标签：#ai

If you've let a coding agent loose on AWS, you've watched it guess. It invents API parameters that don't exist, or hands you an S3 bucket a security review will bounce on sight. The Agent Toolkit for AWS is built to stop that. By the end of this post you'll have it running in whatever editor you use, plus a tour of what's in it and three workflows worth pointing it at. I use Kiro day to day, so I'll walk through that setup first. It also works with Codex, Claude Code, Cursor, and any other agent that speaks MCP, the Model Context Protocol, which is the open standard agents use to connect to outside tools and data. I'll cover those too. What is the Agent Toolkit for AWS? The Agent Toolkit for AWS is a free, AWS-supported set of tools that gives AI coding agents secure access to AWS, current documentation they can read mid-task, and tested procedures for the work they tend to fumble. It plugs into the agent you already use rather than asking you to switch. In practice, that shows up in a few ways, all detailed in the AWS user guide . The agent stops guessing about APIs it never saw. The models behind these agents trained on data that's months or years old, so anything AWS shipped recently is missing or wrong in their heads, and the toolkit hands them current docs and references at request time. For multi-step work like least-privilege IAM or a production serverless stack, it follows a vetted skill instead of reconstructing the steps from half-memory. Every call goes through your own IAM credentials, shows up in CloudWatch, and gets logged to CloudTrail, so you can scope an agent to read-only even when your role can write. And the toolkit costs nothing on its own; you pay only for the AWS resources the agent creates. It's the successor to the MCP servers, skills, and plugins AWS shipped under AWS Labs in 2025. Two things make me reach for it over a raw MCP setup: condition keys that let a policy tell an agent apart from a human, and skills that have been evaluated end

There's a paradox nobody wants to say out loud: the same frameworks companies pick because they're "enterprise-ready," "scalable," and "industry standard" are, for an LLM writing code, a minefield. Angular , React with its whole ecosystem, Nx with its monorepos: these are powerful tools, built by humans to coordinate teams of humans on massive codebases. And for that purpose, they're often the right choice — if your primary constraint is coordinating hundreds of engineers over a decade, the conventions and tooling of an established framework earn their keep. But there's a second actor in the room now. When the one writing the code is an AI, the very traits that make these frameworks "robust" turn into pure friction. The argument I'm making isn't "Angular and React are obsolete." It's narrower: we've historically optimized software architecture for human cognition, and LLMs introduce a different cost model that may favor simpler, more deterministic architectures — at least in some domains. Let's break down why, in three points. 1. The Token Tax (and the Cognitive Bottleneck) An LLM doesn't "understand" code the way we do — it processes it token by token, and every token costs something: money, latency, and context window that could otherwise be spent reasoning about the actual problem. Try asking an AI to generate a simple input form in a typical Angular/Nx context. To do it "properly" it has to: create the component (separate .ts , .html , .css files) declare the @Component with all its metadata import and wire up the right modules possibly touch an NgModule or a standalone-components config navigate 4-5 folder levels inside a typical Nx structure ( apps/ , libs/ , feature-x/ , data-access/ , ui/ ...) All of this before writing a single line of actual logic. That's architectural complexity that, for a human, pays for itself over time thanks to tooling, autocomplete, and internalized conventions. For an LLM generating text sequentially, it's a tax paid on every singl

The Financial Times has a good article on how AI is changing the capabilities of video surveillance, with information from both Israel/Iran and Russia. I wrote about this sort of thing a few years ago, how AI enables mass spying in the way that computers and networks enabled mass surveillance. The interesting development in the article is that AI allows people to ask natural language questions about video footage to AIs—and AIs can answer them. In contrast with older tools restricted to a few dozen preset searches, these new tools allow an almost unlimited range of enquiries by enabling language-based searches on video...

Watching Elon Musk fulminate at Bill Savitt during Musk v. Altman - the case in which Musk sued Sam Altman and OpenAI instead of seeing a therapist about his AI failures - was a bit like watching a toddler have a temper tantrum at his nursery school teacher. Savitt's questions were "designed to trick me," […]

TL;DR Anthropic recently published When AI Builds Itself, an essay explaining how AI is...

I had a lot of work to get through, and for once I didn't want to crawl through it one ticket at a...

WIRED spent months talking to America’s favorite failson as he plotted his return to public life. Now he’s feeding the trolls—and everyone else.

Proton has launched a major upgrade for its Lumo chatbot, giving it image generation and editing capabilities.

Introduction I keep hearing the term loop engineering. It's all over my feed, every AI...

