Mr D

Watching the future form in real time.
AI, technology, money & the shifts most people miss.
Stay curious.

[Mr D @mrd_0x]

Grok Bot is the most powerful AI agent I’ve used.

Set it up correctly, and specialized bots can research, create, coordinate, and run workflows 24/7.

This article shows you how to build an AI workforce and operate a one-person company. https://t.co/jxST6AbnQq

[Mr D @mrd_0x]

@bccxsol we live in AI agents world

[Mr D @mrd_0x]

6 AUTONOMOUS AI INTERNS COULD SAVE YOU 60+ HOURS AND MAKE $3,750+/MONTH

The phrase “autonomous intern” might be the cleanest way to explain where AI agents are right now.

Not a magical employee replacement. Not a senior operator you trust with everything. More like a digital intern that can work 24/7, handle repetitive tasks, follow clear procedures, and cost a tiny fraction of a human salary if you give it the right boundaries.

A single autonomous intern can research leads, draft outreach, summarize meetings, organize files, monitor competitors, clean data, prepare reports, or keep a workflow moving while you sleep. If it saves even 2 hours a day, that is roughly 60 hours a month back. At a $30/hour value on your time, that is already about $1,800/month in recovered capacity.

Now stack them.

One intern handles research. One handles inbox triage. One drafts content. One checks deliverables. One prepares outreach. One tracks numbers. Suddenly you have 5–6 AI workers covering narrow jobs that would normally be spread across several people.

THE MONEY ANGLE

You can use this in two ways.

First, inside your own business. If a small agency spends 20 hours a week on prospecting, reports, content prep, and inbox work, automating even half of that gives back 40+ hours a month. That extra capacity can go into sales, delivery, or taking on more clients without hiring immediately.

Second, you can sell the setup itself.

Build one “autonomous intern” for one painful job:

AI lead researcher for roofers
AI inbox assistant for law firms
AI content intern for real estate agents
AI reporting intern for ecommerce brands
AI follow-up assistant for med spas

Charge $500–$1,500 setup, then $300–$1,000/month to maintain and improve it.

At just 5 clients paying $750/month, that is $3,750/month recurring.

At 10 clients, that becomes $7,500/month.

The key is not pretending the agent is perfect. Give it one narrow role, define what “done” means, force structured output, and keep approval around risky actions like sending messages, spending money, or deleting data.

That is where the model becomes practical.

Build 6 autonomous interns that remove 5–10 hours of repetitive work every week, then either use that leverage yourself or turn the system into a $3,750–$7,500/month service.

[Mr D @mrd_0x]

Grok Bot is the most powerful AI agent I’ve used.

Set it up correctly, and specialized bots can research, create, coordinate, and run workflows 24/7.

This article shows you how to build an AI workforce and operate a one-person company. https://t.co/jxST6AbnQq

[Mr D @mrd_0x]

@federicodonaton Yeah, that's exactly how it works

[Mr D @mrd_0x]

Grok Bot is the most powerful AI agent I’ve used.

Set it up correctly, and specialized bots can research, create, coordinate, and run workflows 24/7.

This article shows you how to build an AI workforce and operate a one-person company. https://t.co/jxST6AbnQq

[Mr D @mrd_0x]

@vmrmax Chief of staff is boss of your company, people can't skip him

[Mr D @mrd_0x]

@i_mika_el Everything individually. Someone does it like you, someone in another way

[Mr D @mrd_0x]

@mikenevermiss So...wait, I can have all three running inside Grok Bot?

[Mr D @mrd_0x]

@Demkinn Yep, that article was you sign to do this

[Mr D @mrd_0x]

@TheViableEdge Automating too early just scales your blind spots. Do it manually first, learn where the edge cases are, then automate the predictable 80% and keep a human check on the risky 20%.

[Mr D @mrd_0x]

The easiest AI agency to start in 2026 doesn’t sell “AI.”

It removes one expensive bottleneck from one type of business.

Most beginners build an impressive automation first, then search for someone willing to buy it. They pitch agents, models, integrations, and complicated workflows. The business owner hears technical language but still doesn’t understand how any of it will make or save money.

Start with the leak instead.

A dental clinic loses potential patients because nobody answers calls after 6 PM. A property manager spends every morning sorting maintenance requests. A freight broker manually copies information from emails into five different systems. A local contractor takes three days to send a quote, and the customer hires someone else before it arrives.

Each problem can become a focused AI service:

APPOINTMENT RECOVERY answers missed calls, qualifies the customer, and books an available time.

INVOICE PROCESSING extracts information from documents, checks for missing fields, and prepares records for approval.

LEAD QUALIFICATION researches new prospects, scores them against predefined criteria, and prepares personalized outreach.

