Elimina los obstáculos técnicos y lanza tu red de pantallas con total seguridad. 🛡️🖥️🚀🌐 #DisplaySolutions #Rockbot #TechSupport

I develop using the #claude models continually and extensively.

I'm running #rockbot against an #openai #gpt model due to cost.

I can say with authority that the #claude models are better for me. They work the way I work, and think the way I think.

I'm fighting #gpt 5.3's core training daily.

Tracking Agent Metrics Running AI agents in production is not like running traditional software. Token costs accumulate continuously, latency spikes are unpredictable, and the messaging infrastructure that connects agents, subagents, and MCP servers all needs to perform reliably. Without observability, you are flying blind. RockBot and its entire ecosystem — subagents, the research agent, MCP servers, and the internal messaging pipeline — are all instrumented with OpenTelemetry (OTel). This means every LLM request, every token consumed, every message dispatched, and every bit of latency is tracked and exported to the observability stack running in my Kubernetes cluster. In my case, I host all the RockBot related services in my Kubernetes cluster, so this blog post focuses on what I’ve done. However, OTel is supported by all major cloud vendors and environments, and nearly any modern instrumentation or monitoring software works with it. As a result, the RockBot framework’s OTel support means that it plugs into Azure, AWS, or almost any other cloud seamlessly - in a manner similar to what I have set up in Kubernetes. ## The OTel Stack in Kubernetes The Kubernetes cluster runs a standard cloud-native observability stack: * **OpenTelemetry Collector** — receives metrics, traces, and logs from all instrumented services and routes them to the appropriate backends * **Prometheus** — scrapes and stores the time-series metrics data * **Grafana** — provides dashboards and alerting on top of the collected data Every component in the RockBot ecosystem—the primary agent, any spawned subagents, the research agent, and the various MCP servers—emits OTel metrics. This gives a unified, aggregated view across the entire agentic system rather than having to piece together logs from individual services. ## What Gets Instrumented Instrumentation falls into broad categories: LLM economics, agent usage, messaging pipeline health, operational health. For example: ### LLM Economics The economics dashboard captures everything related to the cost and efficiency of LLM calls: * **Cost rate ($/hr)** and **total cost (window)** — how much is being spent on LLM inference right now and over a rolling window * **Avg cost per turn** — the average spend per agent conversation turn, a useful signal for understanding task complexity trends * **Token consumption rate** — input and output tokens per minute, broken down by model * **Token efficiency** — the output/input token ratio over time; a rising ratio can indicate the agent is generating increasingly verbose responses * **LLM calls per turn** — how many LLM invocations happen per agent turn, which helps identify whether subagent or tool orchestration is driving up call counts At a glance, the current average cost per turn is **$0.1461** , with roughly **3.5 LLM calls per turn** on average and a total of **6.21 agent turns** tracked during the window. ### LLM Request Throughput and Latency The usage dashboard digs into the raw mechanics of LLM calls: * **LLM requests/min** — the request rate across all agents and services, showing traffic spikes as agents become active * **LLM request latency (p50/p95)** — response times from the LLM backends, with percentile breakdowns to surface tail latency issues * **Input/output tokens (window)** — rolling token totals by model * **Avg tokens/request** — currently sitting around **22.4K tokens per request** , reflecting the context window sizes in use Latency and throughput together tell you whether the LLM routing layer is performing well. If p95 latency climbs while request rate is low, that’s a signal to investigate the upstream model providers or the routing-stats MCP server. ### Messaging Pipeline RockBot uses an internal messaging pipeline to coordinate between the primary agent, subagents, and the various background services. The messaging section of the usage dashboard tracks: * **Message throughput (published/min)** — how many messages are flowing through the pipeline * **Pipeline dispatch latency** — how long it takes from a message being enqueued to it being dispatched (p50/p95) * **Active in-flight messages** — the current backlog of unprocessed messages * **Messaging publish latency** — the time to write a message to the pipeline * **Messaging process latency** — the time from pickup to completion on the consumer side This is particularly useful for spotting backpressure. If in-flight messages climb while throughput stays flat, something downstream is stalling—whether that’s a subagent blocked on a slow tool call, or an MCP server under load. > ⚠️ I know people tend to favor using the HTTP protocol because it is well-understood and deterministically synchronous. In reality though, a queued messaging system is _far_ more resilient, cheaper, and easier to manage. RockBot supports both models, but I almost always default to queued messaging when given a choice. ## Alerting Having dashboards is only half the value. The real payoff is alerts. Grafana alert rules fire on conditions like: * Cost rate exceeding a threshold (unexpected runaway agent behavior) * LLM request latency p95 spiking (model provider degradation) * Message pipeline backlog growing beyond a threshold (subagent stalls) * Token consumption rate anomalies (prompt injection or unexpected task expansion) Alerts land in whatever notification channel you configure—Slack, PagerDuty, email, or any other Grafana-supported contact point. ## Why This Matters Observability for agentic systems isn’t optional—it’s a prerequisite for running them reliably at any scale. A single misconfigured tool or a prompt that causes an agent to loop can silently burn through token budget before anyone notices. An MCP server with degraded performance can cause cascading latency throughout the entire agent ecosystem. By instrumenting everything with OTel and aggregating it in Grafana, you get: 1. **Cost visibility** — know what you’re spending and catch runaway costs early 2. **Performance baselines** — understand normal latency so anomalies stand out 3. **Pipeline health** — ensure the messaging backbone connecting your agents is functioning correctly 4. **Audit trail** — metrics data supports post-incident analysis when something goes wrong The RockBot ecosystem is designed so that every new agent, every new MCP server, and every new subsystem emits OTel metrics by default. As the system grows—more agents, more MCP integrations, more automation—the observability grows with it automatically. If you’re building production agentic systems, treat OTel instrumentation as a first-class requirement, not an afterthought.

