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Confronted with dwindling bee colonies, scientists are arming queens with robots and sensible hives

Robotics

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23 August 2024

Confronted with dwindling bee colonies, scientists are arming queens with robots and sensible hives

By Farshad Arvin, Martin Stefanec, and Tomas Krajnik

Be it the information or the dwindling variety of creatures hitting your windscreens, it won’t have evaded you that the insect world in dangerous form.

Within the final three many years, the worldwide biomass of flying bugs has shrunk by 75%. Among the many development’s most notables victims is the world’s most vital pollinator, the honeybee. In the US, 48% of honeybee colonies died in 2023 alone, making it the second deadliest yr on file. This important loss is due partially to colony collapse dysfunction (CCD), the sudden disappearance of bees. In distinction, European international locations report decrease however nonetheless worrisome charges of colony losses, starting from 6% to 32%.

This decline causes a lot of our important meals crops to be under-pollinated, a phenomenon that threatens our society’s meals safety.

Debunking the sci-fi delusion of robotic bees

So, what might be carried out? Given pesticides’ function within the decline of bee colonies, generally proposed options embrace a shift away from industrial farming and towards much less pesticide-intensive, extra sustainable types of agriculture.

Others are inclined to look towards the sci-fi finish of issues, with some scientists imagining that we may ultimately change dwell honeybees with robotic ones. Such synthetic bees may work together with flowers like pure bugs, sustaining pollination ranges regardless of the declining numbers of pure pollinators. The imaginative and prescient of synthetic pollinators contributed to ingenious designs of insect-sized robots able to flying.

In actuality, such innovations are simpler at educating us over engineers’ fantasies than they’re at reviving bee colonies, so slim are their prospects of materialising. First, these synthetic pollinators must be outfitted for way more extra than simply flying. Every day duties carried out by the frequent bee embrace trying to find crops, figuring out flowers, unobtrusively interacting with them, finding vitality sources, ducking potential predators, and coping with hostile climate situations. Robots must carry out all of those within the wild with a really excessive diploma of reliability since any broken-down or misplaced robotic may cause harm and unfold air pollution. Second, it stays to be seen whether or not our technological data can be even able to manufacturing such innovations. That is with out even mentioning the value tag of a swarm of robots able to substituting pollination supplied by a single honeybee colony.

Inside a wise hive

Bees on one among Hiveopolis’s augmented hives.
Hiveopolis, Fourni par l’auteur

Quite than attempting to switch honeybees with robots, our two newest initiatives funded by the European Union suggest that the robots and honeybees truly group up. Had been these to succeed, struggling honeybee colonies may very well be reworked into bio-hybrid entities consisting of organic and technological parts with complementary expertise. This is able to hopefully enhance and safe the colonies’ inhabitants development as extra bees survive over harsh winters and yield extra foragers to pollinate surrounding ecosystems.

The primary of those initiatives, Hiveopolis, investigates how the advanced decentralised decision-making mechanism in a honeybee colony might be nudged by digital expertise. Begun in 2019 and set to finish in March 2024, the experiment introduces expertise into three remark hives every containing 4,000 bees, against this to 40,000 bees for a traditional colony.

The inspiration of an augmented honeycomb.
Hiveopolis, Fourni par l’auteur

Inside this honeybee sensible dwelling, combs have built-in temperature sensors and heating gadgets, permitting the bees to get pleasure from optimum situations contained in the colony. Since bees are inclined to snuggle as much as hotter places, the combs additionally permits us to direct them towards completely different areas of the hive. And as if that management weren’t sufficient, the hives are additionally outfitted with a system of digital gates that displays the bugs actions. Each applied sciences permit us to determine the place the bees retailer honey and pollen, but in addition once they vacate the combs in order to allow us to reap honey. Final however not least, the sensible hive incorporates a robotic dancing bee that may direct foraging bees towards areas with crops to be pollinated.

Because of the experiment’s small scale, it’s inconceivable to attract conclusions on the extent to which our applied sciences could have prevented bee losses. Nevertheless, there’s little doubt what we now have seen so far give causes to be hopeful. We are able to confidently assert that our sensible beehives allowed colonies to outlive excessive chilly through the winter in a manner that wouldn’t in any other case be potential. To exactly assess what number of bees these applied sciences have saved would require upscaling the experiment to a whole lot of colonies.