SUPPORT TRIAGE reads incoming messages, identifies urgency, retrieves the relevant information, and sends unusual cases to a human.

The offer should never be, “I will install an AI agent.”

The offer should be, “Every qualified lead receives a response within 60 seconds, including evenings and weekends.”

Now the customer can understand the result and calculate its value. If a company is losing an estimated $3,000 per month because of one broken process, charging $750–$1,500 per month to reduce that loss becomes a much easier conversation. Those figures are illustrative—the real price should come from the customer’s volume, margins, and measurable results.

You also don’t need to automate everything on day one. Deliver part of the service manually, observe what repeats, and automate only the stable steps. Keep human approval around payments, sensitive customer information, unusual requests, and important decisions.

One niche. One recurring problem. One measurable result.

That is a stronger business than another “full-service AI automation agency” promising to transform everything.

[Demkinn @Demkinn]

https://t.co/CvEeNTHihW

[Mr D @mrd_0x]

ORICO MINI PC VS NEW MAC MINI. WHAT TO CHOOSE?

the mini PC war just got interesting.

on one side: Apple's new Mac mini with M6, starting at $899, with a 12-core CPU, 12-core GPU, 16GB unified memory, up to 170GB/s memory bandwidth, and Apple claiming up to 4× faster AI performance than the M4 generation.

on the other: ORICO Omini Plus / Omini Pro, basically the Windows/Linux answer to the Mac mini idea. the Omini Plus uses a Ryzen 5 7535HS with 16GB DDR5 and 2TB SSD for around $478–$535, while the more powerful Omini Pro starts around $380–$435 barebones, uses a Ryzen 7 8845HS with Radeon 780M graphics, and can be upgraded to as much as 256GB RAM and 8TB of storage.

so which one would I buy?

MAC MINI

if you want something simple, quiet, extremely power-efficient, and optimized for local AI, content creation, coding, automation, and 24/7 agent workflows, the M6 Mac mini is probably the cleaner machine.

you buy it, plug it in, and it just becomes a permanent workstation.

Apple is clearly pushing the new Mac mini toward always-on AI workloads, and the base M6 already gives you 16GB unified memory with a much stronger AI engine than before.

ORICO

if you want maximum flexibility per dollar, ORICO becomes very interesting.

the Omini Plus gives you 2× USB4, 2× 10Gbps USB-A, 2× USB 2.0, HDMI 2.1, DisplayPort 1.4, dual 2.5Gb Ethernet, and audio inside a roughly 0.8-liter chassis. USB4 also means you can connect an external GPU later if you need more graphics or compute.

the Omini Pro goes even further because you can run Windows or Linux, swap RAM, expand storage, and build something much more customized. up to 256GB RAM and 8TB SSD capacity is a completely different philosophy from Apple's fixed unified-memory approach.

AND HERE'S THE MONEY ANGLE

this is why I think both machines are more interesting than normal desktop PCs.

don't buy one just to browse Chrome.

turn it into infrastructure.

for example, use one machine as an always-on AI backend for a small business service:

AI lead research
content generation
video transcription
customer-support workflows
automated reporting
local document processing
coding agents
data enrichment
scheduled research

say you buy the ORICO for roughly $500 and sell one automation package for $500/month.

one client can theoretically cover the hardware cost.

get 5 clients at $500/month = $2,500/month.

or buy the Mac mini for $899, use it as a dedicated AI/content workstation, and sell a more complete service for $750/month.

5 clients = $3,750/month.

obviously that revenue does not come from owning the computer by itself. you still need the offer, customers, software, and delivery system.

but that's the point.

these machines are cheap enough that compute is no longer the expensive part of starting the business.

SO WHICH ONE?

ORICO Omini: choose it if you want Windows/Linux, upgradeable RAM and storage, lots of ports, eGPU support, and maximum hardware flexibility for roughly half the entry price.

Mac mini M6: choose it if you want the cleaner ecosystem, stronger dedicated AI hardware, excellent efficiency, and something you can leave running 24/7 with minimal maintenance.

if I wanted to experiment, build servers, upgrade everything, and squeeze the most hardware out of every dollar:

ORICO.

if I wanted one tiny box quietly running AI agents, coding, content, and automation every day without thinking much about the hardware:

Mac mini.

either way, the bigger opportunity isn't the computer.

it's realizing that for $500–$900, you can now put a surprisingly capable machine on your desk and build a business around what it does while you're sleeping.

[Mr D @mrd_0x]

LADIES AND GENTLEMEN, WELCOME THE NEW MAC MINI. IT CAN HANDLE WHATEVER YOU THROW AT IT.

