标签：#AI

Users will be able use AI to create newsletters based on their recordings.

You can use Gemini to quickly search your Gmail inbox with natural language.

Yesterday, we kicked off our physical newspaper, The Daily Context, at the AI Engineer World’s Fair...

Proton's Lumo 2.0 is dropping this week, giving users a broader variety of capabilities.

Elastic open-sourced Atlas, a system built on Elasticsearch that maintains three categories of memory for agents. Atlas integrates with agents via MCP and maintains per-user isolation of memories. When evaluated on question-answering capability, it scored 0.89 Recall@10. By Anthony Alford

Hi, I’m David. I’m close enough to middle age that I have no interest in pretending I discovered the future of software development in a week. What I did do was spend one serious week building a small local app with AI assistance, while trying to keep the project understandable. That turned out to be harder, and more interesting, than I expected. The coding agent could move quickly. Sometimes very quickly. It could generate code, refactor, write boilerplate, and help move the project forward. But it could also widen scope, preserve the wrong assumption, “helpfully” redesign something I wanted to keep boring, or act on context that was never meant to become implementation work. The main lesson I took from that week was simple: AI-assisted development is not only a coding problem. It is a context management problem. So I started using a lightweight loop: Task Brief -> think through the problem Codex Contract -> give the coding agent a bounded instruction set Final Review -> test, inspect, patch, and update project memory The result was not perfect AI coding. The result was reviewable AI coding. That distinction felt important enough to write down. The three articles I published three companion articles from that first week. They are meant to stand on their own, but together they describe the workflow, the memory system, and the objections I think are worth taking seriously. 1. Vibe Coding Done Right This is the accessible starting point. It explains how I used a lightweight, spec-driven workflow as a solo developer working with ChatGPT, Codex, VS Code, PowerShell, and a local LLM through LM Studio. The point is not the exact stack. The point is the separation: one place for thinking, learning, and review; another place for bounded implementation; documentation as the memory that keeps the next task grounded. 2. Documentation as Project Memory in AI-Assisted Development This is the more technical case-study piece. The part that surprised me most was documentation. Not

Cost is everything. In just about every agentic conversation, the three things that come up for enterprises implementing AI workloads are: Cost Observability Security and as AI continues to throw everyone for a loop when it comes to cost management (e.g - Uber running out of the yearly token budget in one quarter), the ability to shrink resource (like hardware) usage will be crucial moving forward. In this blog post, you will learn how to cust costs by 90% using Agent Susbtrate in comparison to Agents running in k8s Deployments/Pods. The Cost Comparison Agents need a place to run. The "place to run" needs to be a platform that's easily managed, orchestrated, and has the ability to cluster resources. Resources like CPU, GPU, and memory need to be able to scale and expand. Without this, it's a matter of manually managing servers that Agents are running on and clients to interact with said server. That's why so many organizations choose Kubernetes to run Agentic. When running Agents per Pod, however, that can get costly very quick in terms of hardware (GPU, CPU, memory) and performance (can your cluster scale up and down quickly based on resource needs when it comes to Agents coming up and going down per use?). The tests in this blog post show: Always-on Agents running in k8s. Actors running in Workers via Agent Substrate And the comparison will be 50 always-on Pods in comparison to 50 Actors across 5-7 Workers (Pods). If there are 50 Agents running per Pod and 50 Agents running per Worker with 5-10 Actors per Pod, you can already imagine the hardware resource savings that can be accomplished. Right now, the majority of organizations start off with the "one Agent per Pod" approach as that's the fastest way to show value and get up and running. For the future, however, Agents in Actors via Agent Substrate will be how organizations deploy when they care about efficiency, optimization, and managing cost. Let's dive in from a hands-on perspective. Prerequisites To follow a

June 2026 is shaping up to be the month open models stopped playing catch-up. Three major releases in as many weeks have shifted the landscape, and none of them involve the usual frontier-lab drama. NVIDIA Nemotron 3 Ultra: 550B Parameters, Zero Restrictions On June 4, NVIDIA quietly dropped Nemotron 3 Ultra — a 550-billion-parameter behemoth under a fully permissive open license. That's not "open-weight with strings attached" — it's the most capable model you can download, modify, and deploy commercially without asking permission. Early benchmarks show it competitive with GPT-4.5-class models on code generation and reasoning tasks, while significantly outperforming Llama 4 on mathematical reasoning. If you have the hardware (think 8×H100 nodes minimum), this is the new default for self-hosted enterprise AI. GLM-5.2: China's Answer, MIT License Z.AI launched GLM-5.2 on June 13, and it arrived with full MIT-licensed weights within the week. What makes this noteworthy isn't just the permissive license — it's that GLM-5.2 punches well above its weight class on long-context retrieval and multilingual benchmarks. Developers running locally can deploy it on consumer-grade hardware with quantization, making it a strong contender for privacy-sensitive applications. The API tier starts at ~$18/month, but the real value is in the self-hosted path. Gemini 3.5 Flash Gets Computer Use Google DeepMind also shipped computer use capabilities in Gemini 3.5 Flash this month. Think Claude's computer-use agent paradigm, but running on the fastest Flash-tier model Google offers. Early demos show agents completing multi-step browser tasks — form filling, data extraction, web scraping — at significantly lower latency than competing solutions. The throughline is clear: open models are no longer a compromise . Whether you need 550B monsters for reasoning, MIT-licensed alternatives for compliance, or fast agents for automation, June 2026 delivered on all fronts.

You've got a folder of a few hundred screenshots and you want the text out of each one. Or you want to generate a batch of images for a side project. Or you just want to drop a single "summarize this" call into a script you're writing on a Sunday afternoon. So you open the pricing page for the official API, do the math on per-token billing plus setting up keys and a payment method, and it's hard to justify, because the exact same model will do the exact same thing for free in a browser tab. There are really two ways to get a model like ChatGPT or Gemini to do work for you. The web UI is free, or already covered by a subscription you're paying for anyway, but you drive it by hand. The API is scriptable, but you pay by the token. Most of the time that trade-off is fine. But for a whole category of work like hobby projects, throwaway scripts, research, or anything that doesn't need production-grade reliability, you're stuck picking between "free but manual" and "automated but paid." Which raises the obvious question: why not automate the free web UI? It's just a webpage. You open it, type in the box, click send. It turns out that hides a few fiddly problems, which I ran into enough times that I eventually built a small library for them. In this article we'll work through what it takes to automate these UIs, and at the end I'll show how little code it comes down to. 1. What it takes to drive a chat UI A single round trip with ChatGPT or Gemini breaks down into four jobs: Get your text into the input box Optionally attach a file Wait for the model to finish answering And read the answer back out. Every one of these is harder than it sounds, because the page is a modern single-page app that was never built to be driven by a script. We'll use Selenium with undetected-chromedriver, and for now assume the browser is already open (we'll get to launching it in the next section). To keep the code readable I'll show whichever of the two platforms makes each problem clearest, and

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