开启时会模糊预览图，关闭后正常显示
I log my sessions with /end-session to log: - overview of what we did - files we created/deleted/modified - why we made decisions - what worked/didn’t work - steps for next session then I run /start-session with new agent and it reads the last log to get the context it needs
TLDR; I make software with AI and talk about it. Serial internet biz builder with multiple 6 & 7 figure exits. Always learning.
100% 客户端 JS 运行 Gemini Watermark Remover 一个基于纯 JavaScript 开发专门用于移除 Google Gemini AI 生成图片右下角的水印。 无损直出：没有 AI 幻觉，没有模糊，像素级还原，就像水印从来没存在过一样。 https://t.co/Kat9le6FWx
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Factory should let you publish the chat threads like Amp does would be cool to see the prompts to build a design like this (because I hate designing stuff)
TLDR; I make software with AI and talk about it. Serial internet biz builder with multiple 6 & 7 figure exits. Always learning.
I've been an LLM pessimist for years but I think now that LLM and reasoning tools have now reached a point where they may genuinely transform science and engineering. I do not believe this is AGI, but rather something like bringing generalizing past tools like Google Search or SAT solvers to broader domains. This raises some questions. In coding and writing, the practice of the discipline looks to be transforming entirely. Policing students from not using AI seems difficult, but using AI could genuinely undercut beginner understanding and growth. At the same time, AI looks to be a powerful force multiplier for the expert. I have been asked how students should navigate this situation. My two cents is learning to write and code without AI is probably crucial for the best results. Add onto this other risks of AI such as LLM psychosis. We will see many more beginners claim "breakthroughs" in disciplines they don't understand. This is a shame since it will probably trigger a fallback to credentialism. Unfortunately there are not enough hours in the day for experts to scrutinize beginner work. Until LLM world models grow sophisticated enough to self verify, this is probably unavoidable. What does this mean for science or engineering? I am perhaps a pessimist but I think the singularity is nowhere to be seen. Rather I think AI inefficiency will nearly counterbalance AI gains leading to modest gains. Speaking for myself, I have been moving slower on @deep_chem PRs since I have more distrust of contributed code. Students try to use AI and contribute incorrect code. As a result, I only merge in vetted code by proven existing contributors... AI has slowed progress by raising code review burden. This is an anecdote but I see similar patterns playing out elsewhere. At the same time, experts will use AI to achieve breakthroughs. Increasing efficiency and inefficiency will lie in a tenuous balance. The problem of developing automated verifiers consequently becomes the biggest challenge in AI. I suspect this will be a long slow grind, but there lies the path to real breakthroughs. (As a last comment, this post is entirely AI free! Avoiding AI leads to crisper thought when brainstorming)
Founder and CEO @deepforestsci. Creator of @deep_chem. Author @OReillyMedia. @stanford CS PhD. https://t.co/7LDcegrCsc
there's only one thing you should adopt from this Agile Transformation and it's: "Every day. We stand. For 45 minutes." stand more, sit less, ditch the rest, and focus on what matters
building @VemetricHQ & @snappify_io
RT @DeepakNesss: Basically, one web scraping company is suing another scraping company for scraping the scraped data.
Running 10+ SaaS in parallel at https://t.co/3sUSmGdehM since 2011! ⚡️ 🇮🇹 Italian 🎷 Jazz musician 🥎 Padel addicted