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The tech world has been building up towards the shift to a fully agentic development life cycle for a few years now. AI is changing how software gets built. Across the Puppet ecosystem, we're seeing a shift toward more agentic engineering workflows. AI helps generate code, shape documentation, and accelerate how Puppet modules evolve. This brings real benefits in speed and consistency, but it also raises important questions from the community: How are AI-generated changes validated? How do you ensure consistency across modules? What does this mean for contributors and maintainers? These are exactly the kinds of questions we should be asking! This article will outline how Perforce and the Puppet team are approaching the use of AI in our open source modules and repositories. How We Build Trust in AI-Assisted Contributions At Perforce, AI is a core part of our process and our teams operate within a defined, governed framework for development. We don’t rely on trust in the tool itself. We rely on the processes around it. Whether a change is written by a person, generated with AI, or some mix of both, they are held to the same standards before it’s accepted and released. In practice, that means: Human review is always the gate: Every change is reviewed by maintainers. AI can assist, but it doesn’t replace accountability. AI works within established patterns: AI-generated code isn’t created in isolation. It’s guided by the same module structures, conventions, and expectations that already exist across the ecosystem. Validation is continuous and enforced: AI doesn’t change our standards. It reinforces them. AI-generated changes go through the same checks as any other contribution: Test suites Integration validation Functional verification AI output is a starting point, not a final artifact: Generated code is iterated on, refined, and aligned before acceptance. We treat AI as an accelerator, not an authority. The community plays an important role Open source means visibilit

Logitech's new gaming boss, Robin Piispanen, tells me he likes the idea of gaming handhelds. "It's such a charming value proposition," he says, as we sip iced vanilla lattes at my local cafe. But he's not building one right now. He's not sure when or if Logitech should try again, after its underwhelming experiment with […]

Apple has officially introduced "Apple Upgrade," a new leasing program that aims to make it easier to get your hands on the latest iPhone, Mac, iPad, and Apple Watch models. The service is launching today in the US, and works like a car lease - allowing users to keep a device at the end of […]

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I work on PostSlate, a video editing tool, and this comes out of our own work. We run ML models on-device, face detection and embedding among other things, which means we can't assume anything about the user's GPU. NVIDIA discrete, AMD, Intel integrated, Apple Silicon, all of it. That rules out CUDA immediately, we needed one backend that runs everywhere. We landed on ncnn's Vulkan backend. Numbers on a 4070, fp16: ArcFace R50 (face embedding): 30 ms on ONNX CPU → 3 ms on ncnn Vulkan SCRFD (face detection): 25 ms → 2.5 ms Model size: ArcFace 174 MB (ONNX fp32) → 87 MB (ncnn fp16 weight storage) Of course the real speedup comes from offloading compute to the GPU, but this wouldn't be possible without the power of Vulkan. The speed wasn't even the deciding factor, it's that Vulkan drivers already exist on every machine we ship to. This means that we don't have to force the user to download a specific runtime and no vendor-specific installs. Full writeup with the rest of the numbers: https://getpostslate.com/blog/faster-local-inference submitted by /u/ppchaos [link] [留言]

Your mom never gets you a thoughtless gift, so you shouldn’t get her one, either. Here’s every cool gift WIRED writers would give their mothers.

Grafana Labs has expanded the capabilities of Grafana Assistant, enabling its AI-powered observability assistant to query and correlate data across more than 30 different data sources through natural language. By Craig Risi

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Should you splurge on the new Series 11, or will the SE 3 do? Let us help you figure out which version to get (and which to avoid).

Let them pick out their own hiking boots. Instead, try gifting a useful blade or a nature journal to delight your outdoorsy friend.

Duplicate message processing is something every event-driven system eventually faces. With at-least-once delivery, retries and redeliveries are expected. The challenge is making sure processing the same message twice does not create side effects. I've been looking into the Inbox Pattern as a consumer-side solution: track processed messages keep message tracking and business changes in the same transaction scope the idempotency check per consumer One approach is using a MassTransit pipeline filter so the idempotency logic stays outside the consumers. How do you usually handle this? Do you use Inbox Pattern, custom middleware, database constraints, or something else? submitted by /u/DotDeveloper [link] [留言]

Ben Greene discusses how software engineers can adapt and thrive in an era of rapid AI code automation. Drawing on his startup experience, he explains key mindsets like starting simple, maintaining code comprehension, attacking hard problems first, and focusing on customer impact. He shares why human empathy, agency, and practical problem-solving remain irreplaceable when code is automated. By Ben Greene

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Governments are switching, but I’m not sure it makes a difference : …some municipalities, including Denver, Colorado, are ditching their Flock arrays. But keep in mind that if they’re only switching from Flock to another brand of license-plate readers, like Axon, it’s like a gambling addict trying to kick the habit by switching from FanDuel to DraftKings. […] Despite what you may read on the Flock website, Axon cameras are pretty effective when it comes to hoovering up personal details that can go far beyond your license plate numbers. That means a municipality that opts for Axon cameras instead of Flock units won’t necessarily reduce the amount privacy its citizens lose through their use...

Your Twitter bookmarks organized to make you read them Discussion | Link

In 2023, the Times sued OpenAI and Microsoft for copyright infringement. They’ve since spent more than $20 million on the case, and publisher A.G. Sulzberger has no plans to stop fighting it.

Free voice coding for Claude Code, Codex and more Discussion | Link

As it promised last year, Baidu has started testing its robotaxis in London along with partners Lyft and Freenow.

A scraping job usually fails in boring ways: the browser hangs, a selector starts returning empty strings, a login expires, or the target site returns a captcha halfway through the run. The awkward part is that many platforms still bill you for the work done before the failure. If you run enough jobs, that difference shows up both in your invoice and in the amount of defensive code you need around the scraper. Billing by compute time changes how you build A lot of scraping platforms charge for runtime. Apify, for example, uses compute units: memory multiplied by time. A browser-heavy actor running for ten minutes with 2 GB of RAM consumes roughly a third of a compute unit before any actor-specific result fees. That model is reasonable from the provider side. Chromium processes are expensive. Proxies cost money. Retries use resources. But as the caller, you care about a different unit: did I get the rows I needed? The hard part is that runtime billing makes cost hard to know before execution. A job that normally takes 30 seconds might take 8 minutes when a site slows down. A job that returns malformed data can still count as successful from the platform's point of view. A job that fails after rendering 200 pages still consumed browser time. If your pipeline runs once a day, that may be fine. If it runs continuously, you probably want a local cost model that tracks outcomes, not just requests. type ScrapeRun = { jobId : string ; target : string ; startedAt : string ; finishedAt ?: string ; status : " queued " | " running " | " succeeded " | " failed " ; rowsExpected ?: number ; rowsReceived ?: number ; billedUnits ?: number ; }; function isUsefulResult ( run : ScrapeRun ) { if ( run . status !== " succeeded " ) return false ; if ( run . rowsExpected && ( run . rowsReceived ?? 0 ) < run . rowsExpected * 0.9 ) { return false ; } return ( run . rowsReceived ?? 0 ) > 0 ; } function costPerUsefulRow ( run : ScrapeRun ) { if ( ! isUsefulResult ( run )) return Infinity ; ret

Anthropic and OpenAI employees are expected to give generously after their companies go public. “It’s going to be a wild ride,” says one nonprofit leader.