标签：#AR

I've been tuning Codenames AI , a small web game where an LLM plays Codenames with you. Clue generation is tightly constrained: one word, a count, optional intended targets, JSON on the wire, then deterministic validation before anything reaches the board. As the project started attracting regular players, I wanted to improve the gameplay experience without blowing out costs. Moving one model generation from gpt-4o-mini to gpt-5-mini was my first instinct. The default reasoning setting made responses an order of magnitude slower for this workload. Minimal reasoning looked like the obvious compromise: newer model, responsive gameplay. I expected to compare clue quality, latency, and cost while the surrounding prompt, validator, and consumer contracts stayed put. That last part was wrong. The experiment stopped behaving like an A/B test What showed up was structural, and it showed up in places that had been stable for months. Validation failures started rising. Retries started rising. Entire candidate batches started failing before the game ever saw a clue. The sharpest signal came from a clue-selection path that had run untouched for months, and it hard-failed for the first time. They weren't latency regressions so much as architectural ones. It is easy to read that as "minimal reasoning made the model worse." More often, the failures were exposing gaps in contracts that had looked fine under the previous model. What each failure actually invalidated Eventually every failure traced back to one of three layers: Prompt contracts ask for exactly count targets and, in batch mode, several distinct candidates. Deterministic validators reject target/count mismatches and filter invalid candidates before anything downstream runs. Downstream consumers only see survivors. Empty batches retry with rejection feedback, then fall back if needed. Those layers share one job: enforce the same invariants. The failures below cut across all three rather than mapping one to one. Side comm

Just getting this concept is like money in the bank! I'm too excited for words! At age 67, I have never coded a thing in my life. All of the sudden in 2026, I find that I've become a "vibe" coder with the help of Grok and Gemini. Both in segregated project silos are acting as my PMs and doing a damn fine job of it. As CEO of a brand new company, I'm taking on the entire scope of development for The Avinoam Group, LLC. My business partner Eyal and I have a very cool vision for what we intend to accomplish. And while it is beyond the scope of this post, only 2 crazy non-dev guys would be this bold, with all due respect to the young guns here on DEV. Our core principles incorporate transparency and authenticity. I can think of no better business idea as 4 separate cornerstones (we have 4 separate projects) than the brilliant concept-commitment to #build in public. So, I'm looking forward to telling everyone what we're doing and how we're doing it. We have nothing to hide except a little "secret sauce" that we can't really talk about without shooting ourselves in the foot. Other than that, I want to "show & tell" just like when I was in kindergarten in 1964 and brought "Meet The Beatles" in to Miss Shreiner's class and all the little kids danced! Right now, I'm reading how to get the most out of DEV and being here. At my age, I like to give back. What can an old geezer whose never coded before offer? My life experience and business knowledge. For example, we are developing freepaycalc (Google it if you want) to help devs, makers and freelancers plug the leaks in their "money buckets." and how to kill off that dreaded "scope creep" that so many independent consultants and contractors get trapped in. I don't want this post to be too long. I just wanted to reinforce the value of building in public. If you want to punch through each and every challenge of a complex project, then why not tell brilliant dev colleagues what you're up to? Whatever problem you're facing has a so

Patreon is strengthening its defenses against AI scraping by working with Cloudflare to block bots that train AI models on creators’ content without permission. The move marks a shift away from relying on websites using robots.txt alone to actively block unauthorized AI training.

I tested REI’s best new and revamped outdoor gear of 2026. Here’s what’s worth buying, from tents and sleeping bags to coolers and bikes.

Remarkable's Paper Pure replaces Remarkable 2, and it is quite good.

It’s not just you: AI startups are taking in a huge amount of seed funding, and in the process making things harder for anyone looking for funding even at a pre-seed stage. We’ve covered the trend in detail, and at this year’s TechCrunch Disrupt event, we want to help pre-seed founders now being held to seed-stage […]

The recall comes as the top automotive safety regulator in the U.S. has warned AV companies about their vehicles interfering with first responders.

There's truth to the idea that our devices are spying on us. Smart fridges, washing machines and dishwashers are no exception.

Ben O'Mahony discusses building custom AI-powered Language Server Protocols (LSPs) that go beyond standard rule-based checkers. He explains how to instrument AI agents natively with OpenTelemetry to track concrete user actions (accepting, dismissing, or regenerating code fixes) as implicit labels, creating a continuous data flywheel to distill frontier capabilities into cheaper, local SLMs. By Ben O'Mahony

GitHub热门项目 | Master programming by recreating your favorite technologies from scratch. | Stars: 526,862 | 1,070 stars today | 语言: Markdown

