『L E S S O N S - with Lennox Saint』のカバーアート

L E S S O N S - with Lennox Saint

L E S S O N S - with Lennox Saint

著者： Lennox Saint
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$32,700 in savings. 10 months of runway. non-technical founder building a real SaaS to $10K/month using only AI tools - or going broke trying. this playlist is my full journey from $1,940 MRR to $10K MRR, filmed in real time with real numbers. the wins. the fuck-ups and the dollars. no code experience. no safety net. just AI tools and a deadline. new videos every week. subscribe to see how it ends.© 2026 Lennox Saint マネジメント・リーダーシップ リーダーシップ 経済学
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  • The Rescue Ledger: Can ChatGPT Voice Run an AI Video Team?

    The Rescue Ledger: Can ChatGPT Voice Run an AI Video Team?
    2026/07/28
    ChatGPT Voice had 90 minutes to coordinate three blind reviewers, keep two AI video editors anonymous and return one decision package. The rules were set before the clock started: deliver the work without leaking model identity and stay under the rescue threshold - or get put on probation or fired. The run shows what polished AI demos usually hide. Voice stalls. The sealed model map disappears. One reviewer defends its own process. And technical evidence does not always agree with human taste. The blind reveal matches Claude Fable 5 against GPT-5.6 Sol. The final verdict separates which edit won from whether Voice deserved a place in the workflow. The operating rule is simple: give AI one real job, set a clock, count rescues live and name the decision that stays human. Chapters 00:00 the 90-minute job starts 00:52 what changed in ChatGPT Voice 01:36 the hire-or-fire rules 02:45 three blind reviewers at work 03:46 the anonymous decision package 05:17 the sealed map goes missing 06:09 Fable 5 vs GPT-5.6 Sol 07:27 can ChatGPT grade itself? 08:27 the long and Shorts verdict 10:05 ChatGPT Voice gets its verdict 10:29 where humans keep the vote 12:03 four rules for testing AI at work Watch the YouTube version: https://youtu.be/Xm6kJ-5cn-g Build your own AI content system with Threadify: https://www.threadify.app/yt?utm_source=lenny-podcast&utm_medium=transistor&utm_campaign=chatgpt-voice-ai-video-team&utm_content=show-notes&video_slug=chatgpt-voice-ai-video-team&cta_slot=show_notes&entry_angle=voice-team&lp_variant=plans
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    13 分
  • i asked Codex to map my life. Obsidian broke.

    i asked Codex to map my life. Obsidian broke.
    2026/07/24
    Lennox's Second Brain holds 74,200 notes. On this Mac, Obsidian opened 44,331 of them. Note 44,332 crashed the graph. This episode isn't a tour of a pretty knowledge system. It's the story of why Lennox built one after losing his mum, how perfectionism turned the graph into a new form of procrastination, and why nine crashes finally forced a simpler question: could the system help with a real decision? The answer shows up in PR #122. Codex checked Lennox's notes, Slack and GitHub, caught that a proposed change would undo Samsam's live work, and stopped before the damage was done. You'll hear the exact local test, the fix, the limits, and one practical check you can run this week. Ask your Second Brain a real question you need to decide. If it can't find the right notes and help you choose, stop feeding it. Make it useful first. 00:00 Note 44,332 crashed Obsidian 00:27 The digital graveyard test 00:39 Why Lennox recorded his life 01:57 94% building, 6% distributing 03:13 Nine crashes and the exact limit 05:03 Keep every note without one giant graph 05:28 How it stopped a bad code change 07:26 Test your Second Brain on a real choice Invite Lennox in: https://invite-lennox-in.lennox.chatgpt.site/ Obsidian: https://obsidian.md/
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    9 分
  • my AI studied 1,158 videos, then built this one

    my AI studied 1,158 videos, then built this one
    2026/07/21

    An AI wrote, edited, and packaged this episode. Lennox recorded one take. Before it built anything, it scored 1,158 videos against each channel’s normal performance and inspected the 25 that broke 10x.

    This was not a dataset of 1,158 viral videos. Most were normal. That made the outliers useful. The system turned the patterns into the title, thumbnail, hook, and structure—then the episode reveals the mechanism.

    If you want the same proof-first approach applied to your Threads content, with your voice and final approval preserved, see the Threadify system behind the experiment:

    See Threadify plans through the measured funnel

    This is a real packaging experiment, not proof that one template guarantees views.
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    3 分
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