# AI assistants Published articles for AI assistants. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Deploy, Discover, Inspect, Observe: A Summer Spent Making a Public Vespa MCP Server DevFeed: [Deploy, Discover, Inspect, Observe: A Summer Spent Making a Public Vespa MCP Server]() Original publisher: [Read original article]() Author: eivinbingen oystein viktor mfstort Published: 2026-09-13T00:00:00Z Content type: article Language: en Sources: [Vespa Blog]() Topics: [Model Context Protocol (MCP)](), [MCP Server](), [Cloud](), [AI Agent](), [Large Language Model](), [Command-line interface]() Tags: [ai-agent](), [ai-assistants](), [claude](), [cli](), [cloud](), [codex](), [internships](), [language-models](), [mcp](), [mcp-server]() ### AI overview This article describes the construction of a standalone, publicly hosted Vespa Cloud MCP server. It explains how MCP connects AI assistants and language models to external systems through resources, tools, and prompts, and discusses evaluating MCP usage against terminal access and Vespa CLI access. ### Source excerpt We built a standalone Vespa Cloud MCP server as a summer interns project ## Google Is Testing a Search Bar That Works Outside Chrome DevFeed: [Google Is Testing a Search Bar That Works Outside Chrome]() Original publisher: [Read original article]() Author: Simon Sterne Published: 2026-09-10T16:00:00Z Content type: article Language: en Sources: [Web Designer Depot]() Topics: [Chrome](), [Google](), [Chromium](), [Artificial Intelligence]() Tags: [ai](), [ai-assistants](), [ai-search](), [ai-tech](), [artificial-intelligence](), [browser](), [browser-design](), [chrome](), [chrome-canary](), [chromium](), [everywhere-omnibox](), [future-of-browsers](), [future-of-search](), [gemini](), [gemini-in-chrome](), [google](), [google-chrome](), [google-search](), [project-loom](), [search](), [search-technology](), [ui-design](), [ux-design](), [web-browsers](), [web-design]() ### AI overview Google is testing Project Loom, an experimental floating Search bar that can appear over other Windows apps instead of remaining inside Chrome. The feature is unfinished, with screen sharing, Lens, and AI Mode controls reportedly not yet working. ### Source excerpt Google is quietly testing a way to bring Search outside Chrome and directly on top of whatever app you're using. It's called Project Loom, and this little floating search box could hint at a much bigger future where Google follows you around your desktop. ## Build a memory MCP server on Appwrite DevFeed: [Build a memory MCP server on Appwrite]() Original publisher: [Read original article]() Author: Atharva Deosthale Published: 2026-09-04T00:00:00Z Content type: tutorial Language: en Sources: [Appwrite Blog]() Topics: [Appwrite](), [Model Context Protocol](), [MCP Server](), [OAuth 2.0](), [Serverless](), [Databases](), [Embeddings](), [Claude Code](), [web applications]() Tags: [ai](), [ai-assistants](), [ai-tools](), [api](), [claude](), [claude-code](), [embeddings](), [mcp](), [mcp-server](), [memory](), [net](), [oauth2](), [sdk](), [serverless](), [tutorial](), [tutorials]() ### AI overview This tutorial builds Recall, a small persistent-memory app on Appwrite. It hosts a stateless remote MCP server in an Appwrite Function, stores embedded memories in VectorsDB for similarity search, and uses OAuth2 for user-authorized access. It also explains connecting Claude Code to the server. ### Source excerpt Give your AI tools a shared, persistent memory. Host a stateless MCP server on Appwrite Functions, store memories in VectorsDB, and protect it with your project's OAuth2 server. ## Are AI tutors safe for your kids? DevFeed: [Are AI tutors safe for your kids?]() Original publisher: [Read original article]() Author: Phil Muncaster Published: 2026-08-10T09:00:00Z Content type: article Language: en Sources: [WeLiveSecurity]() Topics: [Artificial Intelligence](), [Chat Bot](), [Machine Intelligence](), [Security]() Tags: [ai](), [ai-assistants](), [education](), [kids-online](), [learning](), [security]() ### AI overview AI tutors are expanding in education, but their capabilities, teaching methods, and safeguards vary widely. The article explains the differences between general-purpose chatbots, teaching-focused Socratic tutors, and curriculum-based intelligent tutoring systems, while highlighting concerns about privacy, security, over-dependence, and whether their effectiveness is sufficiently proven. ### Source excerpt AI tutors can offer useful support, but their quality and safeguards vary widely. Here's what parents should check before handing one to a child. ## Worth Reading 071526 DevFeed: [Worth Reading 071526]() Original publisher: [Read