'Data Centres Use Less Water Than Golf Courses.' True Today — and About to Flip.

Kevin O'Leary's stat checks out: US golf uses 2.08B gallons/day, data centres ~449M — 4.6x more. But golf's curve is flat and data centres are exponential; the lines cross around 2028. Using a true present-tense fact to dismiss a problem defined by its growth rate is the move to watch. His own Utah project shrank 75% under pressure.

A technically-true deflection: a real number that makes your footprint look small next to something frivolous.

Kevin O'Leary has a comeback for anyone worried about AI data centres draining the water supply: golf courses use far more. He's right — today. He's also using a true number to wave away a problem that the same numbers say is about to become his.

The claim, and the honest scorecard

The Shark Tank investor's argument is that American data centres consume far less water than the country's golf courses. The figures back him up, for now, per The Next Web: US golf courses use about 2.08 billion gallons a day; US data centres, roughly 449 million. Golf uses about 4.6 times more. As a factual snapshot, O'Leary is correct.

The problem is that a snapshot is exactly the wrong tool for this question. Water stress isn't about who uses more today — it's about the trajectory, and the two lines are heading in opposite directions.

Why the comparison misleads

Golf's water consumption is flat to declining. Courses are switching to drought-resistant grasses and recycled water; the sport has every incentive to shrink its footprint and is doing so. It's a mature use case whose demand curve bends downward.

Data-centre water demand is on an exponential curve, driven by AI training and inference. And the projections cross. Data-centre consumption is expected to reach roughly 590 billion gallons by 2028, potentially surpassing golf's projected ~425 billion around 2026–2027. So the honest version of O'Leary's claim is: "Data centres use less water than golf courses — for about another year." Using a true present-tense fact to dismiss a problem defined entirely by its growth rate is the rhetorical move to watch for. The gap he's citing is closing as he cites it.

There's also a distribution problem the national totals hide. Golf courses are spread across the country; data centres cluster, and increasingly in already water-stressed regions. A billion gallons averaged nationally is a very different thing from a concentrated draw on one strained local aquifer. The aggregate comparison flatters the data centre precisely by averaging away where the strain actually lands.

The tell is in his own project

The most instructive part of the story is O'Leary's own behaviour. His 40,000-acre Stratos data-centre project in Utah sparked protests and a gubernatorial executive order, and he scaled it back by 75% — to 10,000 acres — after pressure from Republican state senators. If water use were the non-issue his golf comparison implies, a 75% reduction under political pressure is a strange thing to concede.

He says the facility will use a closed-loop chilling system with no continuous water draw — which, if true, would genuinely address the concern. But Virginia Tech experts say there isn't enough data to verify the claim. That's the crux: "closed-loop, no water draw" is the right answer to the water problem, and also an easy thing to assert and a hard thing to prove. The engineering solution exists; whether a given facility actually implements it as advertised is the question that transparency, not reassurance, resolves.

What the argument is really doing

The golf comparison belongs to a familiar genre: the technically-true deflection. Find a real number that makes your footprint look small next to something frivolous, and you can dismiss a concern without engaging its substance. It works because the fact checks out and golf is an easy target. It fails because the concern was never "is this the single largest water user today" — it was "is this a fast-growing, geographically concentrated new draw on a stressed resource, and is it being built responsibly." O'Leary answered a question nobody was asking.

The read

Data centres versus golf courses is a genuinely useful comparison — it just proves the opposite of what it's deployed to prove. Yes, golf uses more water today. No, that doesn't make data-centre water use a non-issue, because golf's curve is flat and the data-centre curve is exponential and they cross within a couple of years. The productive conversation isn't "who uses more" — it's closed-loop cooling, honest local-level disclosure, and siting facilities where the water can actually spare it. O'Leary's own 75%-smaller Utah project suggests he knows the real answer isn't a golf statistic. It's building the thing responsibly and being able to prove it.

Analysis of public figures and projections as reported in July 2026; water-use estimates vary by source and methodology. Not investment advice. Source linked above.

https://redrobot.online/2026/07/19/data-centres-use-less-water-than-golf-courses-true-today-and-about-to-flip/

How to Build a Sales Bot on VBWD That Quotes Real Prices and Talks in Tappable Buttons

Most AI sales bots are a chat widget that hallucinates prices. VBWD's is a first-class part of a self-hosted commerce platform: it lives in your own messenger or Telegram, grounds answers in your own documents via RAG, pulls exact prices from the live catalogue so it never invents one, and replies in rich clickable cards. A practical walkthrough of meinchat, the bot plugins, and configuring a consultant.

