How we evaluate, select, and evolve our technology choices at Oboyu
Our technology decisions reflect our core values:
- Sincere: We choose technologies that genuinely solve problems, not just trendy ones
- Experimental: We're willing to try innovative approaches and learn from failures
- Open: We prefer open-source solutions and share our learnings
When evaluating any technology, we score it across six dimensions:
- Does it directly address our specific challenge?
- Is it designed for our use case or a general solution?
- How well does it handle Japanese language specifics?
- Measurable improvements in speed, accuracy, or efficiency
- Resource consumption (CPU, memory, storage)
- Scalability characteristics
- Active development and maintenance
- Community size and engagement
- Available resources, documentation, and examples
- How easily does it fit into our existing architecture?
- Learning curve for our team
- Migration complexity from current solution
- Long-term sustainability
- Vendor lock-in considerations
- Technology trajectory and evolution
- Open-source preference
- Community values alignment
- Transparency and openness
Challenge: Need fast analytics on embeddings and graph data
Options Evaluated:
| Option | Problem Fit | Performance | Community | Integration | Future | Values | Total |
|---|---|---|---|---|---|---|---|
| PostgreSQL + pgvector | 6/10 | 5/10 | 9/10 | 8/10 | 9/10 | 10/10 | 6.55/10 |
| DuckDB | 10/10 | 10/10 | 7/10 | 9/10 | 8/10 | 10/10 | 9.35/10 |
| Pinecone | 7/10 | 8/10 | 6/10 | 5/10 | 6/10 | 3/10 | 6.25/10 |
Decision: DuckDB Rationale: Perfect fit for embedded analytics, exceptional performance, strong future
Challenge: Need high-quality Japanese text understanding
Options Evaluated:
| Option | Problem Fit | Performance | Community | Integration | Future | Values | Total |
|---|---|---|---|---|---|---|---|
| OpenAI API | 8/10 | 9/10 | 5/10 | 7/10 | 7/10 | 2/10 | 7.15/10 |
| HuggingFace cl-tohoku | 10/10 | 9/10 | 8/10 | 8/10 | 8/10 | 10/10 | 9.05/10 |
| Google Vertex AI | 7/10 | 8/10 | 6/10 | 6/10 | 8/10 | 3/10 | 6.8/10 |
Decision: HuggingFace Models Rationale: Specialized for Japanese, excellent community, aligns with open values
- [ ] **Proof of Concept**: Built working prototype
- [ ] **Benchmarking**: Measured performance vs alternatives
- [ ] **Integration Test**: Verified compatibility with existing stack
- [ ] **Resource Analysis**: Profiled CPU, memory, storage impact
- [ ] **Error Handling**: Tested failure modes and recovery- [ ] **Community Health**: Checked GitHub activity, issue resolution
- [ ] **Documentation Quality**: Assessed learning resources and examples
- [ ] **Maintenance Burden**: Estimated ongoing operational costs
- [ ] **Lock-in Risk**: Evaluated switching costs and alternatives
- [ ] **Value Alignment**: Confirmed fit with open-source preference# Phase 1: Isolated experiment
def experiment_new_technology():
# Small, contained test
results = new_tech.prototype()
return evaluate_results(results)
# Phase 2: Parallel implementation
def parallel_implementation():
# Run alongside existing solution
old_results = current_solution.process(data)
new_results = new_solution.process(data)
return compare_results(old_results, new_results)
# Phase 3: Gradual migration
def gradual_migration():
# Migrate non-critical paths first
if is_critical_path(request):
return current_solution.process(request)
else:
return new_solution.process(request)Every technology decision must include:
- Success Metrics: Clear performance targets
- Failure Criteria: When to rollback or pivot
- Timeline: Evaluation period and decision point
# Always maintain fallback options
class TechnologyGateway:
def __init__(self):
self.primary = new_solution
self.fallback = current_solution
self.success_rate = 0.0
def process(self, request):
try:
result = self.primary.process(request)
self.success_rate = self.update_success_rate(True)
return result
except Exception:
self.success_rate = self.update_success_rate(False)
if self.success_rate < 0.95: # Rollback threshold
return self.fallback.process(request)
raiseFor any NLP technology:
- Can it handle Japanese text without preprocessing?
- Does it understand cultural and linguistic nuances?
- Is it trained on Japanese data, not just translated?
For any infrastructure technology:
- Can it run efficiently on personal devices?
- Does it minimize external dependencies?
- Is operational complexity low?
For any core technology:
- Is the source code available and auditable?
- Can we modify it for our specific needs?
- Does it have a healthy open-source community?
# Automated monitoring of technology health
def evaluate_technology_stack():
for technology in current_stack:
health_score = {
"performance": measure_performance(technology),
"community": check_community_health(technology),
"alternatives": survey_alternatives(technology),
"satisfaction": team_satisfaction(technology)
}
if health_score["overall"] < threshold:
trigger_reevaluation(technology)- Performance degradation > 20%
- Community activity drops significantly
- Better alternatives emerge with > 2x improvement
- Major security or stability issues
- Team productivity impact
- Proof-of-concept first: Saved us from bad choices
- Community research: Active communities = better long-term outcomes
- Japanese-specific evaluation: Generic solutions often fail
- Performance benchmarking: Measurable improvements guide decisions
- Earlier adoption: Sometimes we waited too long to try new tech
- More migration planning: Underestimated switching costs
- Better documentation: Our decision rationale should be clearer
- Team input: Include more voices in evaluation process
- Rust for systems programming: Memory safety + performance
- WebAssembly for edge: Browser-native AI inference
- Mojo for AI: Python-compatible, performance-first AI language
- Modular inference: Composeable AI model architectures
- Automated scoring: Scripts to calculate decision matrix scores
- Impact tracking: Measure actual vs predicted outcomes
- Decision templates: Standardized evaluation formats
- Community input: External feedback on our technology choices
"Good technology decisions compound over time. The frameworks we choose today shape the possibilities of tomorrow. We strive to make choices that honor both our current needs and future potential." - Oboyu Team