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TL;DR: What gadgets, tools, furniture and whatnot do I need for an ideal desk setup for a...

The move comes after the company left potentially sensitive data from the initiative exposed internally.

Employees had previously raised concerns about the initiative, which involves collecting workers’ keystroke data to train AI models.

Amazon is planning to increase the footprint of its new conversational AI assistant Alexa+ to India and is inviting users in the country to test out a Hindi-language version.

Exatamente isso que você leu. Você não precisa ser um "expert" em determinada linguagem, ou ter: 5...

GitHub热门项目 | Scalable datastore for metrics, events, and real-time analytics | Stars: 31,581 | 30 stars this week | 语言: Rust

GitHub热门项目 | A portable accelerated SQL query, search, and LLM-inference engine, written in Rust, for data-grounded AI apps and agents. | Stars: 2,981 | 0 stars today | 语言: Rust

GitHub热门项目 | Tunnel all your traffic over Websocket or HTTP2 - Bypass firewalls/DPI - Static binary available | Stars: 6,841 | 24 stars today | 语言: Rust

Let's be fair. The title of this post is confusing at first, but once you read it in full, I hope you...

"Fractional CTO" has become the title people put on their LinkedIn profile when "senior developer" doesn't sound senior enough. I play this role for some of my clients, so I have opinions about what it actually means — and what it doesn't. The confusion isn't just semantics. If you hire the wrong thing under this label, you pay consulting rates for work that a good contractor would have done better. The Problem with the Label The term covers a surprisingly wide range of people and arrangements. At one end, you have experienced technical leaders — people who have actually run engineering organizations, made architectural decisions that constrained companies for years, hired and let go technical staff, and owned the consequences. At the other end, you have developers who decided their day rate felt more justifiable with a fancier title. Both call themselves fractional CTOs. The market hasn't sorted this out yet. The reason it matters: these are fundamentally different services with different prices, different deliverables, and different risks. Mixing them up is how companies end up paying €200/hour for someone to help them choose a JavaScript framework. What a Real Fractional CTO Actually Owns Owns is the operative word. Not "advises on." Not "contributes to." Owns. Technical architecture decisions. When you're building a system that will handle real volume, real users, and real money — the decisions made in the first few months constrain everything that follows. What database, what caching strategy, how the services are separated, where the complexity lives. A fractional CTO makes those calls and stakes their reputation on them. They're not writing a report about options. They're deciding. Tech stack and vendor selection. When a vendor pitches you something, someone needs to evaluate it who has seen enough vendors to know which ones are overselling. When a developer suggests a library, someone needs to know whether it's the right tool or just the tool that developer

Big Tech is throwing big money into data center buildouts. As national opposition to the facilities grows, some workers are beginning to question whether it’s worth it.

TL;DR For a long time, I told myself I would start documenting my journey later. I thought I needed...

Feel free to include any brands or product names as well. Be as obscure or on the nose as you'd like....

13.5.0 Before We Begin During its design, Rust drew inspiration from many languages, and functional programming had a particularly strong influence on Rust. Functional programming often includes passing functions as values to parameters, returning them from other functions, assigning them to variables for later execution, and so on. In this chapter, we will discuss some Rust features that are similar to what many languages call functional features: Closures Iterators (this article) Improving the I/O Project with Closures and Iterators Performance of Closures and Iterators If you find this helpful, please like, bookmark, and follow. To keep learning along, follow this series. 13.5.1 What Is an Iterator To talk about iterators, we first need to talk about the iterator pattern. The iterator pattern allows you to perform a task on each element in a sequence, one by one. In that process, the iterator is responsible for: Traversing each item Determining when the sequence has finished iterating Rust iterators are lazy: unless you call a method that consumes the iterator, the iterator itself does nothing. In other words, if you write an iterator in your code but never use it, it is as if it did nothing at all. Take a look at an example: fn main () { let v1 = vec! [ 1 , 2 , 3 ]; let v1_iter = v1 .iter (); } v1 is a Vector , and v1.iter() creates an iterator for v1 and assigns it to v1_iter . But v1_iter is not used yet, so the iterator can be considered to have no effect. Now let’s use the iterator to traverse the values: fn main () { let v1 = vec! [ 1 , 2 , 3 ]; let v1_iter = v1 .iter (); for val in v1_iter { println! ( "Got: {}" , val ); } } This is equivalent to using each element in the iterator once in a loop. 13.5.2 The Iterator Trait All iterators implement the Iterator trait. This trait is defined in the standard library and looks roughly like this: pub trait Iterator { type Item ; fn next ( & mut self ) -> Option < Self :: Item > ; // methods with default implementa

