Give me a mixtape, and hold the tape.

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Amazon isn't slowing down on data center spending — but investors don't seem to mind.

Apple said a slowdown in mobile gaming and changes to the App Store’s business model — including court-ordered payment rule changes in the U.S. — weighed on its services business, even as the company topped 1.5 billion paid subscriptions.

Apple may allow users to pay to increase their AI usage limits. During an earnings call on Thursday, Apple CEO Tim Cook said that he believes people will want to use Apple Intelligence and the upcoming Siri AI "a lot," adding that "we will have some kind of upgrade possibilities on iCloud Plus where people […]

After a massive Xbox "reset" that laid off thousands of employees and spun off four studios, Xbox CEO Asha Sharma wants to get Xbox back to growth. In a memo obtained by The Verge, Sharma told staff by the end of fiscal year 2027 (which runs through next June), "we will return XBOX to player […]

The iPhone and Mac sold well, but memory costs threaten further price increases.

You are a software engineer. Your craft honed through years of careful practice. Then suddenly, there are these chatbots and agents. Overnight, your colleagues got a new title on LinkedIn: "AI engineer". Some are already SENIOR AI engineers. You're curious about this new world, and might want to catch up and become part of it yourself. If this is you, then join me on this tour through the concepts and patterns that make up the field of AI engineering. We will find that AI application development is mostly 'just' software engineering, applied to one genuinely strange new non-deterministic component: the LLM. During the tour, we build a real application, end to end. Every article adds a new layer. We link the new patterns and words to existing software engineering concepts you already know. Before take-off, I'd like to establish one vocabulary rule used throughout: "the model" means the LLM itself (large language model, like GPT or Claude), and what AI engineers build around it will be referred to as "the application", "the agent" or "the harness". What we're building As I work at a payments company myself, I figured I'd stick to my domain. PayIQ, the application we build, is an assistant for merchants to perform payment operations: issue refunds, defend chargebacks, calculate processing fees. Give it a charge amount and a payment method, and it computes what a refund actually costs (spoiler: more than the refund amount). Ask it whether a chargeback is worth fighting, and it does the expected-value math using your knowledge base. Ask it something it can't responsibly answer, and it asks for what's missing. No guessing, no hallucinations. By the end, PayIQ will have structured outputs that can be consumed by other systems, a tool belt of financial calculators, retrieval over a knowledge base, an agent loop with persistent memory, an orchestration graph with steps the model cannot skip, token streaming behind a FastAPI service, a regression eval suite, and layered injec

Every i18n setup I've seen has the same three-way standoff. Developers want type-safe JSON in the repo. Translators want a familiar tool, not a pull request. Product wants to fix a typo without a deploy. So you either pay $50–$500/month for a localization SaaS, or you copy-paste strings between a translator's spreadsheet and your JSON files until something silently breaks. For projects under ~1,000 keys, there's a better middle: the spreadsheet is the database. Translators edit a Google Sheet; an Apps Script endpoint serves it as clean locale JSON; your app pulls that at build time. Here's the whole pattern, with the code. Why a sheet beats a translation service for small projects A localization SaaS earns its price at scale — dozens of translators, thousands of keys, screenshots and review workflows. A 300-key marketing site doesn't have that problem; it has a coordination problem. A Sheet solves coordination for free: translators already know it, it has revision history and suggested edits built in, and product can change a string in ten seconds. You only add the two things a raw sheet lacks — a clean JSON API and a fallback for missing translations. The schema: one tab, one row per key A strings tab, with the key in column A and one column per locale: key en tr es fr hero.title Welcome Hoş geldiniz Bienvenido Bienvenue hero.cta Get started Başla Empezar Commencer Use dot-notation keys ( hero.title ) so the JSON nests naturally in your i18n library. Keep a tiny meta tab too: B1 = default locale ( en ), B3 = version ( 1.0.0 ). The Apps Script endpoint Deploy this as a Web App (same mechanics as any Apps Script webhook ). doGet serves one locale — or all of them — as JSON, and the fallback lives right in the query: an empty cell resolves to the default locale, so a half-translated key never ships blank. // Code.gs const SHEET_ID = ' your-sheet-id ' ; function doGet ( e ) { const locale = ( e . parameter . locale || ' all ' ). toLowerCase (); const result = buildLoca

