Helping AI founders & creators grow on X with high-performing content. 10K+ AI audience | Content Strategy & Ghostwriting DM for work 📩

France
Joined July 2024
🚀 27 Most Powerful AI Tools You Should Bookmark 🔖 Save this list — these tools can help with writing, coding, automation, design, video, audio, SEO, research, and more. ✍️ Writing • SurgeGraph • Sudowrite • Hoppy Copy 💻 Coding • Vo • Cursor • Bolt.new 🤖 AI Agents • Manus • n8n • Zapier 🎨 Image Generation • Ideogram • ChatGPT • Midjourney 🎬 Video • Runway • Veo • Hailuo 🎙️ Speech & Audio • Suno • ElevenLabs • Speechify 📈 SEO • SurgeGraph • Google Search Console • AnswerSocrates 🔎 Research • Perplexity • NotebookLM • Deep Research 💬 Chatbots • ChatGPT • Grok • Gemini 🔖 Bookmark this for later. ➕ Follow me for more AI tools, prompts & workflows.
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Peter Saint retweeted
Combining different creative references into one workflow could make video editing so much easier. 🔥
🤝 Paid partnership
the editing techniques and styles you thought were unattainable now just one reference prompt away: - the hook from an Instagram reel - the b-roll style from a TikTok - the captions from a YouTube Short then automatically apply them to your footage with your story get started ↓
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Peter Saint retweeted
Taking inspiration from multiple videos and applying it directly to your own footage is a smart approach to content creation.
🤝 Paid partnership
the editing techniques and styles you thought were unattainable now just one reference prompt away: - the hook from an Instagram reel - the b-roll style from a TikTok - the captions from a YouTube Short then automatically apply them to your footage with your story get started ↓
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Peter Saint retweeted
This gives creators more control over their editing style without starting from scratch every time.
🤝 Paid partnership
the editing techniques and styles you thought were unattainable now just one reference prompt away: - the hook from an Instagram reel - the b-roll style from a TikTok - the captions from a YouTube Short then automatically apply them to your footage with your story get started ↓
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Peter Saint retweeted
Imagine matching the hook, visuals, and captions from different videos with just one prompt. Huge time-saver for creators.
🤝 Paid partnership
the editing techniques and styles you thought were unattainable now just one reference prompt away: - the hook from an Instagram reel - the b-roll style from a TikTok - the captions from a YouTube Short then automatically apply them to your footage with your story get started ↓
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Peter Saint retweeted
One model, multiple quantization options, and a focus on efficiency. This is the kind of flexibility that makes open-source AI exciting.
Aloha!🌺 Meet Ornith-1.5-122B: No Tradeoff Between Strong and Fast Stronger. Faster. Leaner. Ornith-1.5-122B delivers frontier-level capabilities with exceptional inference efficiency. With superior instruction following, stronger task decomposition, and deeper understanding of real-world workflows, it achieves state-of-the-art performance among open-source models of comparable size, while running on a single NVIDIA DGX card!! Open weights under the MIT license. Coding ✅SWE-bench Verified: 0.845 ✅TB2.1: 0.813 ✅CyberGym: 0.800 ✅NL2Repo: 0.512 Agentic ✅HLE: 0.446 ✅BrowseComp: 0.785 ✅MCP-Atlas: 0.746 ✅Toolathlon: 0.708 🤗Hugging Face: huggingface.co/ornith-ai/Orn… We can’t wait to see what you build with Ornith-1.5-122B.
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Peter Saint retweeted
More efficient inference could make capable AI systems easier to integrate into real-world applications and workflows.
