I wanted to share something I built over the last few weeks: isometric.nyc is a massive isometric pixel art map of NYC, built with nano banana and coding agents. I didn't write a single line of code.

Introducing tiny.london A massive isometric pixel map of London, based on amazing NYC demo by @_coenen from 9 months ago My agent created the dataset, finetuned qwen image LoRA adapter, did a multiday inference run and built an efficient tile viewer!

The most emotive model meets the most expressive body Eleven v4 + Reachy Mini with shared emotion tags makes the result feel even more immersive

nice

We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks: Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better: Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better: Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better: Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work! In summary: - As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding. - Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.

Question: when everyone’s agent is looking for the same reservation, ticket, cancellation, who is gonna get it? Is this like high frequency trading where you want to colocate next to the tix/rez servers? Does this become micro bidding using stablecoin contracts? So much fun

很喜欢这种把旅行照片收进田野笔记里的方式。 照片留住现场，旁边那枚略微缺墨的彩色橡皮章，只刻下最值得记住的轮廓。 树、穹顶、海岸都没有被讲满，反而像旅途中随手盖下的一页，安静，又很有地方感。 prompt： 请将我上传的每一张照片分别制作成一张独立的「橡皮章旅行田野笔记海报」，每张照片单独输出，不要多图拼贴。 整体采用4:3横版构图，将画面划分为左右两个区域，但不要绘制明显的分隔线。 左侧约占画面58%，忠实保留原始照片。准确保持主体身份、地形、建筑、植物、人物、空间关系、自然光影、真实质感和原有色彩氛围，仅进行克制的艺术出版物级摄影调色，并加入极轻微的细腻胶片颗粒。为适配版式可以自然裁切，但不得拉伸、扭曲、移动、替换或重新绘制主体。 右侧约占画面42%，使用温暖的米白色旧纸作为背景。纸张具有细微纤维、天然颗粒、轻微使用痕迹和哑光触感，同时保留大面积未经印刷的纸张留白，让空白成为版式的重要组成部分。 分析原始照片，从中提取最具地点辨识度的主体轮廓、建筑结构、地形走势、植物姿态、道路、水岸线或其他关键视觉关系，将其压缩成一枚小型多色橡皮章图像。 不要逐项复制照片中的全部内容。只保留能够让人一眼认出原始地点、主体和场景关系所必需的最少信息。删除人群、车辆、密集窗户、重复建筑、细碎植被、装饰构件和无关背景。 章印位于右侧纸张区域的中下部，整体只占右侧区域高度的约30%—38%，周围必须保留充足留白。章印不能放大成普通插画、完整风景画或品牌Logo。 根据原图构图决定章印的组织方式： - 标志性建筑：保留最有辨识度的外轮廓、屋顶、穹顶、拱门、塔楼或主要结构。 - 山地聚落：将建筑压缩成沿地形排列的少量阶梯状色块。 - 海岸风景：保留山体走势、聚落层级、岸线和少量断续水纹。 - 城市远景：保留主要天际线、一个标志性建筑及一两层远山。 - 自然景观：保留主要山体、树木、水岸或道路的方向关系。 - 近景遮挡物：如果其对原图叙事重要，可作为前景章印轮廓保留。 从原始照片中提取2—4种专色油墨。优先使用炭黑、深绿、砖红、赭黄、灰蓝、灰褐等经过降低饱和度的颜色，但不得强行套用固定色盘。保留原照片最有辨识度的色彩性格，仅允许一个小面积颜色作为视觉强调。 每一种颜色都要呈现为分别手工盖印的效果： 真实橡皮章雕刻纹理、手工刻痕、粗细不均的排线、轮廓缺口、断裂边缘、干燥缺墨、纸张透底、颗粒状油墨、压力不均、局部重影，以及约1—2毫米的轻微套色偏移。 不同色层之间允许出现自然错位，边缘不能数字化平滑。印迹应像真正刻制后压在旧纸上的橡皮章，而不是套用滤镜的照片、平滑矢量插画或线稿Logo。 根据照片中的地点、主题和视觉意象生成文字： 地点英文名称 No. 编号 三个简短英文关键词 西元年份 文字放在章印下方或邻近留白处，使用小型、克制、略带机械误差的打字机字体。排版应像旅行者的田野记录，而不是广告标题。确保所有文字拼写准确，不添加无关标语、品牌或装饰性文案。 整体气质像建筑师、旅行作家或自然观察者保存的田野笔记：安静、克制、触感真实、地域明确、带有手工误差和收藏感。照片负责记录现场，章印负责留下记忆中最值得辨认的部分。 Avoid：明显的中央分隔线、圆形印章、中文红色印章、邮票齿孔、蜡封、贴纸拼贴、旅游纪念品模板、平滑矢量Logo、通用城市图标、完整复制所有建筑、密集刻画、儿童手工感、卡通风格、3D渲染、塑料质感、光滑数字渐变、过度饱和、过多文字、装饰堆积，以及重绘或改变左侧原始照片。

One of my favourite ElevenLabs MCP features: transcribe anything, then immediately ask questions about it. I use it to get through long files or improve presentations & demos. Quick example via Claude + our Speech to Text model: summarizing a Richard Feynman lecture!

Introducing the ElevenLabs MCP, now available in Claude. Your team can manage your voice and chat agents where you already work - review recent performance, create new agents, update configurations, and even estimate LLM costs before changes go live.