Malleable software: Restoring user agency in a world of locked-down apps
The original promise of personal computing was a new kind of clay. Instead, we got appliances: built far away, sealed, unchangeable. In this essay, we envision malleable software: tools that users can reshape with minimal friction to suit their unique needs.
What Actually Works: 12 Lessons from AI Pair Programming | Forge Code
Field-tested practices for productive AI-assisted development. Real lessons from 6 months of daily AI pair programming, including what works, what fails, and why most engineers are doing it wrong.
🎬 Ever wondered how AI could turn your life into a documentary? Watch as I create a seemingly professional documentary about myself in minutes using Gemma 3, Ollama, and ElevenLabs - no film crew needed!
🎯 In this video, you'll learn:
• How to use Gemma 3's multimodal capabilities with multiple images
• Building a simple CLI app with Deno/TypeScript for image processing
• Working with n8n workflows for AI integration
• Creating convincing AI-generated narratives with Ollama
• Complete workflow from capture to final video production
⏱️ Timestamps:
00:00 - Start
00:28 - I'm in a Documentary
01:56 - Gemma3
02:13 - Whats new in Gemma3 with Ollama
02:26 - Tell a story with many images
03:54 - Creating the app with Windsurf
04:49 - It's not in x language
05:19 - Let's look at the code
08:32 - The backend in n8n
🛠️ Tools & Resources Mentioned:
• Gemma 3 27b
• Ollama (https://ollama.com)
• ElevenLabs (https://try.elevenlabs.io/tvlst)
• n8n
• Deno/TypeScript
Want to create your own AI-powered content? Drop a comment below with your ideas or questions!
#AIContent #TechTutorial #AIDocumentary
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humanlayer/12-factor-agents: What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers? - humanlayer/12-factor-agents
How to Build an In-N-Out Agent with OpenAI Agents SDK
In this video, I take a deeper dive look at the OpenAI Agents SDK and how it can be used to build a fast food agent.
Colab: https://dripl.ink/MZw2R
For more tutorials on using LLMs and building agents, check out my Patreon
Patreon: https://www.patreon.com/SamWitteveen
Twitter: https://x.com/Sam_Witteveen
🕵️ Interested in building LLM Agents? Fill out the form below
Building LLM Agents Form: https://drp.li/dIMes
👨💻Github:
https://github.com/samwit/llm-tutorials
⏱️Time Stamps:
00:00 Intro
00:11 Creating an In-N-Out Agent (Colab Demo)
00:40 In-N-Out Burger Agent
04:35 Streaming runs
05:40 Adding Tools
08:20 Websearch Tool
09:45 Agents as Tools
12:21 Giving it a Chat Memory
There were a number of announcements at GitHub Universe that impacted VS Code and Copilot users. This is a roundup of those announcements, including the much-anticipated Copilot Edits, Intent Detection, Model Selection, and Code Reviews. We'll also take a look at some of the amazing extensions created by the VS Code team, including using images for prompts with Vision for Copilot, analyzing CSV data with Data Analysis for Copilot, and GitHub Pull Requests.
🔎 Chapters:
00:00 GitHub Universe announcements
00:30 Demo - Copilot Edits
05:30 Demo - Intent Detection
09:06 Demo - Data Analysis for Copilot
10:19 Demo - GitHub Pull Requests Copilot Integration
13:10 Happy Coding
🔗 Links:
https://aka.ms/get-copilot
https://aka.ms/IntroducingCopilotEdits
Featuring: Rob Conery
#vscode #copilot #githubcopilot
DrSadiqfareed/Full-Page-Handwriting-Recognition: An implementation of a full-page handwriting recognition system using convolutional neural networks and transformers. This project tackles the complex task of recognizing handwritten text without segmentation.
An implementation of a full-page handwriting recognition system using convolutional neural networks and transformers. This project tackles the complex task of recognizing handwritten text without s...
Tomorrow's editor, today. Windsurf Editor is the first AI agent-powered IDE that keeps developers in the flow. Available today on Mac, Windows, and Linux.
Learn how to build a VS Code Extension from scratch. In this fun tutorial, we integrate DeepSeek R1 direction into our editor to build a custom AI assistant.
Go Deeper https://fireship.io/courses
Related Content:
VS Code Extension Template https://code.visualstudio.com/api/get-started/your-first-extension
Ollama DeepSeek R1 https://ollama.com/library/deepseek-r1
DeepSeek R1 First Look https://youtu.be/-2k1rcRzsLA
DeepSeek Fallout https://youtu.be/Nl7aCUsWykg
Building a Vision App with Ollama Structured Outputs
In this video, I look at the Ollama structured outputs and how you can use it to do various tasks, such as named entity recognition and information extractio...
pytudes/ipynb/CherylMind.ipynb at main · norvig/pytudes
There has been much debate on the degree to which Large Language Models (LLMs) have a theory of mind: a way of understanding what other people know and don't know. In this notebook I explore one small part of the issue by asking nine LLM chatbots to solve the Cheryl's Birthday Problem, a well-known logic puzzle in which different characters have different states of knowledge at different times.
A solid pattern to build LLM Applications (feat. Claude)
The thing about modern AI development - both developing things with AI and developing AI things - is that you often need to know the right magic incantations...
Leaping's pytest debugger is a simple, fast and lightweight debugger for Python tests. Leaping traces the execution of your code and allows you to retroactively inspect the state of your program at any time, using an LLM-based debugger with natural language.
It does this by keeping track of all of the variable changes and other sources of non-determinism from within your code.
Code LoRA from Scratch - a Lightning Studio by sebastian
LoRA (Low-Rank Adaptation) is a popular technique to finetune LLMs more efficiently.
This Studio explains how LoRA works by coding it from scratch, which is an excellent exercise for looking under the hood of an algorithm.
Stable Code, an upgrade from Stable Code Alpha 3B, specializes in code completion and outperforms predecessors in efficiency and multi-language support. It is compatible with standard laptops, including non-GPU models, and features capabilities like FIM and expanded context size. Trained in multiple