211 lines
4.4 KiB
Markdown
211 lines
4.4 KiB
Markdown
# **Long-Term Tech Stack**
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This is designed to be:
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* **Simple**
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* **Durable**
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* **Modular**
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* **Vendor-independent**
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* **AI-native**
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* **Low-maintenance**
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* **Future-proof**
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## Core Backend Layer (Stays Python Forever)
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### *FastAPI* {#fastapi}
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Your main backend framework indefinitely.
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* Lightweight
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* Fast
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* Async
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* Great docs
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* Perfect for APIs
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* Perfect for JSON-store backends
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### *Pydantic (v2)* {#pydantic-(v2)}
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For:
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* Validation
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* Schema definition
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* Type enforcement
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* Parsing & serializing JSON
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This gives you the “AI-Native Data Object†foundation.
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### *Python typing* {#python-typing}
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Static typing \+ Pydantic gives you:
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* Safety
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* Predictable data models
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* AI-generated UIs
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* Clean code
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### *JSON object datastore on disk* {#json-object-datastore-on-disk}
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Your architecture:
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`<app-data-root>/json-db/lod/customers/*.json`
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`<app-data-root>/json-db/lod/files/`
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`<app-data-root>/json-db/bc/hubs/*.json`
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This becomes your **source of truth** — and it’s timeless:
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* No SQL migration pain
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* AI-friendly
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* Human-readable
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* Portable across servers
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* Works with agents
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* Simple backups
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* Easy to replicate across clouds
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### *Systemd services* {#systemd-services}
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For:
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* Backend uptime
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* Simple restarts
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* Zero Docker complexity
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* Predictability
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### *CAddy* {#caddy}
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One global reverse proxy.
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* SSL termination
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* Routing
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* Static file serving
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* Stable
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* Minimal overhead
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This will last you a decade.
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## Frontend Layer (Optional TS Later) {#frontend-layer-(optional-ts-later)}
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For now:
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* Simple JS
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* Plain HTML \+ small modals
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* Works fine for LOD
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Long term (as projects expand):
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* **React \+ TypeScript**
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* **HTMX** for hybrid pages
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* Shared component library
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* Shared TypeScript types autogenerated from Pydantic
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* Local static hosting (`<app-install-path>…`)
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You only bring in TS when:
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* The UIs become larger
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* You need better autocompletion
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* Indexing dashboards get complex
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* You build cross-app shared components
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There is *no immediate need*.
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## AI Layer (Strong Long-Term Direction) {#ai-layer-(strong-long-term-direction)}
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### *Python for AI operations* {#python-for-ai-operations}
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Perfect for:
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* Embeddings
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* Chunking
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* Vectorization
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* STT
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* LLM calls
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* Agents
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* Parsing inbound data
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* Publishing pipelines
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### *Agentic pipelines* {#agentic-pipelines-(future)}
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* Chunking → JSONL files
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* Vectorization (Embeddings) → Python
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* OpenAI embeddings
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* Jina embedding
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* Cohere
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* Local embeddings later (GGUF)
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* SentenceTransformers
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* Python is the correct home for:
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* running the embedder
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* transforming the JSONL chunks
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* updating embeddings
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* building vector stores
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* MCP
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* OpenAI’s new agent tools
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* Event-driven systems
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* Scheduled analytical tasks (weeklies)
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* Lightweight Database for Metadata \+ Embeddings
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* SQLite \+ DuckDB
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* Qdrant
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* LanceDB
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* Weaviate (local mode)
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* ChromaDB
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* The simplest long-term option I recommend for you:
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* DuckDB or SQLite for metadata
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* LanceDB or Qdrant for vectors
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* Why?
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* Very fast
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* No server needed
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* Easy to copy/backup
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* Python-native
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* AI-friendly
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* Perfect with JSONL chunk pipelines
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* Your JSONL holds the raw chunks, Your small local DB holds:
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* chunk\_id
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* metadata (source, tags, time ranges)
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* vector embeddings
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* up-to-date indexes
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* Rag Layer \- Python
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* Consiuder agentic RAG sitting in this \- see index main product development document https://docs.example.invalid/private-reference
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### *Python scripts & CLIs* {#python-scripts-&-clis}
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For:
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* Import/export
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* Data normalization
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* Periodic JSON cleanup
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* Building indexes
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* Summaries
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* AI-native publishing
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These will accumulate value into your system over years.
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### *API-Driven* {#api-driven}
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Even inside your system, set up so the pieces talk to each other via API, rather than tight coupling so that modules can be upgraded, replaced, outsourced, etc.
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## Code
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Stay Python-First For:
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AI pipelines
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agents
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orchestration
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APIs
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content processing
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ingestion
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automation
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research tooling
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Add Go/Rust Selectively For:
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high-performance services
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distributed networking
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edge infrastructure
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heavy concurrency
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secure execution sandboxes
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streaming systems
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future NetworkSIG infrastructure
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low-memory edge compute
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