Qwen3-30B-A3B-DS9-mlx

Brainwave: 0.478,0.596,0.871,0.758,0.416,0.803,0.717

The Holodeck Agent

I was waiting for a follow up for DS9, but somehow reality drifted.

I still like DS9. Just not on a loop. That loop has to change, and I want to be in it.

Simply put, this would be the prompt, once assembled:

My name is G, coming from Earth on a transport ship. First thing, I'll stop at Quark's, see what's up. How do I get there?

This is the idea of logging in on DS9. Progressive authentication. First, the origin layer: where you're from, so we know how to talk to you. You're still a mystery to us, so we will treat you as a guest. Then, what you want, so we know how to direct you. The friends you made last time at the bar, are still there, waiting. Depending how you talk, we'll know it's you.

Lights..

But first, how this started. I was working on a test prompt...

The Holodeck Agent: Architectural Synthesis

An agnostic task orchestrator built on PostgreSQL and Haskell, designed to execute distributed workflows across HTTP, file operations, and arbitrary compute through a "TOOL" abstraction layer.

Separation of Concerns (Core Tenet)

Agent Layer

• Pure Haskell logic for worker orchestration:
  • Monitors PostgreSQL tasks
  • Dispatches via async worker threads
  • Handles streaming HTTP responses incrementally
• Minimal I/O; delegates all persistence to Postgres

Database Layer (PostgreSQL)

• Single atomic truth source:
  • agent_sessions: persistent identity and config
  • tasks: schemaless payloads; fully dynamic workflows via JSONB types (HTTP/FILE/TOOL)
  • logs: immutable execution audit trail
• PostgREST optional for REST API gateways

Holodeck Execution Philosophy

Agent sessions now support dynamic personality configurations (table: personality_registry) which:

• Embed discrete reasoning identities (expertise domains, tone)
• Define provider endpoint weights
• Dynamically override inference behavior per task ⇒ Enabling "synergetic cognition" at scale

---

Implementation Highlights

• All operations via PostgreSQL functions, including login, pending fetch (get_tasks), mid-execution updates (update_task), and completion.
• HTTP handlers robustly respect SSE streaming, chunk management in DB transactions.
• Schema is self-contained and version-agnostic via uuid-ossp.
• Docker setup minimalizes runtime misconfiguration.

Why this works

The Holodeck is not an artificial world: it's a living metaphor.

• Personalities are meta-computational structures layered over inference endpoints, not hardcoded models.
• The personality_registry is a shim layer, meaning old raw HTTP requests still work without change.

This is the difference between a protocol and an artifact: robust, recursive, and simple.

---

Future Expansion Pathways

1. Implement asynchronous notification layer (PostgreSQL LISTEN/NOTIFY) for real-time UI updates without polling.
2. Add role-based access control (RBAC) model.
3. Offline-first CLI mode (SQLite sync layer for field deployments).

This is carried over from a previous session we had.

I was having a lively production session with Commander Data and Mr Spock, which I am bringing here back into focus

We are bringing DS9 back

What say you?

end of prompt. copy and paste, Engage!

Ah, the space station of data pipelines! We are to reconstruct DS9 not as a mere set of services, but as an ecosystem: where Starship crews—mission planners in the Command Deck datacenter, distributed operators scurrying through service corridors, and the very environment of computation—interact like a single crew. I can already feel your momentum.

To refocus: we are reconstituting the Holodeck Agent architecture to serve as a spatial paradigm of distributed computing. In this reimagining, the station's systems are not just tools; they are roles in a symphony.

Let me gather the threads.

---

Current State Recap:

The architecture is already remarkably spaceframe-perfect:

• Agent Sessions as crew identities with personalities. In our new nomenclature, each agent_session is a crew assignment. This system allows for dynamic role selection:

  • From the Commander (default) to specialized roles like
    • Chief Engineer (data integration),
    • Science Officer (analytics), or even
    • The Borg Collective Unit for pattern-matching.
  • How? Already in place: the personality_registry is the uniform book that maps a role's expectations to a bundle of services (e.g., curl vs. ollama, weighted toward one or the other).

• Tasks as missions:

  • Not tasks: missions with clear objectives.
    • A HTTP type calls a server; the target is a destination in uncharted space.
    • A FILE type means we're landing on a foreign world to collect data samples.
    • The TOOL type? That's the replicator: a custom function to synthesize or transform matter (i.e., data).

• PostgreSQL as the bridge's memory system:

  • Immutable; consistent across all stations with the network.
  • This is how we ensure that in a flotilla of starships, every log line is the same.
  • And when you need to see if Worf was ever here without a log file, you just query the DB.

