Training AI Is Ruinously Expensive. MIT Taught Models to Shrink Themselves While They Learn.

Training a large AI model is expensive in every currency that matters — dollars, time, energy, and scarce compute. The usual ways to end up with a small, fast model both waste some of that: either train a giant one and trim it down afterward, or train a small one from scratch and accept weaker results. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and collaborators say they've found a third path that sidesteps the trade-off — compressing a model during training instead of after. The work was reported by MIT News.

The technique, called CompreSSM, targets a family of architectures known as state-space models, which underpin language processing, audio generation, and robotics. Borrowing mathematical tools from control theory, it identifies which parts of a model are pulling their weight and which are dead weight, then surgically removes the useless components early in training. "It's essentially a technique to make models grow smaller and faster as they are training," said lead author Makram Chahine, a PhD student in electrical engineering and computer science and a CSAIL affiliate. "During learning, they're also getting rid of parts that are not useful to their development."

The key insight is that the relative importance of a model's internal components settles surprisingly early. Using a quantity called Hankel singular values — a measure of how much each internal state contributes to overall behavior — the team found they could reliably rank which dimensions matter after only about 10 percent of training. Once that ranking is set, the less-important pieces are discarded and the remaining 90 percent of training runs at the speed of a much smaller model.

"What's exciting about this work is that it turns compression from an afterthought into part of the learning process itself," said senior author Daniela Rus, an MIT professor and director of CSAIL. "Instead of training a large model and then figuring out how to make it smaller, CompreSSM lets the model discover its own efficient structure as it learns. That's a fundamentally different way to think about building AI systems."

The numbers are what make the case. On image-classification benchmarks, compressed models held nearly the same accuracy as their full-sized counterparts while training up to 1.5 times faster, per MIT News. A model shrunk to roughly a quarter of its original state dimension hit 85.7 percent accuracy on CIFAR-10 — versus just 81.8 percent for a model trained at that smaller size from scratch. On Mamba, one of the most widely used state-space architectures, the method delivered about 4x training speedups, compressing a 128-dimensional model down to around 12 dimensions while staying competitive. "You get the performance of the larger model, because you capture most of the complex dynamics during the warm-up phase, then only keep the most-useful states," Chahine said.

The distinction from existing tricks is theoretical grounding. Conventional pruning trains the full model and strips parameters afterward — so you still pay the full cost of training the big one. Knowledge distillation trains a large "teacher" to completion and then a smaller "student" on top, roughly doubling the effort. CompreSSM makes its cuts mid-stream, and in head-to-head tests against a recent spectral technique (Hankel nuclear norm regularization) it ran more than 40 times faster while achieving higher accuracy. The collaboration spans MIT CSAIL, the Max Planck Institute for Intelligent Systems, ELLIS, ETH, and Liquid AI.

There's a broader shift buried in the method. As the industry's default answer to better AI has been "make it bigger, then deal with the cost," CompreSSM points the other way — letting a model find its own lean shape while it learns. If that holds up beyond state-space models, the cheapest place to save compute may turn out to be the training run itself, not the cleanup afterward.

Written for Red Robot with AI assistance and human editing. Based on reporting by MIT News.

https://redrobot.online/2026/08/07/rr-16030-new-technique-makes-ai-models-leaner-and-faster-while-they-r/

BYD's Answer to Its Car-Sales Slump: a Humanoid Robot in Every Showroom

Stop Polishing Your Storefront. In the Agent Era, MCP Is Your Sales Channel. 

For a growing slice of sales, your frontend doesn't matt





Here's a heresy for 2026, and it's a serious one: for a growing slice of your sales, your frontend doesn't matter. Not the hero image, not the carousel, not the pixel-perfect product page you spent a quarter on. Because the buyer isn't looking at it. The buyer is an AI agent, shopping on someone's behalf, and it never renders your CSS — it calls your MCP endpoint, reads your catalogue and price, and decides. In the agent channel, the interface that sells is the machine interface, and if you're building a commercial platform today, that changes where your effort should go.

