标签：#IDE

Whether you’re training hard, traveling often, or dealing with poor circulation, these are the best compression boots for anyone looking for better muscle recovery.

Not every great pair of headphones belongs in the gym. These do, thanks to their secure fit, durable design, and impeccable sound.

I've spent more than a decade building data pipelines, and the part nobody warns you about isn't the pipeline logic. It's the tuning. Executor memory, shuffle partitions, cluster size, thread counts. You pick numbers, ship it, and a few weeks later something breaks in a way that's obviously tuning-related but not obviously what to change . The pattern repeats enough times that you start recognizing it before you've even opened the logs. Job's slow, thousands of tiny shuffle tasks, someone way overestimated the partition count. Job dies on OOM, memory's set for last quarter's data volume, nobody updated it since. Cloud bill jumps, a cluster's been sized for peak load and just sits there mostly idle the other 20 hours a day. Every senior data engineer has this pattern-matching running in their head. It's tribal knowledge, and it lives in one or two people's heads on most teams, which means it doesn't scale and it definitely doesn't survive someone leaving. So I built a small tool to make that pattern-matching explicit instead of tribal: it reads your pipeline's config alongside its actual run metrics and tells you what's likely wrong, with the reasoning shown, not just a suggested number. Why rules instead of a model The obvious move in 2026 is to reach for an ML model. I didn't, and it wasn't because I don't think ML has a place here eventually. It's that for this specific problem, a handful of threshold rules already gets you most of the value, and they're something you can actually audit. If a rule fires, I can point at the exact condition and the exact number: average heap usage 28%, peak 47%, five runs, no OOM errors, therefore memory's over-provisioned, shrink it by roughly a fifth. That's checkable. You can look at your own metrics and see whether the reasoning holds. A model's confidence score doesn't give you that, and for something that's about to change a production config, I want the person approving it to be able to say "yes, I see why" rather than "the m

You don’t want any old gaming laptop. Here’s my take on which to get, based on hundreds of hours of testing.

Nice video of the Arctic bobtail squid. As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered. Blog moderation policy.

There’s something magical about using instant cameras that smartphones can’t match. You can capture a moment, print it out, and then give the photo as a gift or hold onto it. Image quality won’t be all that good, but imperfections are part of the allure. We tested instant camera models from popular brands and landed […]

Artificial intelligence is rapidly changing how engineering teams respond to production incidents, offering the ability to summarize incident channels, analyze unfamiliar code, suggest remediation steps, generate pull requests, and increasingly assist with diagnosis. By Craig Risi

I tested the best webcams across various prices to find the top option. Here’s what I learned.

These powerful, high-capacity portable batteries are the best ones when laptop charging is a priority. All have at least 20,000 mAh, enough to charge most laptops twice.

As gaming laptop prices continue to rise, it’s increasingly difficult to find affordable options that aren’t terrible. Here are your best options based on performance and cost.

This burgeoning wearable tech lets you talk to an AI assistant, listen to music, or check out a display screen from the comfort of your very own face.

Microplastics are in everything, especially your coffee. A new generation of plastic-free drip coffee brewers is trying to change this.

Recently, I’ve been spending more time building small projects on my own. One thing I’ve learned is that it’s usually better to keep things simple and ship early instead of trying to make everything perfect. A small project can still teach you a lot about coding, deployment, design, and how people actually use what you build. I’m planning to share some of my development notes and experiments here from time to time. Looking forward to learning from everyone on DEV.

A GitHub product launch is being held back by the CI/CD platform underneath it. On August 6 GitHub filed a Changelog entry announcing that Kimi K3, an open-weight model, is now generally available in GitHub Copilot, then added an editor's note the same day: the rollout is temporarily paused while GitHub mitigates an incident with GitHub Actions. What the entry says, and what it does not Per the note, GitHub will resume the rollout as soon as possible and update the docs with Kimi K3 pricing: $3 per 1M input tokens, $15 per 1M output tokens, and $0.30 per 1M cached input tokens. That is the extent of the disclosure. The Changelog does not describe the Actions incident, does not put a scale on its blast radius, and does not commit to a resume time. It also does not explain how a Copilot model rollout ends up gated on Actions in the first place; a reader can infer that some provisioning or feature-flag step rides the same platform, but the entry does not say so. Availability is qualified in a way worth flagging. Kimi K3 is GA on paper, but the switch that actually turns it on for end users is paused. The operational read There is a coupling here worth naming plainly. GitHub sells Actions as CI/CD for everyone else, and it also uses Actions to ship its own products. When Actions has a bad day, GitHub's launch calendar has a bad day too, in public. That is not a scandal; it is what dogfooding looks like when the changelog is a live document. It is also a data point for any team running a rollout on top of a hosted CI platform: your feature-flag flip is downstream of somebody else's incident queue, and you inherit that queue's MTTR whether or not it is on your status page. Two follow-ups are worth watching. First, whether the resumed rollout entry names the incident and its cause, or whether it stays silent. Second, whether Kimi K3's published pricing survives the pause unchanged. Until then, the GA label is doing work the runtime cannot back up.