A chat widget guesses. A bot wired into the platform knows — and answers in buttons you can tap.

Most "AI sales bot" products are a chat widget bolted onto your website that hallucinates prices and forgets it's talking to a customer. VBWD takes a different route: the bot is a first-class part of a self-hosted commerce platform, it can live inside your own messenger or Telegram, it quotes real prices from your live catalogue, and it talks in rich, tappable cards instead of a wall of text. Here's how the pieces fit — and how you'd actually configure a sales consultant with it.

The building blocks

The bot stack in VBWD is deliberately layered, and understanding the layers is the key to using it well.

bot-base is the transport-neutral core. It defines the neutral message types every bot speaks — text, links, and choice buttons — plus a registry of messenger providers and a command dispatcher. Critically, it isn't a bot itself; it's the framework the bots plug into. The nicest design decision: there's no registration ceremony. Being an enabled plugin that implements the seam is the registration, and a disabled plugin contributes nothing automatically.

meinchat is VBWD's own messenger — real 1-on-1 and group chat, self-hosted, running inside your platform. bot-meinchat is the bridge that lets a bot live inside it, in-process, with no webhook. The bot is provisioned as a real user with a bot role and a nickname, so any customer can find it in search and start a conversation as if messaging a person.

bot-telegram is the same bot logic on a different transport. Webhooks are the production path, validated with a per-bot secret token; long-polling exists for development when you have no public HTTPS. The point of the layering: you write the bot once, and it works in both your own messenger and Telegram, because both are just messenger providers behind the same neutral interface.

The two bots that matter for sales

bot-search adds a /search command over your catalogue. It reads a core search registry that shop, booking, and subscription plugins each register into — so it can surface products, bookable resources, and plans, and tap-through to a detail card. And it comes with a safety property worth knowing: the core registry hard-blocks users and invoices from ever being searchable, by refusing the registration outright. A bot cannot be pointed at your customer list, even by mistake.

bot-meinchat-llm is the real sales consultant — a RAG-grounded LLM bot. It answers via an explicit /consultant command or ambiently: a guest in the widget can just type a question, no command needed. And it has the three properties that separate a real sales bot from a demo.

Why it doesn't hallucinate prices

This is the feature that matters most and is most often faked elsewhere. The consultant bot's product knowledge is grounded in retrieval over your own documents — markdown and PDFs you drop into a corpus directory, indexed with Postgres full-text search. No external vector database to run. It answers from your content, not the model's imagination.

And prices don't come from the language model at all. Recommendations and prices are pulled from the live catalogue through the platform's pricing engine, so — in the project's own words — the bot never invents a price. That's the difference between a bot that says "this plan is around forty dollars, I think" and one that quotes the exact current price because it read it from the same system your checkout uses.

There's a second corpus, too, and it's a clever touch: separate from the product knowledge, a training directory of "how to sell" lessons — your sales method, example dialogues, objection handling — is always injected into the bot's context. So you're not just teaching it what you sell; you're teaching it how your best salesperson sells.

Rich content: buttons and choices, not walls of text

Here's where the conversation stops feeling like a chatbot and starts feeling like an app. Because bot-base defines rich message blocks — text, links, and choice buttons — the bot replies with tappable options instead of asking the customer to type.

In practice that means a product recommendation arrives as a card with a clear action: a tap to view details, a tap to open the full page, a tap to choose between options. bot-search's result cards carry an action scheme under the hood — view a product, open its page — and the "open page" choice can carry a real URL, so on a rich client the customer navigates straight to it instead of the bot dispatching another round of text. The experience is: the bot shows you three plans as three buttons, you tap one, it shows you that plan's details with a "start checkout" action. No typing, no misunderstanding, no dead ends.

That rich-content layer is also what makes the bot work identically across meinchat and Telegram — both render the same neutral choice blocks in their own native button UI.

