🆕 show: HIGH TASTE TESTERS We convene top Al power users to compare what actually works-and what falls apart once you start using it. @davis7 , @benhylak , @dexhorthy , @allisonology , @altryne and @swyx give you unfiltered takes on @OpenAl DevDay, Dots vs Muse vs GrokBot vs Instinct, who's winning coding, personal assistants, and the gap between impressive Al capabilities and useful products. From Pi-powered personal agents and decision models to subagents, context management, prompt injection, and assistants that can spend money: what should you actually use? CHAPTERS 00:00 Introducing High Taste Testers 01:26 OpenAl DevDay: Dots, Spaces, and product execution 10:28 Great models versus great products 14:39 Agents versus assistants 18:08 Building a personal assistant with Pi 21:07 Decision models, Jev, and predictive interfaces 32:46 Model speed, writing quality, and subagents 37:29 Agent loops, forks, and context management 43:19 Haiku, Muse Spark, and Grok 50:00 Local models and coding benchmarks 54:09 Prompt injection and assistants taking action 57:50 Tools, payments, and assistant overload 1:02:16 Delegation, computer use, and everyday workflows 1:10:48 Al hardware and closing thoughts THANKS TO THE TESTERS Ben Davis - @davis7 Ben Hylak - @benhylak Dex Horthy - @dexhorthy Allison Durham - @allisonology Alex Volkov - @altryne 🔗 View on Twitter
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--- tweet_id: "2111244257579409664" author: "latentspacepod" published_at: "2026-10-16T23:53:50+00:00" source_url: "https://x.com/latentspacepod/status/2111244257579409664" --- 🆕 show: HIGH TASTE TESTERS We convene top Al power users to compare what actually works-and what falls apart once you start using it.[@davis7](https://x.com/davis7), [@benhylak](https://x.com/benhylak), [@dexhorthy](https://x.com/dexhorthy), [@allisonology](https://x.com/allisonology), [@altryne](https://x.com/altryne) and [@swyx](https://x.com/swyx) give you unfiltered takes on [@OpenAl](https://x.com/OpenAl) DevDay, Dots vs Muse vs GrokBot vs Instinct, who's winning coding, personal assistants, and the gap between impressive Al capabilities and useful products. From Pi-powered personal agents and decision models to subagents, context management, prompt injection, and assistants that can spend money: what should you actually use? CHAPTERS 00:00 Introducing High Taste Testers 01:26 OpenAl DevDay: Dots, Spaces, and product execution 10:28 Great models versus great products 14:39 Agents versus assistants 18:08 Building a personal assistant with Pi 21:07 Decision models, Jev, and predictive interfaces 32:46 Model speed, writing quality, and subagents 37:29 Agent loops, forks, and context management 43:19 Haiku, Muse Spark, and Grok 50:00 Local models and coding benchmarks 54:09 Prompt injection and assistants taking action 57:50 Tools, payments, and assistant overload 1:02:16 Delegation, computer use, and everyday workflows 1:10:48 Al hardware and closing thoughts THANKS TO THE TESTERS Ben Davis - [@davis7](https://x.com/davis7) Ben Hylak - [@benhylak](https://x.com/benhylak) Dex Horthy - [@dexhorthy](https://x.com/dexhorthy) Allison Durham - [@allisonology](https://x.com/allisonology) Alex Volkov - [@altryne](https://x.com/altryne) [🔗 View on Twitter](https://x.com/latentspacepod/status/2111244257579409664/video/1) ## 视频地址 - [视频 1](https://video.twimg.com/amplify_video/2111242884112695296/vid/avc1/1920x1080/APRCPknj3fOOPU5y.mp4?tag=29)