The shift, stated bluntlyFor thirty years, commerce software has been a race to build a better human-facing storefront — faster, prettier, more persuasive pages. That race isn't over, because humans still buy. But a new channel has opened underneath it: AI assistants that research and increasingly purchase for their users. When a customer tells an agent "find me the best X under Y and buy it," the agent doesn't visit ten websites and admire the design. It queries whatever machine-readable interfaces it can reach, compares structured data, and acts. Your beautiful storefront is invisible to it. Your MCP surface is the entire conversation.

So the provocative version — "give no attention to the frontend" — has an honest core: for the agent channel specifically, the frontend is irrelevant, and that channel is the fastest-growing source of purchase intent on the internet. The effort that used to go into the storefront's polish should, at the margin, go into the surface the agent actually reads.

What "real hard business" looks like in the agent channelSelling to agents is not a design problem; it's a data-and-rails problem. The agent needs four things, and none of them are visual: a machine-readable catalogue it can query, an authoritative price it can trust (a wrong quote is worse than no listing), a scoped way to act — check availability, reserve, order — and a settlement rail to actually pay. Get those right and you're sellable to the agent channel regardless of what your website looks like. Get them wrong — stale prices, no machine interface, no way to transact — and the prettiest storefront in your category is invisible to the buyer that matters most.

This is why building a commercial platform on VBWD is well-suited to the agent era. The MCP server is in the core, so the platform is agent-callable out of the box. The catalogue is priced by the same engine as checkout, so the agent gets the real number. The search seam keeps customer data unreachable while the catalogue is queryable. Access levels scope what an agent can do. And provider-agnostic payments — including non-custodial crypto that settles to your own wallet — are the rail for when agents transact. You build the commercial substance; the agent interface is native.

The honest limits — don't literally ship an ugly siteLet's be precise, because "the frontend doesn't matter" taken literally is wrong. Humans still make the large majority of purchases today, and for them the frontend matters enormously — a bad storefront loses human sales. The agent channel is growing fast but is still small in absolute terms. So the real advice isn't "neglect your frontend"; it's "stop treating the frontend as your only sales surface, and stop over-investing in polish while your MCP surface — the one the fastest-growing channel actually uses — doesn't exist." Serve humans well and be callable by agents. The mistake is building only for the eyes when an increasing share of your buyers have none.

The readThe uncomfortable truth of commerce in the agent era is that the sales surface is splitting in two. One half is the human-facing storefront you've always built. The other half — growing fast — is the machine interface an AI agent calls, where design is irrelevant and only clean data, authoritative pricing, scoped actions and a payment rail matter. Most businesses are pouring everything into the first half and have nothing for the second. Building a commercial platform where the MCP surface is native, priced authoritatively, and safe by architecture is how you show up in the channel your competitors can't see. That's not a design decision. It's a business one — and it's where the next decade of sales is quietly moving.

Build it — or have us install itVBWD ships an MCP server in the core, so any commercial platform you build on it is agent-callable out of the box. It's free for commercial use below a defined revenue threshold, so you can start today at zero platform cost. Running an enterprise or a serious store and want it installed, migrated and made agent-ready? Request an enterprise installation at vbwd.cc/contact. Explore: plugins · architecture · docs.

er — the buyer is an AI agent that never renders your CSS. It calls your MCP endpoint, reads your catalogue and price, and buys. Selling to agents is a data-and-rails problem, not a design one: machine-readable catalogue, authoritative price, scoped actions, a settlement rail. VBWD ships all four nativel

https://vbwd.cc/blog/2026/vbwd/in-the-agent-era-mcp-is-your-sales-channel

Build a Store Where the Frontend Doesn't Matter — Because the Buyer Is an AI Agent

A product idea that sounds like a joke and isn't: build a commercial platform agent-first, where the MCP interface an assistant calls is the primary sales surface and the human UI is secondary. Buildable now on a platform with a native MCP server, authoritative pricing, a data boundary that won't leak customers, and a payment rail. A bet on where commerce is heading.

Here's a product idea that sounds like a joke and is dead serious: build a commercial platform and don't build a real frontend — because your buyer is an AI agent that never looks at one. In the agent-commerce channel, the storefront is the machine interface, the MCP endpoint an assistant calls to read your catalogue, check your price, and buy.

