Indicator Lab Workshops

Workshop 01 · 28 August 2026

Going Sovereign

Build custom agent workflows for investigations—then make them usable on infrastructure and small models you control.

Your host

Tom Vaillant

Tom Vaillant

Investigative Technologist

I'm a visual journalist and creative technologist uncovering stories hidden in data. Using AI to accelerate investigations and building open-source tools for journalists and researchers.

Tom Vaillant
Indicator Lab

Tinker with us

Tools, methods, live workshops, and practical support for putting owned workflows into use.

Indicator Lab · Desktop

Live demo

One place to install and maintain the tool suite

Indicator Desktop Tools screen showing Mycroft, Spotlight, Splash, Navigator, and Scoutpost installation states
WINWindows
MACmacOS
LNXLinux

Indicator Lab · Tool suite

Five tools · one workspace

Five tools for the full investigation cycle

MycroftRun the suite through Goose with a local model, AI subscription, or zero-data-retention provider.
SpotlightMethodology, OSINT research cycles, evidence, fact-checking, and reusable case archives.
ScoutpostMonitor pages, social profiles, councils, aircraft, vessels, and your beat for leads.
NavigatorFind OSINT methods across Indicator’s archives and more than 13,000 tools.
SplashTurn sources and data into reviewed charts, maps, stories, and visuals.

Use each tool independently—or connect the full suite through Mycroft.

Indicator Lab · Membership

Coming September 2026

Practical help when you need it

Learn from working examples and get direct help with your own setup.

Case studiesSee how journalists and newsrooms use AI in real investigations and recurring workflows.
Monthly Lab WorkshopOne useful subject, live—with practical demonstrations, debriefs, and methods.
Lab NotesThe useful ideas, decisions, examples, and resources in a written edition.
Tinkering support · twice a monthBring installations, local models, keys, reporting questions, and broken workflows.
$10 / month · add-on to Indicator ProNext office hours · next week

Indicator Lab Workshops

A live series, not one format

Four ways these workshops can earn the hour

  • Investigation debriefs: journalists unpack a real workflow—including where AI helped and where judgment stayed human.
  • Newsroom operating tours: how teams monitor sources, surface leads, and run recurring reporting work.
  • Practical workshops: build or test a method together, with the setup and failure points visible.
  • Deep-dive workshops: start with the basics, then go deeper—like this Going Sovereign workshop.
At office hours: bring installations, local models, keys, reporting questions, and broken workflows. I’ll also ask which formats and topics are most useful—future Lab Workshops will follow member priorities.

The goal

Why investigators should care

Build reporting workflows you own

  • Keep sensitive material on infrastructure you control.
  • Turn recurring reporting work into a method colleagues can run again.
  • Keep the model replaceable: use cloud for open material, local when custody matters.
01 · Model

First: what’s a model?

Forget the chat window for a minute. Start with the thing underneath it.

Model · The simple version

Text in · text out

A model is a very good next-token guesser

Trained patternsKnowledge encoded in the model’s weights.
Current contextThe words and material placed in front of it now.
No tools by itselfNo files, web search, registers, browser, or APIs.
No durable memoryNothing survives unless another system saves it.
  • It reads the context you give it and predicts what should come next.
  • It cannot fetch evidence, write a case file, or wait for an editor on its own.
  • Without a harness, the only thing it can return is more text.
02 · Harness

Now: what’s a harness?

The harness is what turns a talking model into a system that can do work.

Harness · The simple version

Model + system around it

The harness is everything around the model

Model wrapped by a harnessA model connects to context, tools, durable state, and a human gate inside a harness. HARNESS Contextmethods · sources · assignment Toolsfiles · search · browser · APIs Modelchooses the next useful action Durable statecase files · evidence · progress Human gateapprove · redirect · stop

A harness is code around the model: it builds the context, calls the model, exposes tools, runs selected actions, stores results, and calls the model again when needed.

Harness · Desktop agent apps

Claude or ChatGPT Desktop

A GUI around a harness around a model

That is the useful shorthand for Claude or ChatGPT Desktop. The harness supplies context, tools, and state; the model does the reasoning.

Desktop agent application around a model A desktop app connects to harness code, which calls a Claude or ChatGPT model. MCP tools connect directly to the harness code. tool calls + results Desktop appGUI · conversation · settings Harness codecontext · model calls · tools · state Claude or ChatGPT modelchooses the response or tool call Extensions + MCP toolsexternal data sources and actions

A simplified mental model for a familiar desktop agent app.

Harness · Places to start

Toolkit · framework · runtime

You do not have to build a harness from scratch

Agent toolkitPi

TypeScript building blocks for model providers, tools, sessions, and the agent loop.

Open-source runtimeGoose

A desktop and CLI agent with extensions, MCP connections, and configurable models.

Agent SDKOpenAI Agents SDK

Tools, handoffs, guardrails, sessions, and tracing around OpenAI models.

Agent SDKClaude Agent SDK

Claude Code-style agent capabilities, tools, permissions, and sessions as an SDK.

Agent frameworkGoogle ADK

Model and tool orchestration, workflows, evaluation, and deployment building blocks.

03 · Working example

Building sovereign workflows with AI

Spotlight is the example: an OSINT agent system that worked on frontier models, then became the blueprint for local infrastructure and small models.

Spotlight · What it is

An OSINT agent system

Spotlight is an agent system for running OSINT investigations

It takes a lead through methodology, research cycles, evidence archiving, independent fact-checking, and a reusable case archive.

