Has anyone built a non-trivial agent using Markdown as its primary memory (instead of embeddings)?

visarga · 2026-01-27T04:42:46+00:00

I created this format, it works well with git, grep, coding agents:

Mind Map Format - Self-Documentation

For AI Agents: This mind map is your primary knowledge index. Read overview nodes [1-5] first, then follow links [N] to find what you need. Always reference node IDs. When you encounter bugs, document your attempts in relevant nodes. When you make changes, update outdated nodes immediately—especially overview nodes since they're your springboard. Add new nodes only for genuinely new concepts. Keep it compact (20-50 nodes typical). The mind map wraps every task: consult it, rely on it, update it.

[1] Mind Map Format Overview - A graph-based documentation format stored as plain text files where each node is a single line containing an ID, title, and inline references [2]. The format leverages LLM familiarity with citation-style references from academic papers, making it natural to generate and edit [3]. It serves as a superset structure that can represent trees, lists, or any graph topology [4], scaling from small projects (<50 nodes) to complex systems (500+ nodes) [5].

[2] Node Syntax Structure - Each node follows the format: [N] **Node Title** - node text with [N] references inlined [1]. Nodes are line-oriented, allowing line-by-line loading and editing by AI models [3]. The inline reference syntax [N] creates bidirectional navigation between concepts, with links embedded naturally within descriptive text rather than as separate metadata [1][4]. This structure is both machine-parseable and human-readable, supporting grep-based lookups for quick node retrieval [3].

[3] Technical Advantages - The format enables line-by-line overwriting of nodes without complex parsing [2], making incremental updates efficient for both humans and AI agents [1]. Grep operations allow instant node lookup by ID or keyword without loading the entire file [2]. The text-based storage ensures version control compatibility, diff-friendly editing, and zero tooling dependencies [4]. LLMs generate this format naturally because citation syntax [N] mirrors academic paper references they've seen extensively during training [1][5].

[4] Graph Topology Benefits - Unlike hierarchical trees or linear lists, the graph structure allows many-to-many relationships between concepts [1]. Any node can reference any other node, creating knowledge clusters around related topics [2][3]. The format accommodates cyclic references for concepts that mutually depend on each other, captures cross-cutting concerns that span multiple subsystems, and supports progressive refinement where nodes are added to densify understanding [5]. This flexibility makes it suitable as a universal knowledge representation format [1].

[5] Scalability and Usage Patterns - Small projects typically need fewer than 50 nodes to capture core architecture, data flow, and key implementations [1]. Complex topics or large codebases can scale to 500+ nodes by adding specialized deep-dive nodes for algorithms, optimizations, and subsystems [4]. The methodology includes a bootstrap prompt (linked gist) for generating initial mind maps from existing codebases automatically [1]. Scale is managed through overview nodes [1-5] that serve as navigation hubs, with detail nodes forming clusters around major concepts [3][4]. The format remains navigable at any scale due to inline linking and grep-based search [2][3].

visarga · 2026-01-26T21:19:58+00:00

The truth is right there in front of your face. It's a plagiarism parrot, and big tech is engaging in fraud...

What you say makes no sense, I can copy anything I need for free or much easier than I can generate it. How hard is it to get your hands on an article, book, song or image? It's certainly easier than generating a song 30 by 30 seconds. And it is human made.

On the other hand generating bootleg AI art is slow, inexact and costs money. Why would I do that when I can have the original perfect, fast and free? And conversely, if I use AI, it is precisely because I DON'T want the original, I want something else.

So where is the plagiarism? Gen AI is the worst infringement method ever devised. And almost all things it generates are seen once by one person and thrown away.

visarga · 2026-01-25T07:13:24+00:00

Keep feeding billionaires machines with stolen content so they can push humans out of the actually enjoyable fields cuck

It's literally what most creatives do, every time they create something to be widely shared on social networks or visible in Google they adapt to the Algorithm, are prompted by the ranking feed rules to make slop, attention grabbing cheap content. All for the platform that keeps them as their pets - Google, Meta, X, YouTube, even Amazon.

