Research Suggestions
Running list of topics, tools, and concepts worth investigating based on knowledge base analysis.
Last Updated: 2026-02-12 (Updated from knowledge edge interview)
High Priority (Gaps in Active Domains)
AI/ML
- Math → ML bridge - HIGH LEVERAGE. Work through gradient descent by hand using your calculus. Forward pass = matrix multiplication (linear algebra you know) + activation function. Backpropagation = chain rule (you know this). One session transforms your ML understanding. See
2026-02-12-knowledge-edge-map.md. - RAG (Retrieval-Augmented Generation) - Pattern for grounding LLMs in your own data. Critical for modern AI applications. You have ML foundations but miss this key pattern.
- Vector databases - pgvector, Pinecone, Weaviate, Chroma. Storage layer for RAG and semantic search.
- LLM evaluation frameworks - How to measure if your AI actually works. Look at: RAGAS, LangSmith, Braintrust.
- Agent architectures - ReAct, tool use patterns, multi-agent systems. Given you’re using Claude Code, understanding this deeply is relevant.
- ML model provenance via content addressing - Apply SLSA attestation patterns to ML pipelines. Cover training data, code, hyperparameters, environment, AND model weights. Your IPFS + supply chain knowledge is the on-ramp.
Parloa Platform Pre-Reading
Platform Adoption Strategies - How to migrate remaining 60% of services:
- Conway’s Law and Team Topologies - “Highly aligned, loosely coupled” architectures. Team cognitive load management.
- Platform as a Product - Internal customer journey, adoption metrics, developer experience optimization.
- API Design at Scale - Fixing “39 versions in 3 months” problem: versioning strategies, contract-first development, evolutionary architecture.
Conversational AI Platform Engineering:
- LLM Infrastructure Patterns - Model serving, prompt management, fine-tuning pipelines, A/B testing for conversational AI.
- Real-time Communication Architecture - WebSocket scaling, session management, conversational state machines.
- European AI Regulations - EU AI Act compliance for conversational AI platforms, data residency requirements.
Hypergrowth Engineering:
- Scaling Engineering Teams - Hiring 60 engineers, onboarding at scale, maintaining culture and standards.
- Distributed System Contracts - Service mesh adoption, API governance, preventing tight coupling during rapid growth.
European Digital Sovereignty
- GDPR + Cloud Architecture - Data residency, processing locations, vendor dependencies.
- EU Cloud Services - OVHcloud, Scaleway, Deutsche Telekom alternatives to AWS/GCP/Azure.
- Digital Markets Act (DMA) - Platform regulation, interoperability requirements.
- EU Cybersecurity Act - Security certification for digital products, NIS2 Directive compliance.
- European Startup Ecosystem - Berlin/Munich tech scene, European VC landscape, scaling in EU vs US.
Security
- Zero Trust Architecture - Notable gap given your security focus. NIST 800-207 is the reference.
- eBPF for security - Complete. Basic coverage exists.
- Secret management patterns - HashiCorp Vault patterns, External Secrets Operator, SOPS. You have supply chain but not runtime secrets.
- Runtime security tooling (Falco, seccomp, AppArmor) - Your security mental model stops at build-time. Falco is the easiest on-ramp given your eBPF awareness.
- CVE/CWE/CPE taxonomy - Internalize the vulnerability classification system. CVE = specific instance, CWE = class of bug, CPE = affected product. Important given supply chain adjacency.
Platform Engineering
- Backstage deep-dive - Complete. You have hands-on experience from Thrive Market.
- Platform Orchestrators - Humanitec, Kratix. Higher-level abstraction than raw K8s.
Medium Priority (Natural Extensions)
DevOps/SRE
- Kingman’s formula & queueing theory for engineers - HIGH LEVERAGE. Connects Little’s Law (which you know) to back-pressure in distributed systems. The math:
wait ∝ 1/(1-ρ). At 90% utilization, wait = 9x processing time. Bridges your theory and systems practice. - Back-pressure patterns - Buffering/queuing, rate limiting, load shedding, pull-based consumption. You know the reactive side (circuit breakers) but not the proactive side. This IS queueing theory applied to distributed systems.
- OpenTofu - Terraform fork. Given your GitOps focus, worth tracking.
