Tomorrow Terminal is an AI trading terminal for people who want the receipts before the robot touches anything.
That is the product direction I am taking here: Tomorrow Terminal, a source-aware market research terminal with MCP/API access, Hyperliquid workflows, paper mode, user-set limits, logs, alerts, and a stop button that does what it says.
AI finance tools keep trying to sound like the smartest person in the room.
That is not what I want from them.
If a system helps with research, watches markets, or prepares trades, the job is not to perform confidence. The job is to keep the evidence trail visible and the controls boring enough that you can inspect the workflow after the demo high wears off.
Start with a ticker. Pull filings, financials, market data, news, Reddit, and StockTwits into one company profile. Let the AI analyst form a view, but make it show the receipts. Which filing changed? Which metric moved? Which headline matters? What is social chatter actually saying? Is it attention, or is it evidence?
That distinction matters.
Crowd chatter can explain why people are looking at a company. It should not be treated as proof. A filing excerpt, a table row, a dated news item, and a disclosed metric carry different weight. A useful research system should preserve those differences instead of compressing everything into one smooth paragraph.
The standard should be simple: if an AI research note cannot point back to the source, it should say so.
That standard matters even more when you add agents.
A trading agent without guardrails is just a slot machine with an API key.
The useful version is much less glamorous:
- paper strategies before live capital
- scoped trade keys instead of custody
- hard allocation limits
- daily loss circuit breakers
- one-click flatten
- activity feeds for every action
- human-readable reasons for every rebalance
- source trails back to the research that shaped the strategy
The agent should not make finance feel magical. It should make the workflow inspectable.
There is a big difference between an AI saying it found the right trade and a system saying this user-defined strategy can rebalance within these limits, using these sources, under these constraints, with every action logged.
I care about the second one.
Tomorrow Terminal is built around that idea: research first, sources attached, automation on rails. The scanner can surface unusual attention for manual review. The profile can summarize filings and news. The analyst can answer follow-up questions. The agent layer can test user-defined strategies as paper books, then only move toward live execution inside limits the user sets.
Not because the AI is guaranteed to be right.
Because if it is wrong, you should be able to see where, why, and how far it was allowed to go.
That is the product standard I want for finance agents:
- Show the receipts.
- Keep social data in its lane.
- Start in paper mode.
- Use scoped trade-only keys.
- Let the user set hard limits.
- Log every action.
- Alert when limits trip.
- Make stopping easy.
For agent builders, the interesting surface is the Tomorrow Terminal MCP and REST API. It gives agents access to profiles, prices, discovery feeds, research, and strategy creation behind keys the user controls.
For accountability, the important page is the Tomorrow Terminal public scorecard. It keeps the product honest by making outcomes visible, including the misses.
I also put together a public checklist for this shape of product: Tomorrow Terminal research workflows on GitHub. The useful parts are not the hype words. They are scoped keys, paper/live separation, trade receipts, risk breakers, logs, and a close-all button.
The future of AI finance should not be a confident paragraph and a big green button. It should be source-aware research, user-defined strategies, paper testing, tight rails, audit logs, and a stop button.
Research only. Not financial advice.
Try Tomorrow Terminal: https://tomorrowterminal.com
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