#ai #agentic and #microservices systems both need instrumentation. Combine them like #rockbot, and you _really_ need good metrics to identify issues before they become real problems!

blog.lhotka.net/2026/03/11/Tracking-Agen...

Tracking Agent Metrics VP, Open Source Creator, Author, Speaker

#ai #agentic and #microservices systems both need instrumentation. Combine them like #rockbot, and you _really_ need good metrics to identify issues before they become real problems!

blog.lhotka.net/2026/03/11/T...

Agent Resources and Tools An AI agent, by itself, can’t actually do anything besides chat. And while it can be fun to have philisophical debates in isolation for a while, eventually we all want to get about the business of actually _doing things_. Agents do things by calling functions or tools. These functions and tools are provided to the agent (the LLM) by the hosting runtime. For example, RockBot is a host that provides a set of tools that can be used by the AI agent. The RockBot _framework_ provides access to a whole set of subsystems, each of which provides tools and guidance to the agent on using the tools. The RockBot _agent_ uses all the features of the framework, plus other capabilities. You can think of these tools as being in logical groups by subsystem. ## Tool Discovery Each subsystem provided by the RockBot framework has the ability to provide its own base-level tool guide to the agent. This way the agent immediately knows how to use things like memory, skills, MCP servers, etc. * list_tool_guides, get_tool_guide When a subsystem is registered during app startup, its tool guide is added to the master list of guides, making it easy for the agent to get the appropriate guide to function. Skills layer on top of these guides, allowing the agent to learn over time. ## Scheduling Tools These are tools that allow the agent to schedule tasks to run at specific times. * schedule_task — Schedule one-time or recurring tasks (cron) * cancel_scheduled_task — Cancel a scheduled task by name * list_scheduled_tasks — List all scheduled tasks ## Subagent Tools These tools allow the primary RockBot agent to spin off subagents to work in the background. Each subagent has access to the same tools and skills, but has its own context memory and a slice of working memory for sharing information with the primary and other subagents. * spawn_subagent — Spawn an isolated subagent for complex/long-running tasks * cancel_subagent — Cancel a running subagent by task ID * list_subagents — List active subagent tasks * report_progress (subagent-only) — Report progress back to the primary agent ## Agent-to-Agent (A2A) Tools Sometimes a subagent isn’t enough, and it is necessary to interact with other autonomous agents in the environment. These tools allow the RockBot agent to interact with other autonomous agents. * invoke_agent — Call an external A2A agent by name and skill * list_known_agents — List all known external agents In a business environment, you can imagine how your agent might interact with other agents that help to manage sales, inventory, production, delivery, finance, and other automation across your organization. ### Agent Examples In the RockBot repo there are two agents to demonstrate how to use the RockBot framework to build agents other than RockBot itself. The first is as simple as you can get. The second is a real external agent that the RockBot agent uses when asked to do any research. 1. Sample agent - Agent that echoes any text sent 2. Research agent - Agent that researches a topic and returns consolidated results ## Memory Tools (long-term) RockBot maintains long-term memory, and these are the tools that support that memory concept. * save_memory, search_memory, delete_memory, list_categories > ℹ️ There are no explicit tools for _conversational memory_ because conversational memory is always part of the agent’s context window. Other memories are brought into context on-demand. ## Working Memory Tools (session scratch space) RockBot also maintains working memory, and these are the tools for interacting with that memory. * save_to_working_memory, get_from_working_memory, delete_from_working_memory, list_working_memory, search_working_memory > ℹ️ As you can see, the RockBot framework and agent have three levels of memory: conversational, working, and long-term. This provides a rich way to manage context window usage, subsystem interactions, and long-term concepts in an elegant manner. ## Skill Tools RockBot has skills, and these are the tools it uses to interact with its own set of skills. * get_skill, list_skills, save_skill, delete_skill Skills develop over time automatically, sometimes refining the base-level subsystem tool guides, other times being created out of whole cloth by the agent as it learns. ## Rules & Configuration Tools These are tools used to manage rules that alter the agent’s behavior. These rules are in addition to the built-in `soul.md` and `directives.md` files that are central to the agent’s identity. * add_rule, remove_rule, list_rules, set_timezone ## Web Tools These are tools that allow the agent to search (using a Brave API key) and retrieve web pages. * web_search — Search the web, returning titles/URLs/snippets * web_browse — Fetch a page and return content as Markdown (with chunking) ## MCP Integration Tools These are tools that allow the agent to find and use MCP servers without having those MCP servers and tools always consuming large amounts of context memory. * mcp_list_services — List connected MCP servers * mcp_get_service_details — Get tool details for an MCP server * mcp_invoke_tool — Execute a tool on an MCP server * mcp_register_server — Register a new MCP server at runtime * mcp_unregister_server — Remove an MCP server at runtime * Plus all tools dynamically registered from configured MCP servers > ℹ️ These tools are a built-in equivalent to the separate mcp-aggregator project. ### MCP Server Examples In my current live environment, here are some of the MCP servers I have registered with the RockBot agent. 1. calendar-mcp - access all my email and calendar accounts 2. onedrive-personal - personal OneDrive 3. onedrive-marimer - work OneDrive 4. todo-mcp - a simple to-do list implementation 5. github - read/write to my GitHub repos 6. routing-stats - get info on RockBot LLM routing 7. openrouter - get info on openrouter.ai usage 8. azure-foundry - get info on Azure Foundry usage ## Script Execution When an agent needs to run some code, it uses these tools to execute scripts. The script executes in an ephemeral container that runs in a separate Kubernetes namespace. * execute_python_script — Run Python in a secure ephemeral container In the future we may support other types of script, such as TypeScript or bash. Python was the obvious start point given its broad use, flexibility, and how well LLMs can generate Python code. ## Conclusion Agents without tools aren’t very useful in any real-world scenarios. The RockBot framework provides a range of subsystems you can use when building an agent, and each subsystem provides a set of tools to the agent. The RockBot agent itself has access to all these subsystems and associated tools. And some subsystems, like A2A, MCP, web, and scripts, open the door for the agent do do virtually _anything_ by collaborating with other agents or invoking external tools, APIs, or code.

An #ai #agent needs a good set of resources and tools to be useful. This blog post goes through the tools in the #rockbot framework and how they are used by agents.

blog.lhotka.net/2026/03/09/Agent-Resourc...

Agent Resources and Tools VP, Open Source Creator, Author, Speaker

An #ai #agent needs a good set of resources and tools to be useful. This blog post goes through the tools in the #rockbot framework and how they are used by agents.

blog.lhotka.net/2026/03/09/A...

#rockbot not only uses skills, but it develops and enhances skills over time so it is able to become more effective and helpful over time without manual intervention!

https://blog.lhotka.net/2026/03/06/RockBot-Skills

#ai #agent #dotnet #agentic

How RockBot Learns New Skills VP, Open Source Creator, Author, Speaker

#rockbot not only uses skills, but it develops and enhances skills over time so it is able to become more effective and helpful over time without manual intervention!

blog.lhotka.net/2026/03/06/R...