Apple just turned the Mac mini into one of the most interesting little AI machines you can buy.

The new model starts at $899 with the M6 chip, while the higher-end M5 Pro version starts at $1,699. The M6 brings a 12-core CPU, 12-core GPU, dual 16-core Neural Engine, support for up to 32GB of unified memory, and Apple says AI performance can be up to 4× faster than the previous M4 generation.

And the crazy part is the size.

The Mac mini footprint is only about 12.7 × 12.7 cm, which means you can put serious compute on a desk, under a monitor, in a studio, or even build several of them into a small local server setup without needing a giant workstation.

This is where the money angle gets interesting.

You could use one as a dedicated AI workstation and sell services around it: local transcription, content generation, video processing, automation, coding, data cleanup, image workflows, research, or private AI tools for small businesses.

Example:

You buy the machine for $899.

You offer a local business an AI automation package for $500/month.

The Mac handles scheduled workflows, file processing, reports, lead enrichment, content prep, or local model tasks in the background.

2 clients = $1,000/month.

At that point, the hardware has theoretically paid for itself in the first month before software, taxes, or other operating costs.

Push it further.

Build a small AI service around one niche and charge $750/month to 5 clients.

That is $3,750/month in revenue being supported by a computer that costs less than $1,000 at the base level.

Or turn it into a small local AI box for creators.

Run transcription, clipping, subtitle generation, file organization, thumbnails, research, and content preparation from one machine, then package that as a monthly service instead of selling your time by the hour.

That is the part people miss.

The Mac mini is not interesting because it is small.

It is interesting because the cost of owning permanent compute keeps falling while the number of AI jobs you can run on that compute keeps increasing.

For $899, you are no longer just buying a desktop.

You are buying something that can sit on a desk 24/7 and become the backend for an AI service, automation business, creator workflow, coding setup, research machine, or local inference box.

The laptop is what you work on.

The Mac mini can be what keeps working after you leave.

And that is where a tiny computer starts looking a lot more like an employee.

[Mr D @mrd_0x]

@Demkinn Mac Mini probably the best on the market now

[Mr D @mrd_0x]

LADIES AND GENTLEMEN, WELCOME THE NEW MAC MINI. IT CAN HANDLE WHATEVER YOU THROW AT IT.

Apple just turned the Mac mini into one of the most interesting little AI machines you can buy.

The new model starts at $899 with the M6 chip, while the higher-end M5 Pro version starts at $1,699. The M6 brings a 12-core CPU, 12-core GPU, dual 16-core Neural Engine, support for up to 32GB of unified memory, and Apple says AI performance can be up to 4× faster than the previous M4 generation.

And the crazy part is the size.

The Mac mini footprint is only about 12.7 × 12.7 cm, which means you can put serious compute on a desk, under a monitor, in a studio, or even build several of them into a small local server setup without needing a giant workstation.

This is where the money angle gets interesting.

You could use one as a dedicated AI workstation and sell services around it: local transcription, content generation, video processing, automation, coding, data cleanup, image workflows, research, or private AI tools for small businesses.

Example:

You buy the machine for $899.

You offer a local business an AI automation package for $500/month.

The Mac handles scheduled workflows, file processing, reports, lead enrichment, content prep, or local model tasks in the background.

2 clients = $1,000/month.

At that point, the hardware has theoretically paid for itself in the first month before software, taxes, or other operating costs.

Push it further.

Build a small AI service around one niche and charge $750/month to 5 clients.

That is $3,750/month in revenue being supported by a computer that costs less than $1,000 at the base level.

Or turn it into a small local AI box for creators.

Run transcription, clipping, subtitle generation, file organization, thumbnails, research, and content preparation from one machine, then package that as a monthly service instead of selling your time by the hour.

That is the part people miss.

The Mac mini is not interesting because it is small.

It is interesting because the cost of owning permanent compute keeps falling while the number of AI jobs you can run on that compute keeps increasing.

For $899, you are no longer just buying a desktop.

You are buying something that can sit on a desk 24/7 and become the backend for an AI service, automation business, creator workflow, coding setup, research machine, or local inference box.

The laptop is what you work on.

The Mac mini can be what keeps working after you leave.

And that is where a tiny computer starts looking a lot more like an employee.

[Framez @0xFramez]

https://t.co/HHyttfy1fb

[Mr D @mrd_0x]

@kyzoroXX You should try new Mac Mini

[Mr D @mrd_0x]

@motyka_9 Tim Cook is cooking

[Mr D @mrd_0x]

The Next AI Chip War Isn’t About Training. It’s About How Fast the Machine Answers You.