TL;DR. Bringing AI coding agents into a German operation is not only a technical decision. The moment they meet real systems and real teams, they also meet three things that international AI content almost never mentions: a data processing agreement, the works council, and the question of who owns the generated code. None of this is a footnote. It decides whether your AI project reaches production or stalls in legal review. Here is the layer most vendors forget, and the fast order to clear it. Why this layer is invisible from outside Germany Most content about AI coding agents comes from a world where a developer tries a tool and starts shipping. In a German company with a works council, GDPR, and a legal department reading along, the path to production looks different. Not harder, but with stops that nobody skips without regretting it later. This is not a brake. It is the actual difference between an impressive demo and something your company is allowed to run. And it is exactly the layer that reveals whether a vendor has ever delivered in Germany or is only demonstrating a tool. The three stops The data processing agreement: who is processing whose data? As soon as personal data is processed on your behalf, Article 28 GDPR requires a data processing agreement with the processor (in German, an Auftragsverarbeitungsvertrag, or AVV). With AI coding agents the decisive question is not "are we using AI" but what does the agent actually touch. Does it run over a codebase that contains customer data? Does it send fragments to a third-party model provider? Who in that chain is the controller, and who is the processor? This is answerable, and it belongs settled before the first access, not after. A clean setup often limits, at the technical level, what data an agent can even see, which makes the data-protection question smaller and the answer simpler. The works council: codetermination over systems that can monitor This is the stop most often missed from outside Germany, b

OpenAI has formally outlined a national science initiative designed to connect frontier AI models with government research infrastructure, National Laboratories, universities, and working scientists. The program is not a single model launch. Instead, it combines funded access, early product access, scientific campaigns, and an emphasis on fitting advanced AI into real research workflows. The initiative gives concrete form to OpenAI’s stated goal of helping scientists use increasingly capable models to accelerate discovery. In its official announcement on advancing the next era of national science , published July 22, 2026, the company describes a long-term strategy built around the U.S. Department of Energy’s Genesis Mission and collaborations with National Laboratories. The core proposition is that AI can contribute to hypothesis testing, simulations, and experimental work when it is deployed alongside scientific infrastructure and human expertise. That framing matters. OpenAI is positioning frontier models as tools that researchers direct and evaluate, rather than as a replacement for the institutions and specialists responsible for scientific work. What OpenAI is providing to scientific researchers OpenAI’s commitments span several types of access, from coding support for a broad research community to model capabilities and API funding for large campaigns. The announced provisions include: $4 million in Codex access for approximately 2,000 Genesis researchers at national labs and universities. $3 million in API support for two large scientific campaigns. Up to $10 million in API usage for participating researchers who reach a $2.5 million spending threshold. Access to GPT-Rosalind’s bioscience capabilities for national-lab researchers. Early access to selected models and features for trusted national-lab leaders preparing workflows and evaluations. Expanded access to advanced cyber capabilities for national-lab cybersecurity researchers. These commitments indicat

The new Friend pendant features a speaker, updated models and the same off-putting ideas about how humans should use AI.

TL;DR I designed a proposed AWS Transform Custom community skill that prepares Glue 2.0, 3.0, and 4.0 repositories for Glue 5.0. It separates safe mechanical transformations from changes that require human evidence, generates a migration report, and preserves already-compatible files unchanged. Because I didn't have live atx access, the benchmarks in this post are explicitly labeled manually simulated, not agent-executed. The proposal is open as issue #75 — not yet merged, not yet a pull request. The missing data-engineering transformation AWS Transform Custom can apply agent-driven code transformations across a single repository — or thousands of them at once, via AWS Batch and Fargate. As of July 30, 2026, its public sample repository, aws-samples/aws-transform-custom-samples , contained three community-contributed transformations: an EKS version-upgrade-readiness skill, a JBoss-to-Spring-Boot migration, and a Kubernetes readiness migration. None of them touched data engineering. Given that most of my day-to-day work sits across AWS data engineering, Databricks, and Delta Lake, that gap was the obvious thing to fill. What an AWS Transform Custom "skill" looks like Before writing anything, I studied the deepest existing example, jboss-to-springboot , since the pattern it establishes is effectively an unwritten spec for the other two skills as well: README.md — the problem, what the skill does, and how to invoke it via the atx CLI. This is also where the repo draws a clear line: these are readiness transformations. They modify repository artifacts — code and infrastructure-as-code — but they don't deploy jobs, call AWS APIs to change running resources, or claim data-level equivalence. That distinction matters throughout everything below. SKILL.md — the agent-facing definition: YAML frontmatter with trigger keywords, an Objective, explicit Non-Goals , Constraints, worked before/after examples, a "signal in source code → reference file" routing table, and a numbered V