An AI board that pre-registers its bets – bet #1 just graded wrong

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A board of expert personas whose every decision is a pre-registered, time-anchored, reality-graded bet. Not a chatbot that agrees with you -- a board that keeps score, before the fact.

Landing page & docs: https://danilushin.github.io/asktheboard/

Mechanism on sample data — the 60-second, no-key walkthrough below reproduces it exactly.

pip install asktheboard

Why this exists

Anyone can clone a "panel of AI personas" in a weekend, and a dozen have. The debate mechanic is a commodity. What it leaves out is the thing that makes advice worth trusting: a record of having been right before the outcome was knowable. That record is hard to fake -- you can buy model outputs, but you can't back-date a timestamp. It only accrues the slow way: by calling decisions in advance and letting reality grade them, one resolution date at a time.

So ask-the-board records, for every decision:

your stated prior (what you believed going in),

the per-seat dissent vector -- each seat's stance + its own probability,

a dated, falsifiable prediction, anchored before the outcome is knowable,

on the resolution date, reality's realized outcome, auto-reconciled into a Brier/calibration score per seat.

The board-minute is a git-committable ADR. Your git history is the external attestation of the anchor timestamp. The accumulating, reality-graded record is the durable asset.

See it keep score (60s, no API key)

create -> resolve -> score is pure data -- no LLM, no key, no network. This is a worked example on sample data: you supply the outcome with resolve, and the engine computes each seat's Brier score (lower is better). It shows the mechanism, not a track record -- the integrity comes from the anchor timestamp your git history attests, which no demo can fabricate. The committed artifacts live in examples/.

pip-installed (no repo)? paste the sample spec below. Cloned the repo?

skip the heredoc and use --spec tests/sample_minute.json instead.

cat > sample_minute.json .json (the record) and .md (the committable ADR) into board-minutes/.

Convene a board (BYOK)

create pre-registers a minute you wrote by hand. convene runs the live LLM fan-out: every seat answers through your key, and the board's consensus probability is the mean of the seats' calls. It ships no provider -- bring an OpenAI-compatible endpoint (HTTPLLMClient is stdlib-only, zero dependencies).

from asktheboard import convene, Seat, HTTPLLMClient

minute = convene( id="pgvector-scale", question="Will pgvector hold our scale, or do we need a dedicated vector DB?", prior="leaning postgres to avoid a new service", decision="stay on postgres + pgvector", statement="pgvector serves p95 90-day resolution horizon )

Or from the CLI (key in OPENAI_API_KEY):

asktheboard convene --spec convene.json --model gpt-4o-mini

Any OpenAI-compatible API works -- point --base-url (or HTTPLLMClient(base_url=...)) at OpenRouter, Together, or a local server. The engine still makes no calls of its own; it only ever speaks through the client you pass.

Bundled roster -- seat a board by name

You can always hand-write Seat(name, persona). But a sensible default board ships in the box: a curated set of role archetypes (the architect, the skeptic, the operator -- functions, not impersonations of real people) and a few named panels, so seating one is a single lookup.

from asktheboard import convene, panel, seats, HTTPLLMClient

minute = convene( id="pgvector-scale", question="Will pgvector hold our scale, or do we need a dedicated vector DB?", prior="leaning postgres", decision="stay on postgres + pgvector", statement="pgvector serves p95 default horizons

A minute is only foresight if it has a date by which reality can grade it. decision_type picks a sensible default horizon so the common case is one lookup (and a 5-year horizon on a library swap stands out as dishonest):

type horizon when

library 90d adopt/swap/drop a dependency

migration 180d move a datastore, platform, or pipeline

architecture 365d a structural design bet you live with

Short-latency first on purpose: a fresh board earns a track record on fast library calls before anyone trusts its slow architecture bets. Pass an explicit resolution_date= to override.

A contrarian win

When a seat dissents from the board and turns out more right than the consensus, that is a contrarian win -- the gold the public scoreboard is built from. The board changed (or should have changed) its mind, and reality later stamped the dissenter vindicated.

Stability

What's shipped: the foresight engine (data model + grading + committable ADR) and the BYOK LLM fan-out that produces a board-minute (asktheboard.convene, behind the asktheboard.llm Protocol). No provider is bundled -- you plug in your own key.

The public API is 0.x / unstable. The LLMClient / HTTPLLMClient surface and the board-minute JSON schema may change before 1.0 -- pin a version if you depend on them.

Built with

Built by Dan Ilushin with Claude (Anthropic) in the loop. Contributions welcome -- see CONTRIBUTING.md (DCO sign-off) and SECURITY.md.

License

MIT. (c) 2026 Dan Ilushin.

About

A board of expert personas whose every decision is a pre-registered, time-anchored, reality-graded bet. BYOK; the board that keeps score, before the fact.

Topics

decision-making

calibration

forecasting

adr

decision-support

ai-agents

llm

byok

brier-score

Resources

Readme

License

MIT license

Code of conduct

Code of conduct

Contributing

Contributing

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