Pampering the queen bee

Our second EU-funded undertaking, RoboRoyale, focuses on the honeybee queen and her courtyard bees, with robots on this occasion repeatedly monitoring and interacting together with her Royal Highness.

Come 2024, we are going to equip every hive with a gaggle of six bee-sized robots, which can groom and feed the honeybee queen to have an effect on the variety of eggs she lays. A few of these robots can be outfitted with royal jelly micro-pumps to feed her, whereas others will characteristic compliant micro-actuators to groom her. These robots will then be related to a bigger robotic arm with infrared cameras, that can repeatedly monitor the queen and her neighborhood.

A RoboRoyale robotic arm susses out a honeybee colony.
RoboRoyale, Fourni par l’auteur

As witnessed by the photograph to the fitting and in addition beneath, we now have already been capable of efficiently introduce the robotic arm inside a residing colony. There it repeatedly monitored the queen and decided her whereabouts via gentle stimuli.

Emulating the employee bees

In a second section, it’s hoped the bee-sized robots and robotic arm will have the ability to emulate the behaviour of the employees, the feminine bees missing reproductive capability who attend to the queen and feed her royal jelly. Wealthy in water, proteins, carbohydrates, lipids, nutritional vitamins and minerals, this nutritious substance secreted by the glands of the employee bees permits the queen to put as much as hundreds of eggs a day.

Employee bees additionally interact in cleansing the queen, which entails licking her. Throughout such interactions, they acquire a number of the queen’s pheromones and disperse them all through the colony as they transfer throughout the hive. The presence of those pheromones controls lots of the colony’s behaviours and notifies the colony of a queen’s presence. For instance, within the occasion of the queen’s demise, a brand new queen should be shortly reared from an egg laid by the late queen, leaving solely a slim time window for the colony to react.

Certainly one of RoboRoyale’s first experiments has consisted in easy interactions with the queen bee via gentle stimulus. The following months will then see the robotic arm stretch out to bodily contact and groom her.
RoboRoyale, Fourni par l’auteur

Lastly, it’s believed employee bees may additionally act because the queen’s guides, main her to laying eggs in particular comb cells. The dimensions of those cells can decide if the queen lays a diploid or haploid egg, ensuing within the bee growing into both into drone (male) or employee (feminine) bee. Taking up these guiding duties may have an effect on a minimum of the speed’s total reproductive fee.

How robots can forestall bee cannibalism

This might have one other virtuous impact: stopping cannibalism.

Throughout robust instances, equivalent to lengthy durations of rain, bees need to make do with little pollen consumption. This forces them to feed younger larvae to older ones in order that not less than the older larvae has an opportunity to outlive. Via RoboRoyale, we are going to look not solely to cut back possibilities of this behaviour occurring, but in addition quantify to what extent it happens beneath regular situations.

Finally, our robots will allow us to deepen our understanding of the very advanced regulation processes inside honeybee colonies via novel experimental procedures. The insights gained from these new analysis tracks can be needed to higher shield these worthwhile social bugs and guarantee ample pollination sooner or later – a excessive stakes enterprise for meals safety.

This text is the results of The Dialog’s collaboration with Horizon, the EU analysis and innovation journal.

Farshad Arvin is a member of the Division of Pc Science at Durham College within the UK. The analysis of Farshad Arvin is primarily funded by the EU H2020 and Horizon Europe programmes.

Martin Stefanec is a member of the Institute of Biology on the College of Graz. He has acquired funding from the EU packages H2020 and Horizon Europe.

Tomas Krajnik is member of the Institute of Electrical and Electronics Engineers (IEEE). The analysis of Tomas Krajnik is primarily funded by EU H2020 Horizon programme and Czech Nationwide Science Basis.

tags: c-Analysis-Innovation

The Dialog
is an impartial supply of stories and views, sourced from the educational and analysis neighborhood and delivered direct to the general public.

Utilizing SwiftData with Preview in SwiftUI

iOS Development

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23 August 2024

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Utilizing SwiftData with Preview in SwiftUI

Within the earlier tutorial, I’ve walked you thru the fundamentals of SwiftData, a brand new framework launched in iOS 17 as a substitute for Core Information. You probably have adopted that tutorial, it is best to now be conversant in utilizing SwiftData to save lots of and handle knowledge in a database. The built-in @Mannequin macro and the @Question macro tremendously simplify the method of defining knowledge mannequin and retrieving data from the database, making it extraordinarily straightforward for builders to deal with persistent knowledge.

