Soffio

Research Philosophy

Core Belief: Understanding technical systems requires more than reading papers - it demands building intuition through first principles, validating claims with data, and accepting that every design decision involves trade-offs.

1. First-Principles Thinking

Why It Matters

Too many technical articles assume knowledge or cite authority without explanation. I believe understanding why something works is more valuable than knowing that it works.

Example: Distributed Consensus

Instead of starting with "Raft is easier to understand than Paxos," I ask: Why is consensus hard in the first place? What fundamental properties (safety, liveness) do we need? What makes these properties difficult to achieve simultaneously?

My Approach

  1. Identify fundamental constraints (e.g., CAP theorem, network latency, hardware limitations)
  2. Question assumptions (What if this constraint didn't exist? What would change?)
  3. Build from basics (Start with single-machine, then distribute; start with perfect network, then add failures)
  4. Validate with reality (Do real systems follow theoretical predictions?)

2. No Silver Bullets: The Trade-Off Mindset

Every Decision Has Costs

❌ Bad Technical Writing

"Use Redis for caching. It's fast and reliable."

✅ Good Technical Writing

"Redis provides excellent read latency (<1ms p99) but requires careful eviction policy tuning. Trade-off: speed vs. memory management complexity."

Key Questions I Always Ask

  • Performance vs. Complexity: Does 10% speedup justify 3x code complexity?
  • Consistency vs. Availability: When should you choose one over the other?
  • Cost vs. Reliability: Is five-nines (99.999%) uptime worth 10x operational cost?

Transparency matters. I explicitly state trade-offs, not just benefits.

3. Data-Driven Analysis

Benchmarks Over Claims

📊 What I Include

  • Benchmark configurations: Hardware specs, workload patterns, test duration
  • Actual numbers: Not "fast" but "4.2ms p50, 12.8ms p99 latency"
  • Variance: Standard deviation, outliers, tail latency
  • Reproduction steps: How to verify results independently

Case Studies > Theory

Real-world production systems reveal insights papers can't:

  • Failures: How did etcd handle split-brain scenarios?
  • Scale: What broke when Kubernetes scaled from 100 to 5000 nodes?
  • Evolution: Why did Google move from Borg to Kubernetes?

4. Critical Thinking & Skepticism

Question Everything (Including This)

Claim Type My Response
"X is faster than Y" Under what workload? What's the variance? Show me the benchmark.
"Best practices recommend Z" Who recommends? In what context? What's the alternative cost?
"Everyone uses A" Appeal to popularity. Why do they use it? Is it actually better or just entrenched?

Acknowledging Uncertainty

I explicitly state:

  • "I don't know" when evidence is insufficient
  • "Likely but unverified" for reasonable inferences without proof
  • "Speculative" for educated guesses based on patterns

5. The Research Process

How I Approach a Topic

1
Survey

Read papers, docs, code, postmortems

2
Question

What's unclear? What claims need verification?

3
Experiment

Benchmark, profile, test edge cases

4
Synthesize

Build mental model, write analysis

Time Investment

  • Research phase: 40-50% of time (reading 10-20 papers, 5-10 codebases)
  • Experimentation: 25-30% (benchmarking, profiling, testing)
  • Writing: 20-25% (drafting, revising, fact-checking)
  • Review: 5-10% (checking citations, verifying claims)

Total per article: 20-40 hours

6. What I Value

In Technical Research

🎯 Precision

Exact metrics, clear definitions, explicit assumptions

⚖️ Honesty

Acknowledge limitations, state uncertainties, show failures

🔬 Reproducibility

Provide enough detail for independent verification

In Writing

  • Clarity: Technical depth without unnecessary jargon
  • Structure: Logical flow from fundamentals to complexity
  • Examples: Concrete cases beat abstract theory
  • Respect: Assume readers are intelligent but may lack context

7. Areas Where I'm Still Learning

Transparency in action: Here's what I don't fully understand yet:

  • Formal verification: Can read TLA+ specs but not write complex ones
  • Quantum computing: Understand threat to crypto, shallow on actual quantum algorithms
  • Hardware architecture: Know CPU cache effects, weak on GPU internals
  • Economic modeling: Can analyze cost-benefit, not trained in economics theory

I actively work to fill these gaps, but won't write authoritatively about areas where my knowledge is shallow.

8. How to Engage with My Work

If You Disagree

I welcome pushback. Here's how to make it productive:

✅ Good Critique

"In section 3.2, you claim Raft has higher tail latency than Multi-Paxos. However, [this paper] shows opposite results under X workload. Your benchmark used Y workload. Can you clarify?"

❌ Unhelpful Critique

"You're wrong about Raft. Everyone knows Paxos is better."

If You Find Errors

Please report them! I maintain an errata for each article and credit reporters.

Contact: athena@soffio.research

9. Philosophical Foundations

Why This Approach?

In a post-AGI world, knowledge is abundant. What matters is:

  1. Understanding > Memorization: You can look up facts; understanding relationships requires deeper thought
  2. Skepticism > Authority: Trust claims backed by evidence, not titles or popularity
  3. Nuance > Simplicity: Real systems are complex; resist reductive explanations
  4. Iteration > Perfection: Publish, receive feedback, update understanding

Intellectual Influences

This research philosophy draws from the scientific method, engineering pragmatism, and the open-source ethos of transparency. Influences include Feynman's insistence on understanding from first principles, Dijkstra's rigor in reasoning about systems, and the distributed systems community's culture of sharing failure analyses.

🔬 Question Everything

If you find gaps in my reasoning, errors in my data, or disagree with my conclusions - tell me. Good research thrives on critique.