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+# Paper Explainer Workflow
+
+How to turn a research paper into an animated explainer video.
+
+## Why animate a paper?
+
+A research paper is optimized for precision and completeness. A video is optimized for understanding and retention. The translation is NOT "read the paper aloud with pictures" — it's "extract the core insight and make it feel obvious through visual storytelling."
+
+The paper has one job: prove the claim is true. The video has a different job: make the viewer understand WHY the claim is true, and WHY it matters.
+
+## Who is watching?
+
+Before anything, decide the audience:
+
+| Audience | Prerequisites | Pacing | Depth |
+|----------|--------------|--------|-------|
+| General public | None | Slow, many analogies | Intuition only, skip proofs |
+| Undergrad students | Basic math/CS | Medium, some formalism | Key equations, skip derivations |
+| Grad students / researchers | Domain knowledge | Faster, more notation | Full equations, sketch proofs |
+
+This determines everything: vocabulary, pacing, which sections to animate, how much math to show.
+
+## The 5-minute template
+
+Most paper explainers fit this structure (scale times proportionally for longer videos):
+
+| Section | Duration | Purpose |
+|---------|----------|---------|
+| **Hook** | 0:00-0:30 | Surprising result or provocative question |
+| **Problem** | 0:30-1:30 | What was broken/missing before this paper |
+| **Key insight** | 1:30-3:00 | The core idea, explained visually |
+| **How it works** | 3:00-4:00 | Method/algorithm, simplified |
+| **Evidence** | 4:00-4:30 | Key result that proves it works |
+| **Implications** | 4:30-5:00 | Why it matters, what it enables |
+
+### What to skip
+
+- Related work survey → one sentence: "Previous approaches did X, which had problem Y"
+- Implementation details → skip unless they're the contribution
+- Ablation studies → show one chart at most
+- Proofs → show the key step, not the full proof
+- Hyperparameter tuning → skip entirely
+
+### What to expand
+
+- The core insight → this gets the most screen time
+- Geometric/visual intuition → if the paper has math, show what it MEANS
+- Before/after comparison → the most compelling evidence
+
+## Pre-code workflow
+
+### Gate 1: Narration script
+
+Write the full narration before any code. Every sentence maps to a visual beat. If you can't write the narration, you don't understand the paper well enough to animate it.
+
+```markdown
+## Hook (30s)
+"What if I told you that a model with 7 billion parameters can outperform
+one with 70 billion — if you train it on the right data?"
+
+## Problem (60s)
+"The standard approach is to scale up. More parameters, more compute.
+[VISUAL: bar chart showing model sizes growing exponentially]
+But Chinchilla showed us that most models are undertrained..."
+```
+
+### Gate 2: Scene list
+
+After the narration, break it into scenes. Each scene is one Manim class.
+
+```markdown
+Scene 1: Hook — surprising stat with animated counter
+Scene 2: Problem — model size bar chart growing
+Scene 3: Key insight — training data vs parameters, animated 2D plot
+Scene 4: Method — pipeline diagram building left to right
+Scene 5: Results — before/after comparison with animated bars
+Scene 6: Closing — implications text
+```
+
+### Gate 3: Style constants
+
+Before coding scenes, define the visual language:
+
+```python
+# style.py — import in every scene file
+BG = "#0D1117"
+PRIMARY = "#58C4DD"
+SECONDARY = "#83C167"
+ACCENT = "#FFFF00"
+HIGHLIGHT = "#FF6B6B"
+MONO = "Menlo"
+
+# Color meanings for THIS paper
+MODEL_COLOR = PRIMARY      # "the model"
+DATA_COLOR = SECONDARY     # "training data"
+BASELINE_COLOR = HIGHLIGHT # "previous approach"
+RESULT_COLOR = ACCENT      # "our result"
+```
+
+## First-principles equation explanation
+
+When the paper has a key equation, don't just show it — build it from intuition:
+
+### The "what would you do?" pattern
+
+1. Pose the problem in plain language
+2. Ask what the simplest solution would be
+3. Show why it doesn't work (animate the failure)
+4. Introduce the paper's solution as the fix
+5. THEN show the equation — it now feels earned
+
+```python
+# Scene: Why we need attention (for a Transformer paper)
+# Step 1: "How do we let each word look at every other word?"
+# Step 2: Show naive approach (fully connected = O(n²) everything)
+# Step 3: Show it breaks (information overload, no selectivity)
+# Step 4: "What if each word could CHOOSE which words to attend to?"
+# Step 5: Show attention equation — Q, K, V now mean something
+```
+
+### Equation reveal strategy
+
+```python
+# Show equation dimmed first (full destination)
+eq = MathTex(r"Attention(Q,K,V) = softmax\left(\frac{QK^T}{\sqrt{d_k}}\right)V")
+eq.set_opacity(0.15)
+self.play(FadeIn(eq))
+
+# Highlight Q, K, V one at a time with color + label
+for part, color, label_text in [
+    (r"Q", PRIMARY, "Query: what am I looking for?"),
+    (r"K", SECONDARY, "Key: what do I contain?"),
+    (r"V", ACCENT, "Value: what do I output?"),
+]:
+    eq.set_color_by_tex(part, color)
+    label = Text(label_text, font_size=18, color=color, font=MONO)
+    # position label, animate it, wait, then dim it
+```
+
+## Building architecture diagrams
+
+### The progressive build pattern
+
+Don't show the full architecture at once. Build it:
+
+1. First component appears alone → explain
+2. Arrow grows → "this feeds into..."
