
-   **t**: time, target, temporary
-   **c**: cost, cycle, coefficient
-   **d**: distance, distribution
-   **e**: edge
-   **v**: vertex
-   **u, v, w**: vertex1, vertex2, vertex3
-   **i**: index
-   **i, j**: row, column
-   **i, j, k**: indices
-   **l, m**: line1, line2
-   **n**: dimension, node, next
-   **n, m**: ndim, mdim
-   **w**: weight, frequency (omega)

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### Performance Optimization Tips 🚀

-   **Avoid string comparisons** 🚫
-   **Use sentinel values** 🛡️
-   **Prefer cheaper computations** (avoid `sqrt()`, `sin()`, `cos()`) ⚡
-   **Apply lazy evaluation** 🦥
-   **Use table lookups** 📚
-   **Avoid linear searches**:
  -   Use backward pointers 🔙
  -   Use hash tables, dictionaries, or maps

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### Avoid String Comparisons 🚫

**Inefficient Approach** 👎

```python
if pin == "input":
    # ...
elif pin == "output":
    # ...
elif pin == "in_out":
    # ...
elif pin == "dont_care":
    # ...
else:
    # ...
```

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**Optimized Approach** ⚡

```python
pin_type = {
    "input": 0,
    "output": 1,
    "in_out": 2,
    "dont_care": 3
}
id = pin_type.get(pin, -1)

if id == 0:
    # ...
elif id == 1:
    # ...
else:
    # ... (handle other cases)
```

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### Sentinel Pattern 🛡️

**Less Efficient** 👎

.font-sm.mb-xs[

```python
max = 0
bckt = [Dllist() for _ in range(high)]
# ...
def popleft():
    res = bckt[max].popleft()
    while max >= 0 and bckt[max].empty():
        max -= 1
    return res
```

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**Optimized with Sentinel** ⚡

.font-sm.mb-xs[

```python
max = 0
sentinel = Dllink()
bckt = [Dllist() for _ in range(high+1)]
bckt[0].append(sentinel)  # Sentinel

def popleft():
    res = bckt[max].popleft()
    while bckt[max].empty():
        max -= 1
    return res
# No boundary check needed
```

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### Optimize Computations ⚡

**Inefficient** 👎

```python
mind = 10000
maxd = 0
for u, v in G.edges():
    t = vec[u] - vec[v]
    d = sqrt(t.dot(t))
    if mind > d: mind = d
    if maxd < d: maxd = d
return maxd - mind
```

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**Optimized** ⚡

```python
minq = 10000
maxq = 0
for u, v in G.edges():
    t = vec[u] - vec[v]
    q = t.dot(t)  # Cheaper operation
    if minq > q: minq = q
    if maxq < q: maxq = q
return sqrt(maxq) - sqrt(minq)
```

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### Another Optimization Example ⚡

**Inefficient** 👎

```python
mind = 10000
maxd = 0
for u, v in G.edges():
    t = 1 - vec[u].dot(vec[v])
    d = arcsin(sqrt(t))
    if mind > d: mind = d
    if maxd < d: maxd = d
return maxd - mind
```

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**Optimized** ⚡

```python
minq = 10000
maxq = 0
for u, v in G.edges():
    q = 1 - vec[u].dot(vec[v])
    if minq > q: minq = q
    if maxq < q: maxq = q
return arcsin(sqrt(maxq)) \
       - arcsin(sqrt(minq))
```

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## Advanced Optimization Techniques 🧮

-   **Convex optimization** 🥚
-   **Network optimization** 🖧
-   **Primal-dual methods** ☯

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# Q&A 🎤

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![Q&A](figs/questions-and-answers.svg)

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