Adjacent News vs
SimpleFunctions.

SimpleFunctions adds three layers Adjacent News does not expose: a causal-tree thesis system that decomposes any claim into testable sub-hypotheses, maps them to tradeable contracts, and runs a continuous evaluation heartbeat; an autonomous Portfolio Autopilot backed by a 1M-context LLM and a 7-gate risk cascade that executes live trades; and a 56-tool MCP server that connects the full stack to Claude Code, Cursor, or any MCP-compatible client in one command. These are agent-layer capabilities, not a news feed or charting product.

Both products cover Kalshi and Polymarket. Adjacent News also indexes Limitless and additional venues. SimpleFunctions currently normalises Kalshi and Polymarket into a single schema with 48K+ active contracts. If your analysis depends on Limitless or venues outside those two, that is a genuine gap in SF's current coverage. For Kalshi and Polymarket specifically, SF adds computed indicators and a calibration baseline that Adjacent News does not publish.

You call POST /api/thesis/create with a plain-language claim — for example, 'The Fed will cut rates before Q3 ends.' SF decomposes it into a causal tree of sub-claims, maps each to tradeable contracts on Kalshi or Polymarket where available, and begins an evaluation heartbeat: news scan, price refresh, milestone check, LLM evaluation, and confidence update, run on a continuous schedule. You can inject new signals via /api/thesis/{id}/signal. Public theses are forkable. No current prediction market data product exposes this pipeline.

SF does not offer a standalone news feed, browser extension, Telegram alerts, or RSS distribution. Adjacent News built its product around push-based news-to-market alerting — semantic search maps breaking articles to specific contracts and delivers those signals through browser and messaging channels. SF's approach treats news as one input signal injected into a thesis evaluation loop rather than a standalone distribution product. If push alerting is the core requirement, Adjacent News is purpose-built for that.

Adjacent News's Trade API connects to prediction market venues for order execution. SF's Portfolio Autopilot is a higher-level autonomous agent: it runs a 1M-context LLM against 13 data sources, generates candidate trades from the thesis system and indicator signals, and passes each through a 7-gate risk cascade — kill switch, position limits, drawdown gate, regime check, and others — before executing. It is not a raw trade API; it is a decision-making agent that uses trade execution as one of its actuators.

Yes. /api/calibration returns SF's Brier scores broken down by venue, category, and price bucket, computed over the past 90 days. On T-24h price, SF scores Kalshi 0.20 and Polymarket 0.12. This is SF auditing its own probability accuracy in public. Most prediction market data products — including Adjacent News — do not publish a calibration baseline, which means there is no external reference for how well their implied probabilities track resolved outcomes.

Both expose public REST/HTTP APIs, so migration is syntactically low-friction. Adjacent News uses Auth0 JWT bearer tokens for all endpoints; SF's public read endpoints require no authentication. The semantic search analog in SF is /api/public/scan; the market-detail endpoint is /api/public/market/{ticker}. The main adjustment is that SF returns additional computed fields — implied yield, cliff risk, regime label — that may be useful to pass through to downstream consumers or agents.

Adjacent News is the better fit when your primary workflow is news monitoring mapped to markets: semantic search across breaking stories, push alerts via Telegram, RSS, or browser extension, and TradingView-style charting at trade.adj.news. These are distribution and monitoring capabilities SF does not offer. If you are a researcher or analyst whose main activity is watching how news moves market probabilities in real time, Adjacent News has built dedicated tooling around exactly that.