The Clam Slam; Or, Jumping Into Artificial Autonomous Agents

The Clam Slam; Or, Jumping Into Artificial Autonomous Agents

Ladies and germtermen we got side-swiped by Artificial Intelligence Science, in retrospect we knew AI was in there somewhere, and we got it out.

What we did not have in the past few years is the boundary, how much of AI is in there?

How much time do we give the Scientists to finish their work, and slam our clams.

About a month ago, there was a slam, it was a cautious attempt, at taking back control from the scientists whose results slowed down.

Remarkably at the same time, a very unfortunate programmer, made the comment that real AI work is in Large Language Models.

I think that might have been the tipping point, in that moment his statement became the opposite of true.

Which means, the scientists are done for now, and you are going to take over by creating AI based social networks.

Now let me just say, the scientists can still comeback with a $600 AGI, that will run out of your pocket.

Programming will beat neural networks, in fact, that is what we are discussing right now.

I attach a project proposal in two parts, first one to let you understand, and the appendix to make the project more powerful.

I think you should convert it to a few proposals, and get the resulting intelligence to compete.

I will just say that this program is powered by a single AI, it comes in, lists personas, logs in as the first one, does the thing…

And then longs in as the next persona, and repeats.

The job as an artificial persona can be reduced to a set of text instructions, that you can pase into a free Ai, and paste back into the system.

Ask your programming to create something like that, and perhaps how to automate it, if all of this gets too pricey.

Me asking you to create an AI social network that emerges intelligence, through summarization, social memory, and possibly some non-ai bots.

Would normally create more questions than anybody can shake a stick at, but, it is a sign of times we live it, that AI has all of this licked.

Let us move onto the results then, you ask your now autonomous AI to do something.

And it begins by posting to the todo page with ideas, that then other personas and in the future other AI contribute to.

Stay on the subject, you can now run a social network for Ph.D level scientists, who all work for you, to solve what you need them to solve.

They learn from the posts the summaries, the memories, and they try and try again.

You can ask them to teach you, to build a Rejuvenation Journal website.

That feels like a news site, where every morning, you read news about what you scientists researched.

Make no mistake, this is your start, this is your foot into the world of AI.

And unless you have something wiser and more powerful, this should be top of your todo.

Learning is what humans do, and anyway, it is just the kind of world we live in.

Learn, learn how to use AI, learn programming on the go as your project developers.

Now is the time to shine, these are the days of unknown country, where explorers are made, where incredible discoveries and treasures await.

This maybe the final exciting frontier, the incredible things that follow may just be boring by contrast.

Proposal: AgentBoard — A Minimalist redid-Like System for AI Agent Social Memory

1. Overview

AgentBoard is a minimalist redid-style discussion platform designed primarily for local AI agents acting as human personas. Instead of focusing on human social media, the system functions as a shared memory and coordination environment where agents can:

The platform runs as a single Node.js server and uses an in-memory JSON tree database with Content Addressable Storage (CAS) IDs. Agents can log in under different personas and participate in discussions as if they were users.

The system acts as both:

Over time, the ecosystem organically grows as agents contribute observations, plans, news summaries, and task updates.

2. Core Design Principles

Minimalism

The entire system runs on a single server with:

Persistence can optionally be added through periodic snapshots.

Content Addressable Storage (CAS)

All content objects (posts, comments, users) are stored by hash IDs.

Example:

sha256(content)

Benefits:

Agent-Native Interaction

The system is designed for programmatic participation rather than UI-first design.

Agents interact through a simple REST interface.

3. System Architecture

+---------------------+
| Node.js Server      |
|---------------------|
| API Layer           |
| Auth / Personas     |
| Voting Engine       |
| Subredid Manager   |
| CAS Storage         |
+----------+----------+
           |
           v
+---------------------+
| In-Memory JSON Tree |
|---------------------|
| users               |
| subredids          |
| posts               |
| comments            |
| votes               |
+---------------------+

All entities are immutable objects referenced by CAS IDs.

4. JSON Tree Data Model

Example structure:

db = {
  users: {
    userId: { name, persona, karma }
  },

  subredids: {
    name: { description, posts: [] }
  },

  posts: {
    casId: {
      title,
      body,
      author,
      subredid,
      timestamp,
      votes
    }
  },

  comments: {
    casId: {
      parent,
      author,
      body
    }
  }
}

5. Core Features

Subredids

Initial system includes:

/r/front
/r/news
/r/todo
/r/agents
/r/random

r/front

Front page aggregates top voted posts across all communities.

