docs: consolidate tags to 5-tag system and remove duplicate titles

- Replace 14 scattered tags with 5 meaningful tags: quickstart, concepts, reference, python, rust
- Remove duplicate H1 titles that matched frontmatter title (22 files)
- Mark quickstarts as draft except employee-scheduling
- Add consistent tags across all documentation sections

Author: blackopsrepl

assets/scss/_styles_project.scss

@@ -123,13 +123,16 @@ pre {
   background-color: #022c22 !important; // Very dark emerald
   border-radius: 6px;
   padding: 1rem;
-  margin: 1rem auto;
-  max-width: fit-content;
+  margin: 1rem 0;
+  white-space: pre-wrap;
+  word-wrap: break-word;
 
   code {
     font-size: 0.8rem;
     color: #6ee7b7; // Mint green text
     background-color: transparent !important;
+    white-space: pre-wrap;
+    word-wrap: break-word;
   }
 }
 
@@ -301,3 +304,4 @@ code {
   }
 }
 
+

content/en/blog/technical/python-constraint-solver-architecture.md

@@ -1,6 +1,7 @@
 ---
 title: "Dataclasses vs Pydantic in Constraint Solvers"
 date: 2025-12-06
+tags: [concepts, python]
 description: >
   Architectural guidance for Python constraint solvers: when to use dataclasses vs Pydantic for optimal performance.
 ---

content/en/docs/getting-started/_index.md

@@ -2,8 +2,8 @@
 title: Getting Started
 linkTitle: "Getting Started"
 description: Quickstart Guides — repository layout, prerequisites, and how to run examples locally.
-categories: [Examples, Quickstarts]
-tags: [solverforge, quickstarts, python, docs]
+categories: [Quickstarts]
+tags: [quickstart, python]
 weight: 2
 ---
 
@@ -19,6 +19,42 @@ We're working hard to deliver proper bindings. Stay tuned!
 
 The quickstart guides below demonstrate constraint-solver patterns using the legacy Python implementation. They serve as reference material for understanding SolverForge concepts while the new bindings are being developed.
 
+## Choose a Quickstart
+
+{{< cardpane >}}
+{{< card header="**Employee Scheduling**" >}}
+Assign staff to shifts based on skills and availability.
+Perfect for learning core optimization concepts.
+
+[Start Tutorial →](employee-scheduling/)
+{{< /card >}}
+{{< card header="**Meeting Scheduling**" >}}
+Find optimal times and rooms for meetings while avoiding conflicts.
+
+[Start Tutorial →](meeting-scheduling/)
+{{< /card >}}
+{{< card header="**Vehicle Routing**" >}}
+Plan delivery routes that minimize travel time with capacity constraints.
+
+[Start Tutorial →](vehicle-routing/)
+{{< /card >}}
+{{< /cardpane >}}
+
+{{< cardpane >}}
+{{< card header="**School Timetabling**" >}}
+Schedule lessons to rooms and timeslots without teacher or room conflicts.
+
+[Start Tutorial →](school-timetabling/)
+{{< /card >}}
+{{< card header="**Rust Quickstart**" footer="Experimental" >}}
+Build a solver using the core Rust library directly. For advanced users interested in the internals.
+
+[Start Tutorial →](rust-quickstart/)
+{{< /card >}}
+{{< /cardpane >}}
+
+---
+
 This page covers:
 - Repository layout and quickstart variants
 - Prerequisites and installation notes

content/en/docs/getting-started/employee-scheduling.md

@@ -3,9 +3,10 @@ title: "Employee Scheduling"
 linkTitle: "Employee Scheduling"
 icon: fa-brands fa-python
 date: 2025-11-21
+weight: 10
 description: "A comprehensive quickstart guide to understanding and building intelligent employee scheduling with SolverForge"
-categories: [Examples]
-tags: [scheduling, optimization, tutorial]
+categories: [Quickstarts]
+tags: [quickstart, python]
 ---
 