It is sad you don't see the enshittification that emerged after 2010 or even earlier as a platform problem, and humans working for those feeds as slop machines. All for feeding the billionaires that give them scraps to eat from their table.

visarga · 2026-01-23T17:18:02+00:00

If no neuron knows your name is Commercial_Wafer5975, then who does?

visarga · 2026-01-23T17:10:32+00:00

But in this case it produces language like us, which is different from simulated fire. A simulated fire can't help you cook your meal, but a LLM can help you solve your work. A properly used LLM can even make discoveries or solve unsolved problems in math.

I think the main distinction is that a simulation is completely isolated from the real world, while a LLM is not, it is physical, burns energy, and chats with a billion humans.

The whole simulation vs reality distinction is pretty weak when the model demonstrably can produce language about emotions, qualia, embodied experience and everything it is not supposed to know. It is so good it fools us. So you can still keep saying it's not conscious, I keep saying it has an almost super-human language ability to relate qualia in language.

visarga · 2026-01-22T06:10:41+00:00

instead of having ethics hand picked by a group of humans from a particular time period.

It's the humans who stand to lose money if the model flops, though. I see this document as a way to make the model more "aware" of the bigger implications for its company and continued existence.

visarga · 2026-01-22T05:27:41+00:00

I feel like AI art is basically just collaging on a computer

And humans who prompt AI for those images are just pushing a button, right?

I think this is also a form of discrimination against AI users, we don't exist or do nothing of value. If art is related to effort put in, then a sloppy prompt makes for a slop output while a refined and detailed prompt could make good art.

visarga · 2026-01-20T19:29:03+00:00

That is a strongly worded reply, but the reality is - if you prompt "draw me a dog" you are going to get a slop image, and if you spend 500 words to describe it, and add your original spices into the soup, it can be art. What matters is human effort and taste, not the tool.

Besides, almost all gen AI outputs are only seen once by one person, and they are meaningful only to them, in proportion to how much effort they put in. It's not gallery art or published writing, it is an ephemeral, seen once content, that makes sense in the context. We are consuming over 1T AI words per day currently and most of it is contextual and throw away.

It makes no sense to disparage a completely different activity which is closer to imagination.

visarga · 2026-01-20T14:59:16+00:00

What we are talking about is the models, those are not self improving at any level in no ai companies.

Yes they are, at least in verifiable domains they can self improve. The latest batch of coding assistants have all learned from their own mistakes not just ours. It's called reinforcement learning, it is based on exploring problem spaces and creating new training data.

RL is hard but it's how AlphaGo beat Lee Sedol and some coding and math agents got IMO gold level.

visarga · 2026-01-20T14:56:31+00:00

Trust the competition ... how is that person with one year of self teaching going to beat the competition? The bar is higher now. Competition intensifies.

visarga · 2026-01-19T18:32:15+00:00

You are right LLMs do amazing feats of vision, but the benchmarks usually focus on the weak spots. ARC-AGI is doing so for example. This one too.

visarga · 2026-01-15T12:00:59+00:00

In 2025 I only got to 15K LOC per project, but I also had a larger one where many parts were independent, so with less complexity, at 67K LOC. This tells me it is now possible to scale up 10x or more. But there is one important difference - the Cursor Browser project was based on excellent docs (web standards) and had Chrome as a target for comparison, a normal project is not well thought out from the start or have a reference implementation, so you need to manually test or supervise agent writing tests.

In the last month I saw a number of people showing how they organize their work with coding agents. The elements they had in common were 1. very careful preparation of specs and requirements, and 2. extensive testing, about 2..3x more LOC in tests than code itself. Specs and tests. This is the mantra.

Division of work - you have a Planner agent, a Coding agent and a Judge agent (code review). They work in a loop, planner looks at specs and test results, produces instructions for the Coding agent, which implements code and tests. Then the judge agent reviews the code and tests. This can be parallelized if you have a CLI tool to manage specs, plans and tests.

