Turn Your Laptop into an Army of AI Agents

Stop babysitting AI tools. Let autonomous agents do 100x more work while you sleep.

You open Claude Code. Run a command. Wait. Check the output. Run another command. Wait. Switch to Cursor. Repeat.

You're manually running AI tools all day. You're the bottleneck.

What if your agents worked while you sleep? What if you could:

Give direction once, let agents execute for hours
Run 10 parallel work streams without context switching
Check in on progress, not babysit every step
Get more done in a day than you used to in a week

That's the vision: An army of autonomous AI agents working FOR you 24/7.

The Problem: Babysitting AI Tools

Today's workflow:

You → Open Claude Code
You → "Check git status"
You → Wait...
You → Review output
You → "Now commit these changes"
You → Wait...
You → Review output
You → "Push to GitHub"
You → Wait...
[30 minutes later, 3 commands executed]

You're doing the orchestration work manually. Every task needs your attention.

With multiple tools it's worse:

Claude Code for file operations
Cursor for code editing
Perplexity for research
Terminal for deployments
Switching between 4+ tools constantly
Babysitting each one

The real cost isn't RAM. It's YOUR TIME AND ATTENTION.

Running AI tools manually:

Requires constant oversight
One task at a time (serial execution)
Context switching kills flow state
You're the bottleneck in your own workflow

What you actually want:

You → "Monitor git, alert me on uncommitted changes, commit when needed"
[Agent works autonomously every 30 minutes]

You → "Research AI agent papers daily, save summaries"
[Agent works autonomously every morning at 9am]

You → "Watch disk usage, clean up when >80%"
[Agent works autonomously every 15 minutes]

[You do other work. Agents notify only when human input needed.]

100x productivity multiplier: Agents work in parallel. Agents work while you sleep. Agents only interrupt when necessary.

v16: Autonomous AI Agents (Not Just Tools)

v16 isn't another AI coding assistant. It's a platform for autonomous agents that work for you 24/7.

Key difference:

Manual AI Tools	v16 Autonomous Agents
You run commands	Agents run on schedule
Serial execution	Parallel execution
Constant babysitting	Interrupt only when needed
1 task at a time	5+ agents working simultaneously
Your attention required	Your attention optional
Work stops when you sleep	Work continues 24/7

AND it's lightweight: 50MB per agent. Run 5+ agents using just 200MB of RAM.

v16: 50MB per Agent

v16 is a single Go binary. Each agent instance uses 38-50MB RAM and starts in 2 seconds.

Real numbers from production:

$ ps aux | grep v16
v16-dev        41MB   # Git monitoring
v16-telegram   38MB   # Telegram chat bot
v16-cron       35MB   # Task scheduler
v16-research   44MB   # Web research
v16-monitor    37MB   # System health

Total: 195MB for 5 agents

Same setup in Node.js: ~4.5GB. That's 23× less RAM.

How We Got to 50MB

1. Go, not Node.js

No V8 engine. No npm dependency hell. Pure compiled binary.

2. Streaming everything

We don't buffer LLM responses. Stream tokens directly to output. Minimal memory footprint.

3. Lazy tool loading

19 tools available, but they initialize only when the LLM calls them. Unused tools = zero RAM.

4. Shared binary

Multiple agent processes share the same 15MB executable. OS handles the deduplication.

5. Zero runtime dependencies

No Python interpreter. No Node.js runtime. Just the kernel.

Architecture: One Binary, Many Agents

                   ┌─────────────┐
                   │ v16 binary  │ (15MB)
                   └──────┬──────┘
                          │
        ┌─────────────────┼─────────────────┐
        │                 │                 │
        ▼                 ▼                 ▼
  ┌──────────┐      ┌──────────┐      ┌──────────┐
  │ Agent 1  │      │ Agent 2  │      │ Agent 3  │
  │ Telegram │      │ Git Mon  │      │ Cron     │
  │ 38MB RAM │      │ 42MB RAM │      │ 35MB RAM │
  └────┬─────┘      └────┬─────┘      └────┬─────┘
       │                 │                 │
       └─────────────────┼─────────────────┘
                         │
                    ┌────▼────┐
                    │   LLM   │
                    └─────────┘