original article]() Author: Russ Published: 2026-07-15T18:56:46Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [Artificial Intelligence](), [Confidential Computing](), [Security](), [Cloud](), [Internet](), [Protocol (disambiguation)]() Tags: [ai](), [ai-assistants](), [china](), [cloud](), [corporate](), [cryptographic](), [europe](), [government](), [innovation](), [models](), [open](), [research](), [security](), [worth-reading]() ### AI overview A curated reading roundup examines failures in corporate AI implementation, the role of AI assistants in shaping access to information, confidential computing and sovereign cloud ambitions in Europe, a possible architectural flaw in a security protocol used to establish cryptographic trust, and strategies emphasizing open models and innovation in competition with China. ### Source excerpt Reports of AI corporate implementation failures have continued to mount over the last year. AI assistants now hand you a single, ready-made answer, and a harder question comes with it: who decides what we get to know, and what never makes it into the reply? Vendors are trying to position "confidential computing" as the technical backbone of Europe's sovereign cloud ambitions. But new research shows that a security protocol used to prove cryptographic trust in the system may have a fundamental architectural flaw. The Internet should stop asking only who was in the room. It should ask who can bind the party bearing the loss. America won't beat China by banning AI. We'll win by building the world's best open models and letting innovation--not government--lead the way. ## Introducing MCP server for Registry of Open Data on AWS DevFeed: [Introducing MCP server for Registry of Open Data on AWS]() Original publisher: [Read original article]() Author: Guyu Ye Published: 2026-07-07T20:12:39Z Content type: article Language: en Sources: [AWS Open Source Blog]() Topics: [MCP Server](), [Model Context Protocol](), [datasets](), [Amazon Web Services](), [Open Source](), [Claude Code](), [Kiro](), [Artificial Intelligence]() Tags: [ai](), [ai-assistants](), [aws](), [claude-code](), [datasets](), [kiro](), [mcp-server](), [model-context-protocol](), [model-context-protocol-mcp](), [open-source]() ### AI overview AWS introduces an open-source Model Context Protocol server for the Registry of Open Data on AWS. It lets compatible AI assistants search datasets, inspect metadata, explore bucket contents, and sample files, helping researchers evaluate data through a conversational workflow. ### Source excerpt Today, we are launching an open source Model Context Protocol (MCP) server that brings AI-powered dataset discovery to Registry of Open Data on AWS (RODA). As of today, RODA hosts over 1,100 high-value datasets from more than 400 organizations, spanning satellite imagery, life sciences, climate, geospatial, and more. Ask a research question in Kiro, Claude [...] ## AI-Assisted Production Database Ops with ClusterControl MCP and CCX MCP DevFeed: [AI-Assisted Production Database Ops with ClusterControl MCP and CCX MCP]() Original publisher: [Read original article]() Author: Kyle Buzzell Published: 2026-05-21T10:22:01Z Content type: article Language: en Sources: [SeveralNines]() Topics: [Model Context Protocol](), [Database](), [MCP Server](), [Artificial Intelligence]() Tags: [ai](), [ai-assistants](), [ccx](), [clustercontrol](), [database](), [mcp](), [mcp-server](), [model-context-protocol](), [sovereign-dbaas]() ### AI overview Severalnines describes updates to ClusterControl MCP and the companion CCX MCP for AI-assisted database operations. The article covers database inspection, troubleshooting, operational workflows, and prepared write actions through MCP-compatible clients, with dry-run previews and additional warnings for high-risk operations. ### Source excerpt In December, we introduced how Model Context Protocol could make ClusterControl easier to work with from AI assistants. Since then, Severalnines has expanded that MCP direction across its database operations platforms with ClusterControl MCP and CCX MCP. The latest ClusterControl MCP is the major update, providing a more robust implementation with 69 tools and 20 [...] The post AI-Assisted Production Database Ops with ClusterControl MCP and CCX MCP appeared first on Severalnines. ## Developing a Rust-based IoT device with AI DevFeed: [Developing a Rust-based IoT device with AI]() Original publisher: [Read original article]() Author: John Lee Published: 2026-04-20T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [Rust](), [AI-assisted coding](), [Internet of things](), [ESP32]() Tags: [ai](), [ai-assistants](), [ai-coding](), [blog](), [coding-agents](), [esp32](), [how-to](), [iot](), [llm](), [pitfalls](), [rust](), [verify]() ### AI overview This article describes a rough, hands-on workflow for building a WiFi, BLE, and provisioning device with Rust and AI on the ESP DualKey, an ESP32-S3-based kit from M5Stack. It emphasizes clear specifications, pinned crates, reference implementations, and a tight verification loop. ### Source excerpt AI assistants are most effective on ESP32 Rust firmware when you supply clear specs, pinned crates, reference implementations (often ESP-IDF C), and a tight verify loop. The article discusses good practices, pitfalls, discipline, and entropy. A brief explanation about the device is also included, along with the repository and all artifacts. ## Right-Sizing Engineering Teams for AI DevFeed: [Right-Sizing Engineering Teams for AI]() Original publisher: [Read original article]() Author: Daniel Doubrovkine (dblock@dblock.org) Published: 2026-03-11T00:00:00Z Content type: opinion Language: en Sources: [Daniel Doubrovkine]() Topics: [Artificial Intelligence](), [engineering-culture](), [Code review](), [ai-coding](), [GitHub Copilot](), [pull-requests](), [Claude](), [cursor]() Tags: [ai](), [ai-assistants](), [ai-coding](), [ai-tools](), [claude](), [code-review](), [cursor](), [engineering](), [github](), [github-copilot](), [people](), [pull-requests](), [quality](), [teams]() ### AI overview An opinion article argues that AI coding assistants increase code output without replacing the experienced judgment needed for quality and review. It recommends prioritizing senior expertise over larger mixed-experience teams when planning engineering headcount. ### Source excerpt Before AI coding assistants, a typical engineering team of 8-10 people might have been lucky to have one or two "10x engineers", or "workhorses", the kind of engineer that both keeps project quality and feature velocity high. AI tools have solved the workhorse half of this equation, enabling massive raw output. Today, almost every engineer can produce a high volume of code with GitHub Copilot, Claude, or Cursor. But the quality half of the equation has not kept up. Teams are shipping more code, but a greater fraction of it is AI slop: plausible-looking, locally coherent, globally wrong. Code review remains a human activity. Until that changes, AI assistants cannot substitute for the senior engineer. If AI triples output but the number of senior reviewers stays the same, the ratio of experienced judgment to code produced has gotten roughly 3x worse. The instinctive response to higher individual productivity is to hire fewer people, which is correct directionally but wrong in practice if you cut experience rather than volume. The difference is not in lines of code produced; it is in the accumulated judgment applied at every decision point. A team of four or five senior engineers with AI assistants will, in my experience, outperform a team of ten mixed-experience engineers with the same tools because the ratio of judgment to output stays healthy. This has implications for how engineering leaders should think about headcount planning. A well-functioning engineering team today should be five to seven people, with at most one junior. A reliable signal that you've gotten this wrong: pull requests that sit unreviewed for days, not because people are busy, but because no one feels confident enough to approve them. Smaller, more experienced engineering teams are not a new idea - Fred Brooks noted that you cannot make a late project earlier by adding people. What AI has done is make the argument sharper and more urgent. When every engineer can produce the volume that once requ ## The Golden Ratio of Manager to IC DevFeed: [The Golden Ratio of Manager to IC]() Original publisher: [Read original article]() Author: Daniel Doubrovkine (dblock@dblock.org) Published: 2026-02-04T00:00:00Z Content type: opinion Language: en Sources: [Daniel Doubrovkine]() Topics: [Meta](), [AI Engineering](), [Artificial Intelligence](), [Software]() Tags: [ai-assistants](), [ai-engineering](), [meta](), [opinion](), [organization](), [people](), [teams]() ### AI overview This opinion examines Meta's reported plan for an applied AI engineering organization with up to 50 employees per manager. It argues that flatter structures may reduce unnecessary management layers and that AI assistants are increasing individual contributors' capabilities, while suggesting that managerial roles must continue to evolve. ### Source excerpt In today's shocker, Meta is to "create a new applied AI engineering organization aiming for an ultra-flat structure of up to 50 employees to one manager". Like all software engineers I, too, tend to apply a