78 commits, 4 PRs, and 1 review across 9 repos. This week was a deep dive into network topology simulations and building a more persistent AI coding experience with the new /resume command.
Published Links
Week at a Glance
| Metric |
Count |
| Commits |
78 |
| Pull Requests |
4 |
| Issues |
0 |
| Code Reviews |
1 |
| Discussions |
0 |
| Lines Added |
+21,632 |
| Lines Removed |
-6,597 |
| Streak |
7 days |
Active Repositories
Pull Requests
Code Reviews
Languages
| Language |
Commits |
| Python |
77268658 |
| TypeScript |
15450791 |
| Rust |
9719679 |
| C# |
4170461 |
| HTML |
1909331 |
| MDX |
1825559 |
| Twig |
1525701 |
| Shell |
1262184 |
Blog Post
TL;DR
It’s been one of those weeks where the terminal never really closed. I clocked 78 commits and pushed 4 PRs across 9 different repositories, maintaining a perfect 7-day streak. The bulk of the heavy lifting happened in the P2P space—specifically simulating network attacks—and adding some much-needed session persistence to my AI coding tool, nanocoder. With over 21,000 lines added and about 6,500 deleted, it was a high-output week focused on building out complex systems and then immediately refining them.
WHAT I BUILT
The star of the show this week was definitely P2P-Attack-Simulation. I’ve been obsessed with how decentralized networks handle adversarial conditions, and I finally merged a massive PR for Topology ts simulation. We’re talking 18,756 additions and 6,436 deletions. I essentially rewrote how the network topology is represented in TypeScript to make it more modular. Before, the simulation was a bit too rigid; now, I can spin up different node behaviors and see how the gossip protocols hold up when half the network starts acting malicious. It’s one thing to read about Sybil attacks in a paper, but seeing the message propagation latency spike in your own simulation is a different kind of satisfying.
Over in nanocoder, I spent a good chunk of time on the developer experience. If you’ve used AI coding assistants, you know the pain of losing context or having to restart a session from scratch. I implemented a new Feature /resume command to continue session. This involved a fair bit of state management (about 2,300 lines of code) to ensure that when you come back to a project, the AI knows exactly where you left off. It’s all about reducing friction. I want to spend my time coding, not re-explaining my file structure to a LLM.