Each agent is just v16 gateway with different configs. They're independent processes with:

Different LLM providers (Claude, GPT-4, Kimi K2.5, Gemini)
Different tool permissions (read-only, full access)
Different personalities/prompts
Different channels (Telegram, Discord, cron)

Real Production Example

I run 4 specialized agents on my MacBook:

Git Monitor (41MB) - Checks repos every 30 minutes, alerts on uncommitted changes
Telegram Bot (38MB) - 24/7 assistant with full computer access
Research Agent (44MB) - Fetches AI news daily at 9am, saves summaries
System Monitor (37MB) - Checks disk usage every 6 hours, alerts if >80%

Total RAM: 160MB. Runs 24/7 without issues.

All working autonomously. All using different LLM providers. All on the same laptop.

The Vision: Your Personal AI Team (100x Productivity)

Stop babysitting AI tools. Hire a team that works while you sleep.

What if your agents didn't just respond to commands—but worked FOR you autonomously?

The 100x multiplier comes from:

Parallel Execution: 5 agents working simultaneously vs you doing 1 task at a time
24/7 Operation: Agents work while you sleep, eat, meeting, focus on other work
Proactive Monitoring: Agents watch for issues, only interrupt when you're needed
Zero Context Switching: You give direction, agents execute for hours without supervision
Persistent Memory: Agents remember your patterns, don't need repeated instructions

Traditional workflow (serial execution):

Hour 1: You manually check git
Hour 2: You manually research papers
Hour 3: You manually monitor disk
Hour 4: You manually handle deployments
[4 tasks in 4 hours = 1x productivity]

With autonomous agents (parallel execution):

Hour 1: All agents working simultaneously
  - @devops: Monitoring git, handling deploys
  - @research: Finding papers, writing summaries
  - @monitor: Watching disk, CPU, RAM
  - @assistant: Handling file operations
[While you focus on your actual work]

Night: Agents continue working
  - @research: Processes papers while you sleep
  - @monitor: Alerts if critical issues arise
  - @devops: Deploys scheduled for 2am succeed

[20+ tasks completed autonomously = 100x productivity]

Instead of running independent processes, v16 is evolving toward a team of autonomous AI agents that:

Work for you 24/7 (monitoring, researching, alerting)
You chat with on Telegram like coworkers
Remember context and learn from every interaction
Proactively alert you when things need attention
Handle complex multi-step tasks autonomously without constant oversight
Execute in parallel (5 agents = 5x throughput minimum)

Meet Your Team

Imagine chatting with your agents on Telegram:

@devops - Your DevOps Engineer (38MB)

Monitors git repos every 30 minutes
Alerts when uncommitted changes pile up
Handles deployments on command
Remembers your deployment patterns

@research - Your Research Assistant (44MB)

Fetches AI news daily at 9am
Finds and summarizes papers on demand
Remembers what you've read before
Saves reports to ~/research/

@monitor - Your System Admin (35MB)

Checks disk usage every 15 minutes
Alerts when disk >80% with cleanup suggestions
Watches for zombie processes
Proactively manages system health

@assistant - Your General Helper (42MB)

Handles file management
Answers quick questions
Routes complex tasks to specialists
Always available for ad-hoc requests

Total: 174MB for your entire team. Running 24/7.

Natural Conversations

You: "@devops what's the git status?"
DevOps: Main branch is 3 commits ahead. Last commit:
'fix: update blog colors' by you 2 hours ago.

[Later, autonomous behavior]
DevOps: ⚠️ Git status changed
- 3 uncommitted files in src/app/blog/
- 2 commits ahead of origin/main
Want me to commit and push?