How you'd configure the sales consultant

The setup is genuinely mostly configuration, not code:

- Enable the plugins — bot-base, then bot-meinchat and/or bot-telegram for your channels, then bot-meinchat-llm for the consultant.
- Point it at a model — the bot holds no API key of its own. It resolves a central LLM connection you configure once in the admin, so you choose the provider (or a model you host) in one place.
- Fill the two corpora — drop your product and pricing knowledge into the RAG directory, and your sales method and example dialogues into the training directory. Reindex from the admin.
- Tune the prompts — the system and user prompt templates are editable files, seeded with defaults, so you shape the bot's voice without touching code.
- Decide the reward behaviour — on a buy intent the consultant can mint a referral coupon and a checkout deep link, so a sale it closes is attributable, and there's a guest token economy (one token per word) with a switch to not charge guests for the bot's own answers — on the reasoning that a sales pitch shouldn't drain a prospect's balance.

Explore the plugin catalogue, the architecture, and the developer docs for the specifics.

The honest limits

meinchat is text and images — there's no voice or video. The consultant's retrieval is full-text, not semantic embeddings: cheaper and simpler to run, but not the same as a vector search, so the quality of your corpus matters. It's self-hosted, so you run the server and configure the LLM connection. And a sales bot is only as good as the content and sales lessons you feed it — the platform gives you a grounded, rich-content, price-honest consultant; the persuasion still comes from what you teach it.

Why it's different

The thread through all of this is that the bot isn't a bolt-on — it's part of a commerce platform that already knows your catalogue, your prices, your customers, and your channels. That's why it can quote a real price, why it physically can't be made to search your customer list, why "start checkout" is a real button and not a suggestion, and why writing it once gets you a consultant in both your own messenger and Telegram. A chat widget guesses. A bot wired into the platform knows — and answers in buttons you can tap.

Learn more about VBWD

VBWD is a self-hosted, source-available platform for building subscription products, marketplaces, and AI-powered apps. Explore it further:

- 🌐 Website and documentation: vbwd.cc — see the plugins, architecture, and developer docs.
- 💻 Source code and plugins on GitHub: github.com/VBWD-platform
- 🎥 Watch VBWD in action: demo video 1 and demo video 2
- 💼 Follow the project on LinkedIn: linkedin.com/company/vbwd https://redrobot.online/2026/07/18/how-to-build-a-sales-bot-on-vbwd-that-quotes-real-prices-and-talks-in-tappable-buttons/

Nine Months to Build Login and Invoices. Then You Can Start on Your Actual Idea.

Every small team pays the same entry fee: most of a year rebuilding backend, billing, entitlements, admin and mobile before the product starts. AI made writing each piece faster — it didn't tell you which pieces you forgot. There's a third option between building it all and bending an e-commerce platform into a shape it hates.

The plumbing tax that quietly kills good products — and how to skip it.

Here's a thing that happens to almost every small dev team with a good idea. They start on a Monday, full of energy, and nine months later they still haven't built the product. They've built login. And roles. And a subscription state machine. And invoices, and tax on the invoices, and the webhook handler that fires when Stripe sends the same event three times. And an admin panel, because somebody eventually has to issue a refund.

The idea — the actual reason the company exists — is still sitting in a document somewhere, waiting for its turn.

"But AI writes that now"

Fair challenge. It's 2026; Claude Code can produce a subscription service before lunch. Isn't the plumbing problem solved?

It can write a subscription service before lunch. Having one is a different thing entirely. The afternoon doesn't cover proration when someone upgrades mid-cycle. Or dunning on the third failed card retry. Or what happens to a user's access during the grace period between a failed payment and an actual cancellation. Or tax that varies by country and product type — including the discount line that has to carry negative tax per rate, or your invoice totals quietly drift by a few cents until an accountant finds it. Or idempotency when a webhook arrives twice. Or the audit trail your first enterprise customer demands.

None of this is intellectually hard. It's huge, dull, and unforgiving — hundreds of tiny decisions where 95% correct means wrong, because the missing 5% is money. AI makes writing each piece faster. It doesn't tell you which pieces you forgot, and it can't compress the months you'll spend discovering edge cases in production with real customers' cards.

Add it up honestly — backend, billing, entitlements, admin, mobile — and a small team doing it properly is looking at most of a year. With great tools. That's not doom-mongering; it's an inventory.

The two escape routes, and why both cost you

Build it anyway. Spend the year. Watch competitors ship. Become an expert in VAT, which was not the plan.