13.4.0 Before We Begin During its design, Rust drew inspiration from many languages, and functional programming had a particularly strong influence on Rust. Functional programming often includes passing functions as values to parameters, returning them from other functions, assigning them to variables for later execution, and so on. In this chapter, we will discuss some Rust features that are similar to what many languages call functional features: Closures (this article) Iterators Improving the I/O Project with Closures and Iterators Performance of Closures and Iterators If you find this helpful, please like, bookmark, and follow. To keep learning along, follow this series. 13.4.1 Closures Can Capture Their Environment Closures have a capability that functions do not: a closure can access variables in the scope where it is defined. Take a look at an example: fn main () { let x = 4 ; let equal_to_x = | z | z == x ; let y = 4 ; assert! ( equal_to_x ( y )); } The closure part is: let equal_to_x = | z | z == x ; Some people may find it hard to distinguish the roles of = and == here, so let’s rewrite it another way: let equal_to_x = | z | { z == x ; } In other words, the closure takes z as its parameter, compares it with x (which is 4, because x = 4 was defined above), and returns a boolean. If they are equal, the result is true ; otherwise it is false . Here the closure directly accesses the variable x in the same scope, which functions cannot do. But this feature has a cost: it introduces memory overhead . In most cases we do not need a closure to capture its environment, and we do not want the extra overhead either. That is why functions are not allowed to capture variables from the environment, and defining and using a function never introduces this kind of overhead. 13.4.2 How Closures Capture Values From Their Environment Closures capture values from the environment in three ways, just like functions receive parameters in three ways: Taking ownership, whose trait

The history books are littered with the corpses of corporate record labels started by companies that had no business being in the music industry. Bose thinks it can be the exception to the rule. It thinks it can be Red Bull. And, while Bose has more of a right to dip its toes into the […]

After a full week of intensive Bitcoin programming training, the developers at Zone01 Kisumu moved into the most exciting phase of the bootcamp: building real-world solutions powered by Bitcoin, the Lightning Network, and LND. One thing I learned throughout the experience is that the human mind is truly fascinating. The room was filled with innovative ideas, each attempting to solve a different problem. As the saying goes, no idea is a bad idea—every concept had the potential to make an impact. A total of 17 teams were formed, and each team embarked on a 24-hour hackathon journey to transform their ideas into working products. After an intense day of development came the presentation phase, where we had the privilege of showcasing what we had built. Our team developed Kasi , a WhatsApp chatbot that enables Bitcoin transactions directly through WhatsApp. The goal was to make Bitcoin payments more accessible by leveraging a platform that millions of people already use daily. To build Kasi, we integrated the Twilio API for WhatsApp communication and utilized the Bitnob platform to facilitate Bitcoin transactions. Python was used throughout the development process. The project was brought to life by six developers: Claire, Lamka, Ijay, Dishon, Talo, and myself. Beyond the technical implementation, the hackathon strengthened our understanding of collaborative software development. We practiced Git workflows, team coordination, version control, task management, and effective communication under tight deadlines—skills that are just as valuable as writing code. Although we did not finish at the top of the leaderboard, the experience was incredibly rewarding. Every team brought something unique to the table, and the winners fully deserved their recognition. Congratulations to all the teams that participated and showcased their creativity, determination, and technical skills. One moment from the presentation will stay with me for a long time. As we were demonstrating Kasi to

Cold Court is a brother-sister duo from Philly that seems to love nothing more than shoving all of their influences together in a messy soup that at least superficially resembles the hyperpop you've come to expect from acts like 100 Gecs. But, where songs like "Dumbest Girl Alive" goofily wink at pop punk and emo, […]

Anthropic recently reported that Claude now handles around 95% of its internal analytics requests, letting employees query business data independently instead of relying on data teams. The company attributes this result less to advances in models and more to data governance, semantic definitions, and operational discipline. By Renato Losio

GitHub热门项目 | All components used to run Base | Stars: 703 | 10 stars today | 语言: Rust