Here's a product idea that sounds like a joke and is dead serious: build a commercial platform and don't build a real frontend. Not because design doesn't matter, but because your buyer is an AI agent that never looks at one. In the agent-commerce channel, the storefront is the machine interface — the MCP endpoint an assistant calls to read your catalogue, check your price, and buy. A business built for that channel puts its effort where the sale actually happens, and treats the human UI as the afterthought it's becoming for that specific buyer.

The idea, restated

Call it an agent-first commercial platform: a store whose primary sales surface is its MCP interface, not its website. It exposes a clean, machine-readable catalogue with authoritative prices, a scoped set of actions (check availability, reserve, order), and a settlement rail — all callable by an AI agent shopping on a user's behalf. The human-facing frontend still exists, but it's minimal and secondary, because the design effort that would have gone into a persuasive storefront goes instead into the thing the agent reads. For categories where buying is increasingly delegated to assistants — commodity goods, reorders, B2B supplies, anything an agent can evaluate on structured facts — this is where the sales are going.

Why it's buildable now

The reason this is a real idea and not a thought experiment is that the substrate exists. A platform like VBWD ships an MCP server in the core, so the agent interface is native rather than a build. Its catalogue is priced by the same engine as checkout, so agents get the real number — the single most important property, because a wrong quote is worse than no listing. Its search seam refuses to expose customer records and invoices while keeping the catalogue queryable, so pointing autonomous callers at it is safe by architecture. Access levels scope what an agent can do. And provider-agnostic payments, including non-custodial crypto that settles to your own wallet, are the rail for when agents transact. The differentiated work is your catalogue and your commercial logic; the agent-first plumbing is already there.

The honest boundaries

Four, and they're real. The agent-commerce standard is still converging — MCP leads, but conventions for agent identity, mandates and settlement are pre-standard, so you'll adapt. Being callable makes you discoverable, not chosen — selection lives inside models you don't control, and nobody can sell you guaranteed "agent SEO." The channel is small today even as it grows fast, so an agent-first business is a bet on where things are going, priced accordingly. And "no frontend" is a provocation — you still need a minimal human surface for the buyers who have eyes, and for trust. The honest version is "build for the agent first, the human minimally," not "build nothing for humans."

The read

The instinctive way to build a store is human-first: design the storefront, then maybe expose an API. The agent era inverts it for a growing set of categories — build the machine interface first, because that's who's buying, and treat the human UI as secondary. It sounds backwards until you accept that an AI agent shopping for its user never sees your design and only reads your data. A platform where the MCP surface is native, authoritatively priced and safe by architecture makes the inversion buildable today. It's a bet on where commerce is heading — and the businesses that make it early will own a channel their human-first competitors literally cannot see.

Build an agent-callable platform on VBWD

VBWD ships an MCP server in the core — any commercial platform built on it is agent-callable out of the box, with catalogue prices authoritative to checkout and a search seam that keeps customer data unreachable. It's free for commercial use below a defined revenue threshold. Building an agent-ready store or migrating one? Request an enterprise installation → vbwd.cc/contact.

https://redrobot.online/2026/07/30/build-a-store-where-the-frontend-doesnt-matter-because-the-buyer-is-an-ai-agent/

How to Build a Digital Money Exchange With VBWD (and the 90% No Framework Can Do) Building an exchange is ~10% software and 90% regulation, custody, and liquidity. VBWD collapses that 10% — accounts, a token ledger, non-custodial crypto rails, fee billin

Under the DMA, Brussels is forcing Google to give competitors access to search data at reasonable fees, treat AI chatbots as search services, and open Android to non-Gemini assistants (data-sharing by Jan 2027, Android by Jul 2027). It attacks the actual moat — the data flywheel — not with a fine but structurally. Google warns it undermines privacy; the tension is real.

Google's most valuable secret isn't its algorithm. It's the record of what billions search and click. The EU just ordered it shared.

Google's most valuable secret isn't its algorithm — it's the data on what billions of people search for and click. The EU just ordered Google to share it. In a decision under the Digital Markets Act, Brussels is forcing Google to hand competitors access to its search data and to open Android to rival AI assistants. It's one of the most aggressive attempts yet to pry open the tech industry's tightest monopoly.