LeadURL · document · tip
→
Scope + methodDefine the investigation
→
ResearchSearch · archive · evidence trail
→
Independent checkClaims · gaps · contradictions
→
Report + vaultVerified artifacts you keep
InvestigateOSINTFollow the moneyWeb archivingEvidence groundingReport drafting

Going Sovereign · The transferable blueprint

Custom workflow · owned infrastructure

Making Spotlight local produced a transferable blueprint

The same pattern can create custom sovereign workflows for any newsroom or company—run through its existing harness, or through a small local model when needed.

Custom methodSkills and rules encode how this organization works.
Owned data + stateFiles, evidence, context, and outputs stay where you choose.
Harness + runtimeUse an existing agent app, cloud model, or local model.
Custom outputAlerts, briefs, case files, records, or internal decisions.

Add validation or human approval only where the workflow needs it.

Harness · A newsroom example

One repeated beat task

Example: turn a council agenda into a lead brief

New agendaPDF appears every Friday
→
Beat contextWatchlist · prior decisions · entities
→
Research toolsExtract · compare · search records
→
Editor checksAre the anomalies real and relevant?
→
Lead briefSource-linked changes and next calls

The same controlled path runs next week. The journalist still decides whether there is a story.

Transferable blueprint · Examples

Separate workflows · shared pattern

One blueprint can support very different workflows

Procurement monitoringTenders + ownership records

Surface linked entities, conflicts, and source-backed alerts.

Council + policyAgendas + previous minutes

Find material changes and produce a lead brief with interview questions.

Company intelligenceFilings + archived web

Build an entity map and identify gaps that need reporting.

Internal knowledgeDocuments + staff notes

Create a cited brief or reusable decision record.

Document pipelineLong PDFs + forms

Extract relevant facts into a structured, source-linked record.

Going Sovereign · The challenge

Frontier-capable → small-model usable

How do you move a frontier workflow onto a small model?

Spotlight already worked with frontier models. The local 12B failed because the frontier-shaped harness overwhelmed it.

Frontier workflowLarge model copes with a heavy context and broad tool catalogue.
→
Small model stallsToo much context, brittle tools, lost state, weak procedure discipline.
→
Rebuild for the constraintSmaller context · helper RLM · working harness · procedure tune.

That engineering path—not a model-size comparison—is the Going Sovereign experiment.

Going Sovereign · Result

Laptop-class 12B · before and after

The small-model path became usable

Correct, gated first-turn brief
Frontier-shaped harness · 205 s
Local-engineered path · 74 s
2.8×
Time to first token
Frontier-shaped harness · ~34 s
Local-engineered path · ~0.46 s
74×
Full gated investigation
Beforeloops · dies mid-methodology
Afterresearches · fact-checks · finishes with recovery
✓

Before and after use the same 12B and laptop. The difference is the local-engineered harness plus the narrow procedure adapter.

Going Sovereign · One useful pattern

RLM · Recursive Language Model

A small helper model reads the messy stuff first

RLM stands for Recursive Language Model: in Spotlight, it is a second, smaller model with one job—compress large, noisy inputs before the main investigator sees them.

Long page or transcriptUseful reporting mixed with navigation, boilerplate, and old turns
→
Recursive Language ModelExtracts source-linked leads and a compact checkpoint
→
Main investigatorSees the useful material and stays on task

The RLM is not the evidence and it is not the fact-checker. The untouched source stays in case files you control.

Use the pattern for: meeting transcripts, filings, long PDFs, monitoring feeds, or any input too noisy to send directly to the main model.

Going Sovereign · Under the hood

One human · two model processes

The sovereign topology

Sovereign Spotlight topologyA reporter gates an orchestrator, investigator, fact-checker, and a smaller Recursive Language Model. The model processes run on separate local servers. tasktaskfetch / search / browsedistilcompact older context Reporterapproves at gates Orchestrator · 12Bplans · delegates · stops Investigatorisolated child session Fact-checkerindependent child session Search + browselocal integration tools Recursive LM (RLM)distils pages + old context llama.cpp · :8080hosts the main model :8095hosts RLM

Small models · The practical decision

Bigger is fine when it fits

Use the largest model your infrastructure comfortably supports

Frontier or cloud worksFor open material, use it. No tuning needed.
→
Larger local model fitsGreat. Run the stronger model on your own hardware.
→
Only a small model fitsIf it repeatedly misses the procedure, tune the procedure.

Do not fine-tune to save a working model. Tune only when hardware or custody constraints make the smaller model the useful path.

Procedure tuning · A practical route

RunPod + Unsloth

How do you teach a small model the procedure?

01Generate examples

Run the workflow with a frontier model, keep successful traces, and simulate the failure cases you need to correct.

02Curate the targets

Human-review every decision, tool call, correction, and stopping point before training.

03Train on RunPod

Use Unsloth with LoRA or QLoRA to train a small adapter on rented GPU hardware.

04Prove the workflow

Merge and export, run a coherence check, then repeat the full task, capability, and safety evaluations.

Spotlight used 14 real trajectories and 12 targeted corrections. Total RunPod spend across the iterations was about $10.

Transferable blueprint · Build your own

Start with one useful job

How can you use something like this?

Choose the jobWhat repeated work is worth automating?
→
Define input + outputWhat arrives? What useful artifact should leave?
→
Write the methodSkills, source priorities, checks, fallback rules.
→
Connect tools + dataFiles, APIs, search, internal knowledge.
→
Choose harness + modelExisting agent, cloud runtime, or local model.

Add validation or human approval where mistakes would matter. They are controls you choose—not the point of every workflow.

MonitoringResearchDocument reviewInternal knowledgeStructured alerts
Buried Signals × Indicator

Investigations with AIThe tools to run your own

buriedsignals.com/join