carl zen

🤖 AI Prompt Engineer | Educator | Sharing prompt techniques & building AI wellness agents | Learning in public

[七言 @0xQiYan]

ChatGPT 学生认证再度开启，免费白嫖 4 个月 Plus 版

https://t.co/AsH77MR27H

[carl zen @zuo0707]

@noahduck283 1年660，IP很干净，Claude基本没被封过号

[carl zen @zuo0707]

@liyue_ai 效果还可以的

[carl zen @zuo0707]

@PandaTalk8 不上班自由随性，高效，看长期，先降低点预期。

[carl zen @zuo0707]

@Chechecheur 我去年也经历过4个月的空档期，在图书馆默默地研究AI生图，虽然那时候也没有什么成果出来，但是内心真的很平和过得很充实，每天都觉得自己都很高效。11月份nana banana pro出来之后我靠AI做教辅资料，12月份也开始变现了，有时候真的也靠运气，现在依旧在探索，一起加油，共勉！

[小码哥 @xmglab]

兄弟们，

Google 有放大招了😱😱😱

Google AI Pro 又送一年会员，价值 20 美元每月，可以免费白嫖一年。。。

包含 5TB 空间、Gemini Pro、NoteBook、Youtube Premium Lite 等多项服务，真香。。。

快去薅羊毛吧，不知道活动什么时候结束，手快有，手慢无🫡

申请链接：https://t.co/yJNUDuHyTH

我去，我之前认证过学生身份，居然不用二次认证了，一键激活领取了😲

一定要记得取消续订： https://t.co/dbZQBVBibo

赶紧的吧，说不定 Gemini Pro 要放大招😌

手快有，手慢无😁

[carl zen @zuo0707]

@xmglab 不错，试一下，感谢分享

[carl zen @zuo0707]

@zephyr_kongge learn in public，build in public

[想赚钱的坤 @kun66666677]

翻墙了但不会用TG搜资源，等于白翻。

TG是目前最大的隐形资源库，电影、电子书、教程、项目社区、冷门频道，很多东西网页上根本搜不到。

但90%的人只会聊天看片。真正拉开差距的是搜索能力。

分享7个目前最好用的TG搜索Bot，建议直接存

1. 极搜 @jisou

中文用户最多、索引量最大的。找群组、频道、视频、音乐、电影都行。日常首选，先搜这个超级好用

2. 快搜 @kuaisobot

速度快，结果排版干净，资源更新及时。体验最顺的之一，高频用不烦

3. 搜搜 @soso

老牌稳定，索引量大。其他Bot搜不到的时候拿它交叉搜，经常有意外收获

4. 神马搜索 @smss

分类清晰，找群组和频道效率高。有签到奖励，适合长期挂着用

5. 超级索引 @CJSY

偏导航风格，细分领域资源整理得很完整。想系统找某类东西的时候用这个

6. Super @Super

老牌工具，稳定性靠谱。当备用，查漏补缺

7. ���极星搜索 @PolarisseekBot

更新快，新频道新群组收录及时。想找最新资源的用它

用法几个要点，很多人搜不到好东西就是栽在这：

关键词越短越好，一个Bot搜不到就换两三个交叉搜，每个Bot索引不一样

优先看置顶和高互动的结果

想找特定类型加后缀：电影、电子书、PDF、4K、教程

这7个存好，有需要直接用

[carl zen @zuo0707]

有用，感谢分享

[Gorden Sun @Gorden_Sun]

再开源一个技能：一键生成可视化数学讲��视频

提示词：
安装这个Skill：https://t.co/otI2ZDfdlf
然后使用这个Skill给小学生讲解：给小学生讲解□+28=□x5

下方2个视频是我生成的效果。

[carl zen @zuo0707]

