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The RAM, storage, and component crunch has come for new laptop prices. Plenty of models have gone up in price after being on the market for months, and so have those that’ve been on shelves for over a year. Plus, many new models are releasing at higher prices than the previous generation. So, it really […]

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GPT-5 Pro helped solve a 3-year-old immunology mystery, offering insights into T cell behavior. The breakthrough could support cancer and autoimmune research.

Anthropic’s new Claude Tag brings an always-on AI teammate to Slack. But beyond productivity, the feature is a strategic play to capture organizational context, institutional knowledge, and enterprise workflows.

Anthropic has introduced @Claude for Slack.

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I Am Frankelda, A.I. Artificial Intelligence, and From Russia With Love are among the films deserving of your eyeballs this month.

Beyond the Prototype: Why Teams Need More Than Vibe Coding Over the last year, AI coding tools such as Lovable, Bolt.new, v0, Base44, and others have fundamentally changed how software gets created. A single founder or developer can now go from a rough idea to a working prototype in hours rather than weeks. That kind of acceleration is genuinely exciting, and it has opened software creation to far more people. That democratization is a good thing. Rapid experimentation, faster feedback loops, and lower barriers to entry are changing how products get started. Many successful companies and ideas will emerge because these tools made building more accessible. As I've followed the conversations happening around these tools—through reviews, articles, community discussions, and the experiences being shared by founders and engineering leaders—I've noticed an interesting pattern. The challenge is no longer getting to the first version. The challenge begins after. The Prototype Was Never the Finish Line The prototype works. Stakeholders become excited. Customers show interest. Momentum builds. Then a different set of questions starts to emerge. How do we align everyone on what we're building? How do we evolve an existing application instead of starting over? How do we maintain quality as complexity increases? How do multiple people collaborate without losing context? How do we know whether we're delivering the outcomes we intended? And how do we continuously improve without creating chaos? These aren't failures of AI coding tools. They're simply different problems. Many of today's AI builders are optimized for individual acceleration and rapid exploration. But once a promising idea becomes a product that teams must own, maintain, and evolve together, different requirements naturally emerge. What works for one person experimenting is not always enough for a group of people building something intended to last. Building Software Is More Than Generating Code Software development

Your prompts are fine. Your AI output is still garbage. You write carefully. You're specific. You ask for the format, the tone, the length. Hit enter. The AI responds with something that sounds like it was written by a committee of lawyers having a really bad day. Here's what you don't realize: You're not telling the AI to do something. You're describing the problem, and the AI is solving for the statistical average. The fix isn't more detailed instructions. It's three examples. That's it. Three. Not ten, not one, three. This post is the complete guide to few-shot prompting — the single highest-leverage move in prompt engineering. By the end, you'll have a template you can copy into any AI and watch your output quality jump 5x. Prefer watching? Here's the 3-minute version Otherwise, read on — everything's below. Why Instructions Fail (And Examples Work) When you tell an AI to "be funny," it's working off a fuzzy statistical average of everything labeled "funny" in its training data. When you show an AI what you think is funny, you're giving it a precise pattern to match. Here's the difference: ❌ Instruction: "Write a funny one-sentence movie summary" Result: A lukewarm joke that lands in the middle of the comedy bell curve. ✅ Pattern: Funny summary of The Lion King: Cub loses dad. Cub becomes king. Funny summary of Finding Nemo: Dad fish swims very far for his son. Funny summary of Titanic: [AI fills this in] Result: Boy meets girl. Boat meets iceberg. Oops. Same AI. Different universe. The only thing that changed: you showed it the pattern instead of describing it. The Science (Why This Isn't Magic) Language models predict the next token by pattern matching. They've seen millions of prompt-response pairs and learned: "When a prompt looks like this , the output usually looks like that ." One example could be a fluke. Two examples might be a coincidence. Three examples are clearly a pattern. The AI recognizes the pattern and completes it. This is exactly how humans l

We’re calling for targeted amendments to resolve conflicts with open source licensing and align with international transparency frameworks while preserving regulatory intent. The post GitHub joins coalition advocating for fixes to California AI Transparency Act to protect open source appeared first on The GitHub Blog .

This post is my submission for DEV Education Track: Build Apps with Google AI Studio . What I Built I set out to build the same app as the one mentioned in the Tutorial. Please create an app that generates a unique new Magic the Gathering card, using Imagen for the visuals, and Gemini to create the text descriptions and stats for the card. Apply the "Sophisticated Dark" design theme to the app. Spammed Fix Errors Non-Stop After this other than the Manual Entry option. Demo My Experience You can't trust Gemini Flash even for the Task provided in the Tutorial Standalone at least and well I spammed Fix Errors and they removed the Auto-Fixing of Errors because of idk an infinite loop or something but well the Error Fixing Experience was quite Meh considering I haven't delved into Vue and React in that level yet so I just 'Vibe Coded' and I found out with this experience that Vibe-Coding is UnCool. I think I would do the other course after properly understanding concepts behind it unlike the way I jumped in this One.

