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Current readings
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Checked all 119 current publications. Showing up to 30 connected readings, ordered by original source date.
Lenny's Newsletter host Claire Vo presents Brooke Lacey's workflow for turning a raw phone video into a captioned, jump-cut TikTok with Claude Code, FFmpeg, and Whisper. The episode description says the edit takes about 15 seconds and shows Lacey's process live.
Lacey built a TikTok audience of more than 290,000 by learning to edit short-form videos herself. She describes teaching Claude her cadence and using comments as material for new scripts; the episode also covers how she runs a weekly short-form operation without an editor.
The example matters as a practical account of one creator's editing workflow, not evidence that the same speed or results generalize to other creators. The source does not detail the workflow's setup requirements or establish how much human review the resulting edits need.
What setup and review does this workflow require beyond the live demonstration?
Lenny’s Newsletter presents two accounts of AI-assisted work: Claire’s tests of Meta Muse Wonder and creator Brooke Lacey’s video-editing workflow. Claire reports that Muse Wonder handled agentic tasks such as billing support and inbox triage well in her tests, but struggled with design and finishing prototypes. She plans to try it in some agent workflows, not use it for frontend design.
Lacey describes using Claude Code, FFmpeg, and Whisper to edit phone recordings into captioned videos. She says she taught Claude her jump-cut cadence through instruction and iteration, and uses audience comments to develop video ideas. The workflow removes editing friction, she says, but her experience with video craft is what lets her judge whether the edits work.
Escape Meridian interpretation: Together, the accounts distinguish delegating execution from supplying judgment: Claire’s results vary by task, while Lacey says knowing the craft mattered to teaching the system her style. The descriptions do not establish how these workflows perform beyond the reported tests and examples. What results would Claire’s agent workflows produce outside her tests, and how well would Lacey’s editing process transfer to other creators’ styles?
In Lenny's Newsletter, Claire Vo reports testing Meta’s Muse Wonder and Wonder Full on nearly 20 tasks in the How I AI bench, spanning prototyping, agentic workflows, coding, writing, and creative work. She says the results partly support Meta’s framing of Muse as a “personal and professional agent,” while identifying limits.
Vo scored the tasks with an LLM-as-a-judge pass and her own voice notes. Her overview says Muse ranked first on three agentic tasks, but the supplied page does not identify those tasks or give detailed scores. It also flags weak design output and compares Muse with other models on SVG illustration and short-form video.
The practical question in Vo’s account is where the model’s strengths translate into work she would actually use: the episode also examines email writing, voice-assistant tasks, and whether she will add Muse to her agent workflows. The available page lists those subjects but does not include the detailed results or her final workflow decision. Escape Meridian interpretation: this first-look account of one person’s bench does not establish performance across users or real-world tasks.
Background & sources
Lenny's Newsletter is Lenny Rachitsky's publication and podcast on product management, growth and career questions for product people.
No additional background is recorded for this identity. The references below are the basis for this context.