The Preview function in SwiftUI is very useful because it permits builders to immediately visualize the app’s consumer interface with out the necessity to launch the simulator. Nevertheless, utilizing SwiftData with SwiftUI Preview requires some extra steps. On this tutorial, we are going to discover easy methods to combine SwiftData with SwiftUI Preview successfully.

Observe: When you haven’t learn the SwiftData tutorial, I extremely suggest checking it out first, as this tutorial references a few of the supplies lined in that tutorial.

Revisiting the Information Mannequin and SwiftData

Within the earlier instance, we now have constructed a mannequin class for ToDoItem like this:

import Basis
import SwiftData

@Mannequin class ToDoItem: Identifiable {
    var id: UUID
    var identify: String
    var isComplete: Bool

    init(id: UUID = UUID(), identify: String = "", isComplete: Bool = false) {
        self.id = id
        self.identify = identify
        self.isComplete = isComplete
    }
}

SwiftData simplifies the method of defining a schema utilizing code. You solely have to mark the mannequin class with the @Mannequin macro. SwiftData will then robotically allow persistence for the info class.

So as to drive the info operations (like replace, insert, learn, and delete), we additionally have to arrange the mannequin container. Within the ToDoDemoAppApp.swift, we now have hooked up the modelContainer modifier like beneath:

struct ToDoDemoAppApp: App {
    var physique: some Scene {
        WindowGroup {
            ContentView()
        }
        .modelContainer(for: ToDoItem.self)
    }
}

This configuration is basically all you want earlier than beginning to work with SwiftData.

Preview with SwiftData and In-memory Container

Within the Todo app demo, we now have a ContentView that masses and shows the to-do merchandise within the record view. Right here is the pattern code:

struct ContentView: View {
    @Atmosphere(.modelContext) non-public var modelContext

    @Question var todoItems: [ToDoItem]

    var physique: some View {
        NavigationStack {
            Checklist {
                ForEach(todoItems) { todoItem in
                    HStack {
                        Textual content(todoItem.identify)

                        Spacer()

                        if todoItem.isComplete {
                            Picture(systemName: "checkmark")
                        }
                    }
                }
            }

            .navigationTitle("To Do Checklist")
        }
    }
    }

You can also make the preview work by writing the preview code like this:

#Preview {
    ContentView()
        .modelContainer(for: ToDoItem.self)
}

Nevertheless, on this case, the preview solely shows an empty Todo record as a result of the container doesn’t have any knowledge populated. When you need to have some pattern knowledge, you’ll be able to create a customized mannequin container particularly for the preview. Right here is an instance:

@MainActor
let previewContainer: ModelContainer = {
    do {
        let container = strive ModelContainer(for: ToDoItem.self,
                                           configurations: .init(isStoredInMemoryOnly: true))

        for _ in 1...10 {
            container.mainContext.insert(generateRandomTodoItem())
        }

        return container
    } catch {
        fatalError("Didn't create container")
    }
}()

func generateRandomTodoItem() -> ToDoItem {
    let duties = [ "Buy groceries", "Finish homework", "Go for a run", "Practice Yoga", "Read a book", "Write a blog post", "Clean the house", "Walk the dog", "Attend a meeting" ]

    let randomIndex = Int.random(in: 0..

We instantiate a ModelContainer with an in-memory configuration and populate the container with 10 random to-do gadgets. To make use of this preview container, you merely modify the preview code and specify to make use of the previewContainer:

#Preview {
    ContentView()
        .modelContainer(previewContainer)
}

When you made the modification, the preview pane ought to present you the Todo record view with 10 random gadgets.

Abstract

SwiftUI Preview is a useful function that enables builders to visualise their app’s consumer interface immediately, with out the necessity to launch the simulator. This tutorial supplies complete steering on successfully utilizing SwiftData with SwiftUI Preview. You must learn to create a customized container populated with pattern knowledge particularly for preview functions.

When you get pleasure from studying this tutorial and wish to be taught extra about SwiftUI, don’t overlook to take a look at our Mastering SwiftUI e book for iOS 17 and Xcode 15.

Notifications in iOS 18.1 may very well be the killer Apple Intelligence characteristic

Apple

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23 August 2024

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Notifications in iOS 18.1 may very well be the killer Apple Intelligence characteristic

One thing rings true about Apple’s catchphrase for Apple Intelligence: ‘AI for the remainder of us.’ AI can do some actually spectacular (and problematic) issues. However typically it’s the boring, sensible enhancements which are really most helpful. For instance, day by day ‘the remainder of us’ endure the tedious activity of notification administration. And in iOS 18.1, Apple has made notifications so a lot better due to AI summaries.

