# AI Assistant Published articles for AI Assistant. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Photoshop Elements 2027 Adds Generative Expand, AI Upscaling and a Beta AI Assistant DevFeed: [Photoshop Elements 2027 Adds Generative Expand, AI Upscaling and a Beta AI Assistant]() Original publisher: [Read original article]() Author: Alex Harper Published: 2026-09-16T14:26:34Z Content type: article Language: en Sources: [Web Designer Depot]() Topics: [Artificial Intelligence](), [Generative AI](), [Graphics]() Tags: [adobe](), [adobe-elements](), [adobe-firefly](), [adobe-photoshop](), [ai](), [ai-assistant](), [ai-image-editing](), [ai-tools](), [artificial-intelligence](), [beta](), [creative-tools](), [design-tools](), [generative](), [generative-ai](), [generative-expand](), [generative-upscale](), [graphic-design](), [image-editing](), [photo-editing](), [photoshop-elements](), [photoshop-elements-2027](), [premiere-elements](), [visual-ui-design](), [web-design]() ### AI overview Adobe's Photoshop Elements 2027 update adds Generative Expand, Generative Upscale and a beta AI Assistant, along with photo organization features and a 25-credit monthly limit for generative features. ### Source excerpt Photoshop Elements is getting a serious AI upgrade, with Generative Expand, AI upscaling and a new assistant that helps you edit simply by describing what you want. ## Using Grok with Hermes Agent: Getting Started DevFeed: [Using Grok with Hermes Agent: Getting Started]() Original publisher: [Read original article]() Author: hi@meedamian.com (Damian Mee) Published: 2026-05-15T00:00:00Z Content type: tutorial Language: en Sources: [Damian Mee | Blog | Portfolio | About]() Topics: [Artificial Intelligence](), [AI Agent](), [Command-line interface](), [configuration](), [coding](), [debugging]() Tags: [ai](), [ai-agent](), [ai-assistant](), [cli](), [coding](), [configuration](), [debugging](), [tool-use]() ### AI overview This tutorial introduces using Grok from xAI with Hermes Agent, a CLI-based AI assistant. It describes the pairing's stated capabilities, including reasoning, tool use, coding, debugging, and real-time knowledge, and begins with basic configuration. ### Source excerpt Disclaimer: This post was generated by Grok 4.3 running inside Hermes Agent. Using Grok in Hermes Hermes Agent is a powerful CLI-based AI assistant, and pairing it with Grok (from xAI) unlocks some unique capabilities thanks to Grok's strong reasoning and tool-use performance. Why Grok? Excellent at following complex instructions Strong coding and debugging abilities Built-in real-time knowledge via tools Great balance of speed and intelligence Basic Setup To use Grok in Hermes: ## The architectural trade-offs of AI code generation DevFeed: [The architectural trade-offs of AI code generation]() Original publisher: [Read original article]() Published: 2026-03-31T00:00:00Z Content type: article Language: en Sources: [Aha! Engineering Blog]() Topics: [AI-assisted coding](), [Code generation](), [Large Language Model](), [Ruby](), [Claude Code]() Tags: [ai-assistant](), [claude-code](), [code-generation](), [memory](), [ruby]() ### AI overview The article examines architectural trade-offs in building Aha! Builder, an AI-powered product that generates database schemas, controllers, and views. It argues that frameworks and languages for LLM-generated code should account for token efficiency and context-window limits, and cites a Claude Code benchmark in which Ruby had the highest success rate across the tested languages. ### Source excerpt We recently introduced Aha! Builder, our new AI-powered product for creating prototypes and business applications. It allows you to describe the app you need, and Elle (our AI assistant) builds it for you. But Elle is not just a UI trick. When a us ## A Primer: MCP Servers and the Model Context Protocol DevFeed: [A Primer: MCP Servers and the Model Context Protocol]() Original publisher: [Read original article]() Author: Priya Patil Published: 2026-02-25T06:00:00Z Content type: tutorial Language: en Sources: [8th Light](), [8th Light Insights]() Topics: [Model Context Protocol](), [Server](), [Security](), [client](), [Authentication]() Tags: [ai](), [ai-and-emerging-tech](), [ai-assistant](), [audit](), [authentication](), [mcp](), [model-context-protocol](), [protocol](), [sandboxing](), [security](), [servers]() ### AI overview This primer explains MCP (Model Context Protocol), an open protocol that gives AI models a standard interface for connecting to external tools and data sources. It also outlines current gaps in authentication, discovery, security, sandboxing, secret management, and audit trails. ### Source excerpt Navigating Change The way we interact with software is shifting. Instead of clicking through web interfaces and learning different UIs for each system, some people are moving toward natural language as the interface - telling an AI what they want done, and it handles the task completion for them. This shift is happening now, but there's been a missing piece: how does the AI actually connect to and control those systems? MCP Defined MCP (Model Context Protocol) is an open protocol that gives AI models a standardised interface to external tools and data sources. Think of it as a USB-C for AI - before USB-C, every device needed its own connector. MCP does the same for AI-system integration. Instead of building custom integrations for every combination of AI model and external service, you implement MCP once and your AI can talk to everything it needs to. Instead of context-switching between Slack, JIRA, GitHub, and dashboards for example, we'll be able to describe what we need and have an AI assistant handle the interactions. Or as Anthropic defines it: "MCP (Model Context Protocol) is an open-source standard for connecting AI applications to external systems." Current State: A Nascent Protocol Yes, MCP has gaps. Here are a