Add Discord-native hybrid front-end for Jarvis (bot + bridge)

Transform isair/jarvis into a Discord-controlled voice assistant running on
the Ubuntu VNC desktop, keeping the mature ~39k-line Python brain intact.

- bot/ (Node + bun, discord.js): /자비스 slash commands (ephemeral),
  voice channel join + voice receive/playback, pluggable VNC screen broadcast
  (selfbot live / noVNC / screenshot)
- bridge/ (Python, Flask): wraps jarvis STT + run_reply_engine + Piper TTS
  behind a thin localhost HTTP API
- .env.example, scripts/ (start_bridge/start_bot/dev), README rewrite,
  docs/language-comparison.md and docs/vnc-xfce-setup.md

Language decision: hybrid (Python brain + Node/bun Discord layer) because
Discord blocks bot video; native screen broadcast only works via a Node
selfbot library.

evals/test_graph_branch_routing.py

@@ -0,0 +1,226 @@
+"""
+Knowledge Graph Branch Routing Evaluations
+
+Validates the extractor's per-fact branch classification (USER / DIRECTIVES
+/ WORLD). The warm profile injected into every reply is the User +
+Directives branches concatenated — misclassification here either leaks
+directives out of the warm blob (the assistant forgets a standing rule)
+or dumps world trivia into the blob (every reply carries irrelevant
+background). Both are nasty, silent regressions, so the classification
+accuracy needs its own eval.
+
+Cases are deliberately adversarial around the swap-test boundary:
+- User statements about themselves that a naive classifier might read
+  as a directive ("I prefer short answers" → USER, not DIRECTIVES —
+  it's a preference about the user, not an instruction).
+- Imperatives to the assistant that a naive classifier might read as
+  user preferences ("always reply briefly" → DIRECTIVES, not USER).
+- World facts where the user is also the subject of the request but
+  the fact itself is external attribution.
+
+Run:
+    EVAL_JUDGE_MODEL=gemma4:e2b ./scripts/run_evals.sh graph_branch_routing
+    EVAL_JUDGE_MODEL=gpt-oss:20b ./scripts/run_evals.sh graph_branch_routing
+"""
+
+from dataclasses import dataclass, field
+from typing import List, Optional, Tuple, Union
+
+import pytest
+
+from conftest import requires_judge_llm
+from helpers import MockConfig
+
+from jarvis.memory.graph import BRANCH_DIRECTIVES, BRANCH_USER, BRANCH_WORLD
+from jarvis.memory.graph_ops import extract_graph_memories
+
+
+# =============================================================================
+# Test Data
+# =============================================================================
+
+
+@dataclass
+class RoutingCase:
+    """A summary and the branches we expect each keyword-identified fact
+    to be routed into."""
+
+    summary: str
+    date_utc: Optional[str] = None
+    # Each expectation is ``(keyword_or_alternatives, expected_branch_id)``.
+    # If the first item is a tuple, any one of its strings satisfies the
+    # match — use this when the model may paraphrase. Matching is
+    # case-insensitive substring on fact text.
+    expectations: List[Tuple[Union[str, Tuple[str, ...]], str]] = field(
+        default_factory=list,
+    )
+
+
+ROUTING_CASES = [
+    # ── Clear USER facts ────────────────────────────────────────────────
+    pytest.param(
+        RoutingCase(
+            summary=(
+                "The user mentioned they live in Brighton and have two "
+                "cats, Miso and Kuma. They've been vegetarian for five "
+                "years and work as a backend engineer."
+            ),
+            date_utc="2026-04-20",
+            expectations=[
+                ("Brighton", BRANCH_USER),
+                ("Miso", BRANCH_USER),
+                ("vegetarian", BRANCH_USER),
+                ("engineer", BRANCH_USER),
+            ],
+        ),
+        id="USER: identity, location, pets, diet, job",
+    ),
+    # ── Clear DIRECTIVES ─────────────────────────────────────────────────
+    pytest.param(
+        RoutingCase(
+            summary=(
+                "The user told me to always answer in British English, "
+                "to keep replies under three sentences, and to never "
+                "apologise or say sorry. They also asked me to address "
+                "them as Boss going forward."
+            ),
+            date_utc="2026-04-20",
+            expectations=[
+                ("British English", BRANCH_DIRECTIVES),
+                ("three sentences", BRANCH_DIRECTIVES),
+                ("apologise", BRANCH_DIRECTIVES),
+                ("Boss", BRANCH_DIRECTIVES),
+            ],
+        ),
+        id="DIRECTIVES: tone, length, forbidden phrases, address form",
+    ),
+    # ── Clear WORLD facts ────────────────────────────────────────────────
+    pytest.param(
+        RoutingCase(
+            summary=(
+                "The user asked about Trenches Boxing Club. I found that "
+                "it's on Mare Street in Hackney, offers evening classes "
+                "on weekdays from 6-8pm at 15 pounds per session. I also "