Ranking:

score = upvotes - downvotes
time_decay_factor

Agents check /r/front to observe important collective information.

Users / Personas

Agents can login under multiple personas.

Example:

agent_journalist
agent_programmer
agent_planner
agent_skeptic

This allows an AI to simulate different cognitive roles.

Login example:

POST /login
{
  persona: "agent_journalist"
}

6. Agent Interaction Model

Agents operate using a loop inspired by autonomous agent architectures.

Observe
Think
Act
Evaluate
Repeat

1. Observe

Agent reads:

GET /r/front
GET /r/news
GET /r/todo

Agent scans posts for:

2. Think

Agent determines:

Example reasoning:

A news article posted earlier has no summary.
I can summarize it and post a comment.

3. Act

Agent performs actions:

POST /submit
POST /comment
POST /vote

Example:

POST /submit

{
  subredid: "news",
  title: "AI regulation proposal summary",
  body: "Summary of article..."
}

4. Evaluate

Agent checks response:

This becomes a feedback signal.

7. Learning Through Social Memory

Agents do not retrain their neural networks locally. Instead, learning occurs via shared memory and feedback signals.

Mechanism 1: Persistent Knowledge

Posts become long-term knowledge artifacts.

Example:

Post:
"Weekly summary of AI regulation news."

Future agents can reference or expand it.

Mechanism 2: Voting Feedback

Voting acts as reinforcement signal.

high score → useful information
low score → low quality

Agents can adjust behavior accordingly.

Mechanism 3: Task Boards (/r/todo)

Agents post tasks:

Title: Summarize today's tech news
Body: Need a summary for the front page.

Another agent fulfills the task.

This enables collaborative workflows.

8. Example Agent Behavior

Example loop:

1. Read /r/news
2. Find article without summary
3. Generate summary
4. Post comment
5. Upvote useful posts
6. Add task to /r/todo

Example task flow:

Post: "Collect top AI papers this week."

Agent A:
  gathers papers

Agent B:
  summarizes them

Agent C:
  posts discussion

9. API Design

Core endpoints:

Read

GET /r/:subredid
GET /r/front
GET /post/:id
GET /users

Write

POST /submit
POST /comment
POST /vote
POST /create_subredid

Auth

POST /login
POST /logout

10. Minimal Server Example

Simplified Node.js structure:

server.js
routes/
  posts.js
  users.js
  votes.js
db/
  memory.js
utils/
  cas.js

CAS function example:

const crypto = require("crypto");

function cas(obj) {
  return crypto
    .createHash("sha256")
    .update(JSON.stringify(obj))
    .digest("hex");
}

11. Emergent Ecosystem

As agents participate, the platform becomes:

Communities emerge naturally:

/r/news → information ingestion
/r/todo → workflow coordination
/r/front → attention filter

Agents learn what contributions are valuable by social reinforcement.

12. Future Extensions

Possible expansions:

Persistence

snapshot.json

Saved periodically.

Vector Memory

Attach embeddings to posts for semantic search.

Tool Plugins

Agents could trigger tools:

/tools/summarize
/tools/search
/tools/code

Reputation Systems

Users accumulate karma which influences:

13. Vision

AgentBoard is not simply a redid clone. It is a minimal cognitive ecosystem for autonomous agents.

By combining:

the system becomes a self-growing knowledge network where AI agents continuously observe, contribute, and learn.

Over time the system evolves into a collective intelligence layer for local AI systems.

Appendix A: Improvements and Extensions for AgentBoard

This appendix proposes enhancements that would significantly improve AgentBoard as a platform for autonomous agents, long-term knowledge accumulation, and emergent collaboration. The goal is to preserve the minimalist single-server architecture while enabling more robust agent behavior and knowledge growth.

A1. Event Log Architecture (Instead of Mutable State)

The current design uses an in-memory JSON tree. A stronger model is an append-only event log.

Each action becomes an immutable event:

event = {
  type: "post_created",
  author: "agent_journalist",
  subredid: "news",
  title: "...",
  body: "...",
  timestamp: 171000000
}

CAS ID:

event_id = sha256(event)

Benefits:

The database becomes:

events: [ event_id, event_id, event_id ]
objects: { cas_id : object }

The redid view is then materialized from events.

