2 useful (and 1 fun) homelab projects to try this weekend (May 22


It’s that time of the week again, so let’s dive into three more fun homelab projects to try this weekend! Today, I’ll be talking about setting up a home energy usage monitoring system, a private Pastebin alternative, and a retro LAN party box!

Use Home Assistant to track your home’s energy usage

Energy monitoring smart plugs are only half the battle

I recently deployed an energy-monitoring smart plug to monitor how much electricity my homelab uses. However, that’s only the beginning of my energy monitoring journey.

You see, a homelab is the perfect place to fully track your home’s energy usage. I also have a whole-home energy monitoring solution that natively integrates with Home Assistant that I will be deploying soon.

Once you have Home Assistant running in your homelab, you can then start to aggregate and process energy usage from various sources. Some energy utilities allow you to scrape that data from them, while others (like mine) require you to gather it yourself.

So, I’ve started building out an energy monitoring dashboard in Home Assistant. It’s very simple right now because I only have a single smart plug deployed.


IKEA Inspelning smart plug.


7 neat things you can do with an energy monitoring smart plug

Go beyond simple on-off functionality with power consumption triggers.

However, once I have the rest of the system in place, I’ll be able to see just how much money I’m spending on electricity throughout the house from things like lights, my refrigerator and freezer, and anything else.

If you’ve not thought about energy monitoring in your homelab yet, then this summer is the perfect time to deploy it. It’ll take a while to set up and get configured properly, but it’ll be worth it in the end. For systems like the one I have that go inside of the electrical panel, make sure to hire a licensed contractor if you’re not comfortable working with electricity.

Create a private pasting system to store and share text snippets

Pastebin and Gist are great, but not private

Pastefy's UI with code in the paste area showing syntax highlighting. Credit: Pastefy

Years ago when I was doing Android ROM development, I used Pastebin and Gist all the time. These tools are fantastic and were crucial in my formative years as a developer for sharing snippets of text with other developers.

These days, however, there are much better tools for the job. Instead of using a public pasting site, which is definitely not advised if there are any API secrets or passwords in a paste, you can self-host a system instead. That’s where Pastefy comes in.

Pastefy is designed as both an online, publicly-available Pastebin alternative as well as a self-hosted solution if you’re wanting to host it on your own hardware. I’d definitely recommend self-hosting it.

Pastefy is pretty simple to deploy, as you can use Docker to launch it on your server. Once it’s live, you can start to throw your pastes into the system, and it’ll auto-detect what language it’s in and provide marked-up previews for things like Markdown, SVGs, CSVs, and more. Plus, it offers syntax highlighting for most languages.

So, if you’re still using a public pasting website for your code or homelab, then it’s time to switch to something a bit more private.

Get ready for game night with friends by building a retro LAN party box

A plug-and-play system for local multiplayer games isn’t hard to build

A first-person look at multiplayer gameplay in Call of Duty 4. Credit: Activision

Sometimes you just want to kick back, relax, and enjoy some retro gaming with friends. I’m not talking about Pac-Man or those arcade classics here, I’m talking about modern classics like Call of Duty Modern Warfare (the original), Call of Duty World at War, Battlefield 2, Quake III Arena, Counter-Strike: Source, Team Fortress 2, Halo 2, Halo: The Master Chief Collection, or Minecraft.

There are so many other games that I could list, but this article would be way too long if I did that. However, the same technique that I’m going to talk about here likely applies to your favorite games.

You can use dedicated game server software like CubeCoder’s AMP, which is what I do when I need more advanced game server management. Pterodactyl would also be good. For many of these games, a simple SteamCMD automation setup would work well.

I’d start with spinning up Proxmox on whatever system you want to turn into a retro LAN party box so you can run multiple virtual machines. From there, get a few virtual machines running with various software on them, but Docker will likely be the root for everything. You’ll also want LANCache on one of the virtual machines with plenty of storage space.

With LANCache, you can keep the game itself downloaded on the server, so others can pull it from there instead of having to download from the internet.

Building a retro LAN party box like this will take some time to get properly set up, but it’ll be so worth it in the end. I still remember bringing my desktop to LAN parties at coworkers’ houses and having a blast playing so many games that way. There’s just something special about everyone being in the same room playing the same game that online multiplayer still can’t match.

  • GEEKOM A5 mini PC.

    Brand

    GEEKOM

    CPU

    AMD Ryzen 5 7430U

    Graphics

    AMD Vega 7

    Memory

    16GB DDR4 SO-DIMM

    Storage

    512GB NVMe (expandable)

    The GEEKOM A5 mini PC packs 16GB of user-replaceable RAM, a user-swappable NVMe SSD, plus two other storage slots, giving you plenty of user-upgradability in this compact system. The Ryzen 5 processor packs plenty of power for general tasks, and it’s even great at lightweight gaming and CAD work too.


  • KAMRUI Hyper H1 mini PC.

    Brand

    KAMRUI

    CPU

    AMD Ryzen 7 7735HS

    Graphics

    AMD Radeon 680M

    Memory

    16GB LPDDR5

    Storage

    512GB NVMe

    The KAMRUI Hyper H1 mini PC is perfect for setups that need a high-performance desktop without spending an arm and a leg. It boasts the AMD Ryzen 7 7735HS 8-core 16-thread processor and 16GB of LPDDR5 RAM (which is not user-upgradable). The pre-installed 512GB NVMe drive can be swapped out for a larger one though, and there’s a second NVMe slot for extra storage if needed.


  • ACEMAGIC AI mini PC i9 13900HK.

    Brand

    ACEMAGIC

    CPU

    Intel i9-13900HK

    Graphics

    Intel Iris Xe integrated, Intel Arc A770 dedicated

    Memory

    32GB, upgradable to 96GB

    Storage

    1TB

    This mini PC looks a bit different from the rest, and that’s for good reason. It features a desktop-like i9-13900HK processor that’s paired with a discrete Intel Arc A770 graphics card. That’s right, this mini PC has an actual GPU built-in alongside the integrated graphics that come baked into the i9-13900HK. Alongside that, there’s Windows 11 Pro, 32GB of RAM (which is user-upgradable up to 96GB), dual NVMe slots, and so much more. If you’re looking for the perfect workstation computer or homelab server, this could be it.



Not all homelab projects have to be productivity focused

While the first two projects in today’s roundup are geared toward productivity-type projects, the third is definitely designed around having fun. Having a homelab shouldn’t just be about making things “better” or faster or more productive.

One of my favorite things that I host in my homelab is game servers for friends, and building a LAN party box is a project that would be super fun to do. I don’t have any friends nearby to LAN party with anymore, but I still host online servers for them to use so we can all play in the same Minecraft or Satisfactory worlds.

So, stop keeping your homelab just for productive things. Have fun with it this weekend.



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  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

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McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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