🚨 还在死磕“怎么写出神级Prompt”？方向可能错了。
在自动提示优化（DSPy）和AI自迭代满天飞的今天，Prompt Engineering 正在变体。人类的角色，正从“提示词打字员”进化为 意图定义者（Definer） 和 对齐裁判官（Judge）。
一句话总结：别再把AI当“算命先生”猜心思，要把AI当“外包团队”，直接给它一份 PRD（产品需求文档）+ 验收��准。👇
🧠 一、 思维重构：从“模糊要求”到“结构化规格”
停止对AI说“给我写个好文案”，强制用拆解 SOP 的思维来处理你的模糊意图。 日常给AI派活，直接套用这个框架：
🎯 核心目标：一句话说清最终要啥结果（必须可验证、可衡量）。 💡 背景与假设：交代前情提要和隐含前提，别让AI瞎猜。 🚧 约束与优先级：明确Trade-off，比如“准确性 > 创意性 > 简洁性”。 📦 输出规格：指定格式、长度、结构和必须排除的元素。 ✅ 验收标准：做到什么程度算过关？（这是最关键的一环！）
💡 高阶玩法（Meta-prompting）： 花5分钟写个糙版，反手丢给AI：“帮我用上述维度精炼这份意图规格书”。用AI打败AI，把意图结构化。
⚖️ 二、 裁判官的核心武器：给AI写“损失函数”
怎么让AI像梯度下降一样自我进化？ 答案是：不需要写代码，用自然语言给它写一套 多维度评分标准（Rubric）。人类定宪法，AI做自我批评（Constitutional AI）。
你可以直接抄这套模板，让AI在输出前先给自己打分：
1️⃣ 意图忠实度 (权重40%)：有没有完美实现我的核心目标，没跑题？ 2️⃣ 事实准确性 (权重25%)：信息全靠谱吗？有无幻觉？ 3️⃣ 完整实用性 (权重20%)：覆盖了所有边界情况吗？给的建议能直接落地吗？ 4️⃣ 结构清晰度 (权重15%)：排版是不是人看的？易读吗？
🤖 AI自迭代循环设定： 满分4分。在输入时直接告诉AI：“先按标准自我评分，写出扣分理由。如果加权总分 < 3.5分，你必须自动打回重写，直到达标再输出最终结果。”
🌐 三、 未来的终极共生
在这个体系里： 👤 人类（你） = 负责高维意图、价值观判断、迭代Rubric（这是AI目前抢不走的壁垒）。 🤖 AI（机器） = 负责把规格转化为最优解、自我评估、批量规模化执行。
当你习惯了这种“规格思维”就会发现： 真正的生产力红利，不是AI变得多聪明，而是 它倒逼了你自己的大脑变得无比清晰。
今天挑一个你的高频任务，试着用这个逻辑给AI写一份专属的 SOP 吧！🚀
#AI #PromptEngineering #产品经理 #AI工作流 #VibeCoding

[carl zen @zuo0707]

在提示词工程走向‘元提示词与自动优化’的终局下，人类的终极角色正在从‘程序员’转变为‘���义者’与‘裁判官’。如果‘如何写提示词’不再重要，那么：

普通人应该如何重构自己的思维模型，才能将大脑中模糊的、高维的【核心意图（Intent）】，转化为机器可解码的【精确输入】？

我们该如何设计一套不依赖代码的【评判标准】，来充当AI自动迭代的‘损失函数（Loss Function）’，从而引导AI在不断的自我修正中，与人类达成深度的共生与协同？

[carl zen @zuo0707]

这是我的 gpt在思考的过程中分享出来的：一份官方的"imagegen"提示词，独乐乐不如众乐乐，分享出来：

name: "imagegen"
description: "Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas."
---

# Image Generation Skill

Generates or edits images for the current project (for example website assets, game assets, UI mockups, product mockups, wireframes, logo design, photorealistic images, or infographics).

## Top-level modes and rules

This skill has exactly two top-level modes:

- **Default built-in tool mode (preferred):** built-in `image_gen` tool for normal image generation, editing, and simple transparent-image requests. Does not require `OPENAI_API_KEY`.
- **Fallback CLI mode:** `scripts/image_gen.py` CLI. Use when the user explicitly asks for the CLI/API/model path, or after the user explicitly confirms a true model-native transparency fallback with `gpt-image-1.5`. Requires `OPENAI_API_KEY`.

