Put your attention back in your hands.
A workspace concept for coordinating tasks and tools without letting every notification become your next priority.
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Open the design and demonstrations, then follow the references for more detail.
The design, in more detail
Definition
An executive operating surface and internal task coordinator managing cross-venture execution, persistent SQLite telemetry (spark.db), and attention management.
The problem
Managing multiple deep-tech companies, research laboratories, and civic initiatives simultaneously creates severe cognitive overhead, fragmented documentation, and lost momentum due to constant context switching.
The approach
Spark replaces ad-hoc task managers with a sovereign attention control surface. It structures projects into 18 bounded streams, maintaining continuous telemetry in spark.db and enforcing a 5-tier delegation loop (Level 0 immediate triage to Level 5 autonomous multi-agent execution) guarded by an attention firewall.
How it works
- 18-Stream Bounded Workspaces: Isolates technical context, evidence logs, and roadmap assets across distinct corporate and research domains.
- Single-File SQLite Telemetry: Centralizes all system state, agent actions, milestone completions, and time allocations in spark.db.
- 5-Tier Autonomous Loop: Structures execution from Level 0 (raw command prompt) up to Level 5 (fully autonomous multi-agent task completion).
- Cognitive Attention Firewall: Filters inbound distractions and batches cross-stream requests to protect high-leverage deep work blocks.
- Integrated Command Center: High-density dashboard visualizing active subagent transcripts, task blockers, and financial runway metrics.
Project notes
- Streams Orchestrated
- 18 Concurrent Ventures
- Telemetry Core
- Single-File SQLite (spark.db)
- Autonomy Model
- 5-Tier Delegation Loop
These are the project’s documented design notes. Consult the linked implementation and its version before relying on a specific capability.
Development history & next steps
Schema Architecture
Designed unified spark.db schema tracking tasks, milestones, and cross-stream dependencies.
18 Streams Formalization
Partitioned all commercial, hardware, and civic ventures into bounded autonomous streams.
Level 5 Autonomous Agents
Implemented multi-agent dispatch loops with automatic checkpointing and rollback capabilities.
What comes next
Integrating local offline LLM reasoning directly into the spark.db event trigger system for proactive workload optimization.
Source material & related links
Follow the documentation, repositories, and related sites behind this project.
Spark orchestrator, spark.db schema, subagent scripts, and stream dashboards.
Systems design for multi-agent autonomous project orchestration and attention preservation.
Topics: AI Agents · Operating Systems · Orchestration · SQLite · Productivity · Automation