A Bloomberg terminal without the data feed
Six of the terminal’s seven layers on an agent swarm, with a chat bot as the cockpit
A Bloomberg seat bundles seven functions, and only one of them — the licensed real-time data — is actually protected by contracts. The rest are protected by workflow, which is the kind of moat agents dissolve. The architecture here is sound and the tools are real. The cost claim that headlines it is not: recomputed from its own inputs, the setup runs three to five times the price it advertises.
The interface moat, not the data moat
The useful idea is a decomposition. A terminal is not a stock chart — it is seven functions running side by side, and they are not defended equally. Real-time exchange feeds are contractually locked and always will be. The other six are defended by workflow and integration, which is exactly the kind of moat that a competent agent stack erodes.
So the honest framing is not “replace Bloomberg”. It is: rebuild the six layers that were never really protected, and accept that the seventh is gone. Everything below follows from taking that split seriously.
The three walls this used to hit
Worth stating, because they are the reason a year of attempts at this went nowhere — and the reason it is worth re-testing now rather than assuming the old answer still holds.
Monitoring broke first
Watching hundreds of sources at once was the wall everything hit. No consumer model could run enough agents in parallel to actually cover the news universe, so coverage was always a sample rather than a sweep.
Dashboards needed a frontend engineer
Every route to a live visual layer meant hiring someone or spending months on React before rendering a single chart. The workstation feel was gated behind a skillset unrelated to trading.
A trading day would not fit
Holding a full session of signals in one context was impossible. Everything chunked, and chunking is precisely where the cross-references between a wire story, a filing and a price move get lost.
What is actually being decomposed
Worth looking at before reading the table, because the density is the point. Almost nothing on these screens is the price of a stock — it is breadth, positioning, order books, world clocks, correlation, dealer quotes. The seat is not sold on data alone; it is sold on having all of it arranged at once.
Seven layers, one of them locked
The complete architecture
An engine, a cockpit, and your existing broker
The four capabilities the design leans on
Kimi K3 shipped in July 2026: 2.8T parameters, natively multimodal across text, images and video, a one-million-token context window, and two variants — K3 Max for single-agent work and K3 Swarm Max for parallel. Agent Swarm coordinates up to 300 sub-agents, and the coordinator is trained to decide when to parallelise rather than following a template. Grok Bot is included with SuperGrok Heavy or Cursor Ultra rather than sold separately. The architecture below is not hypothetical.
Where the engine actually ranks
Agentic benchmarks, all models at maximum thinking effort. Worth a look because the whole design rests on the engine being genuinely frontier-class rather than merely cheap — it leads three of these eight and sits second on four more.
Six named teammates, one interface
Why a chat bot is the right front end
The engine is not something you talk to during the day. You do not want to write a query every time you check Fed positioning, which is the whole reason a terminal has a chat function in the first place. So the interface is a set of named bots you message like colleagues — analyst, news, reports, alerts, position, macro — each with its own role, memory and workflow.
The part that makes them more than six chat windows is that every bot on the account shares one persistent cloud computer: browser, filesystem and terminal, living on a cloud VM rather than your laptop. A bot picks up a task, uses that machine to do it, and reports back. That shared machine is also what lets all six read and write the same workspace without any of them knowing about the others.
Plugins, installed once and available to every bot
Bloomberg’s chat function is the layer nobody outside institutional access can touch. The substitute here is arguably better: native access to what every CEO, hedge fund manager, central bank governor and macro commentator posts publicly, in real time. When a well-known manager announces a position publicly, the analyst bot catches it as it lands. A Bloomberg seat does not do that. Every other layer in this build is an approximation of something the terminal already does — this is the one that is genuinely additive.
Eight steps, start to finish
The whole build in one view before the detail. Steps 1 and 2 are the only ones that touch an installer; everything after is configuration and prose.
Install both halves and bridge them
The bridge is the part worth reading twice. The two tools do not integrate — they are joined by pointing them at the same synced folder, which is both why the setup is simple and where it will break.
Set up the cockpit
Sign up for SuperGrok Heavy at grok.com and install the desktop app. Then create the workspace folder structure below — do this before anything else, since every later step assumes those paths exist.