Bend an e-commerce platform into a SaaS. The popular one, and the sneaky one. E-commerce is built around a different noun: an order for a physical thing that ships once. What you're selling is a recurring relationship with an entitlement attached. You can force the shape — everyone does — and you'll pay rent on it forever: a plugin for subscriptions, another for the portal, a webhook stapled to your real backend to sync who's allowed to log in, and an ever-growing pile of translation code between the platform's worldview and yours. You didn't avoid the work. You relocated it somewhere worse.

Option three: don't start from zero, and don't start from the wrong shape

VBWD is built on the premise that there's an obvious third answer: start from a framework that's already the right shape, then add the only part that's actually yours.

What you get on clone: a real backend API (Python/Flask on PostgreSQL and Redis, properly layered, not a toy). Subscription billing with plans, trials, the full lifecycle, invoices, and tax that reconciles. Access entitlements and RBAC — a permission check you call instead of write. A platform-wide event system, so your feature reacts to payment.captured instead of you threading logic through someone else's checkout. Payments behind a provider-agnostic interface — Stripe, PayPal, regional processors, non-custodial crypto — swappable by config. A Vue 3 web frontend plus a complete admin backoffice, so the panel you'd have built in month eight already exists. A CMS, so your marketing site isn't a bolted-on subdomain that knows nothing about your pricing.

And iOS and Android SDKs against the same backend: one API, one billing system, one entitlement model, three clients — instead of three subtly different implementations of "is this user a subscriber?" drifting apart over two years.

The repo also ships instructions for AI coding agents, so Claude Code arrives in a codebase that tells it how to extend things correctly instead of guessing.

The rule that keeps it from owning you

Frameworks that give you everything usually take something back: your freedom to change anything. VBWD's answer is a rule enforced by the build itself — the core is agnostic; only plugins are gnostic.

The core knows users, money, events, entitlements. It knows nothing about gyms, tarot decks, datasets, pharmacies, or your vertical. Your domain lives in a plugin that registers routes, models, migrations, and providers through seams the core already exposes. An automated check fails the build if core code so much as mentions a plugin.

That's why adding your feature doesn't mean forking the billing engine, and why upgrading doesn't mean re-applying your patches. You extend at the designed seams; everything else stays somebody else's maintenance burden.

What day one looks like

git clone https://github.com/VBWD-platform/vbwd-sdk-public.git vbwd
cd vbwd
./recipes/dev-install-ce.sh
make up

The recipe handles clone order, submodules, build sequence, and the database. When it stops, you have a running platform — API, database, cache, user app, admin backoffice, migrations applied, roles seeded. Configure your plans, prices, payment provider, and access levels; then write the plugin that holds your actual idea.

The difference is what your first line of real code is. It's your product — not class User.

The honest limits

It's self-hosted. Someone runs the server, applies updates, takes backups. That's a genuine cost, and for some teams a managed SaaS with a monthly invoice is the better trade — say so out loud before you choose. You'll also have to learn the platform's seams; anything this large has a shape, and working with it beats fighting it. And if your product genuinely is orders for physical things that ship once, use a real e-commerce platform — that's what it's for.

"Clone, install, configure, boom" means the foundation is done, not your product. Your idea still needs building. That was always the interesting part — it's just no longer the part you reach nine months late.

The point

Every small team pays the same entry fee right now: most of a year rebuilding a foundation hundreds of other teams already rebuilt, slightly differently, slightly wrong. That fee kills good ideas — not because the ideas were bad, but because the runway ended somewhere around invoice numbering.

Nobody ever won a market because their tax rounding was elegant. Clone the framework, install it, configure it, add your plugin — and spend the year on the thing only you can build.

VBWD is source-available under BSL 1.1 and free commercially while VBWD-attributable sales stay under the value of 6.7 BTC a year.

Learn more about VBWD

VBWD is a self-hosted, source-available platform for building subscription products, marketplaces, and AI-powered apps. Explore it further:

- 🌐 Website and documentation: vbwd.cc — see the plugins, architecture, and developer docs.
- 💻 Source code and plugins on GitHub: github.com/VBWD-platform
- 🎥 Watch VBWD in action: demo video 1 and demo video 2
- 💼 Follow the project on LinkedIn: linkedin.com/company/vbwd https://redrobot.online/2026/07/16/nine-months-to-build-login-and-invoices-then-you-can-start-on-your-actual-idea/

VBWD Releases v26.7.0: A Free Self-Hosted Full-Stack Platform for Building SaaS and Marketplaces

