Alternative · Forecasting platform
Hypermind vs
SimpleFunctions.
Hypermind is a closed-panel B2B forecasting service — champion human forecasters combined with their Forecasting Machine LLM ensemble, delivering managed probability estimates to enterprise clients. SimpleFunctions is the agent layer above live prediction markets: a causal-tree thesis system that auto-evaluates any claim against real Kalshi and Polymarket prices, an autonomous trading autopilot with a 7-gate risk cascade, computed indicators across 48K+ contracts, and a 56-tool MCP server that drops into any AI development environment in one line.
Verified 2026-04 · public sources only · live SF data from /calibration
Verdict
Pick the one that fits how
you actually work.
Choose SimpleFunctions if
You are building agents, autonomous trading systems, or research pipelines that operate on live prediction market data — calibrated probabilities with public Brier scores, a causal-tree thesis system that decomposes any claim and runs an auto-evaluation cycle against Kalshi and Polymarket, regime classification across the full 48K-contract universe, computed indicators (implied yield, cliff risk, liquidity availability score), and a 56-tool MCP server that integrates with Claude Code or Cursor in one line.
Choose Hypermind if
Your organization needs a managed forecasting service staffed by curated human superforecasters and an AI ensemble — Hypermind has operated this model since 2000 and their product is oriented toward B2B clients who want expert-panel probability estimates as a managed service, not open API infrastructure or DIY agent tooling.
Different product categories. Hypermind delivers managed expert-panel forecasts for B2B clients. SimpleFunctions exposes live market data, a causal thesis system, autopilot, and MCP for agents.
At a glance
Three things that
actually differ.
Everything Hypermind gives you — probability estimates on future outcomes, AI-assisted forecasting signals — SimpleFunctions also gives you, sourced from live Kalshi and Polymarket trading activity across 48K+ active contracts.
On top of that, SF ships a causal-tree thesis system, an autonomous Portfolio Autopilot (1M-context LLM, 7-gate risk cascade), computed indicators (implied yield, cliff risk, liquidity availability score), and a 56-tool MCP server no current forecasting platform exposes.
SF publishes live Brier scores for itself at /api/calibration — Kalshi 0.20, Polymarket 0.12 on T-24h price, past 90 days. Most forecasting platforms claim accuracy; SF lets you check its own numbers with a single curl call.
Side by side
9 dimensions · verified 2026-04SimpleFunctionsKalshi and Polymarket normalised, 48K+ active contracts indexed across politics, economics, crypto, sports, and current events.
HypermindClosed-panel forecasting on a curated question set selected by Hypermind's team; not tied to a public prediction market exchange.
SimpleFunctionsPublic REST API — prices, orderbook depth at /api/public/market/{ticker}?depth=true, cross-venue pairs, trade ideas, world snapshot, no auth required for reads.
HypermindNo public REST/HTTP API documented; access is through a managed B2B engagement with Hypermind's platform.
SimpleFunctionsImplied yield, cliff risk index, liquidity availability score, event overround, τ-days, and regime label pre-computed across 48K+ contracts at /screen.
HypermindProbability outputs from human panel aggregation and Forecasting Machine ensemble; derived market indicators are not exposed.
SimpleFunctionsLive Brier scores at /api/calibration — by venue, category, and price bucket, recalculated over a rolling 90-day window.
HypermindCalibration metrics not publicly published per available public documentation.
SimpleFunctionsPOST /api/thesis/create decomposes any sentence into a causal tree of testable sub-claims, propagates probabilities, scans Kalshi and Polymarket for edges, and runs an auto-evaluation heartbeat.
HypermindNot in scope; Hypermind's product is managed forecast delivery, not a user-facing thesis decomposition or per-sub-claim tracking tool.
SimpleFunctionsPortfolio Autopilot — 1M-context LLM, 13 data sources, 7-gate risk cascade (kill switch, position limits, drawdown gate, regime check) before any execution.
HypermindNot in scope; Hypermind does not offer automated trading against prediction market positions.
SimpleFunctions56 tools via `claude mcp add simplefunctions --url https://simplefunctions.dev/api/mcp/mcp`; works with Claude Code, Cursor, and any MCP client.
HypermindNo MCP server published.
SimpleFunctionsOpen REST + MCP + CLI (npm i -g @spfunctions/cli, MIT), no auth required for read endpoints.
HypermindClosed B2B panel; access requires engaging Hypermind's enterprise platform or sales process.
SimpleFunctionsPublic REST, MCP, and CLI reads require no auth. Authenticated thesis and intent execution: free up to 15M tokens, then pay-per-token.
HypermindB2B pricing not published publicly.
Methodology
Verified 2026-04 from public sources only — Hypermind's documentation, public website, and publicly observable behaviour. We never claim non-public information about Hypermind's internals. SimpleFunctions claims on this page are computed live from /api/calibration, /api/public/cross-venue/pairs, and /api/public/markets — you can re-verify them yourself with curl.
Use cases
Same data, different
best fit per scenario.
Scenario 01
An AI agent needs live prediction market data to inform autonomous decisions and run continuously updated probability estimates.
SimpleFunctions · best fit
SF's /api/agent/world (~800 tokens) gives the agent a compressed world snapshot, with delta endpoints for incremental updates. The 56-tool MCP server means the agent can query prices, thesis state, calibration scores, and place trades through a single integration.