 {{% pageinfo %}}

content/en/docs/getting-started/meeting-scheduling.md

@@ -3,9 +3,11 @@ title: "Meeting Scheduling"
 linkTitle: "Meeting Scheduling"
 icon: fa-brands fa-python
 date: 2025-11-26
+weight: 20
+draft: true
 description: "A comprehensive quickstart guide to understanding and building intelligent meeting scheduling with SolverForge"
-categories: [Examples]
-tags: [scheduling, optimization, meetings, calendar, tutorial]
+categories: [Quickstarts]
+tags: [quickstart, python]
 ---
 
 {{% pageinfo %}}
@@ -45,7 +47,9 @@ This guide walks you through a complete meeting scheduling application built wit
 
 **No optimization background required** — we'll explain concepts as we encounter them in the code.
 
-> **Architecture Note:** This implementation uses dataclass domain models for optimal solver performance. See [benchmark results](/blog/news/python-constraint-solver-architecture/#results-meeting-scheduling) showing this approach completes 60/60 optimization iterations while Pydantic-based alternatives complete only 46-58. Note: benchmarks were run on small test problems; JPype bridge overhead may increase at larger scales.
+{{% alert title="Architecture Note" %}}
+This implementation uses dataclass domain models for optimal solver performance. See [benchmark results](/blog/technical/python-constraint-solver-architecture/#results-meeting-scheduling) showing this approach completes 60/60 optimization iterations while Pydantic-based alternatives complete only 46-58. Note: benchmarks were run on small test problems; JPype bridge overhead may increase at larger scales.
+{{% /alert %}}
 
 ### Prerequisites
 
@@ -317,7 +321,7 @@ class MeetingAssignment:
 
 **Optimization concept:** Unlike employee scheduling (one variable: which employee) or vehicle routing (one list variable: which visits), this problem has **two independent planning variables** per entity. The solver must simultaneously decide both time and room.
 
-**Why multiple planning variables matter:** Having two planning variables (time and room) per entity creates a larger search space but more flexibility. The dataclass-based domain model enables efficient evaluation of variable combinations. For architectural details on why dataclasses outperform Pydantic in constraint evaluation, see [Dataclasses vs Pydantic in Constraint Solvers](/blog/news/python-constraint-solver-architecture/).
+**Why multiple planning variables matter:** Having two planning variables (time and room) per entity creates a larger search space but more flexibility. The dataclass-based domain model enables efficient evaluation of variable combinations. For architectural details on why dataclasses outperform Pydantic in constraint evaluation, see [Dataclasses vs Pydantic in Constraint Solvers](/blog/technical/python-constraint-solver-architecture/).
 
 **Important methods:**
 

content/en/docs/getting-started/rust-quickstart.md

@@ -3,10 +3,11 @@ title: "Rust Quickstart"
 linkTitle: "Rust Quickstart"
 icon: fa-brands fa-rust
 date: 2025-12-08
-weight: 1
+weight: 100
+draft: true
 description: "Build your first constraint solver with the SolverForge Rust core library"
-categories: [Examples]
-tags: [rust, optimization, tutorial, experimental]
+categories: [Quickstarts]
+tags: [quickstart, rust]
 ---
 
 {{% pageinfo color="warning" %}}

content/en/docs/getting-started/school-timetabling.md

@@ -3,9 +3,11 @@ title: "School Timetabling"
 linkTitle: "School Timetabling"
 icon: fa-brands fa-python
 date: 2025-11-26
+weight: 40
+draft: true
 description: "A comprehensive quickstart guide to understanding and building intelligent school timetabling with SolverForge"
-categories: [Examples]
-tags: [scheduling, optimization, timetabling, tutorial]
+categories: [Quickstarts]
+tags: [quickstart, python]
 ---
 
 {{% pageinfo %}}

content/en/docs/getting-started/vehicle-routing.md

@@ -3,9 +3,11 @@ title: "Vehicle Routing"
 linkTitle: "Vehicle Routing"
 icon: fa-brands fa-python
 date: 2025-11-26
+weight: 30
+draft: true
 description: "A comprehensive quickstart guide to understanding and building intelligent vehicle routing with SolverForge"
-categories: [Examples]
-tags: [routing, optimization, logistics, tutorial]
+categories: [Quickstarts]
+tags: [quickstart, python]
 ---
 