#ai #agent #dotnet #agentic

Abre nuevas oportunidades de ingresos con publicidad inteligente en tu propio local. 💰📈✨📺 #AdTech #Rockbot #Monetización

The RockBot Band Over the past several months I’ve been building a set of open source projects that each solve a specific problem in the AI agent space. Individually they’re useful. Together they form the foundation for building truly agentic systems that run in production environments like Kubernetes or Azure. I want to step back and talk about how these projects fit together, because the big picture matters more than any single component. ## The Projects Here’s the lineup: * RockBot — A framework for building agents and multi-agent systems, designed to be cloud-native and manageable. Think of it as the runtime and architecture for agents that communicate through a message bus with full isolation and separation of concerns. * mcp-aggregator — A gateway that sits between your agents (or any LLM client) and all your MCP servers. Agents interact with MCP servers without consuming massive amounts of context memory, and without needing credentials or connection details for each server. * calendar-mcp — An MCP server that provides access to multiple M365, outlook.com, and Gmail email and calendar accounts. Not just one calendar — _all_ of them. Your work calendar, client calendars, personal and family calendars. A real picture of your actual life. * agentregistry — An agent registry for dynamic discovery of A2A and ACP agents, as well as MCP servers. Supports both persistent and ephemeral instances (think KEDA-scaled containers), and agent-to-agent communication via both HTTP and queued messaging. * researchagent — An agent built on the RockBot framework, designed to perform research tasks where results flow back to the calling RockBot agent. Each one addresses a gap I kept running into while building agentic systems. Let me explain how they connect. ## The Agent Runtime: RockBot Everything starts with RockBot. It provides the fundamental architecture: agents as isolated processes communicating through a message bus (RabbitMQ in production). No shared memory, no LLM-generated code running in your host process, no ability for a compromised agent to reach into another agent’s state. I wrote about RockBot’s design in detail in Introducing RockBot, but the key point here is that RockBot is designed from the ground up to run in containers. The framework supports both stateful and stateless agents — state can live in the agent process, in messages, or in external stores, depending on the agent’s needs. Scaling is horizontal for stateless workers, and the registry understands the difference. This matters when you start composing agents together — you need the runtime to support cloud-native deployment, not fight against it. ## A Personal Agent: RockBot Agent The RockBot framework is the foundation, but the RockBot Agent itself is a concrete example of what you can build with it. It’s a personal and professional agent designed to help you manage your life — scheduling, research, information retrieval, task coordination, and more. Unlike the stateless worker agents that spin up, do a job, and disappear, the RockBot agent is stateful by design. It maintains persistent memory about you: your preferences, your projects, your contacts, your communication style. It remembers what you talked about last week and builds on it. This is essential for a personal agent — you don’t want to re-introduce yourself every conversation. The agent uses markdown-based profile files (soul, directives, and style) to define its identity and behavior, and it has a multi-layered memory system with short-term, long-term, and working memory. When it needs to do something beyond its own capabilities — research a topic, check your calendar, interact with external systems — it delegates to other agents and tools through the message bus and mcp-aggregator. The RockBot agent is the hub that ties everything else together from the user’s perspective. ## Tool Access Without the Bloat: mcp-aggregator Agents need tools. MCP (Model Context Protocol) is the emerging standard for giving AI systems access to external capabilities. But if you naively give an agent direct access to a dozen MCP servers, two things happen: your agent’s context window fills up with tool descriptions it may never use, and every agent needs credentials for every server. mcp-aggregator solves both problems. It acts as a single gateway that your agent connects to. The aggregator knows about all available MCP servers, provides concise summaries, and only loads full tool details on demand. Credentials live in the aggregator, not in every agent. I covered this in MCP Aggregator, but the piece I want to emphasize here is how this fits the cloud-native story. In a containerized environment, you configure the aggregator once and every agent in the cluster can use it. Add a new MCP server? Register it with the aggregator and every agent can discover it immediately. No redeployment, no configuration changes across