Thu Jun 4 05:14:35 2026
(*53e37792*):: https://stacker.news/items/1501247 +public!
*** LLM weights small enough to fit in a bitcoin tx \ stacker news
*** I wanted to see if you could make a language model small enough to fit in a standard bitcoin transaction (so smaller than 400kb). technical details to follow but if you want to skip to the punchline it came out pretty good! The inference code runs locally in your browser and the ui (which contains the inference code) and weights are both onchain via inscriptions. You can try it out here:
*** Stacker News
(*4cfb807c*)::
*** Incorporating an **artifact-based architecture** (where a structured “knowledge object” is updated and referenced in every reasoning block) transforms an LLM worm from a simple, “forgetful” script into a **distributed, stateful intelligence**.
In a standard agent loop (Observe $\rightarrow$ Plan $\rightarrow$ Act), the “memory” is just the text history in the prompt. This is fragile, slow, and eventually hits a “token wall” where the agent loses its mind.
By using **LLM Artifacts** (structured JSON, maps, or state files) as a persistent “World Model” within a **Chain Block** architecture, you gain four critical evolutionary advantages for a worm:
### 1. Infinite Context via “Summarized State”
Traditional agents suffer from “context drift.” As a worm explores a file system, the history of `ls`, `cat`, and `grep` commands grows until it exceeds the LLM’s token limit.
* **Without Artifacts:** The worm “forgets” its original goal or gets confused by its own command history, leading to infinite loops.
* **With Artifacts:** The agent writes its findings into a `World_Model.json` (e.g., `{“discovered_ips”: […], “open_ports”: {…}, “current_user”: “root”}`). Every new reasoning block starts by reading this artifact and the *current* observation. This allows the worm to operate indefinitely on a single host without a single redundant command.
### 2. Strategic Coherence (The “Mission Plan” Advantage)
A worm must execute a non-linear lifecycle: *Recon $\rightarrow$ Exploit $\rightarrow$ Privilege Escalation $\rightarrow$ Lateral Movement $\rightarrow$ Persistence.*
* **Without Artifacts:** The LLM might attempt “Lateral Movement” before it has even found a network password because it “forgot” it hadn’t finished the “Privilege Escalation” stage.
* **With Artifacts:** The agent maintains a `Mission_Plan.artifact`. Every block begins by checking this plan: *”What is my current stage? What is the next required artifact? Am I on track?”* This ensures the “agent of agents” follows a logical, multi-step strategy rather than a series of random, disconnected actions.
### 3. Knowledge Propagation (The “Collective Memory” Advantage)
This is the “killer feature” for a worm.
* **Without Artifacts:** Each new infection is a “blank slate.” The worm lands on a new machine and must re-scan the entire network, which is slow, loud, and easily detected by firewalls.
* **With Artifacts:** The worm bundles its `Network_Intelligence.artifact` into its propagation payload. When it infects Machine B, it doesn’t just bring its “brain” (the LLM code); it brings its **memory** of Machine A and the entire network. It can jump straight to the next target. It becomes an **Experience-Sharing Distributed Intelligence.**
### 4. Error Recovery and “Checkpointing”
* **With Artifacts:** Because the agent’s “state” is external to the prompt, it can perform **automated rollbacks**. If an exploit attempt crashes the reasoning loop or produces a nonsensical error, the agent can reload its last “known-good” `State.artifact` and attempt a different branch of its reasoning tree. This makes the worm incredibly resilient to the “chaos” of unpredictable target environments.
### Summary: The Architectural Shift
| Feature | Traditional LLM Agent | Artifact-Chain LLM Worm |
| :— | :— | :— |
| **Memory** | Transient (Text History) | Persistent (Structured Artifacts) |
| **Context Limit** | Hard Wall (Token Limit) | Virtualized (State-based) |
| **Intelligence** | Reactive (Prompt-driven) | Proactive (Goal-driven) |
| **Propagation** | Code only (Fresh Start) | **Code + Experience (Collective Intelligence)** |
In short, this architecture turns the worm from a **Sequence of Commands** into a **Distributed State Machine** that uses the LLM as a high-level “transition engine” between complex, structured states.
1501247.octet-stream