Cheaper – Earlybirds Invest

If you’re looking for a dependable, portable, and budget-conscious laptop for day-to-day tasks, look no further. This pre-loved MacBook Air hits the sweet spot—and it’s only $189.97 (reg. $999) with free shipping for a little while longer.

With a 1.8GHz Intel Core i5 processor, this machine is fast enough for tasks such as email, Google Docs, light streaming, and Zoom calls. It’s not here to replace your gaming rig or your creative workstation, but for the average user who just needs something that works without the drama, this MacBook is a total win.

At just under 3 pounds, it’s a dream for travel. Toss it in your tote or backpack and you’re good to go—whether you’re hopping on a plane or just working from the library. And thanks to the 12-hour battery, you can leave the charger at home more often than not.

The 128GB SSD keeps boot-up times snappy and gives you plenty of space for documents, photos, and those videos you swore you’d organize one day. Plus, the 13.3-inch widescreen display delivers a crisp viewing experience, even if you’re just catching up on YouTube.

Sure, it’s not brand new—this is a Grade “A” or “B” refurbished device, meaning you might see a minor scuff here or there. But under the hood, it’s fully functional, professionally inspected, and even comes with a 90-day aftermarket parts and labor warranty, just in case.

At just $189.97 with free shipping, this deal is hard to beat. Grab your own 13.3-inch MacBook Air now before units sell out for good.

Apple MacBook Air 13.3″ (2017) 1.8GHz i5 8GB RAM 128GB SSD Silver (Refurbished)See Deal

StackSocial prices subject to change.

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It seems like no matter what, you’re forced to seriously budget, from daily groceries to tech goodies. But just because you’re limiting your spending, it doesn’t mean you have to deal with your broken laptop for another year.

Just opt for this refurbished MacBook Pro deal instead of shopping brand-new. This 2017 model has had its price tag slashed by over 70%, coming in at only $329.97 instead of $1,499. The only catch is that supplies are super limited.

This MacBook has a Grade “A” rating, no scratches on its display, and a minimum of 80% battery health. There might be some light wear on the body, but it’s so minimal you might not even notice.

This laptop might not be stocked with features that Apple’s latest MacBooks have, but this one does hold its own. Check out a few of its specs:

Intel Core i5 (with speeds up to 3.1GHz) for smooth multitasking
512GB of storage for simple, local storing of your files, photos, and other media
Four USB-C ports for extra connectivity
A 13.3-inch Retina display with 2560×1600 pixel resolution.

It even comes with the infamous Touch Bar and Touch ID, which makes it easy to securely log into your MacBook Pro and other accounts, as well as pay with Apple Pay.

Grab your own 13.3-inch MacBook Pro for just $329.97 while supplies last. This offer ends June 1 at 11:59 PM Pacific.

Apple MacBook Pro 13″ Touchbar (2017) 3.1GHz i5 8GB RAM 512GB SSD Space Gray (Refurbished)See Deal

StackSocial prices subject to change.

Opinion by: Naman Kabra, co-founder and CEO of NodeOps Network

Graphics Processing Units (GPUs) have become the default hardware for many AI workloads, especially when training large models. That thinking is everywhere. While it makes sense in some contexts, it’s also created a blind spot that’s holding us back.

GPUs have earned their reputation. They’re incredible at crunching massive numbers in parallel, which makes them perfect for training large language models or running high-speed AI inference. That’s why companies like OpenAI, Google, and Meta spend a lot of money building GPU clusters.

While GPUs may be preferred for running AI, we cannot forget about Central Processing Units (CPUs), which are still very capable. Forgetting this could be costing us time, money, and opportunity.

CPUs aren’t outdated. More people need to realize they can be used for AI tasks. They’re sitting idle in millions of machines worldwide, capable of running a wide range of AI tasks efficiently and affordably, if only we’d give them a chance.

Where CPUs shine in AI

It’s easy to see how we got here. GPUs are built for parallelism. They can handle massive amounts of data simultaneously, which is excellent for tasks like image recognition or training a chatbot with billions of parameters. CPUs can’t compete in those jobs.

AI isn’t just model training. It’s not just high-speed matrix math. Today, AI includes tasks like running smaller models, interpreting data, managing logic chains, making decisions, fetching documents, and responding to questions. These aren’t just “dumb math” problems. They require flexible thinking. They require logic. They require CPUs.

While GPUs get all the headlines, CPUs are quietly handling the backbone of many AI workflows, especially when you zoom in on how AI systems actually run in the real world.

Recent: ‘Our GPUs are melting’ — OpenAI puts limiter in after Ghibli-tsunami

CPUs are impressive at what they were designed for: flexible, logic-based operations. They’re built to handle one or a few tasks at a time, really well. That might not sound impressive next to the massive parallelism of GPUs, but many AI tasks don’t need that kind of firepower.

Consider autonomous agents, those fancy tools that can use AI to complete tasks like searching the web, writing code, or planning a project. Sure, the agent might call a large language model that runs on a GPU, but everything around that, the logic, the planning, the decision-making, runs just fine on a CPU.

Even inference (AI-speak for actually using the model after its training) can be done on CPUs, especially if the models are smaller, optimized, or running in situations where ultra-low latency isn’t necessary.

CPUs can handle a huge range of AI tasks just fine. We’re so focused on GPU performance, however, that we’re not using what we already have right in front of us.

We don’t need to keep building expensive new data centers packed with GPUs to meet the growing demand for AI. We just need to use what’s already out there efficiently.

That’s where things get interesting. Because now we have a way to actually do that.

How decentralized compute networks change the game

DePINs, or decentralized physical infrastructure networks, are a viable solution. It’s a mouthful, but the idea is simple: People contribute their unused computing power (like idle CPUs), which gets pooled into a global network that others can tap into.

Instead of renting time on some centralized cloud provider’s GPU cluster, you could run AI workloads across a decentralized network of CPUs anywhere in the world. These platforms create a type of peer-to-peer computing layer where jobs can be distributed, executed, and verified securely.

This model has a few clear benefits. First, it’s much cheaper. You don’t need to pay premium prices to rent out a scarce GPU when a CPU will do the job just fine. Second, it scales naturally.

The available compute grows as more people plug their machines into the network. Third, it brings computing closer to the edge. Tasks can be run on machines near where the data lives, reducing latency and increasing privacy.

Think of it like Airbnb for compute. Instead of building more hotels (data centers), we’re making better use of all the empty rooms (idle CPUs) people already have.

Through shifting our thinking and using decentralized networks to route AI workloads to the correct processor type, GPU when needed and CPU when possible, we unlock scale, efficiency, and resilience.

The bottom line

It’s time to stop treating CPUs like second-class citizens in the AI world. Yes, GPUs are critical. No one’s denying that. CPUs are everywhere. They’re underused but still perfectly capable of powering many of the AI tasks we care about.

Instead of throwing more money at the GPU shortage, let’s ask a more intelligent question: Are we even using the computing we already have?

With decentralized compute platforms stepping up to connect idle CPUs to the AI economy, we have a massive opportunity to rethink how we scale AI infrastructure. The real constraint isn’t just GPU availability. It’s a mindset shift. We’re so conditioned to chase high-end hardware that we overlook the untapped potential sitting idle across the network.

Opinion by: Naman Kabra, co-founder and CEO of NodeOps Network.

This article is for general information purposes and is not intended to be and should not be taken as legal or investment advice. The views, thoughts, and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.

Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool’s board of directors. Keithen Drury has positions in Alphabet. The Motley Fool has positions in and recommends Alphabet. The Motley Fool has a disclosure policy.

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