Aloha!🌺 Meet Ornith-1.5-122B: No Tradeoff Between Strong and Fast Stronger. Faster. Leaner. Ornith-1.5-122B delivers frontier-level capabilities with exceptional inference efficiency. With superior instruction following, stronger task decomposition, and deeper understanding of real-world workflows, it achieves state-of-the-art performance among open-source models of comparable size, while running on a single NVIDIA DGX card!! Open weights under the MIT license. Coding ✅SWE-bench Verified: 0.845 ✅TB2.1: 0.813 ✅CyberGym: 0.800 ✅NL2Repo: 0.512 Agentic ✅HLE: 0.446 ✅BrowseComp: 0.785 ✅MCP-Atlas: 0.746 ✅Toolathlon: 0.708 🤗Hugging Face: huggingface.co/ornith-ai/Orn… We can’t wait to see what you build with Ornith-1.5-122B.
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Peter Saint retweeted
真的没必要去买编程课,AI课了,那钱拿去订一个好模型都比听啥课都强。。大模型赢家通吃是这样的。。 虽然我用codex写代码,但我始终保持一份claude订阅。。写文案做视频opus5.5就完全不用操心。。 AI 是放大器,本身是 0的它也放大不了,本来就会干活的它就可以让你快十倍。。 订阅是在 @GamsGo_Global 市场买的 Claude Pro 共享,今天的价,最低是$6.99/月起,用码 YEDAG 再减3%,是 $6.78/月起。共享号适合轻度用哈。。
🤝 Paid partnership
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Peter Saint retweeted
Codex 官方 $20/月,GamsGo 上 $16.49/月 我平时就用 Codex 写代码,这次让它自己做了张价格对比表、跑了校验: Plus 独享:官方 $20 → GamsGo $16.49 Pro-100:$100 → $68.99 Pro-200:$200 → $145.99 均为 USD/月、独享 1 个月,10 月 20 日页面价。 促销码 CTHTN 再减 3% @GamsGo_Global
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Another AI coding option worth knowing about. Qoder added DeepSeek-V4.1-Flash-1017 on launch day, and it's free to use in terms of Credits until October 24. No paid plan is required, and daily usage limits apply. qoder.com/qoder
🤝 Paid partnership
DeepSeek dropped V4.1-Flash-1017 on Saturday. It's been in Qoder since day one. Now it's FREE through Oct 24. 0.0x credits for free and paid users. Just select it and go.
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Peter Saint retweeted
DeepSeek dropped V4.1-Flash-1017 on Saturday. It's been in Qoder since day one. Now it's FREE through Oct 24. 0.0x credits for free and paid users. Just select it and go.
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Peter Saint retweeted
Thinking about upgrading? Save 60% on CapCut Pro or 40% on Ultra, Oct 20–30. Desktop only. The Jean Phil trend has inspired plenty of creative character videos. But imagine using that style for a real product campaign. You already have a video that works, and now you want to adapt its look for a new launch. Starting from scratch every time isn't the most practical way to create marketing content. #capcutpc #capcutblackfriday
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For AI filmmaking, I’d want to test one specific workflow: retrieve the previous shot’s approved details, generate the next shot, and compare lighting, costume, facial features, and camera direction. That would help measure whether persistent memory improves continuity in practice. #EverMind #EverOS
🤝 Paid partnership
The hardest part of AI filmmaking isn’t creating a beautiful shot. It’s remembering the one before it. As an AI filmmaker, I’ve found that generating beautiful images is often easier than maintaining visual continuity. Imagine a fifteen-second scene. A young Chinese woman stands by a window, wearing an ivory silk blouse and faded blue skirt. Afternoon light falls from the left as the camera approaches. The first shot looks perfect. But in the next, her embroidery changes, her hairstyle shifts, the lighting reverses, and her facial proportions differ. Both shots look convincing individually. Together, they no longer belong to the same film. This happens regularly in my work. We spend hours correcting costumes, faces, lighting, camera direction and anatomy. The problem isn’t necessarily the model. It’s the lack of reliable memory between tasks. What if an AI agent could remember the creative decisions