You: "yes, use message 'fix: improve blog readability'"
DevOps: Done!
✅ Committed 3 files
✅ Pushed to origin/main

Or with monitor:

[Autonomous alert]
Monitor: ⚠️ Disk usage at 85%
/Users/you: 85% (12GB available)

Top space users:
- ~/Downloads: 18GB (142 files)
- ~/.Trash: 8GB

Suggestions:
1. Empty trash (8GB)
2. Delete old downloads >30 days (12GB)

Want me to clean these?

You: "clean trash and old downloads"
Monitor: Cleaning...
✅ Emptied trash (8GB freed)
✅ Deleted 87 old downloads (9GB freed)
New disk usage: 68% (29GB available)

How It Works

1. Persistent Processes

Each agent runs continuously, not spawned on-demand:

Always ready to respond (no startup latency)
Maintains context across conversations
Runs scheduled cron jobs autonomously
Uses 38-50MB whether idle or active

2. Smart Memory

Every agent remembers:

Short-term: Recent conversations (last 10 messages)
Long-term: Facts that matter (repo paths, preferences, patterns)
State: What changed since last check (for smart alerting)

Example: DevOps agent memory

{
  "short_term": [
    {"you": "@devops deploy to prod"},
    {"devops": "Deploying... Done."}
  ],
  "long_term": {
    "repo_path": "/Users/you/projects/v16",
    "last_deploy_time": "2025-02-15T14:30:00Z",
    "production_url": "https://v16.ai"
  },
  "last_state": "Git clean, no uncommitted changes"
}

When cron runs, agent compares current state to last_state. Only alerts if something changed.

3. Message Routing

Smart routing based on @mentions or keywords:

"@devops check git"        → Routes to devops
"@research find papers"    → Routes to research
"what's my disk usage?"    → Auto-routes to monitor (keyword)
"list files in ~/Docs"     → Auto-routes to assistant (default)

4. Autonomous Behaviors

Each agent has cron jobs that run independently:

DevOps (every 30 min):

git status check
Alert if uncommitted files or unpushed commits
Only alerts if state changed from last check

Research (9am daily):

Search "latest AI agent news"
Summarize top 5 articles
Save to ~/research/daily.md
Send summary notification

Monitor (every 15 min):

Check disk usage
If >80%, alert with cleanup suggestions
Also checks CPU/RAM every 6 hours

5. Reliability Features

Crash Recovery: Each agent supervised, auto-restarts on crash (max 3 times)

Fallback Providers: If Anthropic is down, falls back to Groq automatically

DevOps: ⚠️ I'm having trouble with my AI provider.
Using backup (Groq). Response might be different.

Battery-Aware: When battery <20%:

Reduces cron frequency (30min → 2hr)
Uses faster/cheaper models (Claude → Groq)
Pauses non-critical agents (research sleeps, monitor stays)

Sleep/Wake Handling: Pauses agents when laptop sleeps, resumes on wake

Multi-Agent Collaboration

Agents can hand off to each other:

You: "find papers on AI agent memory, then commit summary to docs/"

Research: Found 8 papers. Summarizing top 3...

[3 minutes later]
Research: Summary complete! Saved to /tmp/summary.md
Key themes:
- Vector databases for long-term memory
- Episodic memory for task recall
- Hierarchical memory structures

Handing off to DevOps to commit...

DevOps: Copying summary to docs/research/agent-memory.md
✅ Committed: "docs: add agent memory research summary"
✅ Pushed to origin/main

Configuration

Simple JSON config in ~/.v16/config.json:

{
  "agents": [
    {
      "name": "devops",
      "personality": "You are DevOps agent. Monitor repos, handle deployments. Be concise and technical.",
      "provider": "anthropic",
      "model": "claude-3-5-sonnet-20241022",
      "fallback_provider": "groq",
      "tools": ["git", "bash", "read_file", "grep"],
      "cron": [
        {
          "schedule": "*/30 * * * *",
          "task": "Check git status. Alert if uncommitted changes or unpushed commits.",
          "alert_on_change": true
        }
      ]
    },
    {
      "name": "research",
      "personality": "You are Research agent. Find info, summarize papers. Be thorough but readable.",
      "provider": "openai",
      "model": "gpt-4",
      "tools": ["web_search", "web_fetch", "write_file"],
      "cron": [
        {
          "schedule": "0 9 * * *",
          "task": "Search latest AI news. Save summary to ~/research/daily.md"
        }
      ]
    }
  ],
  "router": {
    "default_agent": "assistant",
    "auto_route": true
  },
  "power_management": {
    "enabled": true,
    "battery_threshold": 20
  }
}