A wide day rather than a deep one — four separate threads across a few projects, each with a lesson worth keeping. I'll teach the patterns and keep the specifics generic. The through-line: make the system honest about what it's actually doing — which queries it fires, whether a service is really up, what a tool will do when you call it twice, and in what order a password change should land. The performance thread got big enough that I split it into its own focused post; here's the short version plus the three other threads. Thread 1 — Stop paying for queries you don't use A sustained sweep through an app (and the package behind it) hunting wasted database work. The highlights: Arm an N+1 detector in dev only. A query detector wired in behind an environment check turns invisible lazy-loads into a visible to-do list. Never in production — it's a developer aid, not a runtime guard. Unused eager loads are N+1s in disguise. Index screens love to with(['creator', 'approver']) for columns a redesign later removed. Not a loop, but the same disease: queries you hydrate and throw away. Delete the eager loads with no consumer in the view. Memoize per-request constants. A default-connection resolver and a sidebar unread count were both recomputed on every call. ??= once, reuse for the rest of the request. Collapse a dashboard's stat queries. ~20 count() calls became one grouped query per table, wrapped in a short-lived cache. A dashboard can tolerate being a few seconds stale; trade live-to-the-second for cheap. The meta-lesson: performance at this layer is mostly removal , and you lock it in with a Pest query-count assertion so nobody quietly re-adds an N+1 six months later. Full write-up in the focused post. Thread 2 — Health checks that actually check Here's a trap I keep seeing in "is it up?" tooling: the check verifies the record exists, or that a config row is present, and calls it green. That's not a health check — that's a config check. The service can be configured per

Performance work has a reputation for being glamorous — the heroic "we cut latency by 80%" story. Most days it's not that. Most days it's a janitorial pass: you go looking for the queries you're firing without realizing it, and you quietly delete them. That was today. One sustained sweep across an app and the package that backs it, chasing the same theme everywhere: stop asking the database for things you don't use. Let me walk through the patterns, because they generalize to any Laravel app of a certain age. First, make the invisible visible You can't fix N+1s you can't see. The first move was wiring up an N+1 detector in the local/dev environment only — beyondcode/laravel-query-detector . It hooks into the request lifecycle, watches your Eloquent relationship loads, and screams (in the console, or as an exception if you want it strict) when it spots the classic loop-and-lazy-load pattern. The "dev-only" part matters. You never want a query detector running in production — it adds overhead and it's a developer aid, not a runtime guard. So it goes in behind an environment check, registered only when the app isn't in production: public function register (): void { if ( $this -> app -> environment ( 'local' , 'testing' )) { $this -> app -> register ( \BeyondCode\QueryDetector\QueryDetectorServiceProvider :: class ); } } Think of it like a smoke detector you only arm while you're cooking. It's noisy by design — that's the point. The noise is a to-do list. Eager loads you don't actually use are just N+1s wearing a disguise Here's the counterintuitive one. We're all trained to fix N+1s by adding with() . But the opposite bug is just as common and almost never gets caught: you eager-load a relationship, and then... never touch it in the view. Index screens are the worst offenders. Someone builds a listing, eager-loads creator and approver so the table can show names, then a redesign drops those columns — but the with(['creator', 'approver']) stays. Now every page load hyd

If you write software for a living and you're considering a trip to China in 2026, the friction you'll hit is not what you expect. The Great Firewall is the headline, but it's rarely what trips up a first-time visitor. What actually breaks your week is the small stuff: a QR code at a noodle shop, a metro turnstile that won't take your foreign card, a hotel Wi-Fi that quietly drops every request to Google. This is a brief survival guide written from a developer's mindset: what's actually changed in 2026, what you can fix before you leave, and what you should just accept. 1. Visa-free entry now covers most Western devs As of late 2025, China extended its 30-day visa-free transit policy to passport holders from 38 countries, including the US, UK, Germany, France, Australia, the Netherlands, and most of the EU. If you're flying in for a vacation, a conference, or even a short remote-work stretch, you may not need to apply for a visa at all — you just need an onward ticket within 30 days. The catch: the rules per nationality drift quarterly, and the official guidance is scattered across embassy pages. I keep a more current breakdown here: FirstTripChina visa-free guide — worth checking the week you book your ticket. 2. The payment problem is the real "API" you need to integrate China runs on two payment rails: Alipay and WeChat Pay. Cash is technically legal but vendors below the level of a 4-star hotel will look at you like you handed them a stone tablet. Foreign credit cards work at airports and big chains; they do not work at the dumpling place you actually want to eat at. The fix that exists in 2026 — and that did not exist three years ago — is "Tour Card" inside Alipay and "International" mode inside WeChat Pay. Both let you link a Visa/Mastercard issued outside China and pay via the same QR system locals use. Setup steps (roughly): Install Alipay (App Store / Play Store, US/EU regions both work). Verify with passport + selfie (KYC takes about 3 minutes). Tap Tour C