各位股东久等了，我把自己真实跑过的直播切片流程整理成了一个 Skill：JPW-Live-Cut。 它不是把逐字稿丢给 AI，让它随便挑 5 段“一键出片”。 它会先在本地用 Qwen3-ASR + ForcedAligner 生成带真实对齐时间码的逐字稿，再覆盖 100% 原片时间轴完成六维评分。符合标准的候选全部保留，不会因为凑够 5 条就提前停止。 接下来，逐字稿和切点、9:16 画面与字幕、最终标题，都要分别经过人工确认，Remotion 才会渲染成片。 我想固化的不是“AI 替我做决定”，而是这条已经反复跑过的制作流程： 本地转录与对齐 → 全时间轴筛选 → 候选审核 → 切点与字幕确认 → 视觉确认 → 标题确认 → 渲染 原直播录像始终不修改，原始声音也不会上传到云端语音服务。 目前 macOS MPS 已验证；Windows 在workbuddy已经验证成功。 仓库放在这里： 如果你手上正好有一场想切的直播，欢迎拿真实素材试一次。遇到问题可以直接提 Issue，我会继续把实际使用中踩到的坑补回这个 Skill。

Hello people of Sol! I've reset usage limits for all ChatGPT Work and Codex users. Together with that, a quick update on GPT-5.6 Sol usage limits. Over the past few weeks, many of you have told us that Sol was using your Codex limits faster than expected. To be clear, we have not reduced usage on any subscription plans. We’ve been digging into what was happening and have landed several improvements. As a result, we expect your usage to last around 18% longer during typical use of Sol. Some of you should already see significantly larger improvements from today. Tomorrow, we’ll also restore the five-hour limit that we temporarily paused while investigating. Here’s what we found: - GPT-5.6 Sol is much more willing to work for longer, make additional tool calls, and coordinate complex workflows across tools and subagents. That makes it better at solving hard problems, but some tasks were using far more than we intended. - Sol also works harder at the same reasoning effort than previous models. High on Sol can use more tokens than High did on GPT-5.5. - Programmatic tool calling, also referred to as code mode, gives Sol much more flexibility to run tool calls in parallel or continue working while waiting. But it also led to more responses per turn, more cached input tokens, and higher usage than expected. - This was particularly noticeable when Sol was waiting for tool calls to finish or running many web searches. We’ve improved how we handle both cases and are continuing to make code mode more efficient. - The impact was also very uneven. The median user actually found Sol quite token efficient, while some power users working on harder tasks saw their usage drain much faster. We were very focused on average and median usage before launch and missed some cases where the long tail could use significantly more usage. Sol is a significant step forward in what Codex can do, but capability and efficiency do not always improve at the same pace, and some issues only become clear once people are using the model at real-world scale. We should have recognized this sooner and been more upfront about it. You keep pushing the frontier and we’ll keep improving efficiency and sharing updates as we go.