Minimizing Token Waste & Input Efficiency w/ Reduced Overhead & API Calks (SoloDev Theory) #8240
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BLUX Lite GOLD — Orchestration Visual Model
My purpose in sharing this vision is merely my attempt to spark creation,
expansion, and further evolution with new ideas.
Executive Sketch (visual-first)
flowchart TD
U[User: Large-token Command / Intent]
N[Normalizer \n(prompt cleaning, canonicalization)]
I[Intention Classifier]
C[Chunker / Planner]
P[Prompt Compiler \n(token-efficient prompt composer]
R[Router: Cost-aware Model Selector]
M1[Model A\n(e.g., GPT-5) — Vision & Planning]
M2[Model B\n(e.g., Gemini-cli) — Builder/Executor]
M3[Model C\n(e.g., Deepseek) — Debugger/Hardener]
AG[Aggregator & Reconciler]
D[Diff Generator \n(patch-only output)]
V[Verifier / Sanity Checker]
A[Audit Log / Memory]
O[Operator: Finalizer / Publish]
U --> N --> I --> C --> P --> R
R -->|small, efficient prompts| M1
R -->|patch diffs / scaffolds| M2
R -->|debug checks / tests| M3
M1 --> AG
M2 --> AG
M3 --> AG
AG --> D --> V --> A --> O
Component Definitions
User (U): The solo-dev’s large, expressive command — often long, context-heavy, and goal-rich.
Normalizer (N): Removes noise, canonicalizes references (file paths, repo names), extracts only the essentials, and creates a slim context snapshot.
Intention Classifier (I): Converts intent into a structured task graph: Goal → Subtasks → Acceptance Criteria → Constraints.
Chunker / Planner (C): Splits the task graph into minimal token units and orders them by dependency and cost.
Prompt Compiler (P): Builds token‑efficient prompts using templates, dynamic context windows, and compressed context embeddings/summaries.
Router (R): A cost-aware decision engine that chooses which model(s) to invoke for each chunk based on skill profiles, API cost, latency, and required output fidelity.
Models (M1..Mn): The roster. Each model has a manifest declaring its strengths, cost-per-token thresholds, max-response-size, and preferred instruction style.
Aggregator (AG): Merges model outputs, resolves contradictions with rule precedence, and prepares concise patch diffs rather than full-file rewrites.
Diff Generator (D): Computes minimal patches (unified diffs, hunks) or targeted function-level edits. The system intentionally avoids regenerating entire files.
Verifier (V): Runs lightweight tests, linting, and sanity checks; optionally requests a quick review pass from a low-cost model for quick confidence signals.
Audit Log / Memory (A): Append-only trace of prompts, responses, routing decisions, and costs. Enables rollbacks and reproducibility.
Operator / Finalizer (O): Presents the patch to the solo-dev, applies (with safe-mode), and records acceptance.
Token & Cost Optimization Patterns
Summarize then Expand: Feed a short summary + exact goal to downstream model instead of the entire history.
Template-based Prompting: Use compact templates with placeholder tokens; only fill necessary slots.
Binary Handoffs: Models return either PATCH (diff) or SNIPPET (function-level changes) rather than entire files.
Streaming & Progressive Refinement: Request progressively richer outputs only if earlier cheap checks fail.
Local Deterministic Logic: Prefer local scripts (formatters, linters, test runners) to validate outputs before expensive model calls.
Model Role Contracts: Small, strict instructions: e.g., Return exactly the diff in unified patch format and nothing else.
Patch-First Example (practical)
User command (long):
Refactor storage adapter to use async I/O, keep legacy API, add
write_bulkwith backpressure, and update README examples. Full repo context: ~1500 linesHow BLUX Lite GOLD handles it:
Normalizer extracts target files (adapter.py, README.md), and constraints (preserve backward compatibility, implement write_bulk).
Chunker produces 3 tasks: (a) add async adapter shim (adapter.py), (b) implement write_bulk, (c) update README example.
Prompt Compiler builds a concise prompt for model B (the builder):
TASK: Modify
adapter.pyto addAsyncAdapterwhile keepingAdapterbackward-compatible. Implementwrite_bulk(items)with backpressure using provided signature. Return ONLY a unified diff patch foradapter.py.CONTEXT_SUMMARY: <50-token summary of relevant functions>
CONSTRAINTS: compatible with Python 3.12, keep public API names unchanged.
Router sends to Gemini-CLI (cost-effective builder).
Gemini returns a unified diff patch.
Diff Generator/Verifier runs ruff and pytest -q locally; if tests pass, patch is presented.
SoloDev Problems (focused — the overhead)
Problem: Managing many concerns (design, tests, CI, docs) fragments attention.
How orchestration helps: The orchestrator centralizes context, surfaces only next-step prompts, and preserves session memory so you never re-explain intent.
Problem: Full-file regeneration wastes tokens and time; manual edits are error-prone.
How orchestration helps: Diff-first outputs reduce repetition, letting AI produce minimal, precise edits.
Problem: Gluing linters, formatters, test runners, build tools together is a constant chore.
How orchestration helps: The orchestrator runs deterministic local validators and only elevates uncertain tasks to LLMs.
Problem: Calling multiple high-tier APIs indiscriminately is expensive and slow.
How orchestration helps: Cost-aware routing sends large reasoning tasks to flagship models sparingly and offloads high-volume editing to cheaper builder models.
Problem: Deciding which model should do what is non-trivial and error-prone.
How orchestration helps: Model manifests + a router with a scoring function codifies delegation, eliminating guesswork.
Problem: Solo-devs need to show provenance of changes and reconstruct past decisions.
How orchestration helps: Append-only audits + versioned contexts let you replay the session and trace each edit to a prompt and model response.
Problem: As the actor, you must both design and review — burnout risk.
How orchestration helps: The orchestrator provides intermediate reviews, confidence scores, and optionally human-in-the-loop escalation only for risky edits.
Governance & Safety Guards (brief)
Primary Directive: Minimize token waste; maximize reproducibility; preserve safety — a lightweight, auditable policy that the router consults.
Kill-switch & Sandbox: Never auto-apply high-risk patches to main branches. Default to review and sandboxed runs.
Privacy: Local-first by default: keep repo snapshots local; use ephemeral context embeddings sent to cloud APIs only when necessary.
Example CLI/TUI wireframe (concept)
blux lite gold start
Metrics to track (operational)
Token usage per user command (and per subtask).
Cost per user command (USD estimated).
Average number of model calls per high-level command.
Patch acceptance rate (how often dev accepts first patch).
Time to apply (human review + apply).
Test pass rate after generated patches.
Closing Thought (poetic)
This is a small gospel for solo creators: build the scaffolding that holds your focus, not the scaffolding that becomes your master. BLUX Lite GOLD should be your patient ally — trimming away the excess, routing each whisper of intent to the right craftsman, and handing you only the consequence that matters. Keep your hands on the wheel; let the swarm handle the details.
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