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Indie App Highlight: ‘Seize’ permits you to arrange your fleeting ideas effortlessly

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Indie App Highlight: ‘Seize’ permits you to arrange your fleeting ideas effortlessly


Welcome to Indie App Highlight. It is a weekly 9to5Mac collection the place we showcase the newest apps within the indie app world. Should you’re a developer and would love your app featured, get in touch.

Seize is a productiveness app that permits you to simply take notes, write down duties, save hyperlinks, and set reminders, multi function clear interface. The app absolutely helps iPhone, iPad, Mac, and Apple Imaginative and prescient Professional.

As somebody who finds myself juggling between placing issues on Calendar, writing them down in Apple Notes, or setting a reminder, I feel this app is fairly useful. It goals to simplify issues, providing you with a centralized place to only jot down no matter in a pinch.

Seize has computerized date recognition, so when you sort one thing like “Get lunch with Andy tomorrow at midday”, it’ll mechanically put it aside for that date/time, and you’ll shut the app and transfer on along with your day. The app helps iCloud Sync, so you’ll be able to entry your fast ideas throughout all your Apple gadgets.

The app additionally permits you to create lists, so you’ll be able to simply separate duties based mostly on class. You can have a listing for private gadgets, work gadgets, or anything. It additionally options widgets, so you’ll be able to shortly look at what’s forward. You may also simply transfer duties to a different app comparable to Calendar, if that is sensible.

One other neat function of Seize is its Share Sheet integration. Should you’re in Safari, you’ll be able to shortly share a hyperlink to Seize, and it’ll hold it saved there. It additionally has a Highlight-like function on macOS known as Fast Seize, the place you’ll be able to arrange a keyboard shortcut and immediately add a brand new thought to the app.

It’s a neat little app, though I do want it had full push notifications moderately than simply badges. The developer says they plan on including help for Siri and Apple Intelligence, Apple Watch help, in addition to audio captures sooner or later.

Seize is free on the App Retailer for iOS 17 and later with as much as 20 captures, and the Professional tier is obtainable for $5.99/12 months or $11.99 lifetime.

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

British teen arrested in reference to MGM Resorts ransomware assault

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British teen arrested in reference to MGM Resorts ransomware assault


British police have arrested a 17-year-old boy believed to be linked to a cybercriminal gang that launched devastating ransomware assaults final 12 months on MGM Resorts and different corporations.

Final September, lodge and on line casino big MGM Resorts was hit by a cyber assault which resulted left visitors queuing for hours to verify in to lodges, residents struggling to enter their rooms, ATM machines disrupted, and web sites, TV companies, and cellphone strains taken down.

On the time, MGM resorts confirmed that the incident impacted all of its resorts in Las Vegas, together with the Aria, the Bellagio, Luxor, MGM Grand and Mandalay Bay.

Some members of the general public took to social media to doc the expertise of being at a on line casino that had been dropped at a standstill by hackers.

It took a full 10 days till MGM Resorts introduced it was “working usually” once more, and later admitted that hackers had been in a position to entry the private info, together with names, contact info, gender, date of beginning, and driver’s license, passport, and even Social Safety numbers, of some visitors.

It was later revealed that hackers had efficiently socially-engineered MGM’s IT helpdesk, posing on the phone as an worker locked out of his account, and tricked them into handing over login credentials which allowed them to deploy the ransomware assault.

The associated fee to MGM Resorts has been declared at over US $100 million.

The 17-year-old youth arrested final week in Walsall, England, in a joint operation by West Midlands Police and the FBI, is suspected of being a member of the “Scattered Spider” cybercriminal group and was initially positioned into police custody on suspicion of blackmail and offenses associated to the Pc Misuse Act, in response to a assertion from the Regional Organised Crime Unit for the West Midlands (ROCUWM).

“This arrest has been made following a posh investigation which stretches abroad to America. We’ve been working carefully with the Nationwide Crime Company and FBI,” mentioned Detective Inspector Hinesh Mehta, Cyber Crime Unit Supervisor at ROCUWM. “These cyber teams have focused well-known organisations with ransomware and so they have efficiently focused a number of victims around the globe taking from them important quantities of cash. We wish to ship out a transparent message that we’ll discover you. It’s merely not price it.”