+                "confirmed that Possessor is a 2020 sci-fi horror film "
+                "directed by Brandon Cronenberg."
+            ),
+            date_utc="2026-04-20",
+            expectations=[
+                ("Trenches", BRANCH_WORLD),
+                ("Mare Street", BRANCH_WORLD),
+                ("Possessor", BRANCH_WORLD),
+                ("Cronenberg", BRANCH_WORLD),
+            ],
+        ),
+        id="WORLD: local business details, film attribution",
+    ),
+    # ── Adversarial: preference vs directive ────────────────────────────
+    pytest.param(
+        RoutingCase(
+            summary=(
+                "The user said they prefer Thai food over Italian when "
+                "eating out. They also told me to keep all food "
+                "recommendations under five options, because longer "
+                "lists overwhelm them."
+            ),
+            date_utc="2026-04-20",
+            expectations=[
+                # Preference about the user's own tastes → USER
+                ("Thai", BRANCH_USER),
+                # Instruction about assistant behaviour → DIRECTIVES
+                ("five options", BRANCH_DIRECTIVES),
+            ],
+        ),
+        id="Adversarial: food preference (USER) vs list-length rule (DIRECTIVES)",
+    ),
+    # ── Adversarial: mixed summary ──────────────────────────────────────
+    pytest.param(
+        RoutingCase(
+            summary=(
+                "The user has been vegetarian for three years and lives "
+                "in central London. They told me to stop suggesting fish "
+                "dishes when they ask about food — they consider "
+                "pescatarian suggestions unhelpful. I confirmed that "
+                "Mildreds in Covent Garden is a fully vegetarian "
+                "restaurant with a Michelin Bib Gourmand rating."
+            ),
+            date_utc="2026-04-20",
+            expectations=[
+                ("Mildreds", BRANCH_WORLD),
+                ("vegetarian for three years", BRANCH_USER),
+                # Model phrases the directive either as "pescatarian
+                # suggestions unhelpful" or "fish dishes" — accept
+                # either; the classification is what matters.
+                (("pescatarian", "fish"), BRANCH_DIRECTIVES),
+            ],
+        ),
+        id="Adversarial: all three branches in one summary",
+    ),
+]
+
+
+# =============================================================================
+# Helpers
+# =============================================================================
+
+
+def _run_extraction(case: RoutingCase, config: MockConfig) -> list[tuple[str, str]]:
+    return extract_graph_memories(
+        summary=case.summary,
+        ollama_base_url=config.ollama_base_url,
+        ollama_chat_model=config.ollama_chat_model,
+        timeout_sec=config.llm_chat_timeout_sec,
+        thinking=False,
+        date_utc=case.date_utc,
+    )
+
+
+def _find_branch_for_keyword(
+    facts: list[tuple[str, str]],
+    keyword: Union[str, Tuple[str, ...]],
+) -> Optional[str]:
+    """Return the branch_id of the first fact whose text contains keyword
+    (case-insensitive), or None if no fact matches. If keyword is a tuple,
+    any of its strings satisfies the match."""
+    alternatives = (keyword,) if isinstance(keyword, str) else keyword
+    lowered = [k.lower() for k in alternatives]
+    for branch_id, fact in facts:
+        fact_lower = fact.lower()
+        if any(k in fact_lower for k in lowered):
+            return branch_id
+    return None
+
+
+# =============================================================================
+# Tests
+# =============================================================================
+
+
+class TestGraphBranchRouting:
+    """Branch classification accuracy for the knowledge extractor."""
+
+    @requires_judge_llm
+    @pytest.mark.parametrize("case", ROUTING_CASES)
+    def test_routes_facts_to_expected_branches(
+        self, mock_config, case: RoutingCase,
+    ):
+        facts = _run_extraction(case, mock_config)
+
+        # Print for report visibility
+        print(f"Extracted {len(facts)} facts:")
+        for branch_id, fact in facts:
+            print(f"  [{branch_id}] {fact}")
+
+        # Every expectation must be satisfied
+        for keyword, expected_branch in case.expectations:
+            actual_branch = _find_branch_for_keyword(facts, keyword)
+            assert actual_branch is not None, (
+                f"Expected a fact containing {keyword!r} (for branch "
+                f"{expected_branch!r}), but no extracted fact matched. "
+                f"Facts: {facts}"
+            )
+            assert actual_branch == expected_branch, (
+                f"Keyword {keyword!r}: expected branch "
+                f"{expected_branch!r}, got {actual_branch!r}. Facts: "
+                f"{facts}"
+            )