+3. Second component appears → explain
+4. Repeat until complete
+
+```python
+# Component factory
+def make_box(label, color, width=2.0, height=0.8):
+    box = RoundedRectangle(corner_radius=0.1, width=width, height=height,
+                           color=color, fill_opacity=0.1, stroke_width=1.5)
+    text = Text(label, font_size=18, font=MONO, color=color).move_to(box)
+    return Group(box, text)
+
+encoder = make_box("Encoder", PRIMARY)
+decoder = make_box("Decoder", SECONDARY).next_to(encoder, RIGHT, buff=1.5)
+arrow = Arrow(encoder.get_right(), decoder.get_left(), color=DIM, stroke_width=1.5)
+
+self.play(FadeIn(encoder))
+self.wait(1)  # explain encoder
+self.play(GrowArrow(arrow))
+self.play(FadeIn(decoder))
+self.wait(1)  # explain decoder
+```
+
+### Data flow animation
+
+After building the diagram, show data moving through it:
+
+```python
+# Dot traveling along the pipeline
+data_dot = Dot(color=ACCENT, radius=0.1).move_to(encoder)
+self.play(FadeIn(data_dot))
+self.play(MoveAlongPath(data_dot, arrow), run_time=1)
+self.play(data_dot.animate.move_to(decoder), run_time=0.5)
+self.play(Flash(data_dot.get_center(), color=ACCENT), run_time=0.3)
+```
+
+## Animating results
+
+### Bar chart comparison (most common)
+
+```python
+# Before/after bars
+before_data = [45, 52, 38, 61]
+after_data = [78, 85, 72, 91]
+labels = ["Task A", "Task B", "Task C", "Task D"]
+
+before_chart = BarChart(before_data, bar_names=labels,
+    y_range=[0, 100, 20], bar_colors=[HIGHLIGHT]*4).scale(0.6).shift(LEFT*3)
+after_chart = BarChart(after_data, bar_names=labels,
+    y_range=[0, 100, 20], bar_colors=[SECONDARY]*4).scale(0.6).shift(RIGHT*3)
+
+before_label = Text("Baseline", font_size=20, color=HIGHLIGHT, font=MONO)
+after_label = Text("Ours", font_size=20, color=SECONDARY, font=MONO)
+
+# Reveal baseline first, then ours (dramatic comparison)
+self.play(Create(before_chart), FadeIn(before_label))
+self.wait(1.5)
+self.play(Create(after_chart), FadeIn(after_label))
+self.wait(0.5)
+
+# Highlight the improvement
+improvement = Text("+35% avg", font_size=24, color=ACCENT, font=MONO)
+self.play(FadeIn(improvement))
+```
+
+### Training curve (for ML papers)
+
+```python
+tracker = ValueTracker(0)
+curve = always_redraw(lambda: axes.plot(
+    lambda x: 1 - 0.8 * np.exp(-x / 3),
+    x_range=[0, tracker.get_value()], color=PRIMARY
+))
+epoch_label = always_redraw(lambda: Text(
+    f"Epoch {int(tracker.get_value())}", font_size=18, font=MONO
+).to_corner(UR))
+
+self.add(curve, epoch_label)
+self.play(tracker.animate.set_value(10), run_time=5, rate_func=linear)
+```
+
+## Domain-specific patterns
+
+### ML papers
+- Show data flow through the model (animated pipeline)
+- Training curves with `ValueTracker`
+- Attention heatmaps as colored grids
+- Embedding space as 2D scatter (PCA/t-SNE visualization)
+- Loss landscape as 3D surface with gradient descent dot
+
+### Physics/math papers
+- Use `LinearTransformationScene` for linear algebra
+- Vector fields with `ArrowVectorField` / `StreamLines`
+- Phase spaces with `NumberPlane` + trajectories
+- Wave equations with time-parameterized plots
+
+### Systems/architecture papers
+- Pipeline diagrams built progressively
+- `ShowPassingFlash` for data flow along arrows
+- `ZoomedScene` for zooming into components
+- Before/after latency/throughput comparisons
+
+## Common mistakes
+
+1. **Trying to cover the whole paper.** A 5-minute video can explain ONE core insight well. Covering everything means explaining nothing.
+2. **Reading the abstract as narration.** Academic writing is designed for readers, not listeners. Rewrite in conversational language.
+3. **Showing notation without meaning.** Never show a symbol without first showing what it represents visually.
+4. **Skipping the motivation.** Jumping straight to "here's our method" without showing why the problem matters. The Problem section is what makes the viewer care.
+5. **Identical pacing throughout.** The hook and key insight need the most visual energy. The method section can be faster. Evidence should land with impact (pause after showing the big number).