VBWD v26.7.0 is free for individual developers and for SMEs whose annual sales stay below the value of 6.7 BTC — one backend serving web, iOS and Android, deployable as compiled instances in any combination.
FOR IMMEDIATE RELEASE — July 13, 2026
VBWD announced today, July 13, 2026 the general availability of VBWD v26.7.0, a self-hosted, full-stack platform for building subscription products, SaaS, booking aggregators and marketplaces. The release is free for individual developers and for small and medium-sized businesses whose VBWD-attributable annual sales stay below the value of 6.7 BTC, under the platform’s Business Source License 1.1.
The release targets two audiences that have historically been priced out of owning their own commerce infrastructure: solo developers and enthusiasts building on the side, and SMEs and agencies that want a subscription-and-marketplace stack they can run themselves without paying a percentage of revenue to a hosted platform. VBWD's licence keeps the software free in commercial production up to the 6.7-BTC threshold, and converts to the permissive Apache-2.0 licence on a fixed future change date.

What VBWD is

VBWD is a source-available platform that provides the parts of a commercial web product that are the same for everyone and costly to rebuild: user accounts and authentication, tiered subscription billing with multi-currency support and tax handling, a full invoice lifecycle, a token and credit system for metered usage, a built-in content-management system, multi-provider payments, an admin backoffice, and a plugin architecture for everything domain-specific. It is built on Python and Flask with PostgreSQL and Redis on the backend, and Vue 3 and TypeScript on the frontend, and it is designed to run on a small self-hosted server so operators keep ownership of their code, their database, and their users' data.

What ships in v26.7.0

Out of the box, v26.7.0 ships subscription billing with plans, trials, upgrades and cancellations; a unified pricing model with per-line tax that reconciles across discounts and refunds; a token economy for usage-based and AI billing; multi-gateway payments through swappable provider plugins including Stripe, PayPal and YooKassa; a CMS with layouts, widgets and per-entity SEO; outbound signed webhooks; and a growing catalogue of plugins spanning e-commerce, bookings, datasets, two-sided marketplaces with vendor payouts, GitHub-gated software distribution, and AI features — including chat and LLM bots grounded in the operator's own content and a Model Context Protocol server that makes an instance callable by AI agents.

iOS, Android and Web on one backend

VBWD is built around a single backend API that serves every client surface. The same server powers the web applications and the native iOS and Android clients, so an operator maintains one source of business logic, one billing engine, and one data model rather than reimplementing accounts, subscriptions and payments separately per platform. New client surfaces consume the same API contract, which keeps behaviour consistent across web and mobile and removes a common class of drift between a company's website and its apps.

DevOps-first: compiled instances, deployed in any combination

The platform is designed for operators, not just developers. VBWD separates cleanly into a backend and two independent frontends — a user-facing application and an admin backoffice — each of which can be built as a compiled, self-contained instance and deployed independently. Operators can run the backend with both frontends, run frontends against a shared backend, scale each component on its own, or compose them across environments in whatever combination a deployment requires. Because the components ship as compiled instances configured through the environment rather than the image, the same artifacts move cleanly from a laptop to a single small VPS to a multi-node setup, which is what makes a self-hosted platform practical to operate over time.

Availability and licensing

VBWD v26.7.0 is available immediately from the project's website and public code repositories. Use is free — including in commercial production — for individuals and for businesses whose VBWD-attributable annual sales remain below the value of 6.7 BTC, measured at the year's average BTC/USD exchange rate. Organisations above that threshold require a commercial licence. Non-production use, including development, testing, evaluation and education, is unlimited and always free, and each release converts to the Apache-2.0 licence on its scheduled change date.
"The goal with v26.7.0 is that a solo developer or a small business can own their entire commerce stack — web, mobile, billing and data — on hardware they control, and pay nothing until the software is genuinely making them money," said Daniil Tkachev, creator of VBWD. "The licence draws the line at real scale, not at getting started."

About VBWD

VBWD is a self-hosted, source-available full-stack platform for building SaaS products and marketplaces, with subscription billing, a plugin system, a built-in CMS, multi-provider payments, and AI capabilities on a single backend that serves web and native mobile clients. It is licensed under the Business Source License 1.1 with a Bitcoin-denominated free-use grant and an Apache-2.0 change licence. Learn more at vbwd.cc, read the documentation, and review the architecture and available plugins.