Hypermind
Hypermind is not oriented toward agent integrations or public API access; connecting it programmatically would require a custom B2B arrangement outside their standard product.
Scenario 02
An enterprise research team wants managed probability forecasts from curated human superforecasters on geopolitical or macro questions.
SimpleFunctions
SF surfaces market-implied probabilities from Kalshi and Polymarket and computes indicators on top of them, but does not offer a managed panel of human forecasters or a curated question-selection service.
Hypermind · best fit
Hypermind is built for exactly this — a closed panel of champion forecasters plus their Forecasting Machine ensemble, run as a managed service for enterprise clients. This is their core product and the audience they have served since 2000.
Scenario 03
A researcher wants to decompose a complex geopolitical thesis into testable sub-claims and track which nodes shift most on breaking news.
SimpleFunctions · best fit
POST /api/thesis/create decomposes any sentence into a causal tree, maps each node to tradeable contracts on Kalshi and Polymarket, and runs an evaluation heartbeat. Inject signals via /api/thesis/{id}/signal to trigger a full re-evaluation cycle.
Hypermind
Hypermind's product delivers aggregate probability estimates; it does not expose a user-facing thesis decomposition or per-sub-claim tracking mechanism.
Scenario 04
A developer wants to audit the accuracy of a forecasting tool before wiring it into a production pipeline.
SimpleFunctions · best fit
GET /api/calibration returns SF's own Brier scores by venue, category, and price bucket, computed over a rolling 90-day window. Current figures: Kalshi 0.20, Polymarket 0.12 on T-24h price. Re-verify any claim with a single curl call.
Hypermind
Hypermind's calibration history or Brier score breakdown is not publicly published per available documentation.
Live data
The SimpleFunctions claims on this page are not marketing copy. Brier scores, market counts, and cross-venue pair counts are computed live from /calibration, /screen, and /api/public/cross-venue/pairs. All public, all free, all CC-BY-4.0.
FAQ
Does SimpleFunctions cover the same types of questions as Hypermind?+
Both operate in the probability-estimation space, but the question sets and mechanisms differ. Hypermind's closed panel forecasts on a curated set of questions determined by the platform. SF indexes live prediction market contracts on Kalshi and Polymarket — 48K+ active contracts spanning politics, economics, crypto, and sports — where prices are set by real-money trading. SF does not offer managed human-forecaster panels; Hypermind does not expose live market prices or a public developer API.
What is SimpleFunctions' thesis system and how does it differ from a forecasting service?+
POST /api/thesis/create takes any claim in plain language — for example, 'The Fed will cut rates twice before October' — and decomposes it into a causal tree of testable sub-claims. Each node is matched against live Kalshi and Polymarket contracts, probabilities are propagated up the tree, and an evaluation heartbeat runs continuously: news scan, price refresh, milestone check, LLM eval, confidence update. You can inject signals via /api/thesis/{id}/signal to trigger a re-evaluation. A forecasting service delivers one aggregate number; the thesis system gives you the full causal chain and lets you stress-test each node individually.
How does SimpleFunctions' Portfolio Autopilot work?+
Portfolio Autopilot is an autonomous trading agent that uses a 1M-context LLM, 13 data sources, and a 7-gate risk cascade before executing any position. Gates include a kill switch, position limits, drawdown gate, and regime check. Each decision cycle reads the current world snapshot, live calibration scores, and thesis state to rank and size trades. Hypermind does not offer autonomous market execution; their product is forecast delivery to enterprise clients.
Can I use SimpleFunctions as an MCP server in Claude Code or Cursor?+
Yes. Run `claude mcp add simplefunctions --url https://simplefunctions.dev/api/mcp/mcp` and all 56 tools are available in any MCP-compatible client. Tools cover prices, orderbook depth, cross-venue arbitrage pairs, thesis creation and signalling, trade ideas, world snapshot, and calibration scores. Hypermind has not published an MCP server.
How does SimpleFunctions handle accuracy transparency?+
GET /api/calibration returns SF's own Brier scores by venue (Kalshi, Polymarket), category, and price bucket, recalculated over a rolling 90-day window. Current numbers: Kalshi 0.20, Polymarket 0.12 on T-24h price. The endpoint is live — any claim on this page can be re-verified with a single curl call. This is a self-auditing mechanism most forecasting platforms do not expose publicly. Hypermind does not publish equivalent calibration metrics in available documentation.
Is there a migration path from Hypermind to SimpleFunctions?+
The products serve different workflows, so migration depends on what you are replacing. If you use Hypermind for probability estimates on macro or geopolitical questions and want to move toward a live-market, API-native workflow, SF's /api/public/scan and /api/public/query endpoints let you search across 48K+ contracts by topic. The thesis system can then add causal structure on top of the raw market prices you find. If you specifically need managed human superforecaster panels, SF does not offer that.
Does SimpleFunctions have a CLI and what can it do?+
Yes. `npm i -g @spfunctions/cli` installs a 60+ command CLI (MIT-licensed) that covers market search, thesis management, portfolio inspection, signal injection, and calibration queries. It operates on the same underlying data as the REST API and MCP server — all three surfaces share the same endpoints. Hypermind does not publish an equivalent developer CLI.
Start for free.
Public endpoints are free for normal usage and rate-limited for reliability. Authenticated endpoints are free up to 15M tokens, then pay per token. No credit card to start.