Install the engine
Download Kimi Work from kimi.com — Apple silicon Mac and Windows — sign in with a Moonshot account, and enable K3 Swarm Max in Settings. Swarm Max is not the default, and without it the 300-agent monitoring layer simply will not spawn.
Point them at the same folder
Configure the engine to sync its workspace with the cockpit’s workspace through Dropbox, iCloud Drive or a shared cloud bucket. There is no API between the two products — this shared folder is the entire integration, which means sync latency is now part of your alerting path.
One shared workspace
Everything hangs off this. The swarm, the dashboard, the bots and the broker all coordinate through files rather than through each other — which is why the pieces can be swapped without rewiring the rest.
How the 300 agents get split
Set up as a routine named terminal-monitor: continuous through market hours, 9:30 to 4 ET, dropping to 30-second intervals off-hours. Each sub-agent owns one source category; when anything clears the conviction threshold the coordinator appends it to live-alerts.json.
The dashboard, described rather than built
This is the single most reusable artifact in the whole setup — the terminal layout as a paragraph. Adjusting the workstation later means editing this text and regenerating, not opening a component file. It generates a full project: package.json, components, styling, API integration. npm install && npm run dev, and it serves on localhost:3000.
What that description produces
Worth reading as a spec rather than a screenshot, since it is the actual argument for what a trading workstation should show at a glance.
One bot per function
Six bots, created one at a time and each given a role description like this one. Note the last line — the design deliberately stops at surfacing signals and leaves the trade to a person.
The one bot that runs on a schedule
The others are reactive — you ask, they answer. The reports bot is the exception, and it is the piece that most resembles the thing people actually miss from a terminal: the morning note waiting for you.
Wiring it together — who touches which file
There is no message bus here. Every connection in the system is a file that one thing writes and another reads, which is worth mapping before you trust any of it.
A day at the desk
The test of whether any of this works is not whether it installs. It is whether an hour with it feels like a workstation, so the piece walks through one: cockpit open on the laptop, dashboard in a browser tab beside it.
You ask the analyst bot where sell-side conviction sits on NVDA this week, and get a synthesised street summary back inside a minute. Prices tick every five seconds. The sector heatmap shifts. The news ticker scrolls whatever the swarm is turning up. Then the alerts bot pings: a major chipmaker has filed an 8-K about capacity expansion, multiple wires confirming, high conviction on semis. You click through to the source in the dashboard news panel, ask the position bot what your current semis exposure is, and decide the trade yourself.
That last clause is the whole design. Every layer here ends at a briefed human, not an order.
Changing it later
Once it is live the dashboard stops being code you maintain and becomes a description you edit.
What you do get
Two figures are simply wrong: SuperGrok Heavy is $300 a month, not $200, and a single Bloomberg seat is $31,980 a year, not $27,660. The bigger problem is that the advertised total does not follow from the inputs given alongside it — $300 a month plus the $500 to $1,000 a month it budgets for API usage is $9,600 to $15,600 a year, not the $3,000 claimed. That makes this roughly two to three times cheaper than a seat, not nine. And the summary’s “less than one percent of the cost” contradicts the “nine times cheaper” a few lines above it; neither figure holds.
Still worth building, for a different reason
Two to three times cheaper than a Bloomberg seat is a much less exciting headline, and it is also not the actual argument. Nobody weighing this was going to buy a seat. The comparison that matters is against having none of it — no monitoring layer, no morning brief, no alert engine, no dashboard.
The other thing the corrected number changes is who this is for. At ten to fifteen thousand a year it is not a hobby setup. It is a real line item, and it should be justified the way any other tooling spend is: against what it replaces in hours, not against a terminal you were never going to lease.
What this does not get you
A monitoring layer that fires HIGH CONVICTION alerts at you all day is a machine for manufacturing urgency, and urgency is not edge — the design keeps execution human for exactly that reason, so do not quietly automate that step later. And the whole thing rests on one JSON file: if the swarm stalls, live-alerts.json simply stops updating, and a dashboard showing stale signals looks identical to one showing a quiet market. Put a heartbeat on it before you trust it.