#architecture

A good collection of practices on automated AI code reviews by Ankit Jain

• The Scalability Crisis: Manual post-PR review is no longer viable. AI agents have nearly doubled code output, causing human review time to spike by 91%, creating a bottleneck that traditional workflows cannot solve.
• The Upstream Pivot: Human value must shift from reviewing implementation to defining intent. Instead of checking syntax, humans spend their energy writing rigorous specs and acceptance criteria before the code is written, which the machine then uses to self-verify.
• The Swiss-Cheese Defense: Rather than one "perfect" human gate, the model uses a stack of imperfect automated layers. By layering signals like agent competition, deterministic guardrails, and adversarial "red-team" agents, the system catches errors where their individual failure modes don't overlap.

Django Modern REST seems to be an emerging REST API library with focus on performance, scalability and pluggable serializers. Supporting msgpack, CBV and less of sync_to_async. Might evaluate it against the my current preferred django-ninja. gemini

Every layer of review makes you 10x slower via apenwarr

On blockchain, UIPC (Universal IPC) and SIPC (Secure IPC) are frameworks that allows different network to talk to each other using a standard protocol. While UIPC acts as the universal courier, SIPC acts as the security guard. Together, they create "Internet of Blockchains". Gemini

• Cosmos IBC & Polkadot - XCM are two industry leading protocols for UIPC
• For the SIPC, Zero-Knowledge Proofs and Rollup architecture also "Trustless bridges" or "Cross-rollup communication" are often more frequently used terms in blockchain literatures.

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann manages to be both rigorous technical manual and something approaching a philosophical treatise on the nature of truth, consistency, and trust in distributed systems. [Claude][https://claude.ai/chat/0f58f2f6-bd56-41a1-a785-8267afa5a3d1]

• Foundation. He starts by asking the questions "What do we actually want in our data systems", answers them as - reliability, scalability and maintainability.
  • Reliability. The question isn't whether failure will happen, it will, but the whether the systems can survive them.
  • Scalability. It's not a binary question of whether a system is "scalable" or "not scalable". Ask - "What happens when specific load parameter increase?"
  • Maintainability - most underappreciated of the three, he argues the majority of cost isn't in initial development but in ongoing maintainence.
• The data model wars - the skill isn't in choosing the "best" database but in understanding which tradeoffs matter for your specific problem.
• Storage engines. Two major approaches to read & write data from disk, neither is universally better.
  • Log-structured storage (like LSM-trees) optimizes for writes, every write is appended and the merged/compacted later.
  • Page-oriented storage (like B-trees) optimizes for reads, data is stored in fixed-sized block, which then get updated in-place, more like filing cabinet with each document has a designated slot.
• Instead of asking "how do I build this?" ask "what does it mean for this to work correctly?"

Notes on "How AI assistance impacts the formation of coding skills" Article HN

• AI speeds up coding but reduces deep understanding and mastery
• Juniors (1-3 years experience) showed speed improvements with AI, but 4+ year developers showed no difference
• Modern software work is more about requirements, specs, documentation, and communication than raw coding skill
• Small sample size (n<8) and study design limitations make results questionable
• Takeaways:
  1. Use AI for high-scoring interaction patterns: Ask conceptual questions and request explanations rather than just code generation
  2. Adopt AI for documentation and specs: Multiple developers report dramatic improvements in tickets, PRs, and documentation quality
  3. Be deliberate about learning: If using AI, actively practice explaining concepts and avoid pure copy-paste workflows
  4. Use AI to reduce grunt work: Let it handle boilerplate, test writing, and repetitive tasks while focusing on architecture and requirements
• The research confirms what many suspected: AI coding assistants create a real trade-off between speed and skill development, but the practical significance is hotly contested. The critical question isn't whether AI reduces learning (it does), but whether deep coding skill remains as valuable as expressing requirements clearly—and whether we're comfortable with a generation of developers who can't function without AI assistance.

Notes from Thoughtworks - Technology Radar vol 33

• text-to-sql solutions aren't working as expected
• pnpm, langGraph, and pydantic recommended for adoption

Inversion of Control principle -- "Don't call us, we'll call you".

• This design principle is used widely in Web Frameworks, GUI programs, etc in the form of event listeners, dependency injections, callbacks to avoid boilerplate code, resuability and testability.
• Some popular examples are DOM, Flask, NestJS, Angular, etc.

"Choose Boring Tech" by Dan McKinley

• "The failure mode of boring tech are well-understood"

Notes from "Django rapid architecture"

• Recommends organizing the codebase with interfaces at the top layer followed by readers and "actions", Finally, 'data' as the bottom layer to organize Django codebase to allow for a simple code structure that allows for scaling