I run a pipeline that generates explainer articles. LLM in the middle, structured output, published in several languages. It had been running for a while and the articles looked good: clean layout, a chart, and near the top of each one a confident little box with a statistic. Something in the shape of "68% of people never change the default." A number, a source, an authoritative ring to it. Not one of those numbers had been researched. The pipeline had never looked up a single statistic in its life. It asked the model for a number and printed whatever came back. I did not find this through a clever eval. I found it while cleaning up something unrelated and actually reading the prompt. The field that forced a lie The output schema had a required field. statistic.text and statistic.source , described in the prompt as an "eye-catching stat" for the top of the article. Required. Every article had to have one. The prompt also, helpfully, told the model what to do when it did not have a real number. It said to round to a safe order of magnitude. And it said to strip the year off the source, so the article would look evergreen instead of dated. Read that back slowly. The instructions were: always produce a statistic, make up a plausible magnitude if you have to, and remove the one piece of metadata that would let anyone check it. That is not a prompt that occasionally allows a hallucination. That is a prompt that requires one, every single time the model does not happen to know a real figure. So it produced them, confidently, in every language, each wearing a real-sounding source: a named institute, an industry association, a government statistics office. None of it had been looked up when it was written. This was content people actually act on, which is exactly the category where being wrong is not a rounding error. There was a second engine doing the same thing in the chart code. The block that generated the data visualization asked the model for "actual statistics from

How I went from toggling a single GPIO pin to deploying intelligent, low-power firmware on the edge — and the lessons that shaped me along the way. The first program I ever ran on a microcontroller did exactly one thing: it blinked an LED. No operating system. No framework. No safety net. Just my code, a register, and a clock ticking a few million times a second. When that LED finally blinked at the rate I intended — not too fast, not stuck on — I felt something I hadn't felt writing software before. On a bare-metal system, nothing happens unless you make it happen. There's no runtime quietly cleaning up after you. That mix of total control and total responsibility is what pulled me into embedded systems, and it's the same thread that eventually led me to running machine learning models on the edge. This is the story of that journey — from a single blinking pin to intelligent devices that sense, decide, and act on their own. The Bare-Metal Beginning Bare-metal firmware is where you learn what a computer actually is. When you write to a memory-mapped register to toggle a GPIO, or configure a UART peripheral one bit at a time, there's no abstraction hiding the hardware from you. You read the datasheet. You read the reference manual. You get the clock configuration wrong, and nothing works — no error message, just silence. Then you fix it, and suddenly bytes are streaming out of a pin at exactly the baud rate you configured. Most of my early growth happened writing low-level peripheral drivers — UART, SPI, I2C, GPIO, ADC — on ARM Cortex-M platforms. These are the unglamorous building blocks, but they teach you the discipline embedded work demands: Every byte and every milliwatt matters. On a resource-constrained MCU, you don't get to be careless with memory or power. Timing is a first-class citizen. An interrupt that fires 50 microseconds late can break the whole system. The hardware is always right. If your code and the oscilloscope disagree, the oscilloscope wins. Th

I sat the Claude Certified Architect exam expecting questions about model parameters, context limits, and API flags. I got something else. The exam barely tests trivia. It tests judgment: given a broken agent and four plausible fixes, which one actually addresses the root cause? The interesting part was how few ideas the whole thing rests on. The same handful of rules kept deciding the "right" answer, and they are the same rules that decide whether an agent holds up once real users touch it. Below are the twelve I kept running into, plus the four traps that look like solutions and are not. This is my own study material, derived from publicly available exam guidance. It reflects how I build, not an official Anthropic position. The twelve rules Enforce determinism in code, not in prompts If a rule has to fire every single time, it is not a job for a prompt. A prompt is a suggestion the model usually follows. "Usually" is not a guarantee. When you need a guarantee, put it in a hook, a gate, or an allowlist. Code enforces. Prose requests. Pick the cheapest fix that hits the root cause Before you build a subsystem, try the levers that cost minutes: a sharper tool description, an explicit acceptance criterion, a config change. Most "we need to build X" moments dissolve once you test the cheap fix first. Reach for the classifier only after the one-line change fails. Bad tool selection? Start with the descriptions When an agent keeps picking the wrong tool, the description is almost always the culprit, not the model. Tool descriptions are the primary signal the model uses to choose. Rewrite them to say exactly when to use the tool and when not to, before you go anywhere near few-shot examples. Over-engineering is almost always the wrong answer Narrowing scope and improving the prompt beat a new subsystem far more often than engineers expect. Every subsystem you add is one more thing to debug, monitor, and keep in sync. Complexity is a cost you pay forever, not once. A bigge

Xreal's XBX a01+ is the best pair of budget AR glasses we've seen yet.

We consulted preparedness experts and WIRED’s team of testers for the essential gear to keep on hand in case of wildfires, earthquakes, and lord knows what else.

FCC chair has been gifted at least $63,000 worth of tickets by CBS or its parent company.

Quick, comfortable, roomy, and agile for a large electric SUV.

Great for indie darlings, but Sony, Microsoft, and Nintendo remain kings of TV gaming.

From the best air fryer to frying pans to knife sharpeners anyone can use, these ideas will keep the curious home chef in your life tinkering away.

The Japanese Microwave measures food temperature directly, and stops cooking when food’s hot. It’s kinda terrific.