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!
Currently it just fetches latest if a version is not specified. It should be pretty easy to use the current project version if available, assuming llm plugins stay in the working directory of the main call. PRs welcome :)
Once the module version is in GOMODCACHE, it's extremely fast, not even noticeable.
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.
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?
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.
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.
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?
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.
> Has anyone found effective tools for maintaining this synchronization between documented architecture and implemented code?
Yes, it's called structure, discipline, and iterative improvement.
Keep the documentation alongside the code. Think in BSD terms: the OS is delivered as a whole; if I modify /bin/ls to support a new flag, then I update the ls.1 man page accordingly, preferably in the same commit/PR.
The man pages are a good reference if you already have familiarity with the employed concepts, so it's good to have an intro/overview document that walks you through those basics. This core design rarely sees radical changes, it tends to evolve - so adapt the overview as you make strategic decisions. The best benchmark is always a new hire. Find out what is it that they didn't understand, and task them with improving the document.
Software development is just one piece of the bigger picture that your manager is concerned with, same way as adding features is just one among your many responsibilities.
Managers understand risk. Communicate the risk of disregarding bugfixes, documentation, technical debt, even if it takes a lot of handholding. Expect no less from your manger: if they can communicate client expectations, budget constraints, and most importantly: long-term strategy, together you may be able to devise a better plan to address your own concerns.
In other words, empathy can go both ways.
And yeah, there are bad managers, just like there are bad coders. Sometimes it's an interpersonal problem. It's called life.
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.
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.
Thanks for the insightful comment! You’re absolutely right — organizing extracted data into a coherent, semantically meaningful structure is critical for high-quality ML training.
Right now, the pipeline focuses on generating OCR outputs optimized for ML models by cleaning, deduplicating, and segmenting content across modalities (text, tables, figures, formulas). For diagrams and tables, we add semantic tags and preserve layout relationships to aid downstream modeling.
I’m planning to add a semantic structuring module that goes beyond basic layout analysis — something that builds hierarchical, MECE-style representations and identifies entity relationships across sections. That’s absolutely the next frontier, and I really appreciate you pointing it out.
Haha good catch! I’m 19 and from Korea, so I’ve been using an LLM to help with replies since my English isn’t perfect yet.
But I designed and built the project myself (with help from some open models/tools) — just wanted to communicate more clearly with the community!
[Hi from Argentina!] LLM have a particular style that will make people suspictious or even angry.
One posibility is to write the answer in Korean and use autotranslation. (And post only the autotranslation.) Double check the technical terms, because autotranslation sometimes choose the wrong synonym.
Another posibility is to write the answer in English inside gmail, and gmail will highlight orthographical and gramar errors. So you can fix them.
Most people here will tolerate a few mistakes if the answer has your own personal style.
Por esa misma razón, un LLM te habría funcionado perfectamente: desplegando tus pensamientos tal como querías, pero sin las distracciones causadas por la mala ortografía o los errores gramaticales. Los LLM son herramientas —como bien sabes— que ya son esenciales y lo serán aún más con el paso del tiempo. Que algunos en esta plataforma se irriten por su uso solo significa que, eventualmente, se convertirán en los dinosaurios del futuro.
For that very reason, an LLM would have worked perfectly for you: laying out your thoughts just as you intended, but without the distractions caused by poor spelling or grammatical mistakes. LLMs are tools—as you well know—that are already essential and will become even more so over time. The fact that some people on this platform get irritated by their use just means they’ll eventually become the dinosaurs of the future.
This reads as es-es (perhaps es-es-corporate) instead of es-ar. I don't like "desplegando" because it's somewhat closer to "unfolding" instead of "laying out". I'm not sure it's incorrect, but I'd have chosen differently.
The problem is that I read the emails from my friends using their voice and speaking style.
I'd do the same with HN comments, but I never heard most (any?) of them. Anyway, each commenter has a personal style, or at least I have an informal list in my head of a few hundreds commenters. I remember someone made a few good comments about some topic, so it adds in my mind weight to their opinion. I remember some details of their lives, like where they live, family, work, unusual past events, which topics they are interested, ..., they are persons!
With too much AI, comments get bland. They all read like the same corporate speak. AI would not add pasta recipes to antirez comments, or yadayada to patio11 comments. Also, the topics I'd trust their opinions are very different.
I don't mind using AI to fix the text. Moreover, in one of my previous comments I recomendad to write it in Gmail. I guess Gmail is using a mix of an expert system and modern AI. I hope someday Google adds that feature to the textbox in Chrome.
The problem is that some people is using AI to write short "somewhat related" comments, that are not wrong but not very relevant. Also to write giant "walls of text" that discuss the topic and the 5 most important ramifications. So there is an overreaction to correct orthography, grammar and "AI style".
> The fact that some people on this platform get irritated by their use just means they’ll eventually become the dinosaurs of the future.
Remember that birds are dinosaurs. And if you think that nobody is scared of birds, you should visit a pen full of rheas (ostrich are a fine substitution). If you have any brilliant ornament on your cloth they will try to eat it and you will be hit by the peak. Also they will steal food from your hands and it hurts. We visit an open zoo with my older daughter when she was a kid. Rheas were locked inside a pen for security reasons, there were a lot of ducks and baby ducks that are cute, and the goose were scary because they are evil and come in organized groups to "ask" for food.
Genuinely curious—could it be for the same reason you used a keyboard to write that comment? It’s efficient, it works. What’s the actual issue with using a tool that helps convey the intended message more clearly and quickly, as long as it reflects what he wanted to say?
why are you offended on behalf of this person? the hindsight that they're simply an English learner obviously makes me feel bad for asking the question and i completely understand the use case, but i don't think it was unreasonable to think that someone who speaks entirely in ChatGPT paragraphs might be a bot, spammer, or the like—particularly because, in a botnet fashion, the original reply was to a comment that also seemed to be LLM-authored
I wasn't offended at all. I was just genuinely curious, because I keep coming across this assumption that if any text is well-crafted, it must have come from an LLM. I think I understand why: we've grown so used to reading sloppy writing, everything from barely coherent text messages to articles in reputable publications filled with typos and awkward phrasing.
Personally, I've always held myself to a high standard in how I write, even in text messages. Some might see that as bordering on perfectionism, but for me, it's about respecting the principle behind communication: to be as clear and correct as possible.
Now that we have tools that help ensure that clarity, or at the very least, reduce distractions caused by grammar or spelling mistakes, of course I'm going to use them. I used to agonize over my comments on Twitter because you couldn't edit them after posting. I would first write them elsewhere and review them several times for any errors before finally posting. For context: I'm a retired 69-year-old physician, and even after witnessing decades of technological advancement, I'm still in awe of what this new technology can do.
Yes, I love beautiful, natural writing. I'm a voracious reader of the great classics. I regularly immerse myself in Shakespeare, Hardy, Eliot, Dickens, Dostoyevsky, Austen, Tolstoy, and many other literary masters. But I also fully embrace this tool that can elevate even the clumsiest writer's work to a clarity we've never had access to before. If that comes at the cost of a bit of stylistic uniformity, that's a reasonable trade-off. It's up to the user to shape the output, review it, and make sure their own voice and ideas shine through.
Back to your original point, I truly wasn't offended on his behalf. I was just curious. As it turns out, he was using an LLM, because his native language is Korean. Good for him. And just to be clear, I didn't intend to make your question seem inappropriate or to embarrass him in any way. If it came across that way, I apologize.
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.
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!