behind every shot? I’ve been studying EverOS documentation, developed by EverMind. I haven’t independently tested EverOS Cloud. This explores how its documented memory architecture might address filmmaking challenges, not a hands-on review. A workflow I’d like to explore First, establish a character sheet: a 22-year-old Chinese woman with consistent facial features, hairstyle, ivory blouse and blue skirt. Define the visual language: soft afternoon light, muted colours, an 85mm aesthetic and restrained camera movement. Next, record approved shots and corrections. Perhaps the lighting was reversed, sleeves too short, or camera movement excessive. Normally, these decisions scatter across prompts, notes and conversations. My proposed workflow would preserve character sheets, shot notes and revision feedback as retrievable context. According to its documentation, EverOS supports memory retrieval, recorded task experiences called Cases, and reusable Skills. The value isn’t simply storing information. It’s retrieving relevant creative decisions before preparing another shot. For the next scene, the agent could reference established details and corrections. The blouse stays ivory. The skirt stays blue. Lighting and camera movement remain consistent. That’s the continuity this workflow aims to support. Memory cannot guarantee perfect generation. Models may still introduce inconsistencies. I would still inspect results, correct anatomy and make final artistic decisions. But reducing manual reconstruction of context could improve production efficiency. Memory should support the director, not replace the director. EverOS also documents a distinction between accumulated task experience and established procedures, with human oversight remaining important. Consistency doesn’t mean repetition. Changes in costume, lighting or composition may be intentional. An assistant should remember decisions without treating them as permanent rules. Creative judgement must remain human. My assessment AI filmmaking doesn’t simply need more image generators. It needs better ways to preserve the reasoning, corrections and decisions connecting shots. EverOS presents a documented approach to persistent agent memory worth investigating in AI production. Practical effectiveness still requires testing, particularly retrieval accuracy, setup complexity and reliable application of corrections. This remains a proposed workflow based on EverMind’s official documentation, not a claim of successful product testing. But the underlying challenge is real. A film isn’t a collection of beautiful frames. It’s the relationship between them. Perhaps one of the most valuable things an AI agent could learn is how to remember that relationship. EverOS: evermind.ai/everos Documentation: docs.evermind.ai/cloud/overv… During the campaign, subscribing to EverOS Pro gives you 3× the standard monthly usage allowance for six months. #EverMind #EverOS #AIFilmmaking
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Peter Saint retweeted
The hardest part of AI filmmaking isn’t creating a beautiful shot. It’s remembering the one before it. As an AI filmmaker, I’ve found that generating beautiful images is often easier than maintaining visual continuity. Imagine a fifteen-second scene. A young Chinese woman stands by a window, wearing an ivory silk blouse and faded blue skirt. Afternoon light falls from the left as the camera approaches. The first shot looks perfect. But in the next, her embroidery changes, her hairstyle shifts, the lighting reverses, and her facial proportions differ. Both shots look convincing individually. Together, they no longer belong to the same film. This happens regularly in my work. We spend hours correcting costumes, faces, lighting, camera direction and anatomy. The problem isn’t necessarily the model. It’s the lack