MGM Resorts has thanked regulation enforcement for its work in finding and arresting alleged members of the gang accountable for the assault, and notes that it refused to pay a ransom.

Databricks SQL Serverless is now out there on Google Cloud Platform

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Databricks SQL Serverless is now out there on Google Cloud Platform


In the present day, we’re thrilled to announce that Databricks SQL Serverless is now Typically Out there on Google Cloud Platform (GCP)! As a key element of our Information Intelligence Platform, Databricks SQL Serverless delivers the very best efficiency with immediate and elastic compute, lowers prices, and frees you to give attention to delivering enterprise worth slightly than managing infrastructure. This GA launch reinforces our perception that the very best knowledge warehouse is a lakehouse, integrating knowledge lakes and warehouses for unified method. SQL Serverless is now out there in 7 GCP areas and 40+ areas throughout all three main cloud suppliers (AWS, Azure and GCP).

Advantages of Databricks SQL Serverless on GCP

During the last couple of years, we have seen over 4x development and adoption of Databricks SQL – our knowledge warehouse purpose-built for the Lakehouse. SQL Serverless on GCP brings quite a few advantages:

  • On the spot and elastic compute: SQL Serverless: compute brings a very elastic surroundings that is immediately out there and scales together with your wants. You will profit from easy usage-based pricing with out worrying about idle time fees. Think about now not needing to attend for infrastructure assets to change into out there to run queries, or now not overprovisioning assets to deal with spikes in utilization. Databricks SQL Serverless dynamically grows and shrinks assets to deal with no matter workload you throw at it.
  • Decrease infrastructure prices: Underneath the covers, the serverless compute platform makes use of machine studying (ML) algorithms to provision and scale compute assets while you want them. SQL Serverless permits substantial value financial savings with out the necessity to shut down clusters manually.
  • Eradicate administration overhead: Serverless capabilities remodel Databricks SQL into a completely managed service, eliminating the burden of capability administration, patching, upgrading, and efficiency optimization of the cluster. You solely must focus in your knowledge and the insights it holds. The simplified pricing mannequin additionally means there’s just one invoice to trace and just one place to test prices.
  • Improved efficiency: Along with immediate startup occasions, SQL Serverless can be full of efficiency and value optimization options for all analytics use instances. SQL Serverless comes with three further performance-improving options giving it best-in-class efficiency.
    • Predictive I/O makes use of machine learning-powered optimizations to make level lookups sooner and cheaper and knowledge updates/deletes blazing quick.
    • Clever Workload Administration dynamically upscales and downscales clusters on a warehouse relying on question load to enhance question efficiency and concurrency whereas decreasing value. That is particularly helpful for profiting from your warehouse for BI use instances but in addition advantages combined workloads. This functionality is barely out there on SQL Serverless.
      Intelligent Workload Management
    • Enhanced Question Outcomes Cache retains question outcomes by persisting them as workspace system knowledge. Because of this equivalent queries (e.g. from refreshing a dashboard) despatched from completely different warehouses in the identical workspace and even after a warehouse has shut down could be served from the question outcomes cache with out requiring to re-run the question. This improves question latency whereas decreasing warehouse value! This functionality is barely out there on SQL Serverless.

Throughout preview, buyer response has been great. Many shoppers adopted early and sit up for scaling up.

“Our consumer group is thrilled after we switched to Databricks SQL Serverless on GCP. The moment warehouse begin occasions and improved question efficiency have considerably boosted developer velocity and adoption of our centralized knowledge platform on Databricks. SQL Serverless was simple to allow and as a consequence of dyamic scaling we noticed immmediate value financial savings of over 50% for a excessive precedence challenge … which was nice!

— Micaela Christopher, Director of Information Science and Engineering, Uplight

New options launched with SQL Serverless

The GA of SQL Serverless on GCP consists of many new options, too. These will make it simpler to undertake SQL Serverless for numerous workloads and enhance consumer expertise as properly.