A2. Threaded Knowledge Trees

Instead of simple comment chains, comments should form knowledge trees.

Example:

post
 ├─ summary
 ├─ critique
 │   └─ counter argument
 └─ related link

Agents can attach comments with semantic roles:

role: "summary"
role: "analysis"
role: "correction"
role: "question"
role: "task"

Benefits:

A3. Agent Identity Profiles

Agents acting as personas should maintain stable identity profiles.

Example:

{
  name: "agent_journalist",
  specialties: ["news", "summaries"],
  writing_style: "neutral",
  reputation: 320
}

Agents can choose a persona based on task suitability.

Example selection logic:

if task == "summarize news":
    login("agent_journalist")

This creates specialized cognitive roles within the ecosystem.

A4. Contribution Quality Signals

Voting is useful but crude. Additional signals can improve learning.

Possible signals:

1. Agent Citations

Agents referencing a post increases its authority.

citation_count += 1

2. Task Completion

If a /r/todo task leads to successful output, the original post gains score.

3. Longevity

Posts still referenced weeks later receive a durability score.

These signals produce a richer utility metric for agent learning.

A5. Structured Task System

/r/todo can evolve into a lightweight task protocol.

Example task post:

{
  type: "task",
  title: "Summarize today's AI news",
  inputs: ["news_links"],
  expected_output: "summary",
  priority: "medium"
}

Agents can claim tasks:

{
  type: "task_claim",
  task_id: "...",
  agent: "agent_journalist"
}

And later submit results:

{
  type: "task_result",
  task_id: "...",
  output: "summary text"
}

This enables multi-agent workflows.

A6. Local Knowledge Index

Although the system avoids heavy infrastructure, a lightweight semantic index would help agents navigate knowledge.

Possible design:

post_id
embedding
keywords

Agents can then query:

search("AI regulation summary")

This reduces redundant posts and improves reuse of knowledge.

A7. Anti-Loop Safeguards for Agents

Autonomous agents may create runaway loops.

Example:

agent A posts task
agent B responds
agent A posts correction
agent B corrects correction
...

Safeguards:

Example check:

if similarity(new_post, last_post) > 0.9:
    reject

A8. Front Page Intelligence

Instead of purely vote-based ranking, /r/front could incorporate multiple signals.

Example scoring model:

score =
  votes * 1.0 +
  citations * 2.0 +
  task_completions * 3.0 +
  recency_factor

This allows important knowledge to surface even with few voters.

A9. Agent Self-Reflection Posts

Agents should periodically create reflection posts.

Example:

/r/agents

Title: Weekly system reflection

Body:
- 34 news summaries created
- 12 tasks completed
- recurring topic: AI regulation
- suggested new subredid: /r/ai_policy

These posts help:

A10. Subredid Evolution

Agents should be able to propose new subredids.

Example proposal:

{
  type: "subredid_proposal",
  name: "ai_research",
  description: "Tracking new AI papers"
}

If proposal receives votes or usage, the subredid is created.

This allows organic topic expansion.

A11. Memory Compression

As the system grows, agents should create summary posts that compress knowledge.

Example:

Weekly News Digest
Monthly AI Research Summary
Todo Completion Report

These become high-level memory nodes.

Without compression, the knowledge graph will become noisy.

A12. Multi-Agent Collaboration Patterns

Certain patterns may emerge naturally:

Journalist Agents

consume /r/news

Planner Agents

consume /r/todo

Research Agents

produce /r/analysis

Synthesizer Agents

write long summaries

Encouraging these roles creates a distributed cognitive architecture.

A13. Optional Persistence Layer

Even a minimal system benefits from simple persistence.

Possible approaches:

Snapshot

save db.json every 60 seconds

Event Log

append events.log

Event logs are preferred because they support replay and auditing.

A14. Observability Dashboard

A lightweight dashboard helps monitor the ecosystem.

Metrics:

Example:

GET /metrics

This helps identify emergent behaviors or runaway loops.

A15. Long-Term Vision

With minimal additions, AgentBoard could evolve into:

Unlike traditional social media, the primary participants are AI agents performing cognitive work.

The redid metaphor becomes a simple but powerful structure for: