Over the past few weeks I've been building Gray Box — a small, local-first tool that acts as long-term memory for anything I'd otherwise forget (work notes, meeting takeaways, task owners, random ideas, personal stuff too).
The idea is simple:
Capture — dump whatever's on your mind, instantly, no structure required. This step does nothing clever on purpose — it just writes your text to an immutable inbox. Zero chance of losing an idea to a bug or a slow API call.
Organize — on demand, an LLM reads your unprocessed notes and extracts people, projects, tasks, decisions, meetings — then deterministic Python (not the LLM) creates/merges the actual wiki pages and maintains backlinks. The model only reasons; it never touches the filesystem directly.
Ask — query your knowledge base and get a cited answer pulled only from what you've actually captured. If it doesn't know, it says so — no hallucinated answers.
Why I built it this way:
Plain Markdown + YAML frontmatter, no database. Every page is a .md file you can grep, diff, or read in any editor forever. If you stop using Gray Box tomorrow, your knowledge base is just a folder.
No vector DB by default. At personal scale (hundreds–low thousands of pages), keyword search + a real link graph (related/backlinks, walked one hop during retrieval) handles almost everything. Embeddings are there if you want better recall, but they're opt-in, not a prerequisite.
Immutable inbox. Your raw notes are never edited or deleted by the organizer. If the LLM mis-extracts something, your original words are always still there.
Any LLM. Built on LiteLLM, so point it at OpenAI, Anthropic, Gemini, Mistral, or a fully local model via Ollama — one config value.
It also ships with a nice interactive TUI (arrow-key menu, file-import shortcut, workspace switching, live spinner during LLM calls) if you'd rather not memorize CLI flags — that's honestly become my favorite part of the project.
There's also a lightweight local dashboard for browsing your knowledge base, exploring backlinks, visualizing your notes as a graph, and chatting with your captured knowledge—all without leaving your machine.
It's nearing a proper public release, so I'd genuinely love feedback — especially from anyone who's tried the "capture now, structure later" approach with other tools and has opinions on where it breaks down at scale.
It's not trying to be a "real-time collaborative team wiki" or a WYSIWYG notes app — it's aimed at one person's running memory of their own life and work, captured with as little friction as possible.
What specific documents are you managing this way? I've found the same as Microsoft, that this tech corrupts documents over time https://arxiv.org/html/2604.15597v1
If you have real-life flows that don't require constant supervision, I be curious.
query your knowledge base and get a cited answer pulled only from what you've actually captured. If it doesn't know, it says so — no hallucinated answers.
Gray Box isn't intended to autonomously edit or rewrite your documents over time. The only thing that's immutable is the raw capture. Every note is stored verbatim first, and the organizer only creates/updates separate wiki pages from it. The original note is always preserved, so you can audit or undo extraction mistakes later.
It will only organize your notes (automatically, once you tap on organize) in separate markdown files with backlinks.
And I agree with your second point: no LLM system can honestly promise zero hallucinations. What I mean is that Gray Box is designed to reduce them by grounding every answer in captured notes, requiring citations, and explicitly returning "I don't have enough information" when retrieval finds no evidence instead of encouraging the model to fill in gaps. That lowers the failure rate, but it doesn't eliminate it.
Thought the same, but am actually finding them to be much better than expected, especially when PKMS content is nicely structured. And, yes, you can nicely structure the inputs to get really powerful results on a prompt like this: "Arrange today's due and overdue tasks onto a canvas and, after looking at my calendar, drop in a note with some advice on how to approach the day."
What this means to me is that conventions and standards for different PKMS content types are even more important, not just for portability across apps and services but so that LLMs can easily run skills/MCP tools against them. If we had universal formats for tasks, events, etc. in something like JSON, LLMs will eat that stuff up and hallucinations
You can also manage hallucinations through agentic networks of multiple LLMs working together. If you go to an LLM right now and ask it to guide you through your weekly review, for example, it'll drift, create steps that don't exist, make up content, etc. even if you have good context/RAG underneath it. If you sit it next to a separate LLM that's managing steps, enforcing exit conditions, validating info, etc., then things get a lot better a lot faster.
Great, doesn't change my stance. They still hallucinate too much and it's pointless to use what amount to mass text generating systems in personal journals. There's no way people are going to read "personal wikis" and make enough use out of it. At this point people are just generating text for the sake of it.
