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Comecei o portal com o objetivo de construir uma suite completa de ferramentas financeiras, calculadoras e simulações para o mercado brasileiro. Com o tempo, fomos além e desenvolvemos módulos como o GreenMetrics Pro (contabilidade de carbono/ESG) e simulações interativas. Alguns destaques recentes: Infraestrutura e SEO: Todo o ecossistema rodando leve, otimizado para SEO técnico (dados estruturados Schema.org, Rank Math, performance nota alta no PageSpeed). Tração e Produto: Calculadoras e ferramentas 100% funcionais, prontas para escala sem necessidade de manutenção pesada de código. Diversificação: Integração de artigos, simuladores e ferramentas práticas que retêm o usuário no ecossistema. O dilema do fundador: Atualmente, o Finaxa atingiu um nível de maturidade em que ele está 100% pronto para crescer e monetizar com força, mas meus novos projetos e focar em outras frentes têm consumido a maior parte do meu tempo. O projeto hoje funciona como um ativo "plug-and-play" redondo para quem já tem audiência no ecossistema financeiro/B2B ou quer rodar mídia/SaaS no topo desse funil. Fica a reflexão para quem constrói múltiplos projetos: como vocês lidam com a decisão de continuar mantendo um ativo estável vs. passar o bastão para quem tem sinergia para escalar? Quem quiser trocar uma ideia sobre a estrutura técnica, SEO ou estratégias de crescimento dele, as DMs estão abertas!

I built Xeyria around a simple problem: AI coding agents can read your code, but they often lose the context behind it. Architecture decisions, discussions, rejected approaches, and unfinished work can disappear between sessions. Xeyria helps developers preserve that project context so they can continue working instead of reconstructing the story. I’ve spent the last couple of months building Xeyria around this problem. I don’t know yet if it will be useful for everyone, or even if I’ve solved the problem in the right way. But if it helps even a few developers, I’ll be happy. One thing I’ve realised during this journey is that building something is only one part of the challenge. Getting it in front of the people you built it for is much harder. That’s the part I’m experiencing right now. So I’m here to learn. If you use AI coding agents, what’s the one thing you wish they remembered between sessions?

I learned something kinda annoying while building a lead-scoring system 😭 A “high-intent” lead can still be the completely wrong customer. The company I was building it for gets a lot of inbound inquiries, and at first one signal seemed pretty obvious: quantity. Someone asking for 200kg of a product sounds way more valuable than someone asking for 5kg, right? Except... not necessarily. That 200kg request could be a distributor looking for a bulk deal, while the smaller request could actually be coming from the exact kind of end customer the company wants. So suddenly the problem wasn't just “is this lead hot or cold?” It was: who is actually behind this inquiry? I ended up treating customer type as its own signal instead of assuming that bigger quantity = better lead. Made me wonder how often this happens in completely different businesses too 👀 If you've had an inbound lead that looked really promising and then turned out to be the wrong kind of customer — what was the misleading signal?

Sam Altman has announced GPT 6.1 Sol, OpenAI’s newest model, and this one is aimed less at simple chat and more at actually getting work done. The model is built around agentic coding, computer use, and professional workflows. In other words, OpenAI is positioning it for tasks where the model needs to reason through a problem, use tools, interact with a computer, write and modify code, and complete longer workflows rather than just give you an answer. One of the more interesting claims from OpenAI is that GPT 6.1 Sol can get close to the performance of its more advanced Astra model on some benchmarks while using significantly fewer tokens. That matters because token usage directly affects both cost and how efficiently an agent can work through large tasks. The API is priced at $2 per million input tokens and $10 per million output tokens. GPT 6.1 Sol is now available through ChatGPT Work, Codex, and the OpenAI API. For developers building AI agents, coding tools, and products that need models to actually operate software instead of simply talk about it, this could be a pretty important release.