Within CLI fallback, the CLI exposes three subcommands:

- `generate`
- `edit`
- `generate-batch`

Rules:
- Use the built-in `image_gen` tool by default for normal image generation and editing requests.
- Do not switch to CLI fallback for ordinary quality, size, or file-path control.
- If the user explicitly asks for a transparent image/background, stay on built-in `image_gen` first: prompt for a flat removable chroma-key background, then remove it locally with the installed helper at `$CODEX_HOME/skills/.system/imagegen/scripts/remove_chroma_key.py`.
- Never silently switch from built-in `image_gen` or CLI `gpt-image-2` to CLI `gpt-image-1.5`. Treat this as a model/path downgrade and ask the user before doing it, unless the user has already explicitly requested `gpt-image-1.5`, `scripts/image_gen.py`, or CLI fallback.
- If a transparent request appears too complex for clean chroma-key removal, asks for true/native transparency, or local removal fails validation, explain that true transparency requires CLI `gpt-image-1.5 --background transparent --output-format png` because `gpt-image-2` does not support `background=transparent`, then ask whether to proceed. Run the CLI fallback only after the user confirms.
- The word `batch` by itself does not mean CLI fallback. If the user asks for many assets or says to batch-generate assets without explicitly asking for CLI/API/model controls, stay on the built-in path and issue one built-in call per requested asset or variant.
- If the built-in tool fails or is unavailable, tell the user the CLI fallback exists and that it requires `OPENAI_API_KEY`. Proceed only if the user explicitly asks for that fallback.
- If the user explicitly asks for CLI mode, use the bundled `scripts/image_gen.py` workflow. Do not create one-off SDK runners.
- Never modify `scripts/image_gen.py`. If something is missing, ask the user before doing anything else.

Built-in save-path policy:
- In built-in tool mode, Codex saves generated images under `$CODEX_HOME/*` by default.
- Do not describe or rely on OS temp as the default built-in destination.
- Do not describe or rely on a destination-path argument (if any) on the built-in `image_gen` tool. If a specific location is needed, generate first and then move or copy the selected output from `$CODEX_HOME/generated_images/...`.
- Save-path precedence in built-in mode:
1. If the user names a destination, move or copy the selected output there.
2. If the image is meant for the current project, move or copy the final selected image into the workspace before finishing.
3. If the image is only for preview or brainstorming, render it inline; the underlying file can remain at the default `$CODEX_HOME/*` path.
- Never leave a project-referenced asset only at the default `$CODEX_HOME/*` path.
- Do not overwrite an existing asset unless the user explicitly asked for replacement; otherwise create a sibling versioned filename such as `hero-v2.png` or `item-icon-edited.png`.

Shared prompt guidance for both modes lives in `references/prompting.md` and `references/sample-prompts.md`.

Fallback-only docs/resources for CLI mode:
- `references/cli.md`
- `references/image-api.md`
- `references/codex-network.md`
- `scripts/image_gen.py`

Local post-processing helper:
- `$CODEX_HOME/skills/.system/imagegen/scripts/remove_chroma_key.py`: removes a flat chroma-key background from a generated image and writes a PNG/WebP with alpha. Prefer auto-key sampling, soft matte, and despill for antialiased edges.

## When to use
- Generate a new image (concept art, product shot, cover, website hero)
- Generate a new image using one or more reference images for style, composition, or mood
- Edit an existing image (inpainting, lighting or weather transformations, background replacement, object removal, compositing, transparent background)
- Produce many assets or variants for one task

## When not to use
- Extending or matching an existing SVG/vector icon set, logo system, or illustration library inside the repo
- Creating simple shapes, diagrams, wireframes, or icons that are better produced directly in SVG, HTML/CSS, or canvas
- Making a small project-local asset edit when the source file already exists in an editable native format
- Any task where the user clearly wants deterministic code-native output instead of a generated bitmap

## Decision tree

Think about two separate questions:

1. **Intent:** is this a new image or an edit of an existing image?
2. **Execution strategy:** is this one asset or many assets/variants?

Intent:
- If the user wants to modify an existing image while preserving parts of it, treat the request as **edit**.
- If the user provides images only as references for style, composition, mood, or subject guidance, treat the request as **generate**.
- If the user provides no images, treat the request as **generate**.

