acmerfight

Agreed. This is pure architectural thinking: you hold the ground truth, enforce the strict IAM boundaries, and outsource the mud-playing to the LLM. Mindless 'vibe coding' without this structural discipline is just tittytainment. The job is contract validation now, not typing.

This is a phenomenal example of exactly what I am advocating.

Notice you didn't ask the AI to 'just design a stereo pedal for me.' You interrogated it, reasoned about netlists, and forced the concepts into your brain through intense friction. That is pure deep work.

Ironically, what you described is exactly using AI to help with deep work. You do the heavy lifting (reading), and use AI strictly for stateless verification and testing your mental model. That is the ideal synergy.

Pure 'vibe coding' is essentially technical 'tittytainment'. Using AI for the horizontal spread while you enforce vertical architectural depth is true deep work.

We actually don't disagree at all—you are perfectly illustrating my point.

Applying strict epistemic discipline (Popper, Russell) to resolve ambiguity and accelerate actual practice is the very definition of deep work. You aren't using AI as a shortcut to skip thinking; you're using it as a Socratic sparring partner to deepen it. This is exactly the paradigm shift I'm advocating for.

Cloudflare’s dual 2025 outages weren't just about a missed Lua nil-check or a sloppy Rust unwrap(). They were the inevitable cost of "hotwiring" production to bypass ancient, broken test harnesses during a security panic.

This article argues that Cloudflare's "Homogeneous Edge" architecture—where every node runs every service for extreme efficiency—has created an infinite failure domain. When commercial constraints demand tight coupling and zero physical isolation, a single "Killswitch" logic error doesn't just fail safe; it becomes a global detonator. A deep dive into how technical debt and architectural gambling eventually cash their checks.

Lately, many people have been talking about AI for Software Engineering (AI4SE), hoping to find the next big startup opportunity there.

My view is straightforward: this path leads nowhere.

Most of these ventures make a fundamental mistake — they try to use a technical hammer to hit a management nail.

That’s doomed from the start.

Many SRE and operations teams predominantly address operational risks through “additional approval processes” — whether for database schema changes, service deployments, client releases, etc.

TikTok and the Scope of the Communications Act of 1934 Are Different The Communications Act of 1934 primarily targets traditional media (e.g., television, radio), while TikTok is an algorithm-driven social media platform where content is user-generated. Its operational model is fundamentally different from traditional media. Directly equating the two is unreasonable and does not align with the realities of the modern digital economy.

Foreign Ownership Does Not Equate to a National Security Threat There is no publicly available evidence proving that TikTok has provided U.S. user data to a foreign government. TikTok has already implemented localization measures for data storage and operations (e.g., the "Texas Project"). In contrast, many U.S. tech companies (e.g., Facebook, Google) have faced scrutiny over data privacy issues but have not been restricted due to foreign ownership. Restricting TikTok solely based on "foreign ownership" lacks factual support.

Economic Impact: TikTok Is a Lifeline for Millions TikTok provides a critical source of income for over 5 million small businesses and 1.5 million creators in the U.S. According to 2023 data, TikTok contributed $24.2 billion to the U.S. economy and supported at least 300,000 jobs. Restricting TikTok would directly threaten the livelihoods of these individuals, causing significant harm to social stability and economic vitality.

A More Reasonable Solution Is Strengthening Regulation, Not an Outright Ban Rather than imposing a blanket restriction on TikTok, it would be more effective to strengthen data privacy protections through legislation, ensuring that all social media platforms (whether foreign or domestic) adhere to the same security standards. For example, TikTok could be required to further localize data storage and undergo independent audits. This approach would safeguard national security while avoiding unnecessary harm to users and creators.

CCState is a semantic, strict, and flexible state management library suitable for medium to large single-page applications with complex state management needs.

The name of CCState comes from three basic data types: computed, command, and state.

You're absolutely right that if someone decides to create a DSL, it needs to be done right. The issue, however, is that most DSLs fail to meet this standard. Extensibility and leveraging existing tooling, as you suggest, are indeed critical factors, but they are rarely prioritized in the ad-hoc creation of niche DSLs.

The IBM JCL example perfectly illustrates the pitfalls of poorly managed DSL evolution. What starts as a seemingly simple and declarative tool often spirals into unintended complexity, becoming a "monster" that burdens users instead of empowering them. This is precisely why the original argument advocates for extreme caution: many developers underestimate these risks and overestimate their ability to design robust, maintainable DSLs.

In most cases, a well-designed library or framework offers a far safer and more effective solution, achieving the desired goals without introducing the long-term costs and complexities of an ill-conceived DSL.

Thank you for sharing your thoughts. You’ve highlighted some great examples of DSLs that have become essential tools in software development, like SQL and Markdown. However, the success of these DSLs often lies in their universality, broad adoption, and well-thought-out design. These qualities make them more than just isolated islands—they are foundational tools backed by strong community support and decades of refinement.

The point of the original argument is not to dismiss DSLs entirely but to caution against the hasty creation of niche, poorly designed DSLs that solve narrow problems at the cost of maintainability, steep learning curves, and a lack of long-term viability. The challenge is that many developers underestimate the complexity of language design and overestimate the benefits of introducing a new DSL for a specific use case.

In conclusion, the advice is not to avoid DSLs altogether but to approach them with caution, ensuring they address real needs without creating undue burden on the team. Thoughtful design and a strong host language can certainly help, but they don’t eliminate the broader risks associated with creating domain-specific abstractions.

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If you're thinking without writing, you only think you're thinking.