Remember when Artificial Intelligence (AI) felt like something from a science fiction movie? Well, it's not just for movies anymore! AI is here, and it's rapidly changing how businesses of all sizes operate. From making customers happier to solving tricky problems faster, AI is becoming a vital tool for success. But how exactly is AI making such a big difference? Many business owners wonder about the real-world uses of AI. That's why we've put together this comprehensive guide. We're going to explore 50 specific ways AI development is transforming modern businesses, helping them work smarter, grow faster, and serve their customers better. Get ready to see how AI isn't just a buzzword, but a powerful engine driving real change in the business world! Boosting Customer Service & Experience (CX) (1-10) AI is making customer interactions smoother, faster, and more personal. Instant Customer Support (Chatbots): AI-powered chatbots answer common questions 24/7, so customers get help right away. Personalized Recommendations: AI suggests products or services customers might like, based on their past choices, making shopping feel more personal. Faster Problem Solving: AI helps support agents quickly find solutions by sifting through information. Predicting Customer Needs: AI can guess what a customer might want or need before they even ask, allowing businesses to be proactive. Voice Assistants for Support: AI voice assistants can handle basic customer calls, freeing up human agents for more complex issues. Sentiment Analysis: AI understands how customers feel about a product or service by analyzing their feedback (reviews, social media posts). Automated Email Responses: AI can draft quick, helpful replies to common customer email inquiries. Targeted Customer Outreach: AI helps businesses send the right message to the right customer at the right time. Improved Loyalty Programs: AI personalizes rewards and offers, making customers feel more valued and increasing their loyalty.

For twenty years, "ranking" meant one thing: get indexed, get crawled, get a position on a results page. Every Shopify store's SEO checklist was built around that single goal. Sitemap submitted, meta tags filled in, Core Web Vitals green, done. That checklist still matters. It's also no longer sufficient, and most stores haven't noticed yet. Two different systems, two different jobs Google's index and an LLM's answer engine are not the same kind of system, even though they both "read" your store. A search index is a retrieval system. It crawls a page, tokenizes the content, stores it, and matches it against a query at request time. Ranking is a function of relevance signals backlinks, click-through behavior, freshness, page experience. The unit of output is a list of links. The user does the synthesis. An LLM-based answer engine is a generation system. When someone asks ChatGPT, Perplexity, or Claude "what's a good Shopify store for sustainable activewear," the model isn't returning a ranked list of crawled pages. It's generating a single answer, and it decides which brands to name in that answer based on which entities it has high confidence are real, relevant, and well-attested across multiple sources. The unit of output is a sentence. The model does the synthesis, and your store either gets a mention in that sentence or it doesn't. This is the gap. A store can be fully indexed sitemap clean, every product page crawlable, ranking on page one for its category and still never get named in an AI-generated answer. Indexing is a necessary condition for citation. It is not a sufficient one. What "citable" actually requires Citation in an LLM context isn't about keyword matching. It's closer to reputation modeling. Three things tend to separate stores that get cited from stores that don't: Entity consistency across the web. The model needs to resolve "your brand" as a single, stable entity across multiple independent sources your own site, marketplaces, press mentions, r

Most developers have experimented with ChatGPT or GitHub Copilot. But when it comes to building AI-powered applications, simply calling an LLM API isn't enough. Understanding what's happening behind the scenes helps you design systems that are scalable, reliable, and cost-effective. In this article, we'll explore four concepts every software engineer should know: tokens, embeddings, transformers, and Retrieval-Augmented Generation (RAG). 1. LLMs Think in Tokens, Not Words One of the biggest misconceptions about Large Language Models (LLMs) is that they understand words like humans do. In reality, they process tokens, which are smaller units of text. For example: Prompt: Explain dependency injection in Spring Boot. is first converted into a sequence of tokens before the model processes it. Why does this matter? API pricing is based on the number of input and output tokens. Longer prompts increase latency and cost. Every model has a maximum context window measured in tokens. When building AI applications, prompt design isn't just about getting better answers—it's also about optimizing performance and cost. 2. Transformers: The Breakthrough Behind Modern AI Before 2017, language models processed text one word at a time using architectures like RNNs and LSTMs. They struggled with long conversations because earlier context was gradually forgotten. The introduction of the Transformer architecture changed this with a mechanism called self-attention. Instead of reading text sequentially, transformers analyze the relationships between all tokens in a sentence simultaneously. Consider this sentence: "The server restarted because it ran out of memory." The model understands that "it" refers to "the server", not "memory", by assigning attention to the relevant words. This ability to capture context efficiently is what powers modern LLMs like GPT, Gemini, Claude, and Llama. 3. Embeddings Enable Semantic Search Suppose a customer searches: "How can I get my money back?" But your

AWS launched Lambda MicroVMs, a new serverless compute primitive that runs each user session or AI agent in its own Firecracker virtual machine with hardware-level isolation, snapshot-based rapid launch, and state preservation for up to eight hours. Reddit community analysis found the minimum setup costs $3.03/day, roughly 9x Fargate spot pricing. By Steef-Jan Wiggers

New OpenAI Signals data shows how ChatGPT adoption is growing globally, with users increasing usage, exploring more capabilities, and driving growth across regions and languages.

OKX is bringing together payments, identity and reputation into a marketplace for AI agents.

Workshops at this year's AI Engineer World's Fair shifted noticeably away from RAG and prompt...

I'm sitting through day one of AI Engineer World's Fair San Francisco, and I'm struck by how...

San Franciscans and attendees at the AI Engineer World's Fair saw a shining swarm of drones flying...

On Sunday night, the sold-out 2026 AI Engineer World's Fair kicked off its orientation at Moscone...

AI agents are hitting the same inflection point. Most people think an agent is the model, the...