Notification summaries are a brand new AI characteristic

Apple Intelligence may have a staggered rollout, however notification summaries are one in all a number of options launching this fall.

In case you have a supported Apple Intelligence machine, then when iOS 18.1 arrives (most likely late October), your notifications from key apps can be extra informative than ever earlier than.

I’ve been utilizing the betas for iOS 18.1, iPadOS 18.1, and macOS Sequoia 15.1 since they arrived in July. In that point, notification summaries are simply the Apple Intelligence characteristic I’ve gotten probably the most profit from.

Higher notifications would possibly sound too boring to be a killer AI characteristic, however that’s precisely what they’ve been for me.

How AI notification summaries work

Notification summaries are designed primarily for communication apps.

Whenever you obtain an e-mail, for instance, AI will present a abstract of that e-mail’s contents within the notification.

The identical is true for messages. If somebody sends you a quick message, you’ll see the precise message contents within the notification—like at all times.

But when a message is a little bit longer, Apple Intelligence summarizes its content material within the notification you obtain. You received’t see the precise message’s textual content in your notification, however what you get is definitely a lot better.

This additionally works when the group chat blows up. Your prime notification will present a abstract of what the dialog is all about.

All summarized notifications are denoted by a small icon, so you realize it’s not the precise message content material being displayed.

Your iPhone’s deeper understanding of the contents of messages additionally powers the brand new Precedence Notifications characteristic. iOS 18.1 extra prominently shows the alerts it thinks are particularly essential. You’ll additionally see summaries inside your Mail inbox.

Why notifications generally is a killer characteristic

I’ve been getting summarized notifications from each the Messages app and Apple Mail in iOS and iPadOS 18.1.

Although the characteristic isn’t probably the most thrilling, it’s offered a significant productiveness and a focus profit for me.

All all through my day, whether or not I’m working or I’m hanging out with household and buddies, notifications fill my units.

When a notification arrives, I’ve to make a judgment name: is that this one thing essential, or not?

If it’s essential, I’ll cease what I’m doing to deal with it instantly
If it’s not essential, I can ignore it and go about my day

This key choice level—which all of us expertise many occasions day by day—is made a lot simpler with Apple Intelligence.

Beforehand, notifications for emails and longer messages had been usually fully ineffective. By together with solely an excerpt of the beginning of a message, they not often offered sufficient information to assist me make an knowledgeable choice round engagement.

As an alternative, what would usually occur is one in all two issues:

Both I’d cease what I’m doing and open the Mail or Messages app, which took extra time and meant higher interruption to what I used to be already doing
Or, I’d ignore the notification, and later uncover both that it was positive to disregard, or I’d be taught it had been essential and I ought to have paid consideration sooner

AI notification summaries have made this drawback a factor of the previous.

Now, my iPhone and iPad present far more informative notifications, which I can shortly look at and resolve whether or not to deal with or ignore.

There’s no must open Messages or Mail until one thing is really essential. And the circulate of my day and my thought sample is minimally disrupted.

Supported apps and units for notification summaries

At the moment within the iOS 18.1 beta, notification summaries can be found for Apple’s Messages and Mail apps.

Within the close to future although, third-party apps will be capable to present the identical utility. Apple’s advertising and marketing supplies present Slack providing summarized notifications, for instance.

Notification summaries are solely out there with Apple Intelligence. Meaning you want a suitable machine, such because the iPhone 15 Professional or an upcoming iPhone 16 mannequin.

One good element: when you have a suitable iPhone, your Apple Watch will profit from AI summaries too.

Have you ever used AI notification summaries? What’s your expertise been like? Tell us within the feedback.

FTC: We use revenue incomes auto affiliate hyperlinks. Extra.

What’s new in Workflows? | Databricks Weblog

Big Data

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23 August 2024

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What’s new in Workflows? | Databricks Weblog

Databricks Workflows is the cornerstone of the Databricks Knowledge Intelligence Platform, serving because the orchestration engine that powers important knowledge and AI workloads for hundreds of organizations worldwide. Recognizing this, Databricks continues to spend money on advancing Workflows to make sure it meets the evolving wants of recent knowledge engineering and AI tasks.