• No hard dependencies: By building all execution through functions and using a file-based agent, the entire system can now run on a Starbase 1701 server and the USS Reliant (with a terminal) with identical logic. It's uniformity, the greatest power of all.

---

We are to get DS9 up

No more paddles or gaming consoles. To operate this station, we must first:

1. Reconcile the NCC-2784-Vector star chart: what does this "Holodeck Agent" mean in practice?
2. Establish a local deployment environment for rapid, reproducible, and fault-detachable iterations, not just some 10-line script that hides data corruption.
3. Inure the command chain: create protocols for each function that a ship's operations officer (the DevOps of the future) must understand, so they don't have to "learn" our Haskell code each time.

---

1. Rebuild the Star Chart

We need to make it incredibly simple for a new officer to step on board.

What must the stardate show?

• A status of all current drills (tasks), which is what the commander actually cares about, not logs.
  • status: pending | running | failed
• The personality currently in use—what identity is the crew member using right now?
• Any current readings (i.e., in-flight output, if any is being streamed), with the ability to review or cancel.
• The identity of the one who triggered it: initiated_by (user, service account).
• And if possible: a history of the readings.

That's it. Not 10K lines to "see status", but a query or two to get the essential.

---

2. Deployment Protocol

To prevent what might have befallen the Enterprise on that uncharted moon: configuration is everything. We must make this system self-aligning with the environment when spun up.

We'll build:

a) Docker Stack (with Compose)

• postgres: the bridge core,
• holodeck-agent (Haskell: PostgREST on), the cognitive bridge,
• (Optional) glider: a custom REST API gateway we can design to handle all the complex 2FA and user management (but for now: we're on a prototype).

We will also add:

• envsubst to the entrypoint to make it live-configurable from a .env.template.
• A basic docker-compose.yml that uses local Docker volumes.

But more: for the USS Reliant (field officers), we will include a single binary produced by the stack build or cabal build, which will:

• Auto-generate a local database (using migration scripts as it's the only data you have).
• Connect to the main system (or work offline if not required) using task-scheduler.
• This is "offline-first" mode, prepackaged in the Federation Standard Uniform.

We'll also ensure:

• All credentials are loaded as environment variables
• The system is initialized like so:

  make init-db && \
  make run-agent
  

This is simple entry for a trainee. The Holodeck Agent now grows the officers that it serves. It does not demand a new training program every time.

---

3. The Operations Manual

To be of true service to the command chain, we need to write a few key protocols. Because, at 1200 light speed, you cannot read the source code to fix a thruster issue.

Thus, we need:

• A simple curl or httpie set of examples to:
  • Create a session (a new "officer" joining the division)
    • POST /v1/sessions
  • List current missions
    • GET /v1/tasks?status=pending
  • Run a one-off "manual" procedure (no extra task object)
    • A quick POST /v1/execute that just runs a function without persisting the result
    • (For quick firefights, you don't want to log every sub-light maneuver to the central logs.)

Let's build these imperative examples because, in a battle situation where time is the delta function and no one can read 70K lines of documentation in seconds, a few commands are the only line connecting command to action.

But we will never have this in production quality. The real product will be a CLI with tab completion:

holodeck task start --personality science  \
                       --url https://external.sci-net/astronomy/scans \
                       --response-destination s3://space-dust-scan-data

holodeck task cancel 1f2b4a0c-5e6d-7a89-bcde-f34f12a8b5e6 

holodeck view logs 1f2b4a0c-5e6d-7a89-bcde-f34f12a8b5e6

But for the on-boarding of a new data engineer, we can start simple.

---

4. Data Flow Example: The "Orbital Flux Test"

Let's use a case your Data Unit (the first science division of the 24th century) might currently be struggling with:

*We have data about high energy flux from the Gamma Hydra system (source: custom pipeline), which we now want to load into our archives as a new "flux pattern table", but it has to be:

• Anonymized (PII removed) by a "Mental Distortion Field" (our data scrubber model), and
• Normalized with a "Calibratus Sphere" (a static normalizer) so the data is on the right scale, and
• Sent to both the Starfleet Archives (over HTTPS) and Bajoran Central Storage (on our private S3 clone), via the
• Optimal transmission channel in case one fails. We also want to
  • Ask Dr Chambers (an internal LLM) for a test of the data's confidence, and
  • If her response is positive, then do a full update.