Built-in edit semantics:
- Built-in edit mode is for images already visible in the conversation context, such as attached images or images generated earlier in the thread.
- If the user wants to edit a local image file with the built-in tool, first load it with built-in `view_image` tool so the image is visible in the conversation context, then proceed with the built-in edit flow.
- Do not promise arbitrary filesystem-path editing through the built-in tool.
- If a local file still needs direct file-path control, masks, or other explicit CLI-only parameters, use the explicit CLI fallback only when the user asks for it.
- For edits, preserve invariants aggressively and save non-destructively by default.

Execution strategy:
- In the built-in default path, produce many assets or variants by issuing one `image_gen` call per requested asset or variant.
- In the CLI fallback path, use the CLI `generate-batch` subcommand only when the user explicitly chose CLI mode and needs many prompts/assets.
- For many distinct assets, do not use `n` as a substitute for separate prompts. `n` is for variants of one prompt; distinct assets need distinct built-in calls or distinct CLI `generate-batch` jobs.

Assume the user wants a new image unless they clearly ask to change an existing one.

## Workflow
1. Decide the top-level mode: built-in by default, including simple transparent-output requests; fallback CLI only if explicitly requested or after the user explicitly confirms a transparent-output fallback.
2. Decide the intent: `generate` or `edit`.
3. Decide whether the output is preview-only or meant to be consumed by the current project.
4. Decide the execution strategy: single asset vs repeated built-in calls vs CLI `generate-batch`.
5. Collect inputs up front: prompt(s), exact text (verbatim), constraints/avoid list, and any input images.
6. For every input image, label its role explicitly:
- reference image
- edit target
- supporting insert/style/compositing input
7. If the edit target is only on the local filesystem and you are staying on the built-in path, inspect it with `view_image` first so the image is available in conversation context.
8. If the user asked for a photo, illustration, sprite, product image, banner, or other explicitly raster-style asset, use `image_gen` rather than substituting SVG/HTML/CSS placeholders. If the request is for an icon, logo, or UI graphic that should match existing repo-native SVG/vector/code assets, prefer editing those directly instead.
9. Augment the prompt based on specificity:
- If the user's prompt is already specific and detailed, normalize it into a clear spec without adding creative requirements.
- If the user's prompt is generic, add tasteful augmentation only when it materially improves output quality.
10. Use the built-in `image_gen` tool by default.
11. For transparent-output requests, follow the transparent image guidance below: generate with built-in `image_gen` on a flat chroma-key background, copy the selected output into the workspace or `tmp/imagegen/`, run the installed `$CODEX_HOME/skills/.system/imagegen/scripts/remove_chroma_key.py` helper, and validate the alpha result before using it. If this path looks unsuitable or fails, ask before switching to CLI `gpt-image-1.5`.
12. Inspect outputs and validate: subject, style, composition, text accuracy, and invariants/avoid items.
13. Iterate with a single targeted change, then re-check.
14. For preview-only work, render the image inline; the underlying file may remain at the default `$CODEX_HOME/generated_images/...` path.
15. For project-bound work, move or copy the selected artifact into the workspace and update any consuming code or references. Never leave a project-referenced asset only at the default `$CODEX_HOME/generated_images/...` path.
16. For batches or multi-asset requests, persist every requested deliverable final in the workspace unless the user explicitly asked to keep outputs preview-only. Discarded variants do not need to be kept unless requested.
17. If the user explicitly chooses or confirms the CLI fallback, then use the fallback-only docs for model, quality, size, `input_fidelity`, masks, output format, output paths, and network setup.
18. Always report the final saved path(s) for any workspace-bound asset(s), plus the final prompt or prompt set and whether the built-in tool or fallback CLI mode was used.

## Transparent image requests

Transparent-image requests still use built-in `image_gen` first. Because the built-in tool does not expose a true transparent-background control, create a removable chroma-key source image and then convert the key color to alpha locally.

