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print(“\n” + “=”*70 + “\n SECTION 6 — NetworkX analytics\n” + “=”*70) def kg_to_networkx(graph): G = nx.MultiDiGraph() for e in graph.entities: G.add_node(e) for s, p, o…

TOOLS = { “calc”: lambda expression: _safe_calc(expression), “kb_search”: lambda query, k=3: _kb_search(query, int(k)), “extract_json”: lambda text: _extract_json(text), “write_file”: lambda path, content: _write_file(path, content), } TOOL_SCHEMAS =…

banner(“§12 CLAUDE.md”) sh(“repowise generate-claude-md”) md = TARGET / “CLAUDE.md” if md.exists(): print(md.read_text()[:4000]) banner(“§13 MCP tools via CLI”) base = [ (“get_dead_code”, “repowise dead-code –safe-only”), (“search_codebase”, ‘repowise…

class RoutedAgent: def __init__(self, server: MCPToolServer, router: HybridMCPRouter, model: str): self.server = server self.router = router self.model = model def discover_exposed_tools(self, exposed_tool_names: List[str]) -> List[Dict[str, Any]]:…

(ROOT / “shop” / “admin.py”).write_text(”’ from django.contrib import admin, messages from django.contrib.auth.admin import (UserAdmin as DjangoUserAdmin, GroupAdmin as DjangoGroupAdmin) from django.contrib.auth.models import User, Group from django.shortcuts…

In this tutorial, we build a realistic Zero-Trust network simulation by modeling a micro-segmented environment as a directed graph and forcing every request to earn access…

class MemoryStoreTool(Tool): name = “memory_store” description = “Save an important fact or piece of information to long-term memory.” def __init__(self, memory: MemoryBackend): self._mem = memory def…

as a programming tool. As a programming tool, it’s really amazing and does an incredible job, and has probably made me a multiple times over more…

entries = df.index[(df[“pos”].diff() == 1)] exits = df.index[(df[“pos”].diff() == -1)] fig, (ax1, ax2, ax3) = plt.subplots( 3, 1, figsize=(13, 10), sharex=True, gridspec_kw={“height_ratios”: [3, 1, 2]}, )…

banner(“Part 5 — Streaming”) mem.attribution(entity_id=”[email protected]”, process_id=”personal-assistant”) stream = client.chat.completions.create( model=MODEL, messages=[{“role”: “user”, “content”: “In two sentences, what do you remember about me?”}], stream=True, ) print(“[stream] “,…