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The Faros AI Engineering Report 2026 is not a survey of developer sentiment. It is two years of telemetry from 22,000 developers across 4,000 teams, measuring what AI adoption actually produces downstream. The findings have a name: the Acceleration Whiplash. The structural explanation has one too. What the telemetry actually shows The output numbers in the Faros report are real and worth stating plainly. Epics completed per developer are up 66.2%. Task throughput per developer is up 33.7%. PR merge rate per developer is up 16.2%. These represent genuine delivery acceleration, and dismissing them would be dishonest. AI coding tools are producing real productivity gains at the business level. The production quality numbers are also real: Metric Change Incidents per PR under high AI adoption +242.7% Median time in code review +441.5% Code churn (lines deleted to lines added) +861% PRs merged with no review at all 31.3% Source: Faros AI Engineering Report 2026: The Acceleration Whiplash . Telemetry from 22,000 developers across 4,000+ teams. Figures represent metric change from lowest to highest AI adoption periods within each organization. Both sets of numbers are true simultaneously. That is the whiplash. Throughput accelerated. The downstream systems built to validate that throughput did not. Plotted together, generation throughput rises steeply while control capacity stays nearly flat -- and the gap between the two curves is the governance debt. Why the systems did not scale Code review, incident response, and architectural validation were all designed for a world where development velocity was human-paced. A senior engineer could review the meaningful PRs in a sprint. An incident postmortem could trace a failure to a specific change and a specific decision gap. Architectural drift was visible because it moved slowly enough to catch. AI-generated code broke these assumptions quietly. Not because the code was obviously bad, but because it was often superficially conv

I built Xaloia AI , a privacy-first AI platform focused on trust and human interaction. And now, I’m shutting it down. Not because the idea was empty. Not because the tech didn’t work. But because I tried to build it from Romania. The Problem Wasn’t the Product Xaloia was built around things that are becoming increasingly important: privacy secure communication human-centered AI But building something meaningful isn’t enough. It needs the right environment to grow. And that’s where things started to break. What I Ran Into Trying to build in Romania, I kept hitting the same walls: Lack of early adopters willing to pay - People are curious about tech, but not ready to invest in new products. Limited startup ecosystem - Fewer accelerators, fewer investors, fewer people who understand what you’re building. Cultural friction around ambition - If your idea isn’t small or conventional, it’s often questioned instead of supported. Low exposure to global markets - Even if you build something good, getting it in front of the right audience is much harder. None of these stop your project instantly. But together, they slowly drain momentum. Why the US Is Different From everything I’ve seen and experienced, the US offers something fundamentally different: Access to capital — people invest earlier Distribution opportunities — platforms, networks, visibility Cultural support for big ideas — ambition is expected, not questioned Faster feedback loops — you know quickly if something works It’s not that success is guaranteed there. It’s that the conditions for success actually exist. The Real Lesson Talent is everywhere. Ideas are everywhere. But opportunity is not evenly distributed. And trying to ignore that reality cost me time, energy, and a product I genuinely believed in. What’s Next Shutting down Xaloia isn’t the end. It’s a reset—with better clarity. Next time, I won’t just focus on building something good. I’ll focus on building it where it actually has a chance to grow.

Here's a thing that happens on every team I've been on. Sprint ends. Someone schedules the retro....

Influential figures, including Nick Fuentes, have been accused of “hijacking” the murder of Henry Nowak to push a racist agenda.

Critics say Trump plan to test AI models is short-sighted, performative.

Compact power banks have gotten a lot faster in the past year — and it’s not just their USB-C charging speeds that have received a boost. The newest Qi2.2-certified models can wirelessly charge an iPhone 16 or later at up to 25W. Combine that with their ability to magnetically snap on via MagSafe, and you’ve […]

A new Supernatural VR workout app arrives this fall months before Meta planned to shut it down.

Plex is increaingly focusing on content discovery and streaming rentals.

Carvana was granted a warrant to buy shares in Slate last year, according to documents obtained by TechCrunch. Guggenheim Partners CEO Mark Walter is heavily invested in both companies.