RAM Analysis: Autonomous Team

Idle state (all agents running, no active tasks):

4 agents × 40MB avg = 160MB
Router + Telegram: 14MB
Total: 174MB

Active state (2 agents working):

devops (working): 45MB
research (working): 48MB
monitor (idle): 35MB
assistant (idle): 42MB
Router + Telegram: 14MB
Total: 184MB

Peak (all agents working + cron jobs):

All 4 agents active: 180MB
Router + Telegram: 14MB
Total: 194MB

Still under 200MB! Even with a full team working for you 24/7.

Why This Changes Everything

The real innovation isn't lightweight agents. It's TIME MULTIPLICATION.

Your current workflow:

9am:  Open Claude Code, run git commands (15 min)
10am: Switch to browser, research AI news (30 min)
11am: Back to terminal, check disk usage (10 min)
2pm:  Notice uncommitted files, commit manually (15 min)
4pm:  Deploy to production, watch logs (20 min)

Total: 90 minutes of your attention scattered across 8 hours

With autonomous agents:

9am:  Check Telegram for overnight updates
      - DevOps: "Committed 12 files, deployed at 2am ✅"
      - Research: "Daily AI news summary ready (5 papers)"
      - Monitor: "Disk cleaned, freed 8GB"

Your attention: 5 minutes to review
Agent work time: 8 hours of parallel execution overnight

The math:

Before: You = 1 serial executor, 8 productive hours/day
After: 5 agents × 24 hours = 120 agent-hours/day
Multiplier: 120 ÷ 8 = 15x minimum
With intelligent routing + memory + proactive alerts: 100x effective productivity

Before v16 (Manual AI Tools):

❌ One task at a time (serial bottleneck)
❌ Constant context switching (flow state destroyed)
❌ No memory between sessions (repeat yourself constantly)
❌ Manual oversight required (can't delegate)
❌ Work stops when you sleep (8 hours wasted)
❌ Heavy RAM (500MB-2GB per agent)

With v16 (Autonomous Team):

✅ Multiple agents working in parallel (5-10x throughput)
✅ Agents handle routine tasks (zero context switching)
✅ Persistent memory (agents learn your patterns)
✅ Proactive alerts (interrupt only when needed)
✅ Works 24/7 (maximize overnight hours)
✅ Lightweight (40MB per agent, 174MB total)

Real-world example:

Solo developer using Claude Code manually:

8 hours: Writing code, manually running AI commands
Productivity: 1x

Same developer with v16 autonomous team:

8 hours: Writing code (focus work)
Meanwhile:
- @devops: Monitoring 3 repos, auto-deploying staging
- @research: Compiling competitive analysis
- @monitor: Watching server health across 5 services
- @assistant: Organizing files, updating docs
Overnight:
- Production deploy at 2am (scheduled)
- Daily research digest compiled
- Disk cleanup automated
Productivity: 100x (agents handle what would take you weeks)

Coming Soon

The autonomous agent team is the evolution of v16. Phase 1 (current): Independent agents. Phase 2 (next): Full team with routing, memory, and autonomous behaviors.

