LLM Wiki – example of an "idea file"
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Re: LLM Wiki – example of an "idea file"
#2Too much context pollution.
Start with short text context, and flow through DAGs via choose your own adventure. We alreadybreached context limits. Nows the time to let LLMs build their contexts through decision trees and prune dead ends.
Re: LLM Wiki – example of an "idea file"
#3Too much context pollution. Start with short text context, and flow through DAGs via choose your own adventure. We alreadybreached context limits. Nows the time to let LLMs build their contexts through decision trees and prune dead ends.
In my experience a wiki can actually drastically reduce the amount of dead context.
I've handed my local agents a bunch of integrated command line tools (kinda like an office suite for LLMs), including a wiki (https://github.com/triblespace/playground/blob/main/facultie... ) and linkage really helps drastically reduce context bloat because they can pull in fragment by fragment incrementally.
Re: LLM Wiki – example of an "idea file"
#4This sounds like compaction for RAG.
Re: LLM Wiki – example of an "idea file"
#5> You never (or rarely) write the wiki yourself — the LLM writes and maintains all of it. You're in charge of sourcing, exploration, and asking the right questions. The LLM does all the grunt work — the summarizing, cross-referencing, filing, and bookkeeping that makes a knowledge base actually useful over time.
I'm not sure how you can get any closer to "turning your thinking over to machines." These tasks may be "grunt work," but it's while doing these things that new ideas pop in, or you decide on a particular or novel way to organize or frame information. Many of my insights in my (analog? vanilla? my human-written) Obsidian vault (that I consider my "personal wiki") have been made or expanded on because I happened to see one note after another in doing the "grunt work", or just by opening one note and seeing its title right beside a previously forgotten one.
There's nothing "personal" about a knowledge base you filled by asking AI questions. It's the AI's database, you just ask it to write stuff. Learn how to learn and answer your own damn questions.
Soon pedagogy will be a piece of paper that says "Ask AI."
I hate this idea that a result is all that matters, and the quicker you can get the result the better, at any cost (mental or financial, short-term or long-term).
If we optimized showers to be 20 seconds, we'd stop having shower thoughts. I like my shower thoughts. And so too my grunt-work thoughts.
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As an aside, I'm not totally against AI writing in a personal knowledgebase. I include it at times in my own. But since I started my current obsidian vault in 2023 (now 4100 self-written notes, including maybe up to 5% Web Clipper notes), I've had a Templater (Obsidian plugin) template I wrap around anything AI-written to 'quarantine' it from my own words:
==BEGIN AI-GENERATED CONTENT==
==END AI-GENERATED CONTENT==
I've used this consistently and it's helped me keep (and develop) my own writing voice apart from any of my AI usage. It actually motivates me to write more, because I know I could always take the easy route and chunk whatever I'm thinking into the AI, but I'm choosing not to by writing it myself, with my own vocabulary, in my own voice, with my own framing. I trick myself into writing because my pride tells me I can express my knowledge better than the AI can.
I also manually copy and paste from wherever I'm using AI into my notes. Nothing automated. The friction keeps me from sliding into the happy path of turning my brain off.
Re: LLM Wiki – example of an "idea file"
#6This sounds very like Licklider's essay on Intelligence Amplification: Man Computer Symbiosis, from 1960:
> Men will set the goals and supply the motivations, of course, at least in the early years. They will formulate hypotheses. They will ask questions. They will think of mechanisms, procedures, and models. They will remember that such-and-such a person did some possibly relevant work on a topic of interest back in 1947, or at any rate shortly after World War II, and they will have an idea in what journals it might have been published. In general, they will make approximate and fallible, but leading, contributions, and they will define criteria and serve as evaluators, judging the contributions of the equipment and guiding the general line of thought.
> In addition, men will handle the very-low-probability situations when such situations do actually arise. (In current man-machine systems, that is one of the human operator's most important functions. The sum of the probabilities of very-low-probability alternatives is often much too large to neglect. ) Men will fill in the gaps, either in the problem solution or in the computer program, when the computer has no mode or routine that is applicable in a particular circumstance.