The AI industry spent years competing to build the largest models. More GPUs, more parameters, more data, and training runs measured in billions of dollars. But once those models are trained, an entirely different problem begins: serving useful answers to millions of people without making them wait or burning money on every request.

That process is called inference, and it may become the most important battleground in AI hardware.

Cerebras Systems has introduced the CS-4, a new AI server built around three WSE-3 Turbo chips. Unlike conventional processors cut into hundreds of small chips from a silicon wafer, Cerebras builds a single processor roughly the size of the wafer itself. The goal is to keep more computation together and reduce the time and energy lost when data moves between separate chips.

That distinction matters because an advanced AI model does not generate an entire answer instantly. It repeatedly processes information and predicts the next token. Every delay in moving data through the system makes the answer slower, while every additional machine required to serve it increases the cost.

Cerebras says its new CS-4 system contains 50% fewer components than the previous architecture, making it easier to install inside data centers. It uses three large chips manufactured on TSMC’s 5-nanometer process, combined with upgraded networking designed to move data faster between machines.

The company is not trying to win with a slightly better chatbot. It is trying to become the engine underneath thousands of them.

This is where the AI market is heading. Model intelligence is improving rapidly, but users are becoming less tolerant of waiting. A research agent that takes 20 minutes to complete a valuable task may be acceptable. A customer-support agent that waits 15 seconds before every sentence is not. Neither is a coding assistant that repeatedly interrupts a developer’s flow.

Speed changes how people use a product. When responses take minutes, AI is something you consult occasionally. When they arrive almost instantly, AI can sit inside customer calls, financial systems, industrial machines, games, search engines, and live business workflows.

Cerebras says it expects to deliver 600 megawatts of computing capacity by the end of 2027. Its leadership is also targeting a fourfold increase in speed by the end of 2026 and as much as 20 times more throughput by the end of 2027. Those are company targets, not guaranteed results, but they show the scale of the ambition.

The opportunity is bigger than competing with Nvidia for chip sales. Faster and cheaper inference could change the economics of every company building on top of AI.

Imagine an AI support system serving 100,000 conversations per day. A small reduction in the cost of each response becomes meaningful at that scale. Now multiply the calculation across banks, hospitals, software companies, governments, retailers, and autonomous machines. The companies controlling inference infrastructure could collect revenue every time intelligence is requested.

That is why the next hardware race will not be decided only by which company can train the most powerful model. It will also be decided by which architecture can operate that model quickly, reliably, and cheaply enough to place it inside everyday products.

Training creates intelligence once. Inference sells access to that intelligence millions of times.

And that may be where the larger business is hiding.

[Demkinn @Demkinn]

https://t.co/CvEeNTHihW

[Mr D @mrd_0x]

A $300/month AI team can now do the first pass of six different jobs.

Not six chatbots sitting in separate tabs. Six specialized agents working inside one system, passing information to each other and escalating only the decisions that require a human.

Here is what the company looks like:

RESEARCH monitors competitors, customer discussions, new products, and industry developments. Every morning, it delivers the five changes that actually matter.

SALES researches prospects, enriches leads, drafts personalized outreach, updates the CRM, and flags the conversations most likely to convert.

CONTENT turns research into posts, articles, newsletters, scripts, and visuals while keeping the same voice across every platform.

SUPPORT reads incoming questions, retrieves the relevant information, prepares answers, and sends unusual cases to a human before anything goes wrong.

OPERATIONS watches projects, deadlines, documents, and recurring workflows. When something stalls, it identifies the bottleneck and suggests the next action.

FINANCE categorizes expenses, tracks invoices, updates cash-flow projections, and warns the owner when the numbers move outside predefined limits.

A seventh agent acts as Chief of Staff. It does not perform every job itself. It routes tasks, checks outputs, resolves conflicts between agents, and gives the owner one short briefing instead of six separate streams of noise.

The illustrative software cost could be around $200–$500 per month, depending on models, tools, and usage. That does not mean six employees have been completely replaced. It means research, preparation, data entry, monitoring, and first drafts can happen before a human touches the work.

The human still owns the important parts: judgment, relationships, strategy, approvals, and accountability.

This is the real shift AI agents are creating. Small companies are gaining access to an operating structure that previously required multiple hires, managers, and expensive software.

The next generation of entrepreneurs will not ask, “What can AI write for me?”

They will ask, “What part of my company can run without waiting for me?”

[Ronin @DeRonin_]

https://t.co/gzpC225HNd

[Mr D @mrd_0x]

@elonmusk That's a dream work

[Mr D @mrd_0x]

@imperiumNash

[Mr D @mrd_0x]

@rugikkk The irony is incredible.
The same person explaining how blockchain could transform finance later became one of its biggest regulators.