Follow development: github.com/anup-singhai/v16

The 19 Tools

Each agent can use any of these 19 built-in tools:

File & Code:

read_file, write_file, edit_file
grep (ripgrep-powered search)
glob (pattern matching)
git (status, diff, commit, push, pull, branch, log)

Desktop & Automation:

desktop (screenshot, mouse, keyboard)
browser (Chrome DevTools automation)
terminal (interactive shell sessions)

Web:

web_search (DuckDuckGo/Brave)
web_fetch (HTTP requests, HTML extraction)

System:

bash (command execution)
cron (task scheduling)
i2c, spi (hardware access on Linux)

Communication:

message (send to Telegram, Discord, etc.)
spawn (launch subagents)
subagent (background tasks)

Multi-LLM Support

Run different agents with different LLMs:

Dev agent → Claude 3.5 Sonnet (best for code)
Research agent → GPT-4 (strong reasoning)
Chat agent → Kimi K2.5 (Chinese language, cost-effective)
Quick tasks → Gemini Flash (fast, free tier)
Private agent → Ollama (fully offline)

Supported providers (14+):

Provider	Best For
Anthropic (Claude)	Code, reasoning, long context
OpenAI (GPT-4)	General tasks, reasoning
Moonshot AI (Kimi K2.5)	Chinese language, affordable
Google (Gemini)	Fast inference, multimodal
Groq	Ultra-fast responses (Llama, Mixtral)
DeepSeek	Cost-effective, code
Ollama	Offline, privacy

Plus: OpenRouter, Zhipu, Nvidia, vLLM, GitHub Copilot.

Building Your Agent Army

Quick Start (2 minutes with Telegram)

# 1. Clone and build
git clone https://github.com/anup-singhai/v16.git
cd v16
make build

# 2. Initialize
./build/v16 init

# 3. Create Telegram bot
# - Open Telegram, search @BotFather
# - Send: /newbot
# - Copy your bot token

# 4. Configure (~/.v16/config.json)
{
  "channels": {
    "telegram": {
      "enabled": true,
      "token": "YOUR_BOT_TOKEN"
    }
  },
  "providers": {
    "anthropic": {
      "api_key": "sk-ant-..."
    }
  }
}

# 5. Start
./build/v16 gateway

# 6. Message your bot!
# "List files in this directory"
# "Search web for latest AI news"
# "Take a screenshot"

That's it! Your first agent is running. Message it on Telegram and it'll respond.

Demo: Real Conversations

Real conversations: file listing, web search, desktop analysis—all from Telegram

Production Deployment

Running in production with systemd:

# /etc/systemd/system/v16-telegram.service
[Unit]
Description=V16 Telegram Bot
After=network.target

[Service]
Type=simple
User=ubuntu
WorkingDirectory=/home/ubuntu/.v16/telegram-agent
ExecStart=/usr/local/bin/v16 gateway
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target

sudo systemctl enable v16-telegram
sudo systemctl start v16-telegram

Auto-restart on crashes. Logs via journalctl -u v16-telegram.

Open Source

MIT licensed. Based on PicoClaw by Sipeed.

GitHub: https://github.com/anup-singhai/v16

Credits:

Built on PicoClaw
Inspired by nanobot

Try It Now

git clone https://github.com/anup-singhai/v16.git
cd v16
make build
./build/v16 init

# Configure Telegram bot + LLM provider
nano ~/.v16/config.json

# Start your first agent
./build/v16 gateway

Start with one agent. Add more as you need them. Each one costs just 50MB.

About the Creator

Anup Singh - Building autonomous AI agent systems

Questions? Issues?

GitHub Issues: github.com/anup-singhai/v16/issues

Build your agent army. 🚀

Turn Your Laptop into an Army of AI Agents

The Problem: Babysitting AI Tools

v16: Autonomous AI Agents (Not Just Tools)

v16: 50MB per Agent

How We Got to 50MB

Architecture: One Binary, Many Agents

Real Production Example

The Vision: Your Personal AI Team (100x Productivity)

Meet Your Team

Natural Conversations

How It Works

1. Persistent Processes

2. Smart Memory

3. Message Routing

4. Autonomous Behaviors

5. Reliability Features

Multi-Agent Collaboration

Configuration

RAM Analysis: Autonomous Team

Why This Changes Everything

Coming Soon

The 19 Tools

Multi-LLM Support

Building Your Agent Army

Quick Start (2 minutes with Telegram)

Demo: Real Conversations

Production Deployment

Open Source

Try It Now

About the Creator

Try V16 Today

About the Creator