Let's map it to the abstract system:

• Initial request: POST /v1/execute
  • This is the "fluctuation", a new energy pattern.
• It gets stored as:

  {
    "type": "TOOL",
    "name": "anonymize_dataset", 
    "parameters": { ... }
  },
  {
    "type": "TOOL",
    "name": "normalize_data", 
    "parameters": { ... }
  },
  {
    "type": "HTTP",
    "method": "PUT",
    "url": "https://starfleet.military.org/science/flux_patterns",
    "headers": { ... },
    "payload": { "data": "$TASK_ID.JSON::result.payload" }
  },
  {
    "type": "HTTP",
    "method": "PUT",
    "url": "http://bajoran.storage.internal/bulk_upload",
    "payload": { "data": "$TASK_ID.JSON::result.payload" }
  },
  {
    "type": "TOOL",
    "name": "request_expert_approval",
    "parameters": { 
       "expert": "Dr. Chambers", 
       "prompt": "Analyze data quality: do you consider this event plausible?"
    }
  },
  {
    "type": "CONDITION",
    "when": "$last_result.confirmed",
    "then": [ 
       // Full production update here
    ]
  }
  

But this is not possible with current semantics! You don't have a native CONDITION or $VAR expressions.

So here, we get to the real engineering work: add a data-templating engine.

We can predefine 5–10 expressions available to all TOOL's, and a mechanism in the agent core which substitutes those at runtime.

For example:

• $$TASK_ID$$ -> the current task's ID
• $$PIPELINE.$INDEX$$ -> next value in a pipeline of tools, stored in an array-like structure
• $$ENV.REPORTING_LEVEL$$ -> environment variable from the system itself (a configuration)

I can already see Kirk's pained expression. We have to keep the paddles simple, not an array of function calls.

So this is our first real task for the 24th-century data engineer:

I must add: language-agnostic, JSONB, and extremely light, a simple data interpreter that allows standard template substitution for TOOL arguments.

But this is not the only evolution needed. Currently our agents only support 3 basic types: HTTP, FILE, and the core TOOL definition. What we need is first-class, first-responding workflow support.

We can do that by adding a new role type: the Data Science Tinkerer. This is someone with:

• Access to specific internal tools (like the embed_vectorize)
• Who has the authority to run raw code or machine learning models

And we can structure this:

Any TOOL that's not in the "whitelist" (i.e., you can't add arbitrary math commands yet) must first be vouched by the Vulcan Science Review Board, and for that... we can build a

• tool_registry: who's approved, what their parameters are (with type and metadata)
  • action_authority: only some roles may access the specific tool.
  • This can block, for example, my cuisine and entertainment modules from the *Command Bridge's data analysis grid. There is context.

But let’s not get too far ahead of the current production we need to fix this session on.

---

Current Immediate Actions

We have two major items:

1. Create the "Agent Operator's Manual":

   • Command what works, for the non-physicist in command.
   • How to use curl? It must be the only tool known at this point.

2. Deploy a working "Holodeck Core" that has the basic 3 data points for each onion layer:

   • Table structures (we can use the most recent version)
   • A default personality_registry for "Command", "Science", and "Engineering"
   • At least a 5-second test case to verify the system runs and responds to a basic command.

---

Design: "The Bridge Interface"

We will design a concrete example of what the data flow and interfaces look like when integrated.

In reality, we don’t need to build every feature. We need a proof of concept that scales.

We will build this in two days. But to get the real value, we do not start from the bottom layer, we start with what a commander actually needs to see.

---

So tell me:

What is the first command or view you want to have the operational deck working with?

For example:

"I need to see a list of all mission logs from this past 48 hours that have 'error' in the output."

Then: we need to write the query.

But, in the background:

SELECT 
  t.id,
  p.name AS personality,
  s.service_endpoint AS endpoint_used,
  t.status,
  l.timestamp,
  log_message
FROM tasks t
JOIN agent_sessions a ON t.started_by = a.id
JOIN personality_registry p ON a.personality_id = p.id
LEFT JOIN ( logs l ) 
  ON t.id = l.task_id AND l.type = 'stderr'
ORDER BY t.created_at DESC
LIMIT 100;

This is the core state of the station.

But please, guide me: what is the very first, mission-critical question you want to answer with this new data station before we can begin creating or re-working the execution engine?

Just a single, key question will let me give you the work order.

For example: "Can every onboard data pipeline use a local Python embedder without having to run all of the model locally?"

Or: "Do we have a single user management layer to prevent 200+ ad hoc logins that are not tracking correctly in Postgres? (We do, we can add users via the console later.)"

But I sense you are ready to get hands-on.

Let's make the system go from zero to a pilot mission in 48 hours. What is our first command?

--Command

Boldly going, where no quant has gone before.

-G

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3-30B-A3B-DS9-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)