“Why don’t we just frickin’ call Apple?”

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

EveryPlate is an actual budget meal kit whose plates taste delicious. Options and ingredients are fewer, but simplicity can also be a virtue.

The superior RX 9070 also launched for $549 just over a year ago.

Back in 2002, Porsche fans sputtered with rage as the Cayenne made its debut at the Paris Motor. More than 20 years later, Porsche now sells more SUVs than anything else in its lineup. Last year, the Macan and Cayenne accounted for 62 percent of all Porsche sales. Now, these SUVs are trolling traditionalists in […]

I kept shipping AI apps with no idea what was happening under the hood — prompts going in, responses coming out, costs creeping up, and zero visibility into any of it. So I built LogLens. Add one line of code and it logs every single AI call your app makes — the full prompt, completion, latency, token count, and cost — all in a clean dashboard. Works with Anthropic and OpenAI out of the box. No framework lock-in. npm install loglens const anthropic = wrapAnthropic(new Anthropic(), { apiKey: 'your-key' }) // that's it — every call is now logged Built the whole thing in ~48 hours using Claude Code. Still early but fully working. Free early access here: llm-watch.vercel.app Would love feedback — what features would make you actually use this day to day? submitted by /u/ProcessAutomatic6941 [link] [留言]

With ideas and project specs scattered across platforms like Notion while actual tasks live in GitHub, keeping them in sync is constant manual overhead—how do you bridge this gap without your PM tools and repos becoming a mess? submitted by /u/asifdotpy [link] [留言]

Google seems to be testing a new feature that will take you to AI Mode by default when you do a search in Chrome.

I got really tired, as a human, of parsing the standard marketing heavy web we have today. I've always loved the simplicity of gopher and gemini web. Recently I found myself manually adding `/llm.txt` to most websites I visit because I find the content for LLMs strait to the point and clear. The only annoyance is web browsers like chrome do not render the markdown. So could the AI revolution actually fix the web for humans as a side effect? Do you find yourself doing the same?

submitted by /u/mariuz [link] [留言]

Add Tab Bar, Mega Menu & more to any website — no code Discussion | Link

submitted by /u/f311a [link] [留言]

I'm working with AWS for many years, and currently I'm working in product with suppose to be cloud agnostic. I started with AWS and now it's time to spin up it into Azure (because many enterprises using azure for some reason). I started in US EAST region in azure and at beginning I had an issue with Postgres Flexible, raised a support ticket, and in the result they recommended me to move to another region. The overall conversation to say this takes about 1 day. I've moved to US EAST 2, and after

“Why wouldn’t you want to be in both Pepsi and Coke?” says one venture capitalist. “It’s the same here.”

From battery life to privacy, there are many hurdles to the idea taking off.

A lot of recent discussion around Edge AI focuses on running increasingly larger local LLMs. Meanwhile modern smartphones already have enough compute for many practical computer vision tasks that don't require massive models at all. I recently built and released an Android feature that performs offline recognition of handwritten and printed Morse code from images and live camera frames. The final solution combines lightweight ML and computer vision techniques running entirely on-device. The AI module is under 5 MB, works fully offline, and runs on Android devices using LiteRT for inference. What made the project particularly interesting was that the entire ML pipeline was built from scratch: data collection, synthetic dataset generation, annotation, model training, evaluation, mobile optimization, and Android integration. Training was performed on a personal GPU workstation using TensorFlow/Keras, while annotation and dataset preparation relied on Label Studio and custom data-generation tools. While the problem itself is fairly niche, the project made me wonder whether we are overlooking a large class of small, highly specialized models that can solve practical tasks locally without requiring cloud infrastructure or large foundation models. What practical Edge AI applications do you think are currently underexplored? Demo video showing the feature running entirely on-device: • Downloading the optional AI module • Real-time camera recognition • Image recognition • Module removal https://youtube.com/shorts/Y2qOK0N1Bvk submitted by /u/VegetableLegal6737 [link] [留言]