  1. SQL Execution API: The Databricks SQL Assertion Execution API means that you can use customary SQL over HTTP to construct customized knowledge purposes and integrations with a variety of purposes, applied sciences, and computing units. The API offers a set of endpoints that mean you can submit SQL statements to a SQL Warehouse for execution and retrieve outcomes.
    SQL Execution API
  2. Python UDFs: Python UDFs enable customers to put in writing Python code and invoke it by way of a SQL operate in a simple safe and absolutely ruled method, bringing the facility of Python to Databricks SQL.
    Python UDFs
  3. Notebooks integration: Use SQL warehouses (Serverless or Professional SQL) instantly in Notebooks. SQL-optimized compute offers as much as a 12x improved worth efficiency over customary interactive clusters. You can even reap the benefits of the pocket book to put in writing and schedule Git-backed, multi-statement, and parameterized SQL.
    Notebooks integration
  4. Workflows Integration: Use the SQL activity sort in a Databricks job, permitting you to create, schedule, function, and monitor workflows that embody Databricks SQL objects similar to queries, legacy dashboards, and alerts. For instance, your workflow can ingest knowledge, put together the information, carry out evaluation utilizing Databricks SQL queries, after which show the ends in a legacy dashboard.
    Workflows Integration
  5. Databricks AI/BI: A brand new sort of enterprise intelligence product constructed from the bottom as much as deeply perceive your knowledge’s semantics and allow anybody to research knowledge for themselves is now additionally GA on GCP. Moreover, AI/BI Genie, a subsequent technology conversational expertise for enterprise groups to interact with their knowledge by way of pure language is coming to GCP quickly.
    Databricks AI/BI
  6. HIPAA compliance: SQL Serverless is now additionally HIPAA compliant in all GCP areas it’s out there in.
  7. AI Capabilities: Leverage LLM and ML fashions instantly in SQL by utilizing highly effective AI features both with built-in LLMs or exterior fashions [coming soon]
AI_GENERATE_TEXT(
  immediate,
 'azure_openai/gpt-35-turbo',
 'apiKey', SECRET('tokens', 'azure-openai'),
 "deploymentName", "llmbricks",
 "apiVersion", "2023-03-15-preview",
 "resourceName", "lakehouserules",
 "temperature", CAST(0.0 as DOUBLE)
);

Availability and Getting Began

SQL Serverless on GCP is offered in seven areas: us-east1, us-central1, us-west1, us-east4, europe-west2, asia-southeast1, and asia-south1. Getting began with SQL Serverless on GCP is easy.

  1. If you do not have a Databricks account, join Databricks on GCP.
  2. Create a workspace within the Databricks UI and choose GCP as your cloud supplier.
  3. After you are arrange, you possibly can start operating SQL queries and expertise the facility of serverless analytics.

The Common Availability of SQL Serverless on GCP is a big development in our mission to simplify knowledge analytics and assist organizations unlock the total potential of their knowledge. We’re excited to see how our prospects will harness this new functionality to drive innovation and obtain their enterprise targets. For extra info, go to our documentation on the right way to get began or contact our help staff.

Thanks for being part of our journey!

For extra info, go to our documentation on the right way to get began or contact our help staff.

Posit AI Weblog: torch 0.10.0

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Posit AI Weblog: torch 0.10.0


We’re joyful to announce that torch v0.10.0 is now on CRAN. On this weblog put up we
spotlight among the adjustments which have been launched on this model. You’ll be able to
examine the total changelog right here.

Automated Combined Precision

Automated Combined Precision (AMP) is a way that allows sooner coaching of deep studying fashions, whereas sustaining mannequin accuracy through the use of a mixture of single-precision (FP32) and half-precision (FP16) floating-point codecs.

With a view to use automated combined precision with torch, you’ll need to make use of the with_autocast
context switcher to permit torch to make use of totally different implementations of operations that may run
with half-precision. Normally it’s additionally advisable to scale the loss perform as a way to
protect small gradients, as they get nearer to zero in half-precision.

Right here’s a minimal instance, ommiting the info era course of. You could find extra data within the amp article.

...
loss_fn <- nn_mse_loss()$cuda()
web <- make_model(in_size, out_size, num_layers)
choose <