Continuous Delivery Spikework Retro

Template Overview

Before committing engineering sprints to unproven technologies, technical spikes de-risk architectural decisions through rapid, time-boxed prototypes. The Continuous Delivery Spikework Retro template captures empirical benchmark findings, technical dead-ends, and clear adoption decisions in plain Markdown.

Markdown Template Structure

Copy the block below or click Download .md File to add this template directly to your personal vault:

---
title: "Spikework Retro: [Spike Topic / Prototype Name]"
date: [YYYY-MM-DD]
lead_researcher: "[[Lead Systems Engineer]]"
collaborators: ["[[Architect Name]]", "[[Senior Developer]]"]
timebox_duration: "[3 Days / 1 Week]"
prototype_decision: "ADOPT / REJECT / EXTEND SPIKE"
tags: [spike, prototype, research, architecture, engineering, agile]
---

# Continuous Delivery Spikework Retro — [Spike Topic]

## 1. Core Hypothesis & Spike Objectives
- **Hypothesis:** Compiling local Whisper models using Apple Metal Performance Shaders will yield 3x faster inference speeds compared to standard CPU matrix operations.
- **Timebox Scope:** 5 working days; prototype limited to evaluating Whisper `tiny.en` and `base.en` models on M-series chips.

## 2. Prototype Architecture & Empirical Benchmarks
| Benchmark Metric | CPU Baseline (M2 Pro) | Metal Shaders Prototype | Performance Delta |
|---|---|---|---|
| Real-Time Factor (RTF) | 0.28 (10 min audio in 2.8 min) | 0.08 (10 min audio in 48 sec) | **3.5x Faster** |
| Peak Memory Allocation | 340 MB | 210 MB | 38% Reduction |
| Package Binary Footprint | +0 MB (Pre-installed) | +14.2 MB (Compiled Shaders) | Acceptable |

## 3. Discovered Technical Dead-Ends & Limitations
- **Dead-End 1:** Attempted dynamic quantization on the fly; introduced noticeable transcription hallucination on accented speech.
- **Dead-End 2:** Metal shaders cannot easily run in background daemon processes without macOS active display server sessions.

## 4. Productionization Decision & Implementation Plan
- **Formal Decision:** **ADOPT (Productionize Metal Acceleration Pipeline)**.
- [ ] [[Lead Systems Engineer]] Author production RFC incorporating benchmark data by Friday.
- [ ] [[Senior Developer]] Design fallback CPU execution routine for non-Apple-Silicon environments.
- [ ] [[QA Lead]] Add automated performance regression test to CI test matrix.

Filled Real-World Example

Here is a realistic example demonstrating how this template captures actual deliberations:

---
title: "Spikework Retro: SQLite FTS5 Custom Tokenizer"
date: 2026-10-09
lead_researcher: "[[Devin Zhao]]"
collaborators: ["[[Tarek Mansour]]"]
timebox_duration: "3 Days"
prototype_decision: "ADOPT"
tags: [spike, prototype, research, architecture]
---

# Continuous Delivery Spikework Retro — SQLite FTS5 Custom Tokenizer

## 1. Findings
Devin Zhao demonstrated that compiling a custom C tokenizer directly into the local SQLite instance reduced wikilink search indexing time by 64% while enabling exact symbol matching for code spans.

## 2. Decision
The team voted unanimously to adopt the custom tokenizer for the upcoming desktop client release.

When to Use and When to Avoid

When to Use

• Use at the conclusion of any time-boxed technical spike or proof-of-concept prototype.
• Use to document quantitative benchmark comparisons between competing technical solutions.
• Use to preserve institutional knowledge regarding discarded dead-ends and failed architectural paths.

When to Avoid

• Avoid using for standard production feature code review retrospectives.
• Avoid using when no prototyping or empirical benchmarking was performed.

On-Device AI Integration

Summarize your recording in Oyma with this local instruction:

Analyze the spike retro audio recording:
1. Synthesize the initial technical hypothesis and quantitative benchmark findings across tested prototypes.
2. Document all discovered technical dead-ends, operational trade-offs, and unexpected system limitations.
3. Record the definitive productionization decision (Adopt / Reject / Extend) and immediate engineering next steps.
Output in clean, structured Markdown.

Your data remains securely on your Mac with zero cloud exposure.