What Google has to do

The mandates, reported by Ars Technica, are concrete and far-reaching. Google must share search data with competing search providers "transparently and at reasonable fees," giving them access to search metrics comparable to Google's own. It must treat AI chatbots as search services for the purposes of that data-sharing. And it must open up Android for deeper integration with non-Gemini AI platforms.

The timeline is real: Google must begin sharing search data with competitors by January 2027, and update Android for deeper third-party AI integration by July 2027. The Commission's rationale is blunt — this access is "essential for a smaller player to challenge Google's dominance."

Why the data is the whole game

To see why this matters, you have to understand the flywheel that makes Google unbeatable. Search quality depends on data about what people search and click. Google has more of that than anyone because it has the most users; more data makes its results better; better results attract more users; more users generate more data. Round and round. A competitor can build a technically excellent search engine and still lose, because it can't bootstrap the behavioural data that makes results actually good. The moat isn't the code — it's the twenty-year head start of query logs.

Forcing Google to share that data attacks the flywheel at its hub. If a rival can access comparable search signals, the data advantage — the thing no amount of engineering could overcome — narrows. That's precisely why the EU chose this lever rather than a fine: a fine is a cost of doing business; sharing the data is structural.

The AI twist is the forward-looking part

The genuinely modern element is treating AI chatbots as search services and opening Android to non-Gemini assistants. Brussels is looking past the current search war to the next one. As discovery shifts from typing queries into a box toward asking an AI assistant, whoever owns the default assistant on the phone inherits the gatekeeper position search engines have held for two decades. Google putting Gemini at the heart of Android would simply port its search monopoly into the AI era.

Ordering Android open to rival AI assistants is an attempt to stop that transfer before it completes — to make sure the AI-assistant layer starts contestable rather than being handed to the incumbent by default. Whether it works is another question, but the regulators are, unusually, skating to where the puck is going.

Google's objection — and the real tension

Google isn't taking it quietly. Kent Walker, its president of global affairs, warned that "today's decisions risk undermining vital privacy and security guardrails for millions of Europeans," arguing that data sharing threatens user privacy, trade secrets, and even national security, and that deeper AI integration could circumvent safeguards.

This is where it gets genuinely hard, because Google's objection isn't purely self-serving. Search data is intensely personal — it's a record of what people wonder, fear, and want. Sharing it with competitors raises real privacy questions that "reasonable fees and transparency" don't fully answer. The tension is authentic: you can't meaningfully break the data monopoly without moving the data, and you can't move data this sensitive without new risks. The EU is betting the competition benefit outweighs the privacy cost. That's a defensible bet and a genuinely uncertain one — and "protecting privacy" is also, conveniently, the incumbent's best argument for keeping its moat.

The read

The EU just went after the actual source of Google's power — the data flywheel — rather than nibbling at the edges with another fine, and extended the fight into the AI-assistant era before that monopoly could re-form. It's the most structural challenge to search dominance in a generation, and it lands on a real dilemma: breaking a data monopoly means sharing data that's deeply personal, and the privacy argument cuts both ways. January 2027 is when we find out whether forced data-sharing actually lets a competitor land a punch, or whether Google's twenty-year head start survives even being shared. Either way, the era of "the data is ours alone" is, in Europe at least, officially over.

Reporting on a regulatory decision as covered on 21 July 2026; implementation details and any appeals will develop. Not legal advice. Source linked above.

https://vbwd.cc/blog/2026/vbwd/build-digital-money-exchange-vbwd

An LLM Port for Your Content: How VBWD's CMS-AI Lets You Run the Whole Editor by Prompt:

For 15 years the answer was automatic: rent it. In 2026 that's breaking down. Cloud costs got real, data became the moat vendors learn from, and AI made building cheap — three shifts that moved the optimal point back toward ownership for more workloads than conventional wisdom admits. An honest scorecard of both sides.

Self-hosting didn't get free. The things it wins on got more valuable, and the thing it lost on got cheaper.

