标签：#product

Run OpenAI Codex CLI natively on Android Discussion | Link

Let AI agents build real animated slideshows Discussion | Link

Um relato honesto de alguém que trabalha com design, vive com TDAH e está cansada de dicas genéricas Tem um tipo de artigo sobre organização que eu já sei de cor. É sempre alguma variação de: “faça uma lista, use Pomodoro, durma 8 horas e beba água”. Só que tem um cenário que quase nunca aparece nessas listas: O momento em que você não é neurotípica, está em burnout, tem duas tarefas importantes com o mesmo prazo e nenhuma técnica milagrosa resolve. É sobre isso que eu quero falar aqui. Sumário: O cenário caótico (e bem real) Por que o Pomodoro não funciona pra todo mundo Burnout em quem tem TDAH O dia em que duas tarefas importantes têm o mesmo prazo Estratégia 1: uma prioridade verdadeira por dia Estratégia 2: subtarefas em vez de cronômetro Estratégia 3: time blocking gentil (agenda que não te esmaga) Estratégia 4: reduzir fricção em vez de exigir mais disciplina Estratégia 5: contratos curtos consigo mesma E quando nada disso parece suficiente? Referências O cenário caótico (e bem real) Imagina o seguinte: Projeto A : entrega do pitch da pós, com prazo na sexta. Projeto B: preparar apresentação do roadmap, também para sexta. Você já está cansada, a cabeça rodando, o corpo em modo economia de energia. Aí você joga no Google “como se organizar” e recebe de volta: “Use a técnica Pomodoro, 25 minutos de foco, 5 de pausa.” E você pensa: “Amiga, eu mal estou levantando da cama. Você quer que eu vire um cronômetro humano?” A real é que muita técnica de produtividade tradicional foi pensada para cérebros neurotípicos. Quando a gente vive com TDAH, burnout ou os dois juntos, essa lógica simplesmente não encaixa tão bem. Por que o Pomodoro não funciona pra todo mundo Pomodoro é ótimo… para algumas pessoas. Mas tem motivos bem específicos para ser um caos para muitos de nós. Por exemplo: A pausa obrigatória, interrompe justo quando o foco finalmente chegou. A sensação do timer contando, aumenta a ansiedade em vez de ajudar. Cada “reinício de ciclo” vira mais uma micro deci

Agentic development environment to run agents at scale Discussion | Link

A solo developer with a $200/month budget can now access the same AI coding power that cost enterprises $50,000/month just two years ago. The secret isn't one tool — it's knowing how to mix and match three different access models to get frontier output at budget prices. I've been running this exact stack for months. Here's the breakdown. The Three Ways to Access AI Coding Models Before we talk strategy, understand your three options. Each has a wildly different cost profile. Option 1: Self-Hosted Open Models With models like GLM-5.2 hitting near-Claude Opus quality under MIT license, self-hosting is finally viable. The math is straightforward. Hardware cost: A dedicated GPU server (RTX 4090 or A100) runs $300–$800/month. An H100 rental starts at $1.99/hour on platforms like RunPod. Break-even point: According to cost analysis from multiple providers, self-hosting becomes cheaper than APIs at roughly 5–10 million tokens per month for premium-tier models [1]. Below that volume, you're paying for idle hardware. The catch: You need DevOps skills. Model deployment, quantization, monitoring, failover — it's real infrastructure work. If you save $500 on compute but burn out managing GPUs on weekends, you lost money. Best for: Teams with predictable, high-volume workloads and existing DevOps capability. Think 100M+ tokens/month where savings hit $5M+ annually [2]. Option 2: Pay-Per-Token APIs The default starting point. You pay exactly for what you use. Current pricing (early 2026, per 1M tokens): GPT-4o: $2.50 input / $10.00 output Claude 3.5 Sonnet: $3.00 input / $15.00 output Gemini 1.5 Pro: $1.25 input / $5.00 output DeepSeek V3: $0.27 blended (yes, really) Together AI (Llama 70B): $0.88 blended [1] The pricing floor crashed when DeepSeek V3 arrived at $0.27/M tokens with GPT-4-class quality. Open-source models routed through providers like Together AI or Cerebras ($6–12/M tokens at 969 tok/s) give you more options than ever. The trap: Pricing scales linearly forever. A

A local command room for AI coding agents. Discussion | Link

Swap out your creaky old box fan for a new model that lights up, mists, or even follows you around the room.

Watches your accounts and tells you what changed Discussion | Link

Know the second Fable 5 is back Discussion | Link

Ship real apps on the AI you already pay for. Discussion | Link

Don’t be fooled by the compact size of this soundbar. It’s a solid option for smaller TVs or spaces without having to sacrifice sound quality.

Meditation shaped by sound, breath, and frequency Discussion | Link

Find a real meal under ¥1,000 anywhere in Japan Discussion | Link

AI-native content operations for any Next.js website Discussion | Link

AI email for urgent mail, replies, and follow-ups Discussion | Link

Designing a modern Production Scheduling Software for film production is a fascinating challenge. Film scheduling is essentially solving a massive, multi-dimensional puzzle where the pieces are constantly changing, and the rules are dictated by art, logistics, weather, and strict labor union contracts. Legacy tools like Movie Magic Scheduling have been the industry standard for decades, but they are often clunky, desktop-bound, and lack real-time collaboration. A next-generation film scheduling tool needs to bridge the gap between complex logistical logic and a modern, collaborative user experience. Here is. what a top-tier SaaS Production Scheduling Software should have the following: The Core Engine: Script Breakdown & Ingestion Before you can schedule, you have to break down the script. This process must be frictionless. Universal Script Import: Flawless parsing of Final Draft (.fdx), Celtx, Fountain, and PDF formats. AI-Assisted Auto-Tagging: The software should automatically read a scene and highlight elements (Cast, Stunts, VFX, Special Effects, Props, Vehicles, Animals, Extras). Customizable Breakdown Sheets: Department heads need to see specific data. The software must allow users to create custom categories and color codes for breakdown sheets. Scene Splitting/Merging: The ability to easily split a scene (e.g., Scene 42A, 42B) or merge scenes if the script changes during prep. The Scheduling Interface (The "Stripboard") The visual representation of the schedule is where the 1st Assistant Director (1st AD) and Unit Production Manager (UPM) will spend 90% of their time. Drag-and-Drop Stripboard: A highly responsive, tactile interface where scenes are represented by colored "strips" that can be dragged, dropped, and reordered. Smart Sorting & Filtering: One-click sorting by Day/Night, Int/Ext, Location, Cast Members, or Script Order. The "Honor" System: The ability to group scenes by specific constraints (e.g., "Schedule all scenes with the child actor first,"

Built Vidilearn — an AI-first CLI for extracting YouTube transcripts, subtitles, chapters, articles, and structured metadata locally with zero API keys. Supports MCP servers, AI agents, RAG pipelines, Codex CLI, Gemini CLI & more. GitHub: https://github.com/Alfo-Tech-Lab/vidilearn

AI-ready React UI for Meta's Ray-Ban Display glasses Discussion | Link

AI-powered lead generation for designers and agencies Discussion | Link

Prove that you said things before anyone else Discussion | Link