of reliable memory between tasks. What if an AI agent could remember the creative decisions behind every shot? I’ve been studying EverOS documentation, developed by EverMind. I haven’t independently tested EverOS Cloud. This explores how its documented memory architecture might address filmmaking challenges, not a hands-on review. A workflow I’d like to explore First, establish a character sheet: a 22-year-old Chinese woman with consistent facial features, hairstyle, ivory blouse and blue skirt. Define the visual language: soft afternoon light, muted colours, an 85mm aesthetic and restrained camera movement. Next, record approved shots and corrections. Perhaps the lighting was reversed, sleeves too short, or camera movement excessive. Normally, these decisions scatter across prompts, notes and conversations. My proposed workflow would preserve character sheets, shot notes and revision feedback as retrievable context. According to its documentation, EverOS supports memory retrieval, recorded task experiences called Cases, and reusable Skills. The value isn’t simply storing information. It’s retrieving relevant creative decisions before preparing another shot. For the next scene, the agent could reference established details and corrections. The blouse stays ivory. The skirt stays blue. Lighting and camera movement remain consistent. That’s the continuity this workflow aims to support. Memory cannot guarantee perfect generation. Models may still introduce inconsistencies. I would still inspect results, correct anatomy and make final artistic decisions. But reducing manual reconstruction of context could improve production efficiency. Memory should support the director, not replace the director. EverOS also documents a distinction between accumulated task experience and established procedures, with human oversight remaining important. Consistency doesn’t mean repetition. Changes in costume, lighting or composition may be intentional. An assistant should remember decisions without treating them as permanent rules. Creative judgement must remain human. My assessment AI filmmaking doesn’t simply need more image generators. It needs better ways to preserve the reasoning, corrections and decisions connecting shots. EverOS presents a documented approach to persistent agent memory worth investigating in AI production. Practical effectiveness still requires testing, particularly retrieval accuracy, setup complexity and reliable application of corrections. This remains a proposed workflow based on EverMind’s official documentation, not a claim of successful product testing. But the underlying challenge is real. A film isn’t a collection of beautiful frames. It’s the relationship between them. Perhaps one of the most valuable things an AI agent could learn is how to remember that relationship. EverOS: evermind.ai/everos Documentation: docs.evermind.ai/cloud/overv… During the campaign, subscribing to EverOS Pro gives you 3× the standard monthly usage allowance for six months. #EverMind #EverOS #AIFilmmaking
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Turning task experience into reusable Skills sounds promising, but the quality of those Skills matters more than their quantity. I'd want to know how developers can inspect the source, verify the conditions, and update a Skill when the workflow changes. #EverMind #EverOS
🤝 Paid partnership
Meet EverOS Cloud. 💛 Your agent shouldn’t have to start from scratch every time a conversation ends. The preferences a user shared. The decisions made during a project. The approach that worked on a previous task. That context should be available when it matters again. EverOS Cloud gives your agents persistent memory through an API: • Remember users and retrieve relevant context across sessions • Turn task experience into Cases and reusable Skill memories • Bring conversations, images, and documents into your memory workflow You build the agent experience. We handle the memory infrastructure, so you can spend less time maintaining the stack and more time making your product useful. Subscribe to EverOS Cloud Pro and get 3× the standard Pro usage allowance for 6 months. Start here: everos.evermind.ai