dozens of agent instances. ## A Real View of Your Schedule: calendar-mcp Most calendar integrations give you access to _one_ calendar. That’s fine if you only have one, but most professionals juggle multiple accounts — work (M365), personal (outlook.com or Gmail), maybe a shared family calendar, client calendars, and so on. calendar-mcp is an MCP server that aggregates across all of these. When your agent needs to schedule something or check your availability, it sees the complete picture. Not just your work calendar, but the dentist appointment on your personal calendar and the school event on the family calendar. This is the kind of tool that becomes much more powerful when accessed through the aggregator. The agent doesn’t need to know about OAuth tokens for three different email providers. It asks the aggregator for calendar tools, the aggregator routes to calendar-mcp, and calendar-mcp handles the multi-account complexity. ## Finding Agents and Servers: agentregistry In a static system, you can hardcode which agents exist and where to find them. In a dynamic cloud environment, that falls apart fast. Containers spin up and down. KEDA scales agents to zero when idle and back up when there’s work. New agents get deployed. Old ones get retired. agentregistry provides dynamic discovery for the entire ecosystem. It knows about: * **A2A agents** — agents that communicate via Google’s Agent-to-Agent protocol * **ACP agents** — agents using the Agent Communication Protocol * **MCP servers** — tool servers available in the environment * **Persistent instances** — always-running services, including stateful agents like the RockBot agent that maintain long-term memory * **Ephemeral instances** — stateless containers that scale to zero and spin up on demand (via KEDA or similar) * **Multiple transports** — HTTP for synchronous communication, queued messaging (like RabbitMQ) for asynchronous agent-to-agent work This is the glue that makes a multi-agent system dynamic rather than static. When RockBot needs to delegate a research task, it doesn’t need a hardcoded address. It queries the registry, finds an available research agent, and sends the task — whether that agent is already running or needs to be spun up. ## Specialized Agents: researchagent The researchagent is a concrete example of how this all comes together. Built on the RockBot framework, it’s a specialized agent designed to perform research — web searches, document analysis, information synthesis — and return structured results to the calling agent. In practice, a user asks the RockBot agent something that requires research. RockBot recognizes the need, queries the agentregistry to find a research agent, delegates the task via the message bus, and gets results back. The research agent uses mcp-aggregator to access whatever tools it needs — web search, document stores, APIs — without having its own MCP server configurations. ## How It All Fits Together Here’s the flow in a realistic scenario: 1. A user asks their **RockBot** agent to find a good time to meet with a client next week and prepare background information on the client’s recent projects. 2. RockBot checks availability by invoking calendar tools through **mcp-aggregator** , which routes to **calendar-mcp**. Calendar-mcp checks the user’s work calendar, personal calendar, and the client’s shared calendar. 3. RockBot delegates the background research to a **researchagent** , discovered through the **agentregistry**. The registry knows a research agent is available (or triggers one to spin up via KEDA). 4. The researchagent uses **mcp-aggregator** to access web search tools, the company’s internal knowledge base, and the CRM — all without having direct credentials to any of them. 5. Results flow back through the message bus. RockBot synthesizes the calendar availability and research results, and presents the user with proposed meeting times and a briefing document. No single project does all of this. But together, they provide the complete infrastructure: an agent runtime with proper isolation, centralized tool access, real-world calendar integration, dynamic agent discovery, and specialized agent delegation. ## Why This Matters The AI agent ecosystem is still young, and most frameworks treat deployment as an afterthought. They work great on a developer’s laptop but don’t have answers for multi-tenant environments, dynamic scaling, credential management, or inter-agent coordination in production. That’s the gap I’m trying to close. Not by building one monolithic framework that does everything, but by building focused components that follow established distributed systems principles and compose together naturally. These projects are all open source under the MIT license: * RockBot * mcp-aggregator * calendar-mcp * agentregistry If you’re thinking about building agentic systems that need to run in real production environments, I’d love for you to take a look. Open an issue, ask questions, or contribute. The pieces are in place — now it’s about making them better.