Would you take a payment to ramp down your electricity use? Would it change anything if you were doing so to help power a local data center? Google just signed a new deal to help pay for a virtual power plant (VPP) in the largest power grid in the US. The agreement is with Voltus,…

Run AI agents across your local files and apps on Windows Discussion | Link

Daily AI stories personalised from your Google apps Discussion | Link

Run multimodal AI locally with an encoder-free architecture Discussion | Link

Amazon's updated search bar will now show you AI-generated images of products as you describe them. For now, the in-app feature only surfaces AI images of clothing and home goods, allowing you to tap on the image that best matches what you're looking for and search for similar-looking items. In a blog post, Amazon positions […]

Cut, extract, mute, and split videos locally Discussion | Link

Hey devs! 👋 I'm the creator of this project, and I'm looking for help with an open issue: https://github.com/PedroVitor-Dev/Ped-Os/issues/1 If you're interested in contributing, reviewing possible solutions, or simply sharing ideas, I'd love to hear your thoughts. Contributions of all sizes are welcome, especially from developers looking to get involved in open source. Thanks in advance! 🚀💚 submitted by /u/Opposite-Stranger- [link] [留言]

What can I say. Anyone claiming that F# is good mostly for finance and data processing and C# for everything else, has probably never written a single line of practical F# code. In previous two parts of the article, I tried to demonstrate that with F# you can achieve the same goals as with C#, but with less verbose, repetitive, structural code. How it started. At some point, developers realized that global state with unrestricted data access causes many side effects, producing insecure, error-prone, and hard-to-maintain code as software grows larger. That is when the idea emerged to bring data and the code operating on it together into a single unit, restricting direct access to the unit’s internal state and making software more secure and predictable. This is how data encapsulation was born. Alongside encapsulation, abstraction was introduced — the process of hiding how behavior works. Encapsulation ( hiding data ) and abstraction ( hiding behavior ) remain two foundational pillars of Object-Oriented Programming. And that is how OOP has worked ever since — developers bring data and behavior together ( classes ) and define abstractions for them ( interfaces ). For example, for C# developers — including myself — this has become a daily routine. And we rarely question it, because OOP languages like C# leave us little choice but to structure code this way. But if you ask yourself whether this repetitive routine is always necessary, the answer is — no. You don’t need OOP concepts to build stateless, streamlined request–response, data-processing pipelines, because in such systems there is no long-lived state to hide and protect. You have a request, and almost immediately you have a response. After that, everything is gone. That is what I tried to demonstrate in the first two parts of this article by applying FP concepts. And even if you have a classical desktop application, you don’t always need to approach it in an OOP way. Functional programming handles side effects no

Amazon will use visual search and AI to show AI generated product images that match your search queries. The retailer says it will help guide users to products.

It's no secret that Marketing Cloud Next (MCN) — or "Growth", or "Agentforce Marketing", or whatever they happen to be calling it this month — is the future of Salesforce marketing products. For the first iterations of it, MCN has seemed focused on attracting new customers with smaller email needs, as an on-core, Flow-based email product for people already in the Salesforce ecosystem. For existing users for Marketing Cloud Engagement (MCE... or traditionally, SFMC... or to the old schoolers like me who used it pre-Salesforce, ex-ExactTarget), and particularly for enterprise users, there have been some deal breakers with missing functionalities. Among those, a lack of AMPscript support has been #1. If you use AMPscript, you know it's infinitely more powerful than any drag-and-drop decision split block will ever be. It allows you to not just personalize an email with tokens/fields in your sending data, but to look up data from other tables, transform how it displays, and create data-driven dynamic versioning of your email. The idea of moving to a new MC that doesn't support AMPscript sounds like giving up one of the most powerful tools in your kit. But not for much longer. It's Here(-ish) In Salesforce's Summer 2026 Release Overview , AMPscript is called out as coming to MCN in PDF 1 , pages 353-355. And they acknowledge that this is a big deal for current MCE customers that want to continue using their historical code skills for great personalized emails. It does say the support will have a "targeted set of functions" which implies that not 100% of functions will be available immediately. AMPscript is a deep language and there are a lot of functions that probably needed dev work to migrate from MCE mechanics to MCN mechanics (for example, I'm sure data extensions are very different on the back end). I'd also guess that system strings might end up a little different from one system to another. So I wouldn't expect a 100% 1:1 recreation. Either way, this is a huge step

After shelving the original executive order last month, Donald Trump finally got on board Monday night.