> The information-processing equipment, for its part, will convert hypotheses into testable models and then test the models against data (which the human operator may designate roughly and identify as relevant when the computer presents them for his approval). The equipment will answer questions. It will simulate the mechanisms and models, carry out the procedures, and display the results to the operator. It will transform data, plot graphs ("cutting the cake" in whatever way the human operator specifies, or in several alternative ways if the human operator is not sure what he wants). The equipment will interpolate, extrapolate, and transform. It will convert static equations or logical statements into dynamic models so the human operator can examine their behavior. In general, it will carry out the routinizable, clerical operations that fill the intervals between decisions.
https://www.organism.earth/library/document/man-computer-sym...
Re: LLM Wiki – example of an "idea file"
#7This is just RAG. Yes, it's not using a vector database - but it's building an index file of semantic connections, it's constructing hierarchical semantic structures in the filesystem to aid retrieval .. this is RAG.
On a sidenote, I've been building an AI powered knowledge base (yes, it uses RAG) that has wiki synthesis and similar ideas, take a look at https://github.com/kenforthewin/atomic
Re: LLM Wiki – example of an "idea file"
#8> You never (or rarely) write the wiki yourself — the LLM writes and maintains all of it. You're in charge of sourcing, exploration, and asking the right questions. The LLM does all the grunt work — the summarizing, cross-referencing, filing, and bookkeeping that makes a knowledge base actually useful over time. I'm not sure how you can get any closer to "turning your thinking over to machines." These tasks may be "g…
Since you're a fellow Obsidian user, you likely remember the early days of back-linking note-taking software like Roam and such. I remember just seeing pictures of the graph being the primary visual symbol representing the depth of learning. I thought "ok well people just want to accumulate stuff." AI tools certainly help with creating a mass of notes.
There's a comment above how this is reminiscent of Licklider's work, but it reminds of the early print culture era, where books were a consumer item, and people just purchased a lot of them to put on shelves built to display them.
Re: LLM Wiki – example of an "idea file"
#9Too much context pollution. Start with short text context, and flow through DAGs via choose your own adventure. We alreadybreached context limits. Nows the time to let LLMs build their contexts through decision trees and prune dead ends.
In my experience a wiki can actually drastically reduce the amount of dead context. I've handed my local agents a bunch of integrated command line tools (kinda like an office suite for LLMs), including a wiki ( https://github.com/triblespace/playground/blob/main/facultie... ) and linkage really helps drastically reduce context bloat because they can pull in fragment by fragment incrementally.
Was also thinking to disambiguate context where you wish to express a tokens function (eg, top) as different from one could use unique ASCII prefix (eg, ∆top) to avoid pollution between the english and the linux binary.
Youd then alias these disambiguated terms and theyd still trigger the correct token autocomplete but would reduce overlap which cause misdirection.
Re: LLM Wiki – example of an "idea file"
#10This is just RAG. Yes, it's not using a vector database - but it's building an index file of semantic connections, it's constructing hierarchical semantic structures in the filesystem to aid retrieval .. this is RAG. On a sidenote, I've been building an AI powered knowledge base (yes, it uses RAG) that has wiki synthesis and similar ideas, take a look at https://github.com/kenforthewin/atomic
eh i'd push back on "just RAG". like yes the retrieval-generation loop is RAG shaped, no ones arguing that. but the interesting bit here is the write loop - the LLM is authoring and maintaining the wiki itself, building backlinks, filing its own outputs back in. thats not retrieval thats knowledge synthesis. in vanilla RAG your corpus is static, here it isnt
also the linting pass is doing something genuinely different - auditing inconsistencies, imputing missing data, suggesting connections. thats closer to assistant maintaining a zettelkasten than a search engine returning top-k chunks
cool project btw will check it out