
No Vig Calculator (True Odds Tool)
Remove bookmaker margin & find true odds
What it does
No Vig Calculator helps you remove the bookmaker’s margin (vig) to reveal true implied probabilities and identify value bets. Perfect for sports bettors who want to make smarter and more profitable decisions. 🔹 Remove vig from American, Decimal & Fractional odds 🔹 Calculate true implied probabilities instantly 🔹 Identify +EV (positive expected value) bets 🔹 Optional bet input for better insights 🔹 Fast, accurate, and 100% free
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Betting Calculators OddsMay 2026 · correctbetting-tools.netlify.app · ▲1Free parlay, odds, hedge & value calculators — no signup
- BGBetting game puzzle (Hamming neighbor sum in linear time)2025 · ▲56
In Spain, there's a betting game called La Quiniela: https://es.wikipedia.org/wiki/La_Quiniela_(Espa%C3%B1a) Players predict the outcome of 14 football matches (home win, draw, away win). You win money if you get at least 10 correct, and the prize amount depends on the number of winners. Since all bets are public, the number of winners and the corresponding payouts can be estimated for each of the 3^14 possible outcomes. We can also estimate their probabilities using bookmaker odds, allowing us to compute the expected value for each prediction. As a side project, I wanted…
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I have recently developed a bot capable of simultaneously scanning over 140+ sportsbooks around the globe. It analyzes millions of odds in real time and uses this data to find overpriced odds. The logic behind it is straightforward: it uses odds from all sportsbooks to calculate the probabilities of an event occurring. Then, with simple maths it determines if the expected value is positive, meaning the potential payout exceeds the risk. Unfortunately, sportsbooks limit your accounts once they realize that you are profitable and not a losing customer. So I have already been limited at more…

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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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