data-driven, mathematical approach to every problem in the world. Yet I would have chosen a more romantic number and applied the golden ratio: roughly 1.6:1, the proportion that shows up in seashells, galaxies, and every second slide about "natural elegance", rather than 50:1, a measure that feels less like harmony and more like a spreadsheet's idea of efficiency. The idea of flattening an organization is not new and can be a good one. I know plenty of managers who have not done any individual contributor work, code or otherwise, in years. This is particularly striking with former strong coders who are promoted to managerial roles. After 2-3 cycles of promotions they are so far detached from what's happening at the individual-contributor level that they become 100% overhead, spending their entire life in meetings and actively preventing real work from being done. It's natural to want to eliminate layers of such people as they simply don't have any impact. And so, the real news at Meta is that it's fighting its own organization design in which, at least in some teams according to my friends who work or have worked there, people managers are discouraged from doing deep technical work, don't own much beyond process, and mostly serve as reporting-structure placeholders. Another reason to flatten an organization is the introduction of AI assistants that have created a major shift in the capabilities of individual contributors. Two years ago you could maybe find one single "10x engineer" in every team--someone who has dramatically higher velocity than their peers. A good manager would recognize these extraordinary abilities, make such an individual their right hand and technical partner, share the responsibility of advancing a project, create effective mentorship, and h ## Terminally online Mistral Vibe. DevFeed: [Terminally online Mistral Vibe.]() Original publisher: [Read original article]() Published: 2026-01-27T16:00:00Z Content type: release Language: en Sources: [Mistral AI Blog]() Topics: [Command-line interface](), [Automation](), [API](), [Code](), [coding](), [Fine-tuning](), [Reinforcement learning]() Tags: [agents](), [ai](), [ai-assistants](), [api](), [automation](), [cli](), [code](), [coding](), [fine-tuning](), [pricing](), [reinforcement-learning](), [updates](), [workflows]() ### AI overview Mistral Vibe 2.0 is a terminal-native coding agent upgrade powered by the Devstral 2 model family. It adds custom subagents, clarification prompts, slash-command skills, configurable agent modes, workflow customization, and automatic CLI updates. The release also describes Le Chat plan availability, pay-as-you-go usage, BYOK support, Devstral 2 API access, and enterprise services such as fine-tuning, reinforcement learning, and code modernization. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## How AI Performs at React Coding and How Developers Can Improve Its Results DevFeed: [How AI Performs at React Coding and How Developers Can Improve Its Results]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2025-12-29T15:31:06Z Content type: tutorial Language: en Sources: [Elevate]() Topics: [React](), [Artificial Intelligence](), [ai-coding](), [coding](), [Development]() Tags: [ai](), [ai-assistants](), [ai-coding](), [benchmarks](), [coding](), [context-engineering](), [guide](), [react]() ### AI overview This article examines how AI coding tools perform for React developers. It reports stronger results on isolated tasks such as scaffolding components and implementing explicit requirements, but weaker results on multi-step integrations involving complex state management and design judgment. It argues that context engineering, specific prompts, structured workflows, guardrails, and deep React knowledge can improve outcomes. ### Source excerpt A data-driven look at what AI can and can't do for React developers - and what you can do about it ## Introducing: Devstral 2 and Mistral Vibe CLI. DevFeed: [Introducing: Devstral 2 and Mistral Vibe CLI.]() Original publisher: [Read original article]() Published: 2025-12-09T12:00:00Z Content type: news Language: en Sources: [Mistral AI Blog]() Topics: [Artificial Intelligence](), [coding](), [Large Language Model](), [Open Source](), [Automation](), [GitHub Copilot CLI](), [Transformer](), [Command-line interface](), [context window](), [Terminal](), [Fine-tuning](), [on-prem]() Tags: [ai](), [ai-assistants](), [automation](), [autonomous-agents](), [claude](), [cli](), [code](), [coding](), [context-window](), [deepseek](), [fine-tuning](), [hardware](), [models](), [on-prem](), [open-source](), [terminal]() ### AI overview Mistral introduces Devstral 2, Devstral Small 2, and the Mistral Vibe CLI. The open coding models target autonomous software engineering, with deployment options spanning APIs, local consumer hardware, and on-premises environments. Devstral 2 supports a 256K context window, codebase-wide changes, failure recovery, and fine-tuning, while Mistral Vibe provides terminal-based code automation. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Making AI assistants better at email: Postmark's new documentation tooling DevFeed: [Making AI assistants better at email: Postmark's new documentation tooling]() Original publisher: [Read original article]() Author: Postmark team (fdossetto+postmark@activecampaign.com) Published: 2025-11-24T18:13:00Z Content type: article Language: en Sources: [Postmark (en-US)]() Topics: [Documentation](), [Artificial Intelligence](), [Model Context Protocol](), [MCP Server](), [API](), [ChatGPT](), [Tooling](), [Claude]() Tags: [ai](), [ai-assistants](), [ai-tools](), [api](), [chatgpt](), [claude](), [code](), [documentation](), [error-handling](), [mcp](), [mcp-server](), [product-news](), [security](), [tooling]() ### AI overview Postmark describes three updates intended to help AI assistants provide more accurate email-integration guidance: an llms.txt file with structured API and documentation context, pre-built prompts for common integration tasks, and an experimental MCP server that connects assistants to Postmark capabilities. ### Source excerpt Getting an AI assistant to help with your email integration shouldn't feel like explaining SMTP to your cat. But until recently, that's kind of what it was like - AI tools could tell you about email APIs, but they often got the details wrong or gave you outdated information. We've been thinking about this problem, and we've shipped three updates to make AI assistants genuinely useful when you're working with Postmark. What we built1. llms.txt: Context for AI assistants We published an llms.txt file that gives AI tools like ChatGPT and Claude accurate, structured information about Postmark's API, features, and best practices. Instead of AI assistants guessing or making assumptions about our documentation, they can now reference this file to give you correct details about authentication, endpoints, message streams, deliverability features, and integration patterns. How to use it: When you need help with Postmark integration, troubleshooting, or understanding specific features, paste https://postmarkapp.com/llms.txt into your AI tool. Your assistant will have the context it needs to provide better, more accurate guidance. 2. Pre-built AI prompts We added an AI prompts section to our documentation with ready-to-use prompts for common integration tasks. Each prompt is designed to generate production-ready code that includes error handling, best practices, and proper Postmark API usage. Copy a prompt, paste it into your AI tool, and get working code. Available prompts: Node.js email integration Rails with ActionMailer setup Laravel Mail configuration Password reset flows Event-driven notification systems Inbound email processing Better Auth integration You'll save time - no more documentation deep-dives just to send a welcome email. You'll also avoid common mistakes since the prompts include our recommendations for deliverability, security, and reliability. 3. Postmark MCP server: AI assistants that actually do things Earlier this year, we launched our Model Context Proto ## Building an MCP Server for Nuxt DevFeed: [Building an MCP Server for Nuxt]() Original publisher: [Read original article]() Published: 2025-11-13T00:00:00Z Content type: tutorial Language: en Sources: [The Nuxt Blog]() Topics: [Model Context Protocol](), [Model Context Protocol (MCP)](), [Nuxt.js](), [MCP Server](), [Documentation](), [Developer experience](), [Tool](), [API](), [JSON]() Tags: [ai](), [ai-assistants](), [api](), [article](), [documentation](), [http](), [json](), [mcp](), [mcp-server](), [nuxt](), [tools]() ### AI overview The article explains how Nuxt built an MCP server that gives AI assistants structured access to Nuxt documentation, blog posts, and deployment guides. It describes MCP resources, tools, and prompts, compares the approach with traditional RAG, and outlines the Nuxt MCP Toolkit module's automatic discovery and HTTP endpoint architecture. ### Source excerpt How we built the Nuxt MCP server to enable AI assistants to access our documentation through structured data and composable tools. ## Introducing Mistral AI Studio. DevFeed: [Introducing Mistral AI Studio.]