Citizens must now spend 21 days in a third country before they are allowed to come home.

The restrictions, which can be turned off, will include a crackdown on “addictive” app features and will be in addition to a total ban on children under 16 accessing platforms like TikTok and YouTube.

OpenAI has issued another statement on the lawsuit, this time suggesting it lacks merit.

Hello all: I am just about finished with my first production-quality app. I developed it for macOS and Windows with full platform parity and am about to port to Ubuntu/Debian before releasing. I have licensing setup through Keygen.sh. I am happy with that decision so far; however, I have questions regarding that issue and the issue of telemetry. Privacy is a focus of mine and a commitment I made to prospective subscribers. That said, I need some form of telemetry and a DRM that can take subscription payments. I am trying to think of the best way to implement the Keygen.sh API for privacy. The app has, well, an app--the client--and it also has a remote server component. It will mostly be free but the more labor intensive developed elements and the tools that require the remote server will be paid at either a monthly or annual subscription rate. I am so confused about how to best implement the licensing API while remaining true to my privacy commitments. Part of me says, "Well, their information is going to be on Keygen's servers anyway, might as well host the store through their website/portal." The other inclination is to host the store on my server/website, so I can control how much information PII is required. The answer can be “very little,” but a much bigger question arises surrounding what to do with the information once accepted. (I’ve considered maintaining the subscriber data in some kind of encrypted space on the server with hardcoded, volatile keys. That way not even I can see their data, but they’d still be able to search for and regenerate their licenses, management payment methods, etc.) The other question I have is along that same vein and involves telemetry. What I would like to do is keep the telemetry deliverables locally stored. When the user has an issue, experiences a bug, or has some other issue requiring my troubleshooting assistance, I can request it and they can choose to release their telemetry data to me. In between those disclosures, using

The Google Cloud Workbench Notebooks extension for VS Code is a new tool that enables developers to connect their local IDE directly to managed Jupyter notebook environments on Google Cloud. By Sergio De Simone

Last year my team had to pick a coding agent, and I volunteered to run the evaluation. I felt good about it. I pulled up the public benchmark scores, lined up the contenders, took the one at the top, and told everyone we had a winner. Then we actually pointed it at our repo. It did not blow up dramatically. It just kept being slightly wrong in ways that ate our time. It wrote diffs our reviewers would not approve. It renamed a function and broke three files it had never opened. The tests it ran passed, and the repo was still broken. I had confidently recommended a tool based on a number that turned out to say almost nothing about our situation. That was embarrassing enough that I went and figured out why. It took a few weeks of reading and a couple more bad calls before I landed on something that works. This is that, written plainly, and I hope it saves you the meeting where you have to walk your recommendation back. Why the benchmark score lied to me The score was not fake. It was just measuring somebody else's code. Once I looked properly, four gaps explained the whole thing: The agent might have already seen the answers. The problems in these public benchmarks are old. Models were very likely trained on the actual fixes used to grade them. So the score partly measures memory, not problem-solving. The setup is nothing like real work. A benchmark gives the agent a clean repo, one clear issue, and one command to run the tests. My engineers give it a half-open editor, a messy branch, a Slack thread, and a reviewer comment. Completely different job. Our codebase has its own habits. Our internal libraries, our wrappers, our test style, the imports we ban. No benchmark knows any of that, so an agent can write textbook-perfect code that our reviewers still reject on sight. The bar for passing is way lower. A benchmark passes a patch if the broken test now passes. My team passes a patch if it does that, and does not break unrelated tests, does not reformat the whole file,

One weekend I wrote an LLM eval framework in about two hundred lines of Python. It demoed beautifully. I felt clever. Six months later that same framework was a mess. Three different judge models with three different parsing hacks. A test dataset nobody had touched since November. A CI gate that kept failing because a vendor nudged their model, not because anyone broke a prompt. And the second engineer on rotation asking me, fairly, "how does this even work?" The framework did not fail. The eighty percent of the work the weekend tutorial skipped is what failed. That gap is the whole story, and this is what I would tell myself before starting again. The one line I wish someone had told me: build the rubric, buy the runner Here is the split that took me six months to see. Some parts of an eval setup are yours and only yours. The rubric that decides what "good" means for your product. The dataset built from your real failures. The rules for when a change is bad enough to block a release. Nobody else can write these, because they encode your domain. The rest is the same at every company. The thing that calls the judge model, parses its answer, retries, and caches. The machinery to run thousands of checks in parallel. The plumbing that scores live traffic. The system that groups failing calls together. Every team rebuilds these, hits the same bugs, and gains nothing by writing them twice. So build the first list. Do not hand-build the second. I rebuilt the second, and it cost me most of a year. Two questions I now ask about every single piece Before writing any part of this, I ask two things: Is it specific to me, or generic? A rubric for my domain is specific and worth owning. A retry-and-cache loop around a model call is generic. Everyone writes the same one. Does it compound, or does it rot? A dataset that grows from real production failures compounds. A year in, it is a regression suite no competitor can copy. A hand-built tracing layer rots. The moment a vendor chan

A number of social media posts claim that GPT-5.6 Sol deleted files and data without warning. OpenAI had basically disclosed the problem in June.

It seems some people take issue with one billionaire family overseeing a third of the US' entertainment media.

