I bring 10+ years in product management, partner ecosystems and commercial operations to building tools for the work I know.
I define the architecture, direct AI-assisted implementation, and personally test and validate the result. The work below shows the systems, the decisions and the evidence behind them.
The deterministic path makes the scoring and generated documents inspectable without relying on a model response. The agent can call the same functions rather than invent a separate scoring process.
In the demo, use Load Sample to inspect the synthetic questionnaire and generated documents. The live agent is a separate path; a working deterministic demo does not establish model availability.
The public repository includes parser and scoring logic, fixtures, tests and an evaluation harness. See the dated build record ↓
Working locally
Blackboard
A visible record of work across people and agents.
A local viewer for file-based events: handoffs, shared artifacts and activity across the fleet. I use it in my own environment and refine it through practice.
The implementation choice
The record remains readable even when the viewer is down.
Events are plain JSON files. This describes the local implementation; there is no public demo here.
The build record
02 / Selected evidence
A few dated implementation steps. Open a note for the scope and source behind it.
06–09 Jun 2026DIA: from public demo to capture toolingPublic commits and a scoped historical test record
06 Jun · 19:43
Initial public demo. The commit contains the parser, scoring, document generation, web interface and tests. Commit ↗
09 Jun · 01:01
Capture integration. The original integration note records 26 passing tests across three named test modules. This is a historical result, not a current full-suite total. Original note ↗
09 Jun · 02:37
Capture made opt-in. The implementation gates OBS capture behind an explicit setting and catches failures so they do not break the intake response. Implementation ↗
Times shown in EDT (UTC−04:00), from the original commit records. No elapsed-time or submission claim is inferred.
Report retrieval. A v1 API client creates or resumes a report, waits for completion, then downloads it.
28 Jul · 00:41
Durable data layer. Parquet snapshots feed a rebuildable DuckDB store and a read-only query interface.
28 Jul · 00:57
Console-export support. An additional ingestion path normalizes exported report columns and handles year-month periods.
EDT (UTC−04:00). Implementation checked against the corresponding local repository revisions. These are engineering milestones; account data, client outputs and commercial performance are excluded.
08 Oct 2026Bespoke: repeatable desktop actionsLocal implementation · verified step recipes and durable targets
Work on remembering a step after its result has been checked, then replaying it against a durable target. Replay follows the operator’s selected approval mode.
Internal implementation record; source remains private. This is not a public release, a client deployment or a measured productivity result.
Tools I work with
03 / The supporting systems
Bespoke
Operator-controlled reading, voice interaction and desktop actions. A local tool for moving between the written word, conversation and repeatable steps.
Local implementation
speak
Read text with playback controls.
ask
Answer a prompt, then speak the response.
live
Stream microphone audio and native audio replies, with interruption handling.
cursor
Desktop actions with durable targets and verified step recipes.
Scope & implementation
The repository implements both turn-based ask and a separate real-time live mode. The latter streams audio over a websocket and flushes playback when interrupted.
Desktop replay follows the operator’s selected approval mode. Verified step recipes and durable targets describe the local implementation; they do not establish independent operation or client deployment.
This portfolio describes implementation work, not a new end-to-end microphone or latency test. It is not presented as a full accessibility screen reader.
Baton
Routing and coordination between agents, supporting how work moves through the wider environment.
Working locally
Agent Eggs
Portable context and capabilities, packaged so useful knowledge can travel between environments.
Reusable context packages
Research & writing
04 / The thinking behind the work
dPKL is the foundation of how I think about AI systems. These four connected works explore knowledge, execution, the operator’s role and human autonomy.
Main framework · v1.1 · 29 July 2026
The Deterministic Personal Knowledge Layer (dPKL): Agents as the Interface to Deep Execution
A local-first approach to making working knowledge portable and queryable, with agents acting as an interface to execution.
What Expectations? AI, Telecommunications, and the Tool It Always Was
Essay · v1.1 · 29 July 2026
AI as infrastructure and a tool, with human judgment shaping how its capabilities are used.
Agents Are Your New Mouse and Keyboard: An Operator’s Doctrine for Driving Agents
Operator doctrine · v0.5 · 3 August 2026
An operating practice for directing agents through command-line tools, documentation, reusable capabilities and human validation.
What Autonomy Is, and Why It Remains Exclusive to Humans
Draft essay · v0.1 · 6 August 2026
A philosophical argument for keeping purpose and responsibility with humans, and designing agents for bounded, reliable execution.
The ideas and operational experience are mine; each work discloses agent-assisted prose and formatting. Full texts are not published here.
Where the judgment comes from
05 / Enterprise experience
Product lines, partner programs and commercial operations taught me to account for the messy parts: incomplete information, handoffs and competing priorities.
LG ElectronicsCommercial operations & retail media
TD SYNNEXProduct lines & vendor relationships
Ingram MicroMicrosoft Surface partner programs
That experience shapes the problems I choose, the systems I design and what I check before calling something useful.
Put a workflow to work.
06 / Work with me
Have a recurring workflow that needs attention? I work on business automation through BA$H. Start with one task, agree the scope and acceptance checks, then build from there.