() Original publisher: [Read original article]() Published: 2025-10-24T12:00:00Z Content type: release Language: en Sources: [Mistral AI Blog]() Topics: [AI Platform](), [Artificial Intelligence](), [MLOps](), [Security](), [DevOps](), [data](), [Large Language Model](), [Deployment](), [Prompt Engineering](), [on-prem](), [VPC]() Tags: [agents](), [ai](), [ai-adoption](), [ai-assistants](), [ai-platform](), [ai-studio](), [autonomous-agents](), [compliance](), [infrastructure](), [multimodal-ai](), [on-prem](), [privacy](), [provenance](), [vpc](), [workflows]() ### AI overview Mistral introduces AI Studio, an enterprise AI platform for customizing, fine-tuning, evaluating, governing, and deploying AI assistants, autonomous agents, and multimodal AI with open models. It addresses the gap between AI prototypes and production by providing evaluation, feedback and dataset workflows, provenance and versioning, governance, and flexible deployment across hybrid, VPC, and on-prem infrastructure. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Make Memory work for you. DevFeed: [Make Memory work for you.]() Original publisher: [Read original article]() Published: 2025-09-02T12:00:00Z Content type: news Language: en Sources: [Mistral AI Blog]() Topics: [Artificial Intelligence](), [AI Platform]() Tags: [ai](), [ai-assistants](), [memory]() ### AI overview Mistral introduces Memories (beta) for Le Chat, designed to help the AI remember useful information while giving users transparency, control, and focus. The feature shows when memory is used and links recalled information to its source, while allowing users to disable memory, use incognito chats, edit or delete memories, and export or import them. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Le Chat. Custom MCP connectors. Memories. DevFeed: [Le Chat. Custom MCP connectors. Memories.]() Original publisher: [Read original article]() Published: 2025-09-02T04:00:00Z Content type: release Language: en Sources: [Mistral AI Blog]() Topics: [Model Context Protocol](), [Artificial Intelligence](), [AI Platform](), [Automation](), [Deployment](), [Cloud](), [on-prem](), [databricks](), [GitHub](), [Notion](), [Prisma](), [pull-requests]() Tags: [agents](), [ai](), [ai-assistants](), [automation](), [cloud](), [databricks](), [docs](), [github](), [integrations](), [mcp](), [on-prem](), [platform](), [prisma](), [pull-requests]() ### AI overview Mistral introduces custom MCP connectors and Memories in Le Chat. The release adds more than 20 secure connectors for enterprise data, productivity, development, automation, and commerce workflows, with options to search, summarize, and act in connected tools. Memories provide personalized responses, user control over stored information, and import from ChatGPT. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Vibe Coding: Best Practices for Prompting DevFeed: [Vibe Coding: Best Practices for Prompting]() Original publisher: [Read original article]() Author: Prashant Sridharan Published: 2025-08-16T07:00:00Z Content type: tutorial Language: en Sources: [Supabase Blog]() Topics: [Prompt Engineering](), [Vibe coding](), [ai-coding](), [cursor](), [Claude Code](), [Code](), [React](), [Supabase](), [Tailwind CSS](), [API](), [Boilerplate]() Tags: [ai-assistants](), [ai-coding](), [ai-tools](), [api](), [best-practices](), [claude-code](), [code](), [cursor](), [react](), [tailwind](), [vibe-coding]() ### AI overview This tutorial explains how to prompt AI coding assistants effectively for vibe coding. It covers providing technical and application context, structuring prompts in layers, refining results iteratively, and maintaining continuity across coding sessions to produce coherent, deployable applications. ### Source excerpt Master the art of communicating with AI coding assistants through effective prompting strategies, iterative refinement, and systematic approaches that turn ideas into deployable applications. ## More Secure Vibes: Chainguard Academy's AI Optimizations DevFeed: [More Secure Vibes: Chainguard Academy's AI Optimizations]() Original publisher: [Read original article]() Published: 2025-07-29T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Documentation](), [Large Language Model](), [LLMs](), [chainguard]() Tags: [ai-assistants](), [ai-models](), [chainguard](), [chainguard-ai](), [chainguard-containers](), [chainguard-edu](), [chainguard-images-documentation](), [documentation](), [llm](), [llms](), [new-features]() ### AI overview Chainguard Academy describes improvements to its documentation ecosystem that make the content easier for AI assistants and models to consume. The article covers a Copy Markdown for LLMs feature and machine-readable endpoints such as AI sitemaps, concept maps, plain-text indexes, and structured metadata. ### Source excerpt Chainguard Academy has implemented several new features to make training LLMs and AI models