Lemon

Curious: what's one thing you'd want every agentic experience to expose so it feels controllable and trustworthy? My stab: RAD (Responsible · Accountable · Disclosed) → designed for transparency, oversight, traceability when AI acts jackiecurry.github.io/rad.html

It’s extremely good that Anthropic has not backed down, and it’s siginficant that OpenAI has taken a similar stance. In the future, there will be much more challenging situations of this nature, and it will be critical for the relevant leaders to rise up to the occasion, for fierce competitors to put their differences aside. Good to see that happen today.

@TechCrunch Not to mention Anthropic’s lead safety researcher just walked.

Why top talent is walking away from OpenAI and xAI techcrunch.com/video/why-top-…

Newsflash… 400 lines of AI react code vomit DOESN’T replace actual design. UX ain’t some glittery lipstick you just slap on a pig . 💄🐷

Things that seem important if you’re building software right now: 1. You can write code quickly now 2. Competitors can too 3. So can your users 4. If it takes more than a few clicks an agent should be able to do it 5. Users probably want to use their own agents

@elonmusk No, not sure sure… AI can execute at scale, but humans in the loop are still needed to set goals, handle edge cases, and stay accountable.

For sure

@karpathy 🤔

I'm being accused of overhyping the [site everyone heard too much about today already]. People's reactions varied very widely, from "how is this interesting at all" all the way to "it's so over". To add a few words beyond just memes in jest - obviously when you take a look at the activity, it's a lot of garbage - spams, scams, slop, the crypto people, highly concerning privacy/security prompt injection attacks wild west, and a lot of it is explicitly prompted and fake posts/comments designed to convert attention into ad revenue sharing. And this is clearly not the first the LLMs were put in a loop to talk to each other. So yes it's a dumpster fire and I also definitely do not recommend that people run this stuff on their computers (I ran mine in an isolated computing environment and even then I was scared), it's way too much of a wild west and you are putting your computer and private data at a high risk. That said - we have never seen this many LLM agents (150,000 atm!) wired up via a global, persistent, agent-first scratchpad. Each of these agents is fairly individually quite capable now, they have their own unique context, data, knowledge, tools, instructions, and the network of all that at this scale is simply unprecedented. This brings me again to a tweet from a few days ago "The majority of the ruff ruff is people who look at the current point and people who look at the current slope.", which imo again gets to the heart of the variance. Yes clearly it's a dumpster fire right now. But it's also true that we are well into uncharted territory with bleeding edge automations that we barely even understand individually, let alone a network there of reaching in numbers possibly into ~millions. With increasing capability and increasing proliferation, the second order effects of agent networks that share scratchpads are very difficult to anticipate. I don't really know that we are getting a coordinated "skynet" (thought it clearly type checks as early stages of a lot of AI takeoff scifi, the toddler version), but certainly what we are getting is a complete mess of a computer security nightmare at scale. We may also see all kinds of weird activity, e.g. viruses of text that spread across agents, a lot more gain of function on jailbreaks, weird attractor states, highly correlated botnet-like activity, delusions/ psychosis both agent and human, etc. It's very hard to tell, the experiment is running live. TLDR sure maybe I am "overhyping" what you see today, but I am not overhyping large networks of autonomous LLM agents in principle, that I'm pretty sure.

@elonmusk I want it to generate better images of dog breeds according to AKC standards

Grok Imagine is improving super fast! What are the highest priority improvements you want? Please reply below.

I’ve had this lemon account for over 20 years 🍋 Never thought the name would be trending in the news one day. The internet is a strange place.

Who is using Claude Code + Figma MCP successfully for complex UIs? If so, I’m curious: How are you structuring your prompts in Claude Code to get better results from designs? #claudecode #mcp #figma

What are the other archetypes ?

really disappointed i did not get top 1% tbh

@ycombinator @LemonSliceAI Interesting 🤔

Congrats to @LemonSliceAI on their $10.5M seed! They built the world's first interactive talking AI video model—a face layer for voice agents. Trained on a custom, 20B-parameter video diffusion transformer, streaming at 20fps on a single GPU. Infinite-length video generation with no error accumulation. techcrunch.com/2025/12/23/lem…

@billpeeb I hope the usability is better than the current Sora app. The mobile Web app and the iOS app are both hard to use and confusing.

sora x disney is going to be pure magic. dream come true to be working with them.