This previous summer season, we held our greatest but Knowledge + AI Summit, the place we unveiled a number of groundbreaking options and enhancements to Databricks Workflows. Current updates, introduced on the Knowledge + AI Summit, embrace new data-driven triggers, AI-assisted workflow creation, and enhanced SQL integration, all aimed toward enhancing reliability, scalability, and ease of use. We additionally launched infrastructure-as-code instruments like PyDABs and Terraform for automated administration, and the final availability of serverless compute for workflows, guaranteeing seamless, scalable orchestration. Trying forward, 2024 will convey additional developments like expanded management stream choices, superior triggering mechanisms, and the evolution of Workflows into LakeFlow Jobs, a part of the brand new unified LakeFlow resolution.

On this weblog, we’ll revisit these bulletins, discover what’s subsequent for Workflows, and information you on the way to begin leveraging these capabilities immediately.

The Newest Enhancements to Databricks Workflows

The previous 12 months has been transformative for Databricks Workflows, with over 70 new options launched to raise your orchestration capabilities. Beneath are among the key highlights:

Knowledge-driven triggers: Precision once you want it

Desk and file arrival triggers: Conventional time-based scheduling isn’t enough to make sure knowledge freshness whereas lowering pointless runs. Our data-driven triggers be certain that your jobs are initiated exactly when new knowledge turns into out there. We’ll test for you if tables have up to date (in preview) or new recordsdata have arrived (usually out there) after which spin up compute and your workloads once you want them. This ensures that they devour sources solely when mandatory, optimizing price, efficiency, and knowledge freshness. For file arrival triggers particularly, we have additionally eradicated earlier limitations on the variety of recordsdata Workflows can monitor.
Periodic triggers: Periodic triggers will let you schedule jobs to run at common intervals, resembling weekly or every day, with out having to fret about cron schedules.

AI-assisted workflow creation: Intelligence at each step

AI-Powered cron syntax technology: Scheduling jobs may be daunting, particularly when it includes advanced cron syntax. The Databricks Assistant now simplifies this course of by suggesting the right cron syntax primarily based on plain language inputs, making it accessible to customers in any respect ranges.
Built-in AI assistant for debugging: Databricks Assistant can now be used instantly inside Workflows (in preview). It offers on-line assist when errors happen throughout job execution. For those who encounter points like a failed pocket book or an incorrectly arrange activity, Databricks Assistant will supply particular, actionable recommendation that can assist you shortly determine and repair the issue.

Workflow Administration at Scale

1,000 duties per job: As knowledge workflows develop extra advanced, the necessity for orchestration that may scale turns into important. Databricks Workflows now helps as much as 1,000 duties inside a single job, enabling the orchestration of even essentially the most intricate knowledge pipelines.
Filter by favourite job and tags: To streamline workflow administration, customers can now filter their jobs by favorites and tags utilized to these jobs. This makes it straightforward to shortly find the roles you want, e.g. of your staff tagged with “Monetary analysts”.
Simpler collection of activity values: The UI now options enhanced auto-completion for activity values, making it simpler to cross data between duties with out guide enter errors.
Descriptions: Descriptions enable for higher documentation of workflows, guaranteeing that groups can shortly perceive and debug jobs.
Improved cluster defaults: We have improved the defaults for job clusters to extend compatibility and scale back prices when going from interactive improvement to scheduled execution.

Operational Effectivity: Optimize for efficiency and value

Value and efficiency optimization: The brand new timeline view inside Workflows and question insights present detailed details about the efficiency of your jobs, permitting you to determine bottlenecks and optimize your Workflows for each pace and cost-effectiveness.
Value monitoring: Understanding the fee implications of your workflows is essential for managing budgets and optimizing useful resource utilization. With the introduction of system tables for Workflows, now you can monitor the prices related to every job over time, analyze tendencies, and determine alternatives for price financial savings. We have additionally constructed dashboards on prime of system tables you can import into your workspace and simply customise. They may also help you reply questions resembling “Which jobs price essentially the most final month?” or “Which staff is projected to exceed their funds?”. It’s also possible to arrange budgets and alerts on these.

