basselt @basselt

@sama Right after this video came out that is... x.com/maxleedev/stat…

maxleedev @maxleedev

12 months ago

current chat interfaces suck, so i built a canvas for llms

477 828 16K 2.2M 9K

I’d like to share a tip for getting more practice building with AI — that is, either using AI building blocks to build applications or using AI coding assistance to create powerful applications quickly: If you find yourself with only limited time to build, reduce the scope of your project until you can build something in whatever time you do have. If you have only an hour, find a small component of an idea that you're excited about that you can build in an hour. With modern coding assistants like Anthropic’s Claude Code (my favorite dev tool right now), you might be surprised at how much you can do even in short periods of time! This gets you going, and you can always continue the project later. To become good at building with AI, most people must (i) learn relevant techniques, for example by taking online AI courses, and (ii) practice building. I know developers who noodle on ideas for months without actually building anything — I’ve done this too! — because we feel we don’t have time to get started. If you find yourself in this position, I encourage you to keep cutting the initial project scope until you identify a small component you can build right away. Let me illustrate with an example — one of my many small, fun weekend projects that might never go anywhere, but that I’m glad I did. Here’s the idea: Many people fear public speaking. And public speaking is challenging to practice, because it's hard to organize an audience. So I thought it would be interesting to build an audience simulator to provide a digital audience of dozens to hundreds of virtual people on a computer monitor and let a user practice by speaking to them. One Saturday afternoon, I found myself in a coffee shop with a couple of hours to spare and decided to give the audience simulator a shot. My familiarity with graphics coding is limited, so instead of building a complex simulator of a large audience and writing AI software to simulate appropriate audience responses, I decided to cut scope significantly to (a) simulating an audience of one person (which I could replicate later to simulate N persons), (b) omitting AI and letting a human operator manually select the reaction of the simulated audience (similar to Wizard of Oz prototyping), and (c) implementing the graphics using a simple 2D avatar. Using a mix of several coding assistants, I built a basic version in the time I had. The avatar could move subtly and blink, but otherwise it used basic graphics. Even though it fell far short of a sophisticated audience simulator, I am glad I built this. In addition to moving the project forward and letting me explore different designs, it advanced my knowledge of basic graphics. Further, having this crude prototype to show friends helped me get user feedback that shaped my views on the product idea. I have on my laptop a list of ideas of things that I think would be interesting to build. Most of them would take much longer than the handful of hours I might have to try something on a given day, but by cutting their scope, I can get going, and the initial progress on a project helps me decide if it’s worth further investment. As a bonus, hacking on a wide variety of applications helps me practice a wide range of skills. But most importantly, this gets an idea out of my head and potentially in front of prospective users for feedback that lets the project move faster. [Original text: deeplearning.ai/the-batch/issu… ]

This guy literally shows how to build AI apps with zero code using Google's Firebase Studio

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with @agi_inc and taught by @divgarg and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape DeepLearning.AI's website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here: deeplearning.ai/short-courses/…

🔍 Shandu AI Research A system that generates cited reports using LangGraph workflows. Features intelligent scraping, multi-source synthesis, and parallel processing to streamline research tasks. Check out this research tool 📚 github.com/jolovicdev/sha…

@minchoi So true

@nutlope @YoussefUiUx @nutlope can't find yet a prompt that helps, any application this tool generates for me, ends with crash

Interesting observation: More descriptive prompts typically do much better on LlamaCoder. Going to add an additional LLM call for prompt rewriting (e.g take "build a mortgage calculator" and rewrite it into something a lot more descriptive) before passing it into the final LLM!

@stephterroir Hey, so you are no longer living at Arta building?

@nutlope Great work 👏

@alexshyba Good luck 🤞

@bertsesame Raam like to watch sesame street when he was little, and now he is 6 years old he sleeps with sesame street stories.

Did you know that it's possible to render #Scriban using #Sitecore #PowerShell Extensions?

@FrachriBuy وهي على امازون ب ٨٩

@FrachriBuy نفس الجهاز ب ٨٦ درهم أونلاين

@Jad_Ghosn من إقطاع لإقطاع قطعو البلد

@nrashmy @sitecore_master Thanks for the article, I have more errors showing other than those you challenged. Shall I share it at your blog? Maybe you can find a solution

@JoeAkkawi Draw it on the tiles

will not buy bread from #spinnyes #food anymore.