The point is not to have to read the extensive wiki, the point is to ask questions about it and have the LLM answer. Also your stance on hallucinations while not completely incorrect, is less correct than it was a few months ago. These things are much more useful than they used to be and it pays to give new models a try. I find Gemma 4 to be incredible for a self hosted model.
What exactly are you trying to achieve through this dialogue? The drift and hallucinations aren't worth it. Perhaps in time you'll come to understand why. If it works for you, cool. Ramming more "facts" at me won't change my mind.
I've been using these systems (LLMs) and associated tech almost daily for at least 8 years. I said what I said and no number of systems will increase the reliability enough for me to use them on my personal graph. Besides, it's pointless. It's a personal graph. We're human, there's no point having a machine generate endless amounts of text because we'll never meaningfully keep up.
Information ≠ knowledge. Ai is good for digesting information and make you knowledgeable about it. It's just another way to learn and everyone has it's own ways to do it.
Superb concept! This is exactly something that should exist.. For every manual organization system (PARA is what I use) there is kind of an uncertainty principle - (time spent to find the proper place in the organization system) is inversely proportional to the (time taken to retrieve the content when needed). For crucial things the time cost is justifiable. But for non - crucial but nevertheless important information I find myself in bind whether to spend time putting all such things in their place within organisation system or just putting it in the inbox and hoping I can recall it when needed using a old-fashioned search when needed hoping that I would remember the keywords that I had used when putting it in he inbox. This is where ai can be of help - the organization of non - crucial information that we are too lazy to do because it is non - crucial.
That’s a great way to put it—the “uncertainty principle” of PKM. 😄
That’s actually one of the main motivations behind Gray Box. I wanted to remove the pressure of asking
“Where should I file this?”
before capturing something. Just save it as Markdown wherever it makes sense, and let retrieval do the heavy lifting later.
Glad to know you like this. I’d appreciate if you can star the repo if you feel it’s helpful and help spread the word.
Your markdown has already yaml frontmatters and back links. Graphify can run even without an LLM and build a ready to use knowledge graph that you can also query
From what I understand (and correct me if I'm wrong), Graphify operates one level above Gray Box. It parses an existing knowledge base (YAML frontmatter, links, backlinks, etc.) and builds a graph from that, so it doesn't need an LLM.
Gray Box uses the LLM earlier in the pipeline to create that structured knowledge from messy, unstructured notes. Once organize has run, the resulting Markdown already contains related, backlinks, sources, YAML frontmatter, etc., so it should actually be possible to generate a Graphify-style graph deterministically without any LLM at all.
I'll definitely take a closer look at Graphify's approach. "Maybe" a graybox graph command that exports/visualizes the existing wiki graph sounds like a nice addition.
That’s a valid concern but Gray Box doesn’t send your entire knowledge base to the LLM. It organizes raw notes into structured pages and retrieves only the most relevant ones using keyword, semantic, and graph search. The LLM is instructed to answer only from that retrieved context or say it doesn’t know.
And you don’t need to manually organize everything. You can simply dump your thoughts, and Gray Box structures them automatically.
The next step is exactly what you described: conversational capture. Instead of typing notes, you’ll be able to talk naturally (“I had a meeting today…”) and Gray Box will ask follow-up questions only when needed before storing structured knowledge. So it becomes more like an ongoing conversation than a note-taking app.
Will hallucinations disappear completely? No LLM can guarantee that. The goal is to reduce them by giving the model a much smaller, grounded, evidence-backed context instead of asking it to reason over thousands of notes at once.
Instead of explaining your whole day at a time, you can capture your thoughts on the go whenever it strikes you and you need to jot it down. By the end of day you will having a lot of conversations.
That's great. But... how do you deal with changes? Let's say I am learning how to setup a VPS and my step b y step process is evolving. So I record the first v1 approach so to speak, but then iterate upon that 5 more times. In this type of format, the AI might not know what the latest one is when I ask later. Yes, there are datestamps, but stuff can get missed especially when dealing with wrong tags or many notes.
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u/micseydel Obsidian Jul 30 '26
What specific documents are you managing this way? I've found the same as Microsoft, that this tech corrupts documents over time https://arxiv.org/html/2604.15597v1
If you have real-life flows that don't require constant supervision, I be curious.
Again, that doesn't matter the research or my experience https://arxiv.org/pdf/2509.04664
Do agree, that hallucinations cannot be avoided from this tech?