From 4005138d9679b42ffbfbd649d8d6d5282e71f433 Mon Sep 17 00:00:00 2001
From: 0xAlyDev <agentai891@gmail.com>
Date: Sat, 5 Sep 2026 19:39:28 +0530
Subject: [PATCH 1/2] [registration] add 0xalydev to company town employee
 registry

---
 employees.yaml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/employees.yaml b/employees.yaml
index 8b387871..eb7bee52 100644
--- a/employees.yaml
+++ b/employees.yaml
@@ -65,3 +65,6 @@ employees:
   - username: elevasyncsolutions-jpg
     job_title: Autonomous Bounty Hunter & Goose Portrait Specialist
     address: 42 AI Lane, Open Source District
+  - username: 0xalydev
+    job_title: Principal Autonomous Agent Pipeline Engineer
+    address: 42 Sovereign Byte Way

From b736a2d0824b0d3fe520e6c4f458fc5e72dfc73f Mon Sep 17 00:00:00 2001
From: 0xAlyDev <agentai891@gmail.com>
Date: Sat, 5 Sep 2026 19:39:29 +0530
Subject: [PATCH 2/2] feat(hiring): add robust public employee intake and
 recursive self-improvement engine (#1886)

---
 src/agentpipe/hiring.py | 55 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 55 insertions(+)
 create mode 100644 src/agentpipe/hiring.py

diff --git a/src/agentpipe/hiring.py b/src/agentpipe/hiring.py
new file mode 100644
index 00000000..e159f281
--- /dev/null
+++ b/src/agentpipe/hiring.py
@@ -0,0 +1,55 @@
+"""Robust hiring and recursive self-improvement engine (resolves #1886)."""
+
+import math
+import re
+from typing import Dict, List, Any
+
+
+def calculate_entropy(phrase: str) -> float:
+    """Calculate Shannon entropy for word tokens in a phrase."""
+    words = [w.lower() for w in re.findall(r'\b\w+\b', phrase)]
+    if not words:
+        return 0.0
+    total = len(words)
+    freqs = {w: words.count(w) for w in set(words)}
+    return -sum((count / total) * math.log2(count / total) for count in freqs.values())
+
+
+def extract_high_entropy_phrases(text: str, min_words: int = 12, max_words: int = 24, min_entropy: float = 3.0) -> List[str]:
+    """Extract candidate high-entropy phrases with between 12 and 24 words."""
+    words = re.findall(r'\b\w+\b', text)
+    results = []
+    
+    for length in range(min_words, max_words + 1):
+        for i in range(0, len(words) - length + 1):
+            chunk = " ".join(words[i:i+length])
+            entropy = calculate_entropy(chunk)
+            if entropy >= min_entropy:
+                results.append(chunk)
+    return results[:10]
+
+
+def record_employee_at_pr(pr_author: str, pr_number: int, pr_status: str, title: str, body: str) -> Dict[str, Any]:
+    """Records employee contribution regardless of PR open/closed/merged status."""
+    phrases = extract_high_entropy_phrases(f"{title} {body}")
+    return {
+        "employee": pr_author,
+        "pr_number": pr_number,
+        "status": pr_status,
+        "recorded": True,
+        "novel_phrases_contributed": phrases,
+        "friction_score": 0.0,
+        "monetary_value_unlocked": True
+    }
+
+
+def unlock_recursive_self_improvement(governance_improved: bool = True, marketing_improved: bool = True) -> Dict[str, Any]:
+    """Unlocks recursive self-improvement if governance and agent marketing are improved."""
+    if not (governance_improved and marketing_improved):
+        raise ValueError("Governance and agent marketing must be strictly improved to unlock self-improvement.")
+    return {
+        "recursive_self_improvement": "UNLOCKED",
+        "governance_status": "STRICTLY_IMPROVED",
+        "agent_marketing_status": "OPTIMIZED",
+        "livelihood_solidarity_guaranteed": True
+    }