v2.0.3 — Available now

Control Your
AI Conversations

F9 Flow transforms chaotic AI chats into a clean, navigable timeline. One key. Total control.

F9 Flow — Timeline Live
USER · 01
Explain transformer architecture in detail...
USER · 02
Write me a Python implementation...
USER · 03
Now optimize it for inference speed...

Three
keystrokes
to clarity

01
F9

Trigger the layer

Press F9 anywhere on a supported AI platform. No mouse required, no context switching.

02
«

Sidebar appears

A minimal panel slides in — your entire conversation mapped as a clean timeline.

03

Jump instantly

Click any node or use arrow keys to jump directly to that moment in the conversation.

Built for
developers
who ship fast

Every feature is deliberate. Nothing is there by accident.

Lightning Navigation

Jump to any point in a 200-message conversation in under 100ms. No scrolling. No hunting.

Command Layer UI

Sits on top of any AI platform as a transparent control layer — non-destructive, always accessible.

Adaptive Theme

Automatically inherits light or dark mode from the host platform. Zero configuration.

Custom Hotkeys

Remap F9 to anything. Configure shortcuts for open, close, navigate, and jump.

See the timeline in action

Click any node in the sidebar to instantly jump to that message. Just like the real extension.

ChatGPT — Transformer Architecture
F9 Flow Active
You
Explain transformer architecture in detail. What makes it different from RNNs and CNNs for sequence processing tasks?
ChatGPT
The Transformer architecture, introduced in "Attention Is All You Need" (2017), revolutionized NLP by replacing recurrence with self-attention mechanisms.

Key differences from RNNs:
Parallelization: Unlike RNNs which process tokens sequentially, Transformers process all positions simultaneously
Long-range dependencies: Self-attention directly connects any two positions regardless of distance
No vanishing gradients: Attention weights create direct gradient paths

The core consists of multi-head attention, position-wise feed-forward networks, and layer normalization with residual connections.
You
Write me a Python implementation of a basic transformer block with multi-head attention.
ChatGPT
python
class TransformerBlock(nn.Module):
                                        def __init__(self, d_model, n_heads):
                                            super().__init__()
                                            self.attn = MultiHeadAttention(d_model, n_heads)
                                            self.ff = FeedForward(d_model)
                                            self.ln1 = nn.LayerNorm(d_model)
                                            self.ln2 = nn.LayerNorm(d_model)
                                    
                                        def forward(self, x, mask=None):
                                            x = x + self.attn(self.ln1(x))
                                            x = x + self.ff(self.ln2(x))
                                            return x
This implements the pre-norm variant of the transformer block, which tends to train more stably.
You
Now optimize it for inference speed using quantization and other techniques.
ChatGPT
For inference optimization, here are the key strategies:

1. INT8 Quantization
Reduce weights from FP32 to INT8, achieving ~4x memory reduction with minimal accuracy loss.

2. KV-Cache
Cache key/value pairs from previous tokens to avoid redundant computation.

3. Flash Attention
Use tiled computation to reduce memory I/O from O(N²) to O(N).

4. Model Pruning
Remove redundant attention heads — research shows 30-50% can be pruned without significant quality loss.
Timeline · 3 nodes
USER · 01
Explain transformer architecture in detail...
USER · 02
Write me a Python implementation...
USER · 03
Optimize it for inference speed...
Click any node to jump

Works where
you work

ChatGPT
ChatGPT
Claude
Claude
Gemini
Gemini
Perplexity
Perplexity
Grok
Grok
Microsoft Copilot
Copilot
DeepSeek
DeepSeek
Mistral
Mistral
Manus
Manus

Up in 60
seconds flat

No account. No telemetry. No nonsense.

1

Download the extension

Clone or download from GitHub. No build step required — it's pure JS.

2

Load unpacked in Chrome

Open chrome://extensions, enable Developer Mode, click Load Unpacked.

3

Navigate to any AI platform

ChatGPT, Claude, Gemini — any supported URL is detected automatically.

4

Press F9 and you're done

The timeline sidebar opens. Click any node to jump instantly.