Resources for editors

- Website and documentation: vbwd.cc
- Source code and plugins: github.com/VBWD-platform
- Product demos: video 1 and video 2
- LinkedIn: linkedin.com/company/vbwd https://redrobot.online/2026/07/13/vbwd-releases-v26-7-0-a-free-self-hosted-full-stack-platform-for-building-saas-and-marketplaces/

VBWD Releases v26.7.0: A Free Self-Hosted Full-Stack Platform for Building SaaS and Marketplaces

VBWD v26.7.0 is free for individual developers and for SMEs whose annual sales stay below the value of 6.7 BTC — one backend serving web, iOS and Android, deployable as compiled instances in any combination.
FOR IMMEDIATE RELEASE — July 13, 2026
VBWD announced today, July 13, 2026 the general availability of VBWD v26.7.0, a self-hosted, full-stack platform for building subscription products, SaaS, booking aggregators and marketplaces. The release is free for individual developers and for small and medium-sized businesses whose VBWD-attributable annual sales stay below the value of 6.7 BTC, under the platform’s Business Source License 1.1.
The release targets two audiences that have historically been priced out of owning their own commerce infrastructure: solo developers and enthusiasts building on the side, and SMEs and agencies that want a subscription-and-marketplace stack they can run themselves without paying a percentage of revenue to a hosted platform. VBWD's licence keeps the software free in commercial production up to the 6.7-BTC threshold, and converts to the permissive Apache-2.0 licence on a fixed future change date.

What VBWD is

VBWD is a source-available platform that provides the parts of a commercial web product that are the same for everyone and costly to rebuild: user accounts and authentication, tiered subscription billing with multi-currency support and tax handling, a full invoice lifecycle, a token and credit system for metered usage, a built-in content-management system, multi-provider payments, an admin backoffice, and a plugin architecture for everything domain-specific. It is built on Python and Flask with PostgreSQL and Redis on the backend, and Vue 3 and TypeScript on the frontend, and it is designed to run on a small self-hosted server so operators keep ownership of their code, their database, and their users' data.

What ships in v26.7.0

Out of the box, v26.7.0 ships subscription billing with plans, trials, upgrades and cancellations; a unified pricing model with per-line tax that reconciles across discounts and refunds; a token economy for usage-based and AI billing; multi-gateway payments through swappable provider plugins including Stripe, PayPal and YooKassa; a CMS with layouts, widgets and per-entity SEO; outbound signed webhooks; and a growing catalogue of plugins spanning e-commerce, bookings, datasets, two-sided marketplaces with vendor payouts, GitHub-gated software distribution, and AI features — including chat and LLM bots grounded in the operator's own content and a Model Context Protocol server that makes an instance callable by AI agents.

iOS, Android and Web on one backend

VBWD is built around a single backend API that serves every client surface. The same server powers the web applications and the native iOS and Android clients, so an operator maintains one source of business logic, one billing engine, and one data model rather than reimplementing accounts, subscriptions and payments separately per platform. New client surfaces consume the same API contract, which keeps behaviour consistent across web and mobile and removes a common class of drift between a company's website and its apps.

DevOps-first: compiled instances, deployed in any combination

The platform is designed for operators, not just developers. VBWD separates cleanly into a backend and two independent frontends — a user-facing application and an admin backoffice — each of which can be built as a compiled, self-contained instance and deployed independently. Operators can run the backend with both frontends, run frontends against a shared backend, scale each component on its own, or compose them across environments in whatever combination a deployment requires. Because the components ship as compiled instances configured through the environment rather than the image, the same artifacts move cleanly from a laptop to a single small VPS to a multi-node setup, which is what makes a self-hosted platform practical to operate over time.

Availability and licensing

VBWD v26.7.0 is available immediately from the project's website and public code repositories. Use is free — including in commercial production — for individuals and for businesses whose VBWD-attributable annual sales remain below the value of 6.7 BTC, measured at the year's average BTC/USD exchange rate. Organisations above that threshold require a commercial licence. Non-production use, including development, testing, evaluation and education, is unlimited and always free, and each release converts to the Apache-2.0 licence on its scheduled change date.
"The goal with v26.7.0 is that a solo developer or a small business can own their entire commerce stack — web, mobile, billing and data — on hardware they control, and pay nothing until the software is genuinely making them money," said Daniil Tkachev, creator of VBWD. "The licence draws the line at real scale, not at getting started."

