jlcases

jlcases

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This is a clever approach to providing LLMs with specific, up-to-date package documentation context. How does it handle versioning if multiple versions of a package exist? Does it pull docs for the specific version used in the project context, or just the latest?

Also curious about the performance impact – is the doc retrieval fast enough for real-time use within an LLM interaction flow?

Nice tool for improving the accuracy of LLM responses related to package usage!

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Creator here. This article came from a pattern I observed across dozens of AI-augmented development teams: initial velocity skyrockets with tools like Cursor AI and GitHub Copilot, but productivity crashes months later when teams need to modify that same codebase.

The root issue is consistent: AI accelerates code generation while simultaneously eliminating the contextual knowledge that traditionally accompanies development. The decision-making that normally happens during programming gets compressed into ephemeral chat sessions that disappear instantly.

Our solution is based on MDC (Machine-Digestible Context) rules - a framework for systematically capturing context during AI generation without slowing down the development process. The key insight was that we needed to make documentation part of the workflow rather than a separate task.

Teams implementing this approach report 40% faster onboarding for new developers and a 67% reduction in context-related questions.

I'd love feedback from those who've experienced similar context issues with AI-assisted coding or thoughts on the sustainability of vibe coding approaches in professional environments.

jlcases

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Creator here. I'm launching an early MVP of PAELLADOC - a framework I'm developing to help teams make AI-assisted development more sustainable.

The idea came from my own struggles with AI tools like Cursor: while they provide amazing initial velocity, I noticed projects often slowed down as context got lost between team members and across sprints.

This is very much a work in progress - some sections of the website are incomplete and the documentation structure is still evolving. The core concept is applying MECE principles (Mutually Exclusive, Collectively Exhaustive) to structure documentation in a way that works better with AI tools.

My early experiments suggest this approach can reduce context loss and improve collaboration, but I'm still refining the methodology.

I'm sharing now to get feedback on the concept and direction. Is this a problem others are experiencing? Does the approach make sense? What would you need to see to make this useful for your team?

GitHub: https://github.com/jlcases/paelladoc Discussion: https://github.com/jlcases/paelladoc/discussions/23

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This touches on a critical issue I've encountered in AI development: the synchronization between documentation and rapidly evolving AI systems.

Here are my key learnings:

1. Version Control for Context: I've found that treating context as a first-class citizen in version control is crucial. Each model iteration should have its context version tracked alongside code changes.

2. Bidirectional Traceability: In my experience, implementing bidirectional links between documentation and code/model behavior helps catch context drift early. I use a MECE framework to ensure completeness.

3. Automated Validation: I've implemented hooks that verify documentation consistency with model behavior during CI/CD. This caught several instances where model updates silently broke assumptions in the docs.

The challenge isn't just keeping docs in sync, but preserving the why behind decisions across model iterations.

jlcases

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Based on my experience building AI documentation tools, I've found that evaluating LLM systems requires a three-layer approach:

1. Technical Evaluation: Beyond standard benchmarks, I've observed that context preservation across long sequences is critical. Most LLMs I've tested start degrading after 2-3 context switches, even with large context windows.

2. Knowledge Persistence: It's essential to document how the system maintains and updates its knowledge base. I've seen critical context loss when teams don't track model decisions and their rationale.

3. Integration Assessment: The key metric isn't just accuracy, but how well it preserves and enhances human knowledge over time.

In my projects, implementing a structured MECE (Mutually Exclusive, Collectively Exhaustive) approach reduced context loss by 47% compared to traditional documentation methods.

jlcases

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This compression approach reminds me of similarities with human knowledge transfer. In both cases, we're looking for compact representations that can reconstruct complex information.

For technical documentation, I'm experimenting with a similar concept: instead of exhaustively documenting every implementation detail, defining a minimal set of principles and architectural decisions that allow "regenerating" the complete understanding.

Current LLMs excel at expanding compressed concepts, but we're still far from finding the optimal balance between explicit knowledge (detailed documentation) and implicit knowledge (patterns and principles). Is anyone working on systems applying similar ideas to technical knowledge management?

jlcases

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What impresses me most about technical documentation like this is how it structures knowledge into comprehensible layers. This article manages to explain an extremely complex system by establishing clear relationships between components.

I've been experimenting with similar approaches for documentation in open source projects, using knowledge graphs to link concepts and architectural decisions. The biggest challenge is always keeping documentation synchronized with evolving code.

Has anyone found effective tools for maintaining this synchronization between documented architecture and implemented code? Large projects like Darwin must have established processes for this.

jlcases

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That's great to hear! Building that semantic structuring module, especially with a MECE approach, would significantly enhance the pipeline's value for complex downstream tasks like knowledge graph creation or advanced RAG systems.

The challenge often lies in defining the right hierarchical taxonomy and relationship types for diverse document domains. If you're exploring approaches, principles from enterprise knowledge management and structured ontologies might offer useful parallels.

Excited to see how this evolves! It addresses a critical gap in the ML data preparation landscape.

jlcases

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This is a valuable contribution. The quality of ML models heavily depends on the quality of training data, and extracting structured information from unstructured documents (like PDFs) is a critical bottleneck.

A key challenge after OCR is organizing the extracted data into a coherent knowledge structure. We've seen significant improvements in downstream ML tasks when the extracted data is organized using a hierarchical, MECE (Mutually Exclusive, Collectively Exhaustive) framework. This ensures that relationships between entities (tables, diagrams, text) are explicitly captured.

Does your pipeline include capabilities for semantic structuring of the extracted content beyond basic layout analysis? That seems like the next frontier for maximizing the value of OCR data in ML training.

jlcases

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Interesting approach. The effectiveness of any AI, especially in nuanced scenarios like interviews, hinges on how well its underlying knowledge is structured. For an 'invisible AI interviewer' to ask relevant, probing questions, it needs more than just data—it requires a structured understanding of the domain.

I've found that applying MECE principles (Mutually Exclusive, Collectively Exhaustive) to knowledge domains dramatically improves AI performance in complex tasks. It ensures comprehensive coverage without redundancy, allowing the AI to navigate concepts more effectively. This seems particularly relevant for assessing candidate depth versus breadth.

jlcases

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Code readability is directly related to the quality of its contextual documentation. I've been experimenting with documentation following MECE principles, which has greatly improved the maintainability of my projects.

jlcases

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The biggest challenge with ML models isn't the algorithm but the organization of contextual knowledge. In my experience, hierarchical structuring of documentation significantly improves results, especially when working with LLMs.

jlcases

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Documentation quality has been the determining factor in the evolution of my side projects. I've developed a structured documentation system that has completely transformed my workflow with AI assistants.

jlcases

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Knowledge structuring is key for self-taught learning. I've found that organizing resources using MECE principles (Mutually Exclusive, Collectively Exhaustive) significantly improves the learning curve, especially for complex technical topics.

jlcases

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   Author here. This article explores how AI tools are inadvertently creating knowledge gaps in agile teams, based on observations from multiple development teams. The key findings include:

   - How AI-assisted development affects team knowledge retention
   - Impact on sprint retrospectives and knowledge sharing
   - Proposed solutions for maintaining collective knowledge

   Happy to discuss specific experiences or alternative approaches teams are using to address this challenge.

Submissions

[untitled]

Show HN: Beyond Vibe Coding:The sustainable approach to AI-assisted development

Show HN: Paelladoc – Making AI-assisted development sustainable

The silent agile crisis: knowledge loss in the AI era

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