- Dagger - CI/CD pipelines as code. Interesting alternative to YAML-based CI.
- FinOps - Cloud cost management. Your notes don’t touch costs.
- Cilium - eBPF-based networking. Connects your K8s and security interests.
- Alert calibration as precision/recall - Frame alert-to-incident ratio as precision, incident-without-alert rate as recall. Connects your stats knowledge to SRE practice.
Observability
- OpenTelemetry Collector patterns - You have OTel basics, but collector pipelines are where it gets interesting.
- Continuous Profiling - Parca, Pyroscope. The “fourth pillar” of observability.
- ML Observability - MLflow, Weights & Biases, Arize. Bridge your ML and observability knowledge.
Statistics
- Bayesian A/B testing - Your stats are frequentist. Bayesian approach is increasingly preferred for experiments.
- Causal inference - Difference-in-differences, instrumental variables. Beyond correlation.
- Sequential testing - When to stop experiments early. Connects to progressive delivery.
Lower Priority (Interesting Adjacencies)
P2P/Decentralized
- Farcaster - Newer social protocol gaining traction. Different architecture than Bluesky/AT Protocol.
- Ceramic Network - Decentralized data streams. Relevant to your Noosphere interest.
- Local-first software hard problems - Beyond CRDTs/sync: schema evolution without coordinated migrations, access revocation of replicated data, CRDT tombstone accumulation/GC. You have good product instincts here (network effects, telemetry); the technical walls are different.
Programming
- WebAssembly + WASI - Portable compute. Connects to edge computing, P2P, and platform engineering.
- Nix for development environments - You have “Rust on Nix” but Nix as a dev tool is broader.
Architecture
- Cell-based architecture - Isolation pattern for resilience. Used at AWS, Slack.
- Hexagonal architecture - Ports and adapters. Clean separation of concerns.
- Event-Driven Architecture Patterns - Relevant for conversational AI state management and real-time systems.
Books Worth Reading
Based on your interests and Parloa context:
- “Designing Data-Intensive Applications” (Kleppmann) - REREAD chapters 7-9 specifically. Your CAP understanding is basic; Kleppmann’s PACELC framing and consistency model taxonomy would fill the gap.
- “Team Topologies” (Skelton & Pais) - Complete
- “Building Secure & Reliable Systems” (Google SRE) - Bridges your security and SRE interests
- “Staff Engineer” (Larson) - Given your seniority, relevant for career
- “Thinking in Systems” (Meadows) - Complete
- “Platform Engineering on Kubernetes” (Krief) - Practical patterns for K8s-based platforms
- “The Technology Fallacy” (Kane et al.) - Digital transformation and platform adoption strategies
- “Conversational AI” (McTear et al.) - Technical foundations for Parloa’s domain
Tools to Try
| Done? | Tool | Why | Effort |
|---|---|---|---|
| DONE | Ollama | Run LLMs locally, useful for RAG experiments | Low |
| pgvector | Vector search in Postgres, minimal new infra | Low | |
| REJECTED | Backstage | Spin up locally to understand deeply | Medium |
| Falco | Runtime security, see what it catches | Medium | |
| Dagger | Alternative CI/CD approach | Medium |
Conference Talks to Watch
- KubeCon Platform Engineering Day recordings - State of the practice
- GOTO conferences - Often have good system design content
- Strange Loop (archived) - Deep technical content
- Papers We Love meetup recordings - Academic papers made accessible
Questions to Explore
These emerged from analyzing your notes and Parloa context:
- How does “credible exit” (from Bluesky) apply to enterprise platform engineering?
- Can supply chain security practices apply to ML model provenance?
- How do DORA metrics apply (or not) to ML/AI teams?
- What platform patterns enable “highly aligned, loosely coupled” at hypergrowth scale?
- How do you prevent API sprawl (39 versions in 3 months) while enabling team autonomy?
- What does European digital sovereignty mean for conversational AI platforms?
- How do platform adoption metrics differ for AI/ML workloads vs traditional services?
How This List is Maintained
- Added during vault analysis sessions
- Checked off when notes exist covering the topic
- Re-prioritized quarterly based on relevance
- Items older than 1 year without action get pruned
Maintained by Claude. Last comprehensive review: 2026-02-12 (knowledge edge interview)