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FINAI 2.0 — an ultra-low-latency predictive ML layer for markets. Feature engineering → training → strict no-look-ahead sim → inference, in Rust/C++ at 4–50 µs/tick, enabling tick-level backtesting. Per-bar signals (probabilities + raw) across tickers & timeframes, asset-agnostic, strictly causal. Transparent by design: live public board + immutable S3 audit trail — every signal timestamped, independently verifiable. For market makers, execution desks & systematic funds.
We built FINAI 2.0 out of frustration: serious research on market microstructure needs tick-level simulation, and Python just can't run it at scale. So we rebuilt the whole pipeline — feature engineering → training → strict no-look-ahead simulation → production inference — in Rust/C++, at 4–50 µs per tick. That speed is the unlock: we can backtest at tick resolution, honestly, with no look-ahead.
The engine emits per-bar predictive signals across assets and timeframes — from crypto ticks to US equities. The part we care about most: you don't have to trust us. There's a live public board with a real-time signal stream and an immutable S3 audit trail — every signal timestamped and independently verifiable.
We're a small team a bit obsessed with causal, no-leakage ML — we publish only what survives out-of-sample. Would genuinely love feedback, especially from anyone who's fought the same latency / look-ahead battles. Happy to answer anything 🙏
Plugged FINAI into some of my own tick data and the latency numbers actually held up, which honestly surprised me. The public audit board is a nice touch too, kind of rare to see that level of transparency in this space.
About FINAI by Synlabs on Product Hunt
“Ultra-low latency predictive ML layer”
FINAI by Synlabs was submitted on Product Hunt and earned 0 upvotes and 3 comments, placing #151 on the daily leaderboard. FINAI 2.0 — an ultra-low-latency predictive ML layer for markets. Feature engineering → training → strict no-look-ahead sim → inference, in Rust/C++ at 4–50 µs/tick, enabling tick-level backtesting. Per-bar signals (probabilities + raw) across tickers & timeframes, asset-agnostic, strictly causal. Transparent by design: live public board + immutable S3 audit trail — every signal timestamped, independently verifiable. For market makers, execution desks & systematic funds.
FINAI by Synlabs was featured in Fintech (47.2k followers) and Artificial Intelligence (473.7k followers) on Product Hunt. Together, these topics include over 125k products, making this a competitive space to launch in.
Who hunted FINAI by Synlabs?
FINAI by Synlabs was hunted by Nikita. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Nikita
Hey Product Hunt! 👋 Nikita here, CEO of Synlabs.
We built FINAI 2.0 out of frustration: serious research on market microstructure needs tick-level simulation, and Python just can't run it at scale. So we rebuilt the whole pipeline — feature engineering → training → strict no-look-ahead simulation → production inference — in Rust/C++, at 4–50 µs per tick. That speed is the unlock: we can backtest at tick resolution, honestly, with no look-ahead.
The engine emits per-bar predictive signals across assets and timeframes — from crypto ticks to US equities. The part we care about most: you don't have to trust us. There's a live public board with a real-time signal stream and an immutable S3 audit trail — every signal timestamped and independently verifiable.
We're a small team a bit obsessed with causal, no-leakage ML — we publish only what survives out-of-sample. Would genuinely love feedback, especially from anyone who's fought the same latency / look-ahead battles. Happy to answer anything 🙏
— Nikita