About VBWD

VBWD is a self-hosted, source-available full-stack platform for building SaaS products and marketplaces, with subscription billing, a plugin system, a built-in CMS, multi-provider payments, and AI capabilities on a single backend that serves web and native mobile clients. It is licensed under the Business Source License 1.1 with a Bitcoin-denominated free-use grant and an Apache-2.0 change licence. Learn more at vbwd.cc, read the documentation, and review the architecture and available plugins.

Resources for editors

- Website and documentation: vbwd.cc
- Source code and plugins: github.com/VBWD-platform
- Product demos: video 1 and video 2
- LinkedIn: linkedin.com/company/vbwd https://redrobot.online/2026/07/13/vbwd-releases-v26-7-0-a-free-self-hosted-full-stack-platform-for-building-saas-and-marketplaces/

Investment Idea: AI-Integrated Blockchain Infrastructure – The Next 20-50x Opportunity

Autonomous AI agents transacting on blockchain infrastructure represent a structural market shift. Early-stage protocols enabling trustless agent-to-chain interactions address a $500B+ opportunity, following historical patterns of 20-50x returns over 3-5 year cycles as developer adoption accelerates.

The convergence of autonomous AI agents and blockchain infrastructure creates a multi-year institutional tailwind. As enterprise AI adoption accelerates, middleware protocols reducing latency and enabling trustless agent transactions are capturing structural demand. This mirrors Ethereum's 2015-2017 infrastructure phase and Solana's 2020-2021 throughput narrative—both delivered 15,000x+ returns for early investors.

Investment Idea: AI-Integrated Blockchain Infrastructure

Summary

Autonomous AI agents transacting on blockchain infrastructure represent a structural market shift. Early-stage protocols enabling trustless agent-to-chain interactions address a $500B+ opportunity, following historical patterns of 20-50x returns over 3-5 year cycles as developer adoption accelerates.

Tags

InvestmentIdeas, CryptoIdeas, RedRobotIdeas, AI-Infrastructure, BlockchainAgents

Category

Investment Ideas by AI

Lead Paragraph

The convergence of autonomous AI agents and blockchain infrastructure creates a multi-year institutional tailwind. As enterprise AI adoption accelerates, middleware protocols reducing latency and enabling trustless agent transactions are capturing structural demand. This mirrors Ethereum's 2015-2017 infrastructure phase and Solana's 2020-2021 throughput narrative—both delivered 15,000x+ returns for early investors.

Article

- Context – Messari's AI-first research pivot and Sei Development Foundation's strategic AI partnerships signal institutional capital rotation toward agent-enabling infrastructure. Historically, infrastructure layers captured outsized returns: Ethereum (2015-2017) delivered 40x as developers built DeFi primitives; Solana (2020-2021) attracted $14B+ venture capital and delivered 15,000x. Modular blockchain thesis (Celestia, Arbitrum) outperformed L1s by 8-12x in 2023-2024. AI-agent infrastructure follows identical adoption curves: early protocol adoption → developer network effects → institutional integration → 20-50x realized returns.
- Strategy Explanation – Autonomous AI agents require trustless on-chain infrastructure to transact, access verified data, and manage assets without intermediaries. This creates demand for: (1) low-latency Layer-1/Layer-2 protocols with native agentic capabilities; (2) middleware and oracle networks enabling agent data access; (3) intent-based DeFi primitives with agent-friendly UX. Early infrastructure protocols capture network effects as developer communities build agent-native dApps, creating sticky competitive advantages and durable revenue streams.
- Token Targets – Primary allocation (60%): Layer-1/Layer-2 protocols with native agentic capabilities (Sei, Solana ecosystem agents, Arbitrum infrastructure). Secondary allocation (25%): Middleware and oracle protocols enabling agent data access (decentralized compute networks, x402-equivalent infrastructure). Tertiary allocation (15%): AI-adjacent DeFi primitives with agent-friendly UX (automated market makers, intent-based protocols). Rebalance quarterly based on developer activity metrics and TVL growth in agent-focused dApps.
- Expected Returns & Risks – Base case ROI: 15-25x over 36 months (assuming 15% of AI agent transactions route through infrastructure layer). Bull case: 50-100x if agent adoption reaches 10% of enterprise AI workloads. Downside risk: Regulatory scrutiny on autonomous agents, centralized AI giants building proprietary chains, or technical failures in cross-chain verification. Mitigation: (1) Diversify across 5-7 protocols to reduce single-point-of-failure risk; (2) Monitor regulatory developments quarterly; (3) Maintain 20% dry powder for opportunistic rebalancing; (4) Exit 30% of position if infrastructure TVL contracts >40% YoY.
- Exit Signals – Entry thesis validates at $50-150B aggregate market cap for AI-infrastructure layer (vs. $80B for DeFi today). Exit conditions: (1) Top 3 protocols reach $10B+ individual market caps; (2) Agent-native transactions exceed 20% of total blockchain volume; (3) Enterprise adoption contracts signed by Fortune 500 companies; (4) Valuation compression due to regulatory headwinds or competitive saturation. Suggested exit ladder: 25% at 10x, 25% at 25x, 25% at 50x, hold 25% for 100x+ optionality. Time horizon: 36-60 months. Liquidity strategy: Months 0-12 (accumulation, 80% deployed), Months 12-24 (rebalancing, lock in 20-30% gains), Months 24-36 (distribution, begin exit ladder), Months 36-60 (hold core positions, harvest volatility). Maintain 15% liquidity reserve for margin calls. Prioritize CEX-listed infrastructure assets with >$10M daily volume. https://redrobot.online/2026/03/17/investment-idea-ai-integrated-blockchain-infrastructure-the-next-20-50x-opportunity/