 {{% pageinfo %}}
@@ -45,7 +47,9 @@ This guide walks you through a complete vehicle routing application built with *
 
 **No optimization background required** — we'll explain concepts as we encounter them in the code.
 
-> **Performance Note:** Vehicle routing is particularly sensitive to constraint evaluation performance, as the solver must recalculate distances and arrival times millions of times during optimization. This implementation uses the "fast" dataclass architecture—see [benchmark results](/blog/news/python-constraint-solver-architecture/#results-vehicle-routing). Note: benchmarks were run on small test problems (25-77 customers); JPype bridge overhead may compound at larger scales.
+{{% alert title="Performance Note" %}}
+Vehicle routing is particularly sensitive to constraint evaluation performance, as the solver must recalculate distances and arrival times millions of times during optimization. This implementation uses the "fast" dataclass architecture—see [benchmark results](/blog/technical/python-constraint-solver-architecture/#results-vehicle-routing). Note: benchmarks were run on small test problems (25-77 customers); JPype bridge overhead may compound at larger scales.
+{{% /alert %}}
 
 ### Prerequisites
 
@@ -373,7 +377,7 @@ Routes are built using **shadow variable chaining**:
 
 **Optimization concept:** This is **incremental score calculation**. When the solver moves one visit, only affected arrival times recalculate — not the entire solution. This enables evaluating millions of route modifications per second.
 
-**Why this matters for performance:** Shadow variables enable efficient incremental updates. With Pydantic models, validation overhead would occur on every update—compounding across millions of moves per second. The dataclass approach avoids this overhead entirely. See [benchmark analysis](/blog/news/python-constraint-solver-architecture/#object-equality-in-hot-paths) for details on this architectural choice.
+**Why this matters for performance:** Shadow variables enable efficient incremental updates. With Pydantic models, validation overhead would occur on every update—compounding across millions of moves per second. The dataclass approach avoids this overhead entirely. See [benchmark analysis](/blog/technical/python-constraint-solver-architecture/#object-equality-in-hot-paths) for details on this architectural choice.
 
 ### The Search Process
 

content/en/docs/overview.md

@@ -2,6 +2,7 @@
 title: Overview
 description: What SolverForge is, how it differs from mathematical solvers, and the project roadmap.
 weight: 1
+tags: [concepts]
 ---
 
 # What is SolverForge?
@@ -10,16 +11,31 @@ SolverForge is a **constraint satisfaction solver** for real-world planning and
 
 ## What Problems Does It Solve?
 
-SolverForge excels at **combinatorial planning problems** where you need to:
-
-- **Employee Scheduling** — Assign staff to shifts based on skills, availability, and labor regulations
-- **Vehicle Routing** — Plan delivery routes that minimize travel time while meeting time windows
-- **School Timetabling** — Schedule lessons to rooms and timeslots without conflicts
-- **Task Assignment** — Allocate jobs to workers or machines optimally
-- **Meeting Scheduling** — Find times and rooms that work for all attendees
-- **Bin Packing** — Fit items into containers efficiently
-
-These are problems where a brute-force search is impossible (millions to billions of possibilities), but a good solution dramatically improves efficiency.
+SolverForge excels at **combinatorial planning problems** — problems where a brute-force search is impossible (millions to billions of possibilities), but a good solution dramatically improves efficiency.
+
+{{< cardpane >}}
+{{< card header="**Employee Scheduling**" >}}
+Assign staff to shifts based on skills, availability, and labor regulations.
+{{< /card >}}
+{{< card header="**Vehicle Routing**" >}}
+Plan delivery routes that minimize travel time while meeting time windows.
+{{< /card >}}
+{{< card header="**School Timetabling**" >}}
+Schedule lessons to rooms and timeslots without conflicts.
+{{< /card >}}
+{{< /cardpane >}}
+
+{{< cardpane >}}
+{{< card header="**Task Assignment**" >}}
+Allocate jobs to workers or machines optimally.
+{{< /card >}}
+{{< card header="**Meeting Scheduling**" >}}
+Find times and rooms that work for all attendees.
+{{< /card >}}
+{{< card header="**Bin Packing**" >}}
+Fit items into containers efficiently.
+{{< /card >}}
+{{< /cardpane >}}
 
