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Layer

One API for Polymarket and Kalshi

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This is 1 of 113 launches in algorithmic and prediction market trading tools — see how it stacks up on momentum and crowding →

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Details

External ID
50009330
Source
HN
Company
—
Product
Layer
Website domain
uselayer.sh
Launched
Oct. 8, 2026
Cohort
—
Upvotes
5
Upvotes percentile
0.10
Tags
—

Description

Hi HN, I'm David. I built Layer, an API + SDK or building trading bots/ai agents across prediction markets.The idea came from something i kept running into while playing with trading bots. Once your bot needs access to more than 1 venue, you suddenly have to deal with different market schemas, contract definitions, prices, fees and APIs before you can reason about where to trade. That gets messy real fast, especially with prediction markets. The same real-world event can exist on Kalshi & Polymarket, but the contracts can be represented differently.Here's my broader thesis: as more trading becomes automated, bots/agents will need a common interface to access different financial markets. Prediction markets felt like a good place to start because the fragmentation is obvious.With Layer's SDK, you can: - pull normalized prediction-market data - match equivalent markets across Kalshi and Polymarket - compare prices across venues - see where a trade has a better payout - identify potential cross-venue arbitrage opportunitiesAdditionally, I built an example bot called Spread using Layer. It finds matched markets across venues and compares their prices after fees, so you can see which venue pays more for the same bet.Layer is still super early and i would love your feedback, especially from people building trading bots/agents, or prediction-market tools.Feel free to break it, build on it. Would love to see what you do with it.

Enrichment

Niche
algorithmic and prediction market trading tools
Vertical
Fintech
Function
Data infrastructure
Audience
Developer
AI stance
Not AI
Project type
Commercial product
Normalized one-liner
unified api for prediction markets
Manually corrected
False

Could you build this?

Partial While wrapping the respective APIs is straightforward, automatically matching semantic event contracts across Kalshi and Polymarket with confidence scores and resolving settlement rule discrepancies requires sophisticated NLP entity resolution and real-time execution routing.

What it would actually take: The system requires an orchestration backend in Go or Rust with WebSocket adapters for Kalshi and Polymarket order books, combined with an LLM-powered entity alignment pipeline that continuously parses event settlement clauses to output equivalence confidence scores. The core technical hurdle is handling disparate order types, non-atomic cross-exchange execution (preventing legging risk), and real-time order-book normalization with sub-millisecond precision.

Discussion

1 comment analyzed.

Competitors

Other products that read as similar to this one — 292 launches clear the similarity bar, closest 8 shown.

Attention rank: #283 of 293 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 344 days after the earliest competitor.

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