A growing collection of pocket-size gadgets lets you easily make recordings and extract info from them. Here are our favorites.

These services deliver freshly roasted, delicious coffee picks right to your door—each with its own twist.

Building software is easy. Building something people actually want to use is the hard part. For the last few months, I've been working on CV Mimarı, a resume builder designed to make creating ATS-friendly resumes simple, fast, and accessible. 👉 https://cvimarı.xyz My goal wasn't to build "another resume builder." I wanted to remove the usual pain: confusing editors unnecessary account creation complicated formatting resumes that look good but fail ATS screening The idea was simple: Spend your time improving your experience, not fighting with Word formatting. What it currently does Today the project includes: Resume templates AI-powered resume improvements ATS score checking Resume optimization Cover letter generation Resume examples and guides PDF export Modern responsive interface I tried to keep everything clean and straightforward instead of adding dozens of unnecessary options. Sometimes software tries so hard to become "professional" that it forgets people just want to click a button and move on with their lives. Why I'm posting here I'm not looking for compliments. I'm looking for problems. Imagine you were using this to apply for your next job. I want brutally honest feedback. Things like: Is something confusing? Does the UI feel slow? What would make you leave the site? Which feature feels unnecessary? What's missing? Would you actually trust this with your resume? If something is bad... Tell me. If something is ugly... Tell me. If something makes you want to close the tab... Definitely tell me. The biggest challenge One thing I've learned is that building features is much easier than understanding users. I can spend a weekend implementing a new AI feature. But discovering why someone leaves after 20 seconds? That takes dozens of real users. That's why I'm asking for feedback before continuing to add more features. The roadmap Some ideas I'm considering: More resume templates Better AI suggestions Portfolio integration LinkedIn import Resume version history

I put handheld, wearable, and misting fans through a sweltering summer to see which ones kept me coolest.

Intro Day 20! I lined up 10 AIs that turn a single photo into a few seconds of video. Half ran locally on my DGX Spark, half in the cloud 🐱 What I used: DGX Spark (LTX-2.3 / Wan 2.2) / 8 cloud models via fal.ai / ComfyUI / ffmpeg The setup Item Value Input One identical photo (my cat on a desk) Length 6 seconds Settings Identical The only variable The prompt Easy prompt The cat looks at the camera and meows once. It opens its mouth, meows, then closes it. Its tail flicks and its ears twitch. Hard prompt The cat stands upright on its hind legs in a kitchen, wearing a small apron, holding a knife in its front paws and chopping vegetables on a cutting board. Steam rises from a pot behind it. Please, just watch it Some of the cats came out with very long legs. Anyway. First half is the easy prompt, second half the hard one. On the easy prompt, local and cloud were a fair match . On the hard one... cloud, I think...! Three rankings below. Ranking 1: Time Time per 6-second clip on the hard prompt. Rank Model Where Time 🥇 LTX-2.3 Cloud 41s 🥈 Wan 2.7 Cloud 92s 🥉 Happy Horse 1.1 Cloud 97s 4 Veo 3.1 Cloud 128s 5 Kling 3 Pro Cloud 205s 6 Seedance 2.0 Cloud 210s 7 LTX-2.3 Local 315s 8 Wan 2.2 Local 651s 9 daVinci-MagiHuman Cloud 710s 10 HunyuanVideo 1.5 Cloud 796s A 19x spread. Look at 1st and 7th. Same model, LTX-2.3 , nearly the same resolution. The only difference is where it ran — 7.6x . Local setup DGX Spark (GB10, 128GB unified memory, ~273GB/s). ComfyUI headless, workflows over its API. LTX-2.3 is distilled fp8 at 8 steps. At 1088×1920 peak memory hit 77.8GB, about 60% of 128GB. That was the ceiling. Dropping to 512×768 finishes in 70s, but with one-fifth the pixels. Wan 2.2 is I2V-A14B fp8, 20 steps, 480×640. Higher resolution does not finish in reasonable time. Ranking 2: Cost Rank Model Per 6 seconds 🥇 Local Electricity only 🥈 LTX-2.3 (cloud) $0.36 🥉 Wan 2.7 $0.90 4 Kling 3 Pro $1.01 5 Happy Horse 1.1 $1.08 6 Veo 3.1 $2.40 7 Seedance 2.0 $4.09 — HunyuanVideo / MagiHum

After Uber and Waymo ended their partnership in Phoenix earlier this year, experts and robotaxi watchers wondered whether the companies' improbable bromance was fraying. Not so, Uber CEO Dara Khosrowshahi said today. The two companies are committed to continue working together in Atlanta and Austin, and the partnership remains "very strong." "Waymo is a very […]