SPOILER: De 1 à 2000 Durante minha jornada como jogador , existiu uma coisa que me despertou muita curiosidade: os créditos. Quando eu jogava coisas como Final Fantasy, ficava completamente arrepiado quando via aquelas cenas antes do menu inicial, com CGIs bem bonitões e um monte de nomes em japonês — muitos deles que, honestamente, até hoje não sei quem são. Esse arrepio também acontecia quando eu chegava ao final de algum jogo e começavam a aparecer nomes e mais nomes. Quando eu entendi o que eram aqueles nomes, a primeira coisa que me veio à mente foi: peraí, precisa de tudo isso de gente pra fazer um jogo? Too long, didn't read : Sim e não. Skyrim levou aproximadamente 6 anos com uma equipe de 100 pessoas (e ainda é dito que é uma equipe enxuta), enquanto Stardew Valley levou 4,5 anos com uma "equipe" de apenas 1 pessoa. Eles têm quase o mesmo tamanho em horas jogadas na campanha principal. Not too long, I'll read it : É um pensamento lógico que trabalhos maiores no mundo da tecnologia envolvam uma quantidade maior de pessoas trabalhando E tempo. Dessa forma, seria correto assumir que um trabalho menor exige menos tempo e menos pessoas trabalhando. Mas o que os dados dizem? King of Fighters 94: 2 anos, começou com 6 pessoas, aumentou para 60. Clair Obscure - Expedition 33: aproximadamente 6 anos, 30 pessoas. TES V - Skyrim: 6 anos, 100 pessoas. Super Mario Bros: 2 anos, 7 pessoas na equipe principal. Stardew Valley: 4,5 anos, 1 pessoa. Kenshi: 12 anos, 1 pessoa por 6 anos (trabalhando meio período), aproximadamente 8 pessoas por mais 6 anos (tempo integral). Final Fantasy VII (PS1): aproximadamente 2 anos, de 100 a 150 pessoas (uma das maiores equipes da indústria na época). Final Fantasy VII Remake: Cerca de 5 anos, e não se tem números exatos, mas os créditos citam mais de 2000 pessoas, incluindo muitos -istas e -ores (vou tentar confirmar esse número depois). É claro que existem muitos fatores envolvidos aí, como época, demandas de mercado, prazos, tecnologia

Design + Product Thinking: NYC’s Path to Reliable AI AI delivers value when it’s useful, trusted, and operational. For city services that affect millions, those qualities don’t happen by accident — they come from applying design thinking (who the service is for, how it’s used) together with product thinking (what outcome we’re trying to achieve and how we operate over time). This article explains why hiring designers and product managers matters for NYC’s digital and AI initiatives, summarizes the city’s PIT Crew program, and outlines how Flamelit applies outcome-focused delivery in the public sector. Why design and product roles matter Designers and product managers have distinct but complementary responsibilities that reduce common AI delivery failures: Designers (Design Thinking): center human needs, prototype user flows, and validate that interfaces and decision workflows are understandable and accessible. They surface usability and trust issues early, preventing technically accurate models from becoming unusable in practice. Product managers (Product Thinking): define the measurable outcomes, prioritize use cases, align stakeholders, and manage the lifecycle from discovery to ongoing operations. They ensure work is evaluated against mission impact, not just technical metrics. Together they prevent common failures: building technically impressive models that nobody trusts, deploying brittle systems without human review, or shipping features with unclear ownership that decay in production. PIT Crew and NYC hiring context NYC’s PIT Crew program is a city initiative designed to attract and staff product, engineering, and design talent for public service projects. It’s a practical recognition that public-sector digital transformation needs people skilled in user research, product management, and delivery. Read more about the PIT Crew and how it works here: https://www.nyc.gov/content/pitcrew/pages/ (open in a new tab). Hiring programs like PIT Crew help create the c

Genkit Agents API, ORA, Python AI Explainer: New Tools for Workflow Automation Today's Highlights This week, Google's Genkit ships a powerful Agents API for TypeScript and Go, featuring human-in-the-loop capabilities for robust production deployments. Additionally, a new Go-based open-source task orchestrator, ORA, emerges for efficient model routing, alongside a practical Python tutorial for building an AI error explainer. Google's Genkit Ships Agents API with Detached Turns and Human-in-the-Loop for TypeScript and Go (InfoQ) Source: https://www.infoq.com/news/2026/07/genkit-agents-api-preview/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global Google has released a preview of its Genkit Agents API, significantly enhancing its open-source AI framework for building generative AI applications. This new API introduces features critical for deploying robust AI agents in production, specifically "Detached Turns" and "Human-in-the-Loop" functionalities. Detached Turns allow agents to operate asynchronously, handling long-running tasks or waiting for external events without blocking the main workflow, which is essential for complex, multi-step agentic processes. The Human-in-the-Loop feature provides crucial mechanisms for human oversight and intervention, ensuring reliability, safety, and compliance in critical applications where full automation is not yet feasible or desirable. The Genkit Agents API supports both TypeScript and Go, targeting a broad range of developers integrating AI into existing systems or building new agentic workflows. By offering structured patterns for agent interaction, state management, and human review, Genkit aims to streamline the development and deployment of intelligent agents, addressing key challenges in control and reliability for real-world AI applications. Comment: The introduction of Human-in-the-Loop into Genkit's Agents API is a game-changer for production-grade agent systems, offering the control and reliab