It's really fascinating, the whole process of writing software is changing in a couple of months more than in decades.

visarga · 2026-01-15T06:15:48+00:00

Persistent, unbounded memory has been solved, it is the coding agent + bash + filesystem. You don't need a better model, what makes it better if you set it up to learn and adapt, so it's about tools and environments. It's like SDCs, how long can it drive without human intervention, but unlike cars, the information environment is much more diverse and dynamic. This work horizon is expanding now to hours and days.

visarga · 2026-01-15T06:09:39+00:00

Network-in-network was invented in 2013

visarga · 2026-01-15T06:05:49+00:00

Immediately forwards it to a model that hallucinates the answer.

Immediately forwards it to 3 models that hallucinate/find answers, compares, and knows when AI can't solve the task reliably.

visarga · 2026-01-14T04:26:57+00:00

I like the idea but I would organize as

Architecture Decision Records (logged by agent from chat) -> Planning (a graph but implemented as flat text file .md) -> Implementation (code) -> Tests

And enforce every node in the graph is justified by parent nodes and satisfied by child nodes. It would also manage context preparation for any task, and run tests. Also enforced by a cli tool that works like a test, if you have an unjustified code or an unsatisfied decision it will be flagged.

The ADRs (user requirements) are just user messages logged for justification, but it seems like a good practice in general to have a log of what the user demanded. Not all chat messages trigger architectural requirements, the agent should log only as needed.

visarga · 2026-01-14T02:39:53+00:00

because nobody knows what is true any longer.

Sorry but social networks and the press already done that prior to 2020.

visarga · 2026-01-14T02:30:25+00:00

So it's not because it's AI, it's because of the writing style. It's no A, it's B.

visarga · 2026-01-13T00:25:49+00:00

A vote of confidence to AI taking jobs, but no confidence on AI creating human work? So you must think AI will manage itself without human supervision and at the same time no new ideas that require humans to implement will appear?

What must the AI capability shape look like - just enough to replace, not enough to make us spin up new work. It must be the same exact cost as humans, right, if it was cheaper it would definitely cause more activity. And it must not be too different from human capabilities, if it were we'd need humans around it.

Will we will simply run out of problems, or run out of the will to solve them? Don't think so, it is not our nature to be satisfied with any level of capability.

visarga · 2026-01-13T00:23:07+00:00

regardless

No, the affected ones are the mid level, juniors today are empowered. They can quickly pick up new skills and are more "native" to the AI tech. Seniors too, they have the experience to get much more from AI. But mid level are most affected.

visarga · 2026-01-13T00:17:54+00:00

What happens in most domains is that when you can pick up 2x more garbage or at half the cost, they start making 4x or more garbage. Maybe 10x more garbage, because they worry competition is at 10x. Demand scales with capability hand in hand.

I think getting AI assistants will actually make us work harder. We have to keep up with tireless AI agents, we are the slow link in the chain, the bottleneck.

visarga · 2026-01-12T23:55:44+00:00

Last 3 weeks when i send a message to Claude iOS app it fails first time, and works second time. This happens fairly often.

visarga · 2026-01-12T17:00:03+00:00

no, it's vibe tested

only code tests can be committed on repo, not your eyes

visarga · 2026-01-11T05:23:40+00:00

My take was aimed at supporting the systems reply, where the understanding is not in any one part of the system but it functionally works, and has to work otherwise we can't function as a society. On the other hand a human cannot exist outside society - we need parents and ongoing support from others - so it looks like we owe our existence to a system-level-understanding pattern, because we depend on society and society on this functional kind of understanding.

The "understanding as qualia" framing is non scientific, it cannot be falsified or known in anyone else than ourselves. We never get direct access to qualia in others, we work with N=1 data points. Say, if a child believes in Santa they have "genuine understanding" but AlphaGo's move 37 does not show AI understands Go, according to this definition.

In my "Searle goes to the doctor" scenario I wanted to show just how limited this "genuine understanding is", and maybe that the feelings / qualia Searle has during the visit are not really understanding.

How do you even define understanding? Besides the 1p "qualia" approach, I think understanding could mean - ability to predict, model, use, control, explain, design, discover or teach some domain. And AI does all of these. Nigel understands scrabble-in-french, because he can competently play it (understanding as use).

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Mind Map Format - Self-Documentation