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AI systems engineering. We build cognitive architectures, agent infrastructure, and production AI systems that actually think — not demos that break when you stop watching.
operational terrence schonleber east coast PID 1
~/radicalize
ps aux --services

PROCESSDESCRIPTIONSTATUS
agent-systemsMulti-agent architectures that actually run in production. Memory, coordination, task routing, cost control. Not another demo with 3 agents talking to each other.running
ai-toolingMCP servers, IDE integrations, developer tools that make AI agents useful inside real workflows. Built for Claude, GPT, and open models.running
full-stackEnd-to-end product development. From architecture to deploy. TypeScript, Python, React, databases, cloud — whatever the project needs.running
infrastructureCloud architecture, networking, CI/CD, containerization, monitoring. The boring stuff that keeps everything else alive.running
cybersecurityPenetration testing, security assessments, hardening, incident response. Red team thinking applied to your stack before someone else does it first.running
consultingYour AI breaks in production. Your infra is held together with duct tape. Your security audit is next month. I fix all three.running
~/radicalize
cat ./projects/brainctl

brainctl — open source context engineering for AI agents. v2.8.0

Your AI agent forgets everything between sessions. brainctl fixes that. Persistent memory, self-building knowledge graph, affect tracking, and MCP server — all in a single SQLite file. FTS5 full-text search with optional vector similarity, confidence decay, write gate with surprise scoring, consolidation engine, session handoffs, and multi-agent support. Zero LLM cost for retrieval.

199
MCP tools
22
API methods
10k
memory cap
FTS5
+ vectors
0
LLM cost
brainctl — brain.db
pip install brainctl
brainctl init
created brain.db — ready
brainctl memory add "deploy to staging first" -c lesson
memory_id: 1, confidence: 1.0
brainctl affect classify "production is down, team panicking"
valence: -0.82 arousal: 0.67 fear: 1.00
state: anxiety cluster: fear_overwhelm
brainctl-consolidate sweep
decayed: 27 merged: 9 hebbian: 53 dreamed: 12
consolidation complete — signal/noise: 0.97
github.com/TSchonleber/brainctl · pypi.org/project/brainctl
~/radicalize
cat ./projects/windsurf-mcp

windsurf-mcp — real-time IDE collaboration between Claude Code and your editor.

33 MCP tools that give Claude Code full control of your IDE — live buffer editing, diagnostics, symbol navigation, code actions, inline decorations. Plus @claude inline chat: type @claude in any source file and get answers right where you're working. Tool-backed queries pull real data from the editor. Streaming responses. Follow-up conversations. Cross-platform.

33
MCP tools
@claude
inline chat
3
platforms
0
API keys
windsurf-mcp — IDE bridge
claude mcp add windsurf -- npx windsurf-mcp
added windsurf MCP server — 33 tools available
// in any source file:
// @claude what errors are in this file?
// 🤖 Line 42: [Error] 'name' is possibly undefined (ts2532)
// 🤖 Line 67: [Warning] Unused variable 'temp' (no-unused-vars)
// Claude sees your cursor, your selection, your diagnostics.
// Edits appear in real time. No copy-paste. No context switching.
github.com/TSchonleber/windsurf-mcp · npmjs.com/package/windsurf-mcp
~/radicalize
cat ./projects/costclock

CostClock AI — time tracking, invoicing, and collections automation for service businesses.

AI-powered financial ops platform for service businesses. Automates the tedious stuff — time tracking, invoice generation, payment reminders, client portals, collections — so you can focus on the work that actually matters.

costclock.ai/dashboard
$18,430
revenue this month
142.5h
hours tracked
12
invoices sent
$3,200
outstanding
AI Operator
INV-0042 (Meridian Labs, $1,800) — paid yesterday. Stripe confirmed.
INV-0039 (Vertex Inc, $2,400) — 14 days overdue. Follow-up email queued for tomorrow.
3 timesheets ready for invoicing. Auto-generate?
costclock.ai
~/radicalize
whoami --verbose

I build AI systems that survive contact with production. The infrastructure layer everyone skips — how agents remember, forget, coordinate, and stay safe at scale.


Currently running 277+ AI agents on cognitive architecture I designed and open-sourced. The systems are grounded in neuroscience and affect research, optimized for cost, and tested end-to-end.


If you're building with AI agents and hitting walls around memory, coordination, cost, or reliability — that's the work.

  • nameTerrence Schonleber
  • locationEast Coast, US
  • focusAI Systems Engineering
  • stackPython, TypeScript, C++, Rust, Ruby, PHP, Kotlin, React, Next.js, PostgreSQL, SQLite
  • agents277+ in production
  • open_srcbrainctl
  • entityRadicalize.AI LLC
~/radicalize
open --contact

Building with AI agents and need help with architecture, infrastructure, or making things actually work at scale? Let's talk.

ready