Enhanced SQL Integration: Extra Energy to SQL Customers

Activity values in SQL: SQL practitioners can now leverage the outcomes of 1 SQL activity in subsequent duties. This characteristic allows dynamic and adaptive workflows, the place the output of 1 question can instantly affect the logic of the subsequent, streamlining advanced knowledge transformations.
Multi-SQL assertion help: By supporting a number of SQL statements inside a single activity, Databricks Workflows affords larger flexibility in establishing SQL-driven pipelines. This integration permits for extra subtle knowledge processing with out the necessity to swap contexts or instruments.

Serverless compute for Workflows, DLT, Notebooks

Serverless compute for Workflows: We have been thrilled to announce the final availability of serverless compute for Notebooks, Workflows, and Delta Reside Tables at DAIS. This providing was rolled out to most Databricks areas, bringing the advantages of performance-focuses quick startup, scaling, and infrastructure-free administration to your workflows. Serverless compute removes the necessity for advanced configuration and is considerably simpler to handle than traditional clusters.

What’s Subsequent for Databricks Workflows?

Trying forward, 2024 guarantees to be one other 12 months of great developments for Databricks Workflows. This is a sneak peek at among the thrilling options and enhancements on the horizon:

Streamlining Workflow Administration

The upcoming enhancements to Databricks Workflows are centered on enhancing readability and effectivity in managing advanced workflows. These modifications goal to make it simpler for customers to arrange and execute subtle knowledge pipelines by introducing new methods to construction, automate, and reuse job duties. The general intent is to simplify the orchestration of advanced knowledge processes, permitting customers to handle their workflows extra successfully as they scale.

Serverless Compute Enhancements

We’ll be introducing compatibility checks that make it simpler to determine workloads that might simply profit from serverless compute. We’ll additionally leverage the ability of the Databricks Assistant to assist customers transition to serverless compute.

Lakeflow: A unified, clever resolution for knowledge engineering

In the course of the summit we additionally launched LakeFlow, the unified knowledge engineering resolution that consists of LakeFlow Join (ingestion), Pipelines (transformation) and Jobs (orchestration). All the orchestration enhancements we mentioned above will turn out to be part of this new resolution as we evolve Workflows into LakeFlow Jobs, the orchestration piece of LakeFlow.

Attempt the Newest Workflows Options Now!

We’re excited so that you can expertise these highly effective new options in Databricks Workflows. To get began:

Quick Frontend Improvement with David Hsu

Software Engineering

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23 August 2024

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Quick Frontend Improvement with David Hsu

Retool is a platform to assist engineers rapidly construct inner frontends. It does this by abstracting away repetitive features of frontend improvement. The platform was began in 2017 and has obtained funding from Sequoia, Stripe Co-Founders, and Nat Friedman.

David Hsu is the founder and CEO of Retool. He joins the present to speak about why he began coding, learning philosophy and pc science, Retool’s tech stack, and extra.

Paweł is the founder at flat.social the world’s first ‘flatverse’ start-up and glot.house, an AI-powered language studying app. Pawel’s background is as a full-stack software program engineer with a lean and experimental strategy in the direction of product improvement. With a powerful grounding in computing science, he spent the final decade getting early-stage merchandise off the bottom – each in startup and company settings. Comply with Paweł on Twitter, LinkedIn and his private web site – pawel.io.

codesanitize

Confronted with dwindling bee colonies, scientists are arming queens with robots and sensible hives

Debunking the sci-fi delusion of robotic bees

Inside a wise hive

Pampering the queen bee

Emulating the employee bees

How robots can forestall bee cannibalism

Utilizing SwiftData with Preview in SwiftUI

Revisiting the Information Mannequin and SwiftData

Preview with SwiftData and In-memory Container

Abstract

Notifications in iOS 18.1 may very well be the killer Apple Intelligence characteristic

Notification summaries are a brand new AI characteristic

How AI notification summaries work

Why notifications generally is a killer characteristic

Supported apps and units for notification summaries

What’s new in Workflows? | Databricks Weblog

The Newest Enhancements to Databricks Workflows

Knowledge-driven triggers: Precision once you want it

AI-assisted workflow creation: Intelligence at each step

Workflow Administration at Scale

Operational Effectivity: Optimize for efficiency and value

Enhanced SQL Integration: Extra Energy to SQL Customers

Serverless compute for Workflows, DLT, Notebooks

What’s Subsequent for Databricks Workflows?

Streamlining Workflow Administration

Serverless Compute Enhancements

Lakeflow: A unified, clever resolution for knowledge engineering

Attempt the Newest Workflows Options Now!

Quick Frontend Improvement with David Hsu

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