TraderBI
Trading journal built for serious Brazilian day trader
TraderBI launched on May 20, 2026 with 4 votes, #2,419 of 20,538 launches that month and more than 75% of that year's launches.
What it does
Most trading journals are built for US stock traders. TraderBI is for Brazilian day traders — WIN, WDO, and prop firms on CME and Forex. → Auto-imports from Profit Pro and Black Arrow → BRL + USD with live FX rates → Prop Trading Dashboard — drawdown & violations (unique in Brazil) → Analytics by setup, time, and asset → AI on YOUR trades, not generic advice → Risk calculator with auto-violation detection Free plan. No credit card. Built by a trader, for traders.
Para o day trader brasileiro que opera WIN, WDO e mesa proprietária — e quer parar de adivinhar por que perde. O TraderBI mostra o que a planilha esconde.
Para quem opera WIN, WDO, FOREX ou mesa proprietária — e quer parar de adivinhar por que perde. Você sente, mas não tem dados. E sem dados, você confia no instinto errado — e repete o mesmo erro na semana seguinte. Não é falta de disciplina. É que a ferramenta errada cria atrito — e atrito mata o hábito antes dele se formar. Cada plataforma te dá um número diferente. Você some com o lucro antes de contar. Ou conta prejuízo duas vezes. Você opera na intuição. O TraderBI separa o que funciona do que você acha que funciona. Reconhecer o problema não é suficiente. Você precisa ver o padrão nos dados para quebrar o ciclo. O P&L da plataforma te diz quanto você perdeu. Não te diz onde errou, nem…from traderbi.com.br
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Growth · 12d ago · wallhop.io
Source: Product Hunt launch ↗
Launched alongside, May 2026
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Parallel agents, diff reviewer, and multi-model comparisons
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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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