https://gds.blog.gov.uk/2026/06/02/building-for-the-future-m... https://www.adyen.com/press-and-media/adyen-payments-gov-uk

been building AI agents for a while and noticing a pattern: the LLM reasoning part works. the part that breaks is everything around accounts, logins, and verification. agent gets to "sign up for this service" and then: - email verification loop breaks - OTP times out while the agent is mid-step - captcha or bot detection fires - session expires between steps the model figured out what to do. the infrastructure around it didn't cooperate. curious if this matches what others are building. where do your agents actually fail in production? is it the reasoning, or is it the plumbing? submitted by /u/kumard3 [link] [留言]

Google's AI app Pixel Studio launched less than two years ago, but is now being shut down.

submitted by /u/ProfessorDeep8754 [link] [留言]

We're now deep into developer conference season, and one of the themes so far is the relentless conviction from Big Tech companies that AI is going to change everything about how we do everything. Nvidia's Jensen Huang made that clearer than anyone this week, when he described a completely new way of using our laptops […]

Hey HN, Guanming and Bill here from General Instinct ( https://general-instinct.com/ ). After years of working in robotics, we kept running into the same problem: the best models never fit the hardware we actually had available. The models that performed best were usually designed around datacenter assumptions: large GPUs, lots of memory bandwidth, and reliable network access. But most physical systems have the opposite constraints. That led us down the path of figuring out how much of a frontie

I work as a Developer Advocate helping people ship web apps as native mobile apps, and I kept answering the same questions over and over. So I wrote a guide that summarizes everything I recommend, based on 13+ years working in mobile development. From what I've seen, every time someone in a web dev community asks "how do I publish my React/Vue/Angular app to the App Store?", the answers are either "just make a PWA" (which doesn't actually get you on the stores) or links to tutorials that assume you already know what a provisioning profile is and have Xcode configured on a Mac. This one starts from zero. It's structured as an index, not a wall of text. Each step links to specific posts, videos, tools, and automation resources so you can go as deep as you need, at your own pace. Some things I cover that I rarely see explained well: - Why PWAs won't get you into the App Store or Google Play (and what to use instead) - How to generate iOS certificates directly from your browser, without a Mac or Keychain Access - The Google Play closed testing requirement that blindsides most first-time publishers: personal accounts created after November 2023 need 12 testers actively opted in for at least 14 consecutive days before you can go public. - Why you should install the Live Update plugin in your very first release, even if you have nothing to update yet. Adding it later means another full native build and another review cycle, which is the last thing you want when you're trying to push a critical fix. - How to build for iOS without owning a Mac Honest feedback welcome: is there a step that's unclear, or something you think is missing? https://capawesome.io/blog/11-steps-to-get-your-web-app-on-the-app-store/ submitted by /u/DayanaJabif [link] [留言]

Supabase, an example of an open source project becoming a fast-growing company, has greatly benefited from AI tools like Claude, Codex, and other vibe-coding platforms.

I feel like this might start becoming a lot more common, due to all the hype and marketing surrounding AI. Seems a lot of ppl believe anyone can build an app in one-shot and we don’t need experienced engineers anymore. When they ask a question like “why can’t we just one-shot the app”, it puts you on the hot seat to explain the entire development process to them, and all the steps it takes to get to a production quality app. AI can be used to assist and augment that process but you need a human in the loop to know and understand what they are doing. Rather than just telling them “go ahead and try it yourself”, how would you respond to this? How would you explain why AI can’t just make the entire app? How would you breakdown the step by step process of building an entire application? Thanks submitted by /u/throwaway0134hdj [link] [留言]

Run AI coding agents hands-free from a heads-up display Discussion | Link

Disclosure: I work on the benchmark below, so flagging that up front. We've been testing whether LLMs can critique recent science-paper summaries — catch planted flaws, overclaims, and missing evidence — and, separately, how calibrated they are about their own judgments (confidence scored with Brier, a strictly proper rule). The pattern that keeps showing up: the models best at spotting problems are also among the most confidently wrong when they miss. Critique skill and calibration look like different axes, not the same one. There's also a clear gap between raw accuracy and knowing when to abstain. It's open (Apache-2.0) if you want to poke at it: Leaderboard: https://huggingface.co/spaces/BGPT-OFFICIAL/refute-leaderboard Dataset: https://huggingface.co/datasets/BGPT-OFFICIAL/refute Curious how others think about measuring calibration vs. raw capability — is a proper scoring rule enough, or do you need explicit abstention metrics too? submitted by /u/connerpro [link] [留言]

Even if you've done nothing wrong, it's never a good idea to hand your phone to the cops. But international travelers at American airports often have no choice - even if they're US citizens. When Minnesota labor organizer Janette Zahia Corcelius returned home from a three-week trip to Europe in late April, she was detained […]

I'm working on a personal one-man project. It's very simple : it's a static website generated from some data stored in a JSON file. I have a prototype written in TypeScript/TSX, consisting of fewer than ten files (views), each containing an average of less than fifty lines of code. Only two of the pages retrieve data from JSON; the rest are simple TSX files that describe the project (pages like "About" or the "Privacy" page). Given how simple it is, I thought I'd go the extra mile and focus heavily on ensuring the entire code, from the build process to the distribution, is 1000% correct, stuff like: crazy type safety 0% chance of logical and consistency errors validation of JSON against a schema HTML (and attributes) that conforms to the specifications (no mains inside spans, no booleans inside hrefs...) I started exploring the various programming languages that allowed me to do all these things at once, and I found OCaml . It: is statically typed has yojson to parse JSON and into a nested OCaml tree data structures has tyxml to build valid HTML. If I understood correctly, it has the distinctive feature of performing strict checks on (HTML) element's attributes while libraries in other languages simply accept any string To be honest, I also looked into Elm , which seems to be even more lenient when it comes to error handling; however, its HTML generation library doesn't seem to have strict controls over attributes, not nearly as strict as tyxml. Is there something even more powerful that allows me to achieve what I want (code safety and error free) or is OCaml already the best? If so, what has been your experience with it? Any advice? I'll say it again: the project is so simple that you could rewrite it in any programming language in an hour, it's no problem for me. It's a chance to learn something new. Thanks in advance. submitted by /u/Wise_Stick9613 [link] [留言]

Saw a YC company has raised 200 million at a billion dollar valuation. https://www.starcloud.com/starcloud-4. Additionally - with the impending spacex ipo this seems like a big focus. Can someone with a stronger physics background explain why anyone would think this is a good idea?