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Peter Saint retweeted
Meet EverOS Cloud. 💛 Your agent shouldn’t have to start from scratch every time a conversation ends. The preferences a user shared. The decisions made during a project. The approach that worked on a previous task. That context should be available when it matters again. EverOS Cloud gives your agents persistent memory through an API: • Remember users and retrieve relevant context across sessions • Turn task experience into Cases and reusable Skill memories • Bring conversations, images, and documents into your memory workflow You build the agent experience. We handle the memory infrastructure, so you can spend less time maintaining the stack and more time making your product useful. Subscribe to EverOS Cloud Pro and get 3× the standard Pro usage allowance for 6 months. Start here: everos.evermind.ai
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One detail worth investigating is the difference between the API response and the later Memory Bank counts. If Case and Skill extraction happens asynchronously, exposing processing status and record provenance could make debugging agent-memory pipelines much easier. #EverMind #EverOS
🤝 Paid partnership
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Peter Saint retweeted
Agent Memory 我已经用了好久了。手头同时跑好几个 Agent 和 Bot,最烦的就是换个工具或者团队交接时,之前排障踩过的坑又要从头交代一遍。所以在公司我一直想把记忆做成云端共享,让大家做过的工作变成下一次任务所有人都能直接调用的经验。 这次体验 EverOS Cloud,刚好把团队云端记忆这块补齐了。不管你是主力用 Antigravity、Codex 这类开发 Agent,还是自己搭的 Grok Bot、Muse、Hermes 等 Personal Bot,都能通过支持的 API 或 MCP 等方式接入。对于手头跑着好几个 Bot 的玩家来说,只要是已写入或导入云端、且允许访问的内容,它都能在不同 Agent 之间把上下文串联起来,不用换个窗口或换个 Bot 就从头交代背景。 它的能力主要分成三块。第一块是 User Memory,用来记录用户偏好与长期上下文;第二块是文档知识库,把文档、PDF、表格等业务资料转成可检索的记忆;第三块是 Agent Memory,专门把每次任务过程记录成 Case,从重复经验里提炼出可复用的 Skill,结合团队 SOP 给后续任务提供参考,正式流程调整依然由团队审核。 录屏里我专门拿 User Memory 做了一个演示,直接从控制台复制官方的 Agent Prompt 丢给 Antigravity 自动配好 SDK,把一次接口排障的上下文写入 User Memory,新开任务前再调 API 把这条记忆检索出来用。 进后台的 Memory Bank 就能直接翻看存下来的每条记忆,记偏了随时能调 API 纠正、删除或评价,而且 Cloud 版和自托管版共享核心 API,后续做核心功能迁移时也很省心。 现在订阅 Pro,每月额度变 3 倍,连享 6 个月。 Cloud 注册体验 everos.evermind.ai #EverMind #EverOS
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Peter Saint retweeted
换一个 AI 编辑,上一轮的判断还能留下来吗? 一篇稿子写完,除了成稿,我还想留下审稿时用过的判断:哪些话有依据,哪些承诺不能写,换个产品后哪些做法值得参考。 这次与 EverMind 合作,我用 EverOS Cloud 给写作流程加了一份“交班笔记”:保存上一轮工作记录,按新任务检索,再交给没有旧对话的新编辑。 我用了两个虚构产品做演示,写作、AI 审稿和 Cloud 调用都实际执行。这个小实验想回答的是:一次任务里的判断,怎么交到下一次任务手里? 1. 把判断连同出处留下来 第一轮写“纸舟文档助手”的批量导入说明。初稿明确区分了三件事:导入完成、后续检索命中、文件内容正确。它们需要分别确认。 独立 AI 审稿没有提出必须修改的问题。原稿保留,助手另整理案例注释,注明做法及适用条件。随后把任务资料、稿件、审稿与案例注释、修订决定整理成四条消息,通过 API 提交给 EverOS Cloud;查询时看到了保存的记忆记录。 2. 带着新任务,找相关经验 第二轮换成“巡灯值班台”,写交接摘要功能。 搜索时,我把新产品、受众和写作目标说明白,还加了一句:“需要与这类写作任务相关的历史经验,不需要替新产品补充能力或代写最终稿。” Cloud 返回了上一轮记录,保留了“导入结果、检索命中与内容核对的区别”这一判断。 接下来,接入流程把返回的完整历史记录数组交给新编辑,同时标注“旧任务资料,不是新产品事实”,再提供本轮产品资料。 3. 看新编辑实际交出了什么 没有继承旧对话的新编辑,在成稿中写到: “摘要保留的是条目已有的最近更新时间,不会核实现场进展、更新任务状态,也不会判断旧条目是否已经失效。” 这条保存→检索→交给新编辑的链路跑通了。EverOS Cloud 负责保存与检索;写作、审稿、案例注释和上下文组装,是我们自己接的流程。 结果也有边界:这轮有无历史记忆的稿件,在独立盲评中没有实质差异,不能据此说质量更好或效率更高。返回记录经过压缩,还遗漏了案例注释中明确的适用与不适用条件,关键条件仍要回看原文。 所以,我会给下一轮任务留这样一份交班笔记: 任务:给谁写什么,帮助读者作什么判断? 依据:哪份资料、哪个版本,完整原稿在哪里? 审阅:谁审的,认可或修改了什么,为什么? 做法:下次遇到相似任务,可以检查什么? 边界:何时适用,何时不能套用,什么变化后要重核? 这是基于本次体验提出的记录建议,不代表这些字段已被 Cloud 完整保存并取回,也不证明产品会自动学习或生成 Skill。 对做产品说明、分析报告、Agent 工作流的人,可以先拿一份可公开的工作记录试接入:下一次任务来时,看看能取回什么、漏了什么、新编辑怎么使用。 可以注册 EverOS Cloud (everos.evermind.ai)开始体验;有持续用量需求,再评估 Pro。本次活动 Offer:开通 Pro,享半年 3 倍月度额度。 #EverMind #EverOS
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