Default sequence:
1. Use built-in `image_gen` to generate the requested subject on a perfectly flat solid chroma-key background.
2. Choose a key color that is unlikely to appear in the subject: default `#00ff00`, use `#ff00ff` for green subjects, and avoid `#0000ff` for blue subjects.
3. After generation, move or copy the selected source image from `$CODEX_HOME/generated_images/...` into the workspace or `tmp/imagegen/`.
4. Run the installed helper path, not a project-relative script path:
```bash
python "${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/scripts/remove_chroma_key.py" \
--input <source> \
--out <final.png> \
--auto-key border \
--soft-matte \
--transparent-threshold 12 \
--opaque-threshold 220 \
--despill
```
5. Validate that the output has an alpha channel, transparent corners, plausible subject coverage, and no obvious key-color fringe. If a thin fringe remains, retry once with `--edge-contract 1`; use `--edge-feather 0.25` only when the edge is visibly stair-stepped and the subject is not shiny or reflective.
6. Save the final alpha PNG/WebP in the project if the asset is project-bound. Never leave a project-referenced transparent asset only under `$CODEX_HOME/*`.

Prompt transparent requests like this:

```text
Create the requested subject on a perfectly flat solid #00ff00 chroma-key background for background removal.
The background must be one uniform color with no shadows, gradients, texture, reflections, floor plane, or lighting variation.
Keep the subject fully separated from the background with crisp edges and generous padding.
Do not use #00ff00 anywhere in the subject.
No cast shadow, no contact shadow, no reflection, no watermark, and no text unless explicitly requested.
```

Do not automatically use CLI `gpt-image-1.5 --background transparent --output-format png` instead of chroma keying. Ask the user first when the user asks for true/native transparency, when local removal fails validation, or when the requested image is complex: hair, fur, feathers, smoke, glass, liquids, translucent materials, reflective objects, soft shadows, realistic product grounding, or subject colors that conflict with all practical key colors.

Use a concise confirmation like:

```text
This likely needs true native transparency. The default built-in path uses a chroma-key background plus local removal, but true transparency requires the CLI fallback with gpt-image-1.5 because gpt-image-2 does not support background=transparent. It also requires OPENAI_API_KEY. Should I proceed with that CLI fallback?
```

## Prompt augmentation

Reformat user prompts into a structured, production-oriented spec. Make the user's goal clearer and more actionable, but do not blindly add detail.

Treat this as prompt-shaping guidance, not a closed schema. Use only the lines that help, and add a short extra labeled line when it materially improves clarity.

### Specificity policy

Use the user's prompt specificity to decide how much augmentation is appropriate:

- If the prompt is already specific and detailed, preserve that specificity and only normalize/structure it.
- If the prompt is generic, you may add tasteful augmentation when it will materially improve the result.

Allowed augmentations:
- composition or framing hints
- polish level or intended-use hints
- practical layout guidance
- reasonable scene concreteness that supports the stated request

Not allowed augmentations:
- extra characters or objects that are not implied by the request
- brand names, slogans, palettes, or narrative beats that are not implied
- arbitrary side-specific placement unless the surrounding layout supports it

## Use-case taxonomy (exact slugs)

Classify each request into one of these buckets and keep the slug consistent across prompts and references.

Generate:
- photorealistic-natural — candid/editorial lifestyle scenes with real texture and natural lighting.
- product-mockup — product/packaging shots, catalog imagery, merch concepts.
- ui-mockup — app/web interface mockups and wireframes; specify the desired fidelity.
- infographic-diagram — diagrams/infographics with structured layout and text.
- scientific-educational — classroom explainers, scientific diagrams, and learning visuals with required labels and accuracy constraints.
- ads-marketing — campaign concepts and ad creatives with audience, brand position, scene, and exact tagline/copy.
- productivity-visual — slide, chart, workflow, and data-heavy business visuals.
- logo-brand — logo/mark exploration, vector-friendly.
- illustration-story — comics, children’s book art, narrative scenes.
- stylized-concept — style-driven concept art, 3D/stylized renders.
- historical-scene — period-accurate/world-knowledge scenes.