on Chainguard documentation easier. Learn more today. ## Analyze Your Nx Cloud Runs With Your AI Assistant DevFeed: [Analyze Your Nx Cloud Runs With Your AI Assistant]() Original publisher: [Read original article]() Author: Juri Strumpflohner Published: 2025-06-25T00:00:00Z Content type: tutorial Language: en Sources: [Juri Strumpflohner]() Topics: [ci](), [Model Context Protocol (MCP)](), [Cloud APIs](), [Conversational AI](), [Architecture & optimization](), [build times]() Tags: [ai](), [ai-assistants](), [build-times](), [ci](), [devops](), [mcp](), [model-context-protocol-mcp](), [nx](), [nx-cloud](), [optimization](), [resource]() ### AI overview This article explains how Nx Cloud MCP connects AI assistants to live CI data so technical teams can analyze pipeline runs conversationally, identify recurring build and test failures, compare cache performance, and optimize development workflows. ### Source excerpt Learn how to use Nx Cloud MCP to analyze CI data conversationally with AI assistants, identify failure patterns, and optimize your development pipeline through data-driven insights. ## Scan your AI-generated code from Cursor using Model Context Protocol (MCP) DevFeed: [Scan your AI-generated code from Cursor using Model Context Protocol (MCP)]() Original publisher: [Read original article]() Author: Manoj Nair Published: 2025-06-23T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [cursor](), [MCP Server](), [Model Context Protocol (MCP)](), [code security](), [Vulnerabilities](), [interoperability](), [Command-line interface](), [snyk-code]() Tags: [ai](), [ai-assistants](), [application-security](), [awareness](), [blog](), [cli](), [code-security](), [cursor](), [customer](), [developer](), [interoperability](), [mcp-server](), [model-context-protocol-mcp](), [open-source-security](), [related-content](), [security](), [snyk-code](), [vulnerabilities]() ### AI overview This article explains how Snyk's CLI MCP server integrates with Cursor to provide in-agent security for AI-generated code. It describes real-time detection of known vulnerabilities in code and open-source packages, with minimal configuration through MCP interoperability. ### Source excerpt Secure your AI-generated code from Cursor in real-time with Snyk's CLI Model Context Protocol (MCP) server. Detect vulnerabilities and accelerate secure development without compromising agility. ## 8 essential tips for using Figma Make DevFeed: [8 essential tips for using Figma Make]() Original publisher: [Read original article]() Author: Alexia Danton Published: 2025-06-10T00:00:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Prompt Engineering](), [App](), [Artificial Intelligence]() Tags: [2025](), [ai-assistants](), [app](), [best-practices](), [code](), [design](), [figma](), [flow](), [generation](), [v1](), [visualization]() ### AI overview This article presents eight practical tips for using Figma Make, Figma's prompt-to-app and prompt-to-code feature. It emphasizes detailed initial prompts that specify the task, context, design elements, expected behavior, and constraints, along with iterative reframing and starting fresh when necessary. It also discusses using AI assistants to refine prompts and provide code snippets. ### Source excerpt Here, we share our team's favorite prompts, pro tips, and best practices for using Figma Make to help you get the most out of our recently launched prompt-to-code feature. ## Introducing Mistral Code DevFeed: [Introducing Mistral Code]() Original publisher: [Read original article]() Published: 2025-06-04T12:00:00Z Content type: article Language: en Sources: [Mistral AI Blog]() Topics: [ai-coding](), [AI Platform](), [Software Engineering](), [ide](), [Visual Studio Code](), [agentic-coding](), [Refactoring](), [Deployment](), [Security](), [observability](), [Cloud](), [Open Source]() Tags: [agentic-coding](), [ai](), [ai-assistants](), [ai-coding](), [ai-platform](), [cloud](), [coding](), [developers](), [ide](), [local](), [observability](), [on-prem](), [open-source](), [security](), [vscode]() ### AI overview Mistral Code is an enterprise AI coding assistant that combines coding models, an in-IDE assistant, local deployment options, administrative controls, observability, and support. It is built on the open-source Continue project and is designed for secure, compliant deployment in cloud, reserved-capacity, or air-gapped on-premises environments. The platform supports autocomplete, code search and retrieval, agentic coding, chat assistance, and multi-step refactoring, with private-repository fine-tuning and lightweight model distillation capabilities described. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. [Next page]()