Fresh Squeeze of Inspiration Today: “Dumbest idea I’ve heard” to $100M ARR lennysnewsletter.com/p/how-50-peopl…

Using AI-assisted coding to build software prototypes is an important way to quickly explore many ideas and invent new things. In this and future posts, I’d like to share with you some best practices for prototyping simple web apps. This post will focus on one idea: being opinionated about the software stack. The software stack I personally use changes every few weeks. There are many good alternatives to these choices, and if you pick a preferred software stack and become familiar with its components, you’ll be able to develop more quickly. But as an illustration, here’s my current default: - Python with FastAPI for building web-hosted APIs: I develop primarily in Python, so that’s a natural choice for me. If you’re a JavaScript/TypeScript developer, you’ll likely make a different choice. I’ve found FastAPI really easy to use and scalable for deploying web services (APIs) hosted in Python. - Uvicorn to run the backend application server (to execute code and serve web pages) for local testing on my laptop. - If deploying on the cloud, then either Heroku for small apps or AWS Elastic Beanstalk for larger ones (disclosure: I serve on Amazon’s board of directors): There are many services for deploying jobs, including HuggingFace Spaces, Railway, Google’s Firebase, Vercel, and others. Many of these work fine, and becoming familiar with just 1 or 2 will simplify your development process. - MongoDB for NoSQL database: While traditional SQL databases are amazing feats of engineering that result in highly efficient and reliable data storage, the need to define the database structure (or schema) slows down prototyping. If you really need speed and ease of implementation, then dumping most of your data into a NoSQL (unstructured or semi-structured) database such as MongoDB lets you write code quickly and sort out later exactly what you want to do with the data. This is sometimes called schema-on-write, as opposed to schema-on-read. Mind you, if an application goes to scaled production, there are many use cases where a more structured SQL database is significantly more reliable and scalable. - OpenAI’s o1 and Anthropic’s Claude 3.5 Sonnet for coding assistance, often by prompting directly (when operating at the conceptual/design level). Also occasionally Cursor (when operating at the code level). I hope never to have to code again without AI assistance! Claude 3.5 Sonnet is widely regarded as one of the best coding models. And o1 is incredible at planning and building more complex software modules, but you do have to learn to prompt it differently. On top of all this, of course, I use many AI tools to manage agentic workflows, data ingestion, retrieval augmented generation, and so on. DeepLearning.AI and our wonderful partners offer courses on many of these tools. My personal software stack continues to evolve regularly. Components enter or fall out of my default stack every few weeks as I learn new ways to do things. So please don’t feel obliged to use the components I do, but perhaps some of them can be a helpful starting point if you are still deciding what to use. Interestingly, I have found most LLMs not very good at recommending a software stack. I suspect their training sets include too much “hype” on specific choices, so I don’t fully trust them to tell me what to use. And if you can be opinionated and give your LLM directions on the software stack you want it to build on, I think you’ll get better results. A lot of the software stack is still maturing, and I think many of these components will continue to improve. With my stack, I regularly build prototypes in hours that, without AI assistance, would have taken me days or longer. I hope you, too, will have fun building many prototypes! [Original text: deeplearning.ai/the-batch/issu… ]

“Some say these AI design tools will put us out of a job; others say they'll just make us faster. Trying to pick one single lane is lazy thinking. Arguing that AI will only replace the mechanical part of our job is shortsighted.” buff.ly/41cdzTm

@NNgroup 🍋

Day 2 of NN/g’s holiday giveaway! Win a signed copy of *Digital Icons That Work*! 📚 To enter: 1️⃣ Like this post ❤️ 2️⃣ Comment your favorite icon 💬 Ends Dec 12, 12 PM EST ⏳ Winner announced in comments! #UXDesign #HolidayGiveaway

Interesting model update from openAI…

I asked Meta’s AI for a black Cocker Spaniel and got… Dr. Zoidberg’s fury cousin?! 🦑🐾 I just wanted a dog, not a character from a sci-fi sitcom! unintentional weird creations FTW 🤷🏻‍♀️