To be useful, #rockbot needs other agents and #mcp servers, because it follows the principle of least privilege. Each has permission to do just what it does. #agentic #ai in action.

https://blog.lhotka.net/2026/03/03/The-RockBot-Band

The RockBot Band VP, Open Source Creator, Author, Speaker

To be useful, #rockbot needs other agents and #mcp servers, because it follows the principle of least privilege. Each has permission to do just what it does. #agentic #ai in action.

blog.lhotka.net/2026/03/03/T...

Rockbot: la base sólida sobre la que las mejores marcas construyen su éxito. 🏗️💎🚀📊 #BusinessGrowth #Rockbot #MarketingDigital

Perhaps it can give the presentations too, then you can spend the time creating new features for #rockbot 😁

Last night #rockbot noticed that I have some conference submissions (calls for speakers) open, so it created talk titles/abstracts for me to submit.

Very thoughtful 😎

Desde Barcelona para el mundo: Rockbot redefine el concepto de medios para empresas. 📍🌍💻✨ #BarcelonaTech #Rockbot #GlobalBusiness

Creamos experiencias atractivas que se traducen en resultados reales para tu negocio. 📈✨🎯🤝 #ROI #Rockbot #BusinessSuccess

Maximiza el potencial publicitario de tu espacio físico con tecnología de vanguardia. 🚀🎯📺💰 #Advertising #Rockbot #Innovation

Why am I creating #rockbot? Because I want to have a personal/professional #ai bot that I can trust - that has clear security boundaries and which could be connected to my business services in addition to my personal and work emails and calendars.

github.com/MarimerLLC/r...

Original post on fosstodon.org

If feels like #rockbot is finally starting to really come together. Minimal tool calling failures and hallucinations, and hopefully I've got the time zone issues nailed down. It is strange how big things are easy, but the niggling details are always the hardest part! […]

Venta de programas y servicios de IT adaptados al 100% a tus necesidades. 🤝💻🔥📉 #B2B #Rockbot #TechSales

How RockBot Remembers Many AI models have no persistent memory. Every conversation starts fresh. They don’t remember what you told them yesterday, last week, or five minutes ago in a different chat window. For a casual ass...

A discussion of the #ai #agent memory system used by #rockbot to remember conversations, activity, collaboration, and long-term memory.

blog.lhotka.net/2026/02/24/A...

Lesson learned: use at least #claude haiku if your agent uses tools, cheaper models hallucinate way too much.

#rockbot is starting to shape up pretty well today; good tool use, subagents, #a2a agents - truly productive #ai.

github.com/marimerllc/r...

GitHub - MarimerLLC/rockbot: Autonomous personal agent designed to be cloud-native Autonomous personal agent designed to be cloud-native - MarimerLLC/rockbot

#rockbot now has a _ton_ of new features after the weekend! Supports #a2a protocol, subagents, background tasks, 3 levels of memory, and has major identity updates.

I gave up trying to use #deepseek and have been using #haiku, and that helps - worth the extra cost imo.

github.com/marimerllc/r...

Like a human needs to dream, so does an autonomous agent. #rockbot is designed with a dream subsystem so it can organize all sorts of memories, skills, and other information to make its life better and to be more productive when it "wakes up".

https://github.com/MarimerLLC/rockbot

GitHub - MarimerLLC/rockbot: Autonomous agent designed to be cloud-native Autonomous agent designed to be cloud-native. Contribute to MarimerLLC/rockbot development by creating an account on GitHub.

Like a human needs to dream, so does an autonomous agent. #rockbot is designed with a dream subsystem so it can organize all sorts of memories, skills, and other information to make its life better and to be more productive when it "wakes up".

github.com/MarimerLLC/r...

I'm trying to make #rockbot able to compensate for "cheap" models like deepseek terminus, but it is really hard! I'm not convinced that it isn't better to just pay for a good model like Claude sonnet or opus.

I'm working on #rockbot and am discovering that date/time is almost as hard as dealing with double values and rounding!

Rockbot Achieves Remarkable Rankings in Built In's 2026 Best Workplaces Awards Rockbot has garnered multiple accolades in Built In's 2026 Best Places to Work awards, reflecting its commitment to workplace culture and employee satisfaction.

Rockbot Achieves Remarkable Rankings in Built In's 2026 Best Workplaces Awards #United_States #Oakland #remote_work #Built_In #Rockbot

Rockbot Launches Cutting-Edge Smart Amp for Enhanced Audio Solutions in Businesses Introducing the Rockbot Smart Amp, an innovative all-in-one media player and amplifier for businesses, offering seamless audio management and monitoring.

Rockbot Launches Cutting-Edge Smart Amp for Enhanced Audio Solutions in Businesses #USA #Oakland #Rockbot #audio_solutions #Smart_Amp

Emilia
20251966

The parts used to put the bot together
This bot was made with part found on a trip to Arizona
Head: rock found from Arizona
Eyes: sewing buttons
Body: rock found from Arizona
Feet: computer stand feet

#RockBot #SculptureArt #CraftyBot #ButtonEyes #ComputerPartsArt #TechArt