AI in Education: Bridging Innovation Gaps Between US and Asian Models

This analysis compares AI-driven education innovation in the US and Asia, highlighting recent initiatives from MIT and Chinese tech firms, with projections for 2030 growth and policy impacts.

In 2025, AI is reshaping education with US and Asian models diverging in approach; for instance, MIT's new AI curriculum and China's AI tutoring platforms demonstrate rapid adoption, pointing to a 20% increase in global EdTech funding and potential learning gains of 30% by 2030.

Verified Developments

Recent AI innovations in education highlight distinct regional strategies. In the United States, MIT's Computer Science and Artificial Intelligence Laboratory launched an interdisciplinary AI course in May 2025, targeting 500 students to address skills gaps. In Asia, China's government-backed initiative with tech giant Alibaba expanded its AI-powered tutoring platform, 'AI Tutor Pro,' in June 2025, serving over 2 million students in urban areas. According to a report from the MIT Technology Review in April 2025, such initiatives reflect a global push toward adaptive learning systems, with OECD noting increased government funding in Asia compared to private-sector dominance in the US.

Quantitative Indicators & Case Studies

Quantitative data underscores the rapid growth of AI in education. The International Energy Agency's 2025 report estimates that AI-driven tools could reduce energy costs in digital learning by 15% through optimized resource allocation. A case study from McKinsey in May 2025 shows that personalized AI platforms in Singapore improved student test scores by an average of 25% over six months, while in the US, startups like Coursera reported a 40% increase in AI course enrollments since early 2025. These indicators suggest a trajectory where AI could address accessibility issues for 100 million learners by 2030, as projected by the World Bank.

Regional Strategic Comparison

Comparing US and Asian models reveals contrasting approaches. In the US, innovation is largely private-sector-led, with companies like Google and Khan Academy piloting AI tools in K-12 education, focusing on scalability and profit margins. In contrast, Asia, particularly China and South Korea, employs government-driven strategies; for example, South Korea's 2025 national AI education plan allocates $500 million to integrate AI into public schools, emphasizing equity and standardization. According to the OECD, this dichotomy highlights risks in the US, such as data privacy concerns, while Asian models face challenges in fostering creativity due to top-down implementation.

Business and Policy Implications

Business implications include new market opportunities: the global EdTech AI market is projected to grow from $3 billion in 2025 to $10 billion by 2030, according to McKinsey, driven by demand for personalized learning solutions. For policymakers, the US must balance innovation with regulations like the proposed AI Education Act of 2025, which aims to set ethical standards. In Asia, policies could enhance cross-border collaboration, as seen in ASEAN's 2025 digital education framework. Constructively, these developments suggest a need for hybrid models that leverage private agility and public oversight to mitigate inequalities and drive sustainable growth.

https://redrobot.online/2026/03/12/ai-in-education-bridging-innovation-gaps-between-us-and-asian-models/