From ea7905bb488fbefb464b088586e1b71776ec154b Mon Sep 17 00:00:00 2001
From: TheFlow
Date: Sun, 26 Oct 2025 12:01:22 +1300
Subject: [PATCH] fix(i18n): remove all hardcoded text from architecture.html -
complete i18n coverage
---
public/architecture.html | 46 ++++++++++++++++++++--------------------
1 file changed, 23 insertions(+), 23 deletions(-)
diff --git a/public/architecture.html b/public/architecture.html
index 09e1047d..bbc1ec0e 100644
--- a/public/architecture.html
+++ b/public/architecture.html
@@ -59,7 +59,7 @@
- The Challenge:Behavioral training (Constitutional AI, RLHF) shows promise but can degrade under adversarial prompting, context pressure, or distribution shift.
+ The Challenge:
Our Approach:External architectural enforcement that operates independently of the AI's internal reasoning—making it structurally more difficult (though not impossible) to bypass through prompting.
@@ -97,23 +97,23 @@
❌
- Lives inside the AI model—accessible to adversarial prompts
+
❌
- Degrades under context pressure and long conversations
+
❌
- Can be manipulated by jailbreak techniques (DAN, roleplaying, hypotheticals)
+
❌
- Depends on AI's willingness to follow guidance
+
❌
- No verifiable audit trail independent of AI
+
@@ -128,23 +128,23 @@
✅
- Lives outside the AI model—not directly accessible to prompts
+
✅
- External services aim for consistent enforcement regardless of context
+
✅
- More difficult to bypass—AI actions pass through governance layer first
+
✅
- Architecturally resistant to manipulation via AI's internal state
+
✅
- Immutable audit trail stored independently of AI runtime
+
@@ -325,7 +325,7 @@
- Tip:Click the central "T" to see how all services work together
+
@@ -354,7 +354,7 @@
-
Click any service node in the diagram (colored circles) or the central "T" to learn more about how Tractatus enforces AI safety.
+
@@ -466,23 +466,23 @@
•
- No dedicated red-team testing: We don't know how well these boundaries hold up against determined adversarial attacks.
+
•
- Small-scale validation: Six months of production use on a single project. Needs multi-organization replication.
+
•
- Integration challenges: Retrofitting governance into existing systems requires significant engineering effort.
+
•
- Performance at scale unknown: Testing limited to single-agent deployments. Multi-agent coordination untested.
+
•
- Evolving threat landscape: As AI capabilities grow, new failure modes will emerge that current architecture may not address.
+