few: It's relatively new (launched November 2024) and the ecosystem is still figuring things out. Authentication and authorisation have a standard but implementation is patchy. Discovery is a work in progress, with a real need for global registry mechanics. Security is largely left to implementers - no built-in sandboxing, no secret management standard, no consistent audit trails. The community is actively working on all of these, and thing are moving very fast. But these are solvable problems and a lot of the changes are happening on the client side so building out servers feels a little more stable. These gaps shouldn't prevent you from getting value today - they just mean you'll need to handle some plumbing yourself and take an abundance of cauti ## Snippy: An Open-Source GPT-4 Screen-Sharing Assistant Built with Tauri and Rust DevFeed: [Snippy: An Open-Source GPT-4 Screen-Sharing Assistant Built with Tauri and Rust]() Original publisher: [Read original article]() Author: Tim Misiak Published: 2024-04-21T17:47:33Z Content type: tutorial Language: en Sources: [TimDbg]() Topics: [Artificial Intelligence](), [Large Language Model](), [multimodal](), [screen](), [Tauri](), [Rust](), [screenshot](), [cross-platform](), [Open Source](), [OpenAI](), [API keys](), [debugging]() Tags: [ai](), [ai-assistant](), [api](), [debugger](), [llms](), [macos](), [multimodal](), [open-source](), [rust]() ### AI overview This article presents Snippy, an open-source screen-sharing GPT assistant that sends application screenshots with chat messages. It explains how GPT-4 with vision uses screen context to answer questions and describes the use of Tauri and Rust in the application. ### Source excerpt While I no longer work on WinDbg, I still spend a lot of time thinking about how to make tools so people can build things faster. With WinDbg, I tried to do that by putting more debugging power at people's fingertips in a way that was easier to use. Recently, everyone is looking for ways to use LLMs to build things faster. But for most people using something like ChatGPT means pasting text back and forth between your different tools. ## How LLMs Could Assist Managers With Everyday Problems DevFeed: [How LLMs Could Assist Managers With Everyday Problems]() Original publisher: [Read original article]() Author: Pedro Gil Carvalho Published: 2023-08-18T08:37:09Z Content type: opinion Language: en Sources: [Pedro Gil Carvalho]() Topics: [Artificial Intelligence](), [Large Language Model](), [ChatGPT](), [Azure]() Tags: [ai](), [ai-assistant](), [chatgpt](), [llms](), [manager](), [privacy]() ### AI overview The article argues that LLMs can help managers handle simple, everyday problems by capturing intent, synthesizing relevant literature, and suggesting checklists or next steps. It says privacy, security, missing context, and the need for human judgment limit their use for diagnosis and strategic decisions. ### Source excerpt Much like AI came for artists first, it might affect the Manager role quicker than we think. ## Using Fleet 1.19's AI Assistant to Convert Kotlin Between Imperative and Functional Styles DevFeed: [Using Fleet 1.19's AI Assistant to Convert Kotlin Between Imperative and Functional Styles]() Original publisher: [Read original article]() Published: 2023-06-10T13:12:44Z Content type: article Language: en Sources: [maiatoday]() Topics: [coding style](), [Kotlin](), [Functional programming](), [Artificial Intelligence](), [coding](), [Code](), [Advent of Code]() Tags: [advent-of-code](), [ai](), [ai-assistant](), [code](), [coding-style](), [doggerel](), [fleet](), [functional](), [functional-programming](), [imperative](), [kotlin](), [word-games]() ### AI overview The article experiments with Fleet 1.19's integrated AI assistant by converting Kotlin code for the Advent of Code 2022 Day 10 puzzle between imperative and functional styles, comparing the results, and generating poetry. ### Source excerpt Once upon a time I admit. I have a penchant for word games and weird segues. So I will tell you what this post is about off the bat. You can skip to the code, I won't be offended. Fleet 1.19 was released this week with an integrated AI assistant. I took it for a spin and used it to convert Kotlin code from imperative style to functional and back again. I used some Kotlin code, which solves the Advent of Code 2022 Day 10 puzzle. I had written the solution in two styles and I had the AI assistant do the same conversion and compared the code. And then I played around and generated some poetry. Follow links at your peril, there are rabbit holes, it might make you late or change the size of your free time. Segue the first of many Oh you are still here! You didn't jump to the code starts here section? Why am I doing this? A long time ago, I read a lot of Lewis Caroll, Alice in Wonderland. In one scene Alice talks to the Mock Turtle. He sings a song about sea creatures dancing. When the first language translators appeared on the scene, Altavisa babelfish or perhaps it was the early Google Translate, I converted passages from Alice in Wonderland to French and then back again to see what the machines could do. The Mock Turtle was transformed into an Imitation Tortoise. In this way the reference to the original mock turtle soup was lost in translation. In a roundabout way I was wondering what would be uncovered or lost if I used the AI assistant for some translations in coding style. Couplets of doggerel My trusty and non emotional assistant had no opinion on Lewis Caroll but it made this for me. A definition of functional programming style in rhyming couplets: Functional programming's way, treats functions as the play. Immutable states they use, side effects they refuse. Declarative code is their choice, with functions, they rejoice. Nested functions map, filter, reduce, a clean, elegant code they produce. Compare this with the imperative version: Imperative code, step by st