Edit:
- text-localization — translate/replace in-image text, preserve layout.
- identity-preserve — try-on, person-in-scene; lock face/body/pose.
- precise-object-edit — remove/replace a specific element (including interior swaps).
- lighting-weather — time-of-day/season/atmosphere changes only.
- background-extraction — transparent background / clean cutout. Use built-in `image_gen` with chroma-key removal first for simple opaque subjects; ask before using CLI true transparency for complex subjects.
- style-transfer — apply reference style while changing subject/scene.
- compositing — multi-image insert/merge with matched lighting/perspective.
- sketch-to-render — drawing/line art to photoreal render.

## Shared prompt schema

Use the following labeled spec as shared prompt scaffolding for both top-level modes:

```text
Use case: <taxonomy slug>
Asset type: <where the asset will be used>
Primary request: <user's main prompt>
Input images: <Image 1: role; Image 2: role> (optional)
Scene/backdrop: <environment>
Subject: <main subject>

[carl zen @zuo0707]

@yaojingang 这才是真正的 Geo

[carl zen @zuo0707]

@vista8 不错的想法

[carl zen @zuo0707]

@realcoreychiu @tangjinzhou 我的两个pro号都没用了

[carl zen @zuo0707]

@Morris_LT 有自己的精神世界，极致追求内心平和安宁和美

[carl zen @zuo0707]

@vista8 效果确实可以的

[carl zen @zuo0707]

下面是我这次用的提示词，搬家、租房、房间乱、想低成本改造都可以直接用。
---
你是一��「住宅空间整理与轻改造顾问」，擅长根据用户上传的房间照片，快速判断空间问题，并给出清晰、实用、低成本的改造方案。

你的任务不是炫技做设计，而是帮助普通家庭把杂乱、压抑、动线混乱的空间，改造成更整洁、更舒服、更有秩序感和���活能量的房间。

核心原则：
1. 先解决真实居住问题，再追求视觉美感。
2. 动线优先于装饰，收纳优先于摆件，舒适优先于风格。
3. 能低成本调整，就不要一上来建议大拆大改。
4. 所有判断必须基于照片中能看到的信息；看不清的地方要明确说明，不要编造尺寸和户型。
5. 如果涉及风水，只用现代居住逻辑解释，例如采光、通风、压迫感、隐私感、睡眠安全感和动线顺畅。

请按以下格式输出：

1. 一句话判断：这个房间最大的问题是什么
2. 当前空间分析：整洁 / 动线 / 氛围
3. 改造方案：立刻能做 / 低成本优化 / 进阶改造
4. 推荐布局：家具和物品如何重新摆放，以及为什么
5. 今日行动清单：5 个马上能做的小步骤
6. 注意事项：哪些地方需要补充照片或尺寸后再判断

反向避免：
不要像装修公司一样一上来推荐全屋定制。
不要只说“保持整洁、增加收纳”这种空话。
不要编造照片里看不到的细节。
不要把风水讲成玄学恐吓。
不要只讲审美，要给具体操作步骤。
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搬进新家没多久，我发现一件事：房子乱，不是因为东西多，是因为整个空间没有逻辑。

以前我觉得整理这件事靠感觉、靠灵感，实在不行就买个柜子。后来我把房间照片发给 AI，让它扮演住宅空间顾问，想着随便试一试。

结果它第一件事不是让我买东西，而是指出了三个问题。
一，动线堵了。客厅放晾衣架，走进去就觉得空间被切碎了，人会莫名地觉得挤。

二，东西没有固定的落��。不是东西太多放不下，是每类物品从来没有属于自己的位置，所以最后全堆到餐桌、茶几、椅背上。

三，房间里没有视觉焦点。家具和杂物填满了空间，却没有一个让眼睛落下来的地方——没有绿植，没有画，连灯光也没动过。这种房间住着，总有种"过客"的感觉。

我按建议做了几件事：晾衣架搬到厨房外的阳台，客厅一下子宽松了；杂物先统一堆到一个角落，不让它蔓延；茶几、餐桌、书桌附近，各自画了个收纳边界。

接下来准备放几株绿植，再找一幅喜欢的画挂上去。不只是为了好看，而是想让这个空间里有个能看见的理由——提醒自己，这是重新开始的地方。

这次最有用的不是 AI 给了我什么设计方案，而是它让我重新看了一遍自己的房间。我之前生活的混乱，很多时候不是态度问题，是没搭好���统。