To put theory into practice, the professors have designed eight assignments for this course (based on Java). These assignments cover the core concepts of the course. More importantly, they have also set up an Online Judge where you can submit your code and get instant feedback (note: the OJ is currently open only to users with educational email addresses).
These assignments are exceptionally well-crafted, featuring a clever, progressive structure. The entire learning path is like a well-designed “skill tree”:
- The Constant Propagation algorithm you implement in Assignment 2 becomes the foundation for Dead Code Detection in Assignment 3.
- Assignment 4 builds on this, challenging you with the more complex Interprocedural Constant Propagation, where you begin to analyze real-world method calls.
- Next, Assignments 5 and 6 guide you through implementing two different precisions of Pointer Analysis — one of the core challenges in the field.
- Finally, in Assignment 7, you will use the algorithm from Assignment 6 to improve the precision of your earlier constant propagation analysis.
Since nowadays we have many input sources, e.g. Twitter, newsletters, blog RSS feeds, etc. They're already overwhelming for me. Browsing the "newest" feed has a very low signal-to-noise ratio.
This article contains my observations and thoughts as an active user in the YouWare community. During this time, my perspective has gone through an interesting evolution: from initial excitement about Vibe Coding, to a period of deep immersion in creating, and now to a more calm and considered view of the platform’s ecosystem.
In fact, their approach to using vibe coding in production comes with many restrictions and requirements. For example:
1. Acting as Claude's product manager (e.g., asking the right questions)
2. Using Claude to implement low-dependency leaf nodes, rather than core infrastructure systems that are widely relied upon
3. Verifiability (e.g., testing)
BTW, their argument for the necessity of vibe coding does make some sense:
As AI capabilities grow exponentially, the traditional method of reviewing code line by line won’t scale. We need to find new ways to validate and manage code safely in order to harness this exponential advantage.
First of all, there are certainly many issues with abusing vibe coding in a production environment. I think the core problem is that the code can't be reviewed. After all, it's ultimately people who are responsible for the code.
However, not all code requires the same quality standards (think perfectionism). The tools in this project are like blog posts written by an individual that haven’t been reviewed by others, while an ASF open-source project is more like a peer-reviewed article. I believe both types of projects are valid.
Moreover, this kind of project is like a cache. If no one else writes it, I might want to quickly vibe-code it myself. In fact, without vibe coding, I might not even do it at all due to time constraints. It's totally reasonable to treat this project as a rough draft of an idea. Why should we apply the same standards to every project?
I’ve always wanted to do an analysis of how I use Claude Code. Then I found out that someone had already done it. I looked at my own usage data, and for now, there’s nothing particularly interesting. I wonder if anyone has any ideas.
We’re caught in a paradox: the more information overwhelms us, the more we rely on summaries. Yet every summary is just a slice of the original, shaped by presets and biases.
If our attention is the most valuable capital we have, then each time we read, we are making an investment. How do we ensure a higher return on that investment?
My proposed approach is this: to view trust as a cognitive abstraction. This means shifting our investment from uncertain content to more reliable people. Let’s explore this mental model.
TL;DR — This post is a deep reflection on insights shared by YouWare’s founder (a user-generated software platform) during a recent podcast conversation. It explores what it really means to build AI-native products, rather than simply wrapping LLMs in traditional software shells.
Key ideas:
- Do less, align with the biggest variable — AI: feature restraint is a virtue when model progress is the true leverage.
- Token speed as a core metric: DAU may mislead; the faster and more effectively a product consumes tokens, the more tightly it aligns with model intelligence.
- Wrappers vs. Containers: great AI products don't just function — they shape user behavior and learning (e.g., Midjourney's use of Discord).
- Coding as a new creative medium: coding should evolve from expert work to point-and-shoot creativity, like photography did.
Curious to hear thoughts from others building AI-native tools — or grappling with similar product trade-offs. Really, any thoughts are welcome.
These assignments are exceptionally well-crafted, featuring a clever, progressive structure. The entire learning path is like a well-designed “skill tree”:
- The Constant Propagation algorithm you implement in Assignment 2 becomes the foundation for Dead Code Detection in Assignment 3.
- Assignment 4 builds on this, challenging you with the more complex Interprocedural Constant Propagation, where you begin to analyze real-world method calls.
- Next, Assignments 5 and 6 guide you through implementing two different precisions of Pointer Analysis — one of the core challenges in the field.
- Finally, in Assignment 7, you will use the algorithm from Assignment 6 to improve the precision of your earlier constant propagation analysis.