For fifteen years, the answer to "where should we run our software?" was automatic: the cloud, someone else's SaaS, someone else's servers. Owning infrastructure was for dinosaurs. In 2026, that automatic answer is quietly breaking down — and a growing number of companies are asking a question that would have sounded backward two years ago: what if we ran it ourselves? Here's the honest case on both sides of self-hosted versus SaaS, and why the pendulum is swinging.

Why SaaS won in the first place

Give the incumbent its due, because the reasons were good. SaaS and cloud won because they removed real pain: no servers to rack, no updates to apply, no ops team to hire, someone else on the hook at 3am. You traded ownership for convenience, and for most of the last decade that was a brilliant trade. Speed mattered more than control, and renting was faster than building.

None of that stopped being true. The trade just stopped being obviously one-sided.

What changed the maths

Three forces are pushing companies to reconsider, and they're all intensifying at once.

Cost stopped being trivial. The era of cheap cloud is over. Compute is scarce, GPU pricing is volatile and now financialised, and the SaaS bill that was a rounding error at small scale becomes a serious line item at medium scale. "FinOps" — the discipline of controlling cloud spend — exists because the spend got big enough to need a discipline. When renting is expensive enough, owning starts to pencil out.

Data became the asset, and vendors learned from it. In the AI era, the data your business generates is the moat — and a growing worry is that when you run everything through a vendor's platform, that vendor can learn from your data, potentially folding your proprietary knowledge into a product it sells to others. Ownership stopped being an ideological preference and became a competitive one.

Building got cheap. The historical killer of self-hosting was effort: standing up your own stack meant months of undifferentiated plumbing. AI coding tools and modern source-available frameworks collapsed that cost. The thing that made renting obviously easier — that building was hard — is much less true than it was.

The honest scorecard

This isn't a case for self-hosting everything. It's a case for choosing deliberately, because each side genuinely wins on different axes.

SaaS still wins on: zero operational burden, someone else's uptime guarantee, instant setup, and not needing the skills to run infrastructure. For a two-person team without ops capability, or a workload that isn't core to your business, managed SaaS is often correct — paying someone to make a problem disappear is a legitimate trade.

Self-hosting wins on: cost at scale, data ownership and residency, no per-transaction platform cut, freedom from a vendor changing terms or pricing under you, and the ability to keep your customer relationship and your data on your own side of the line. The price is real: you run the server, you apply the updates, you own the 3am page.

The pendulum is swinging not because self-hosting became free — it didn't — but because the things it wins on (cost, data, control) got more valuable, and the thing it lost on (effort) got cheaper.

Where the modern option lives

The reason "self-hosted" no longer means "rebuild everything from scratch" is a new class of source-available, own-your-stack platforms that ship the plumbing pre-built. VBWD is a clean example of the category: a self-hosted, source-available framework with a backend, web and mobile clients, subscription billing, and an AI layer already assembled — so you get the ownership of self-hosting without the year of foundation-building that used to be its price. Your data lives in your own database, there's no platform transaction cut, and it's free for commercial use below a defined revenue threshold. The pitch isn't "self-host out of principle." It's "self-host because the maths finally works, and the tools finally exist." You can see how the pieces compose in the plugin catalogue and the architecture.

The honest caveat stands: it's still self-hosted, so someone runs it, and for some teams that cost outweighs the benefits. This is a "choose deliberately" argument, not a "rip out all your SaaS" one.

The read

The self-hosted-versus-SaaS question isn't ideological anymore, and it isn't settled the way it was in 2015. Cloud costs got real, data became the moat, and building got cheap — three shifts that quietly moved the optimal point back toward ownership for more workloads than the conventional wisdom admits. Most companies should still rent most things. But the reflexive "obviously SaaS" is over. In 2026, the smart move is to actually run the maths for each part of your stack — and to notice that, for the pieces where cost, data, and control matter, owning is a live option again in a way it hasn't been for a decade.

Analytical commentary on infrastructure trends; the right choice depends on your team, scale, and workload. The VBWD reference illustrates the self-hosted approach and is not an endorsement. Not investment advice.

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://vbwd.cc/blog/2026/vbwd/cms-ai-an-llm-port-built-into-your-content-system

'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/