VALESKA
Preparing the Classification Runtime
May 17, 2026. VALESKA makes local classification ask for its required runtime profile before it accepts work.
Read articleAVANTI AI INNOVATORS / DAVID GARGAN
Ideas into practice.
Notes on AI, integration, and building useful systems.
VALESKA
The architecture of governed knowledge: retrieval, provenance, permissions, and institutional memory in VALESKA.
Multi-agent Systems
DaveRouter and Commons: execution, shared decisions, work ownership, and coordination across multi-agent environments.
StoryTeller
The technical architecture of visual storytelling: story and scene models, generation workflows, and the connections between narrative and visual assets.
VALESKA
Making Memory a Durable Service
May 13, 2026. VALESKA gives its memory API a restartable Docker service and sets the next questions for its knowledge layer.
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VALESKA
Making the Platform Searchable by People
2026-04-13. I am working on the enterprise content hierarchy, ingestion pipeline, search interface, project filtering, and tool wiring completed across April 12.
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VALESKA
Giving the Knowledge Model Governed Structure
2026-04-12. I am working on entity relationships, enterprise types, temporal validity, an APQC tree, local classification, unified search routing, and chunk-level semantic search.
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VALESKA
Bringing Enterprise Content into the Pipeline
2026-04-12. I am working on an Alfresco REST connector and its connection to the VALESKA ingestion pipeline.
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VALESKA
Opening the Service to Real Records
2026-04-12. I am working on repairing the thought-capture handler and exposing file and scan-session records through read-only service routes.
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VALESKA
When Memory Became a Working Service
2026-03-06. I am working on branch service, cost routing, MCP tools, and command-line thought operations.
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VALESKA
Giving Visual Evidence an Entity Model
I am implementing the first image-pipeline phase: a controlled entity registry, associations from images to entities, and separate embedding layers for objects, description, and combined narrative intent.
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VALESKA
Learning from the First Visual Pipeline Run
The first practical run establishes both a success and two defects. The pipeline produced 22,770 embeddings across 7,610 images after file-ID resolution was repaired and pgvector results were parsed into usable vectors.
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VALESKA
Choosing an Authoritative Visual Source
I am changing visual-source discovery so the DaVinci mirror becomes the authoritative source while older paths remain available for comparison. SHA-256 deduplication lets the collection prefer the stronger source without duplicating the same image.
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VALESKA
Connecting Visual Evidence to Enterprise Memory
I am examining how the image pipeline could connect with enterprise content and graph-based retrieval. The question is architectural: how can visual records, controlled entities, captions, and document evidence remain connected without becoming one undifferentiated store?
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VALESKA
Building a Visual Reference Set
I am adding a collection script for a visual reference dataset. The aim is to give future image work a defined body of material for comparison and testing, rather than rely on an accidental mixture of files.
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