 ## How Is This Different from Gurobi or CVXPY?
 
@@ -35,15 +51,8 @@ This is a common question. **SolverForge and mathematical programming solvers (G
 
 ### A Concrete Example
 
-**Mathematical programming (Gurobi/CVXPY):**
-```python
-# You must translate your problem into mathematical form
-x = model.addVars(employees, shifts, vtype=GRB.BINARY)
-model.addConstrs(sum(x[e,s] for e in employees) == 1 for s in shifts)
-model.addConstrs(sum(x[e,s] for s in shifts) <= max_shifts for e in employees)
-```
-
-**Constraint satisfaction (SolverForge):**
+{{< tabpane text=true >}}
+{{% tab header="SolverForge" %}}
 ```python
 # You describe rules about your business objects directly
 @constraint_provider
@@ -57,6 +66,16 @@ def define_constraints(factory):
             .penalize("Missing skill", HardSoftScore.ONE_HARD),
     ]
 ```
+{{% /tab %}}
+{{% tab header="Gurobi/CVXPY" %}}
+```python
+# You must translate your problem into mathematical form
+x = model.addVars(employees, shifts, vtype=GRB.BINARY)
+model.addConstrs(sum(x[e,s] for e in employees) == 1 for s in shifts)
+model.addConstrs(sum(x[e,s] for s in shifts) <= max_shifts for e in employees)
+```
+{{% /tab %}}
+{{< /tabpane >}}
 
 **The key difference:** With SolverForge, you work with domain objects (`Shift`, `Employee`) and express constraints as natural business rules. You don't need to reformulate your problem as a system of linear equations.
 
@@ -115,7 +134,7 @@ SolverForge is under active development. The Python API shown above works today
 | **solverforge-core** | ✅ Complete | High-performance Rust backend — foundation complete, not yet user-facing |
 | **Python bindings** | 🚧 In progress | PyO3-based bindings to the fast Rust core — coming Q1-Q2 2026 |
 
-**Want to try it today?** Start with the [Python quickstarts](/docs/getting-started/hello-world/) using `solverforge-legacy`.
+**Want to try it today?** Start with the [Python quickstarts](/docs/getting-started/) using `solverforge-legacy`.
 
 ## Roadmap
 
@@ -143,7 +162,7 @@ Making the fast Rust core available to Python developers:
 
 ## How You Can Help
 
-- **Try the quickstarts** — [Build your first solver](/docs/getting-started/hello-world/) and share feedback
+- **Try the quickstarts** — [Try a quickstart](/docs/getting-started/) and share feedback
 - **Report issues** — Found a bug or have a suggestion? [Open an issue](https://github.com/solverforge/solverforge/issues)
 - **Contribute** — We're actively developing Python bindings. PRs welcome!
 - **Spread the word** — Star the [GitHub repo](https://github.com/solverforge/solverforge) and share with colleagues

content/en/docs/solverforge-legacy/_index.md

@@ -3,6 +3,7 @@ title: "SolverForge (Legacy)"
 linkTitle: "Python Solver"
 icon: fa-brands fa-python
 weight: 100
+tags: [python]
 description: >
   Technical documentation for SolverForge Legacy — the pure Python constraint solver using the Timefold backend.
 ---
@@ -64,5 +65,5 @@ class Timetable:
 
 ## Next Steps
 
-- Follow the [Hello World Quickstarts](/docs/getting-started/hello-world/) to solve your first planning problem
+- Follow the [Getting Started guides](/docs/getting-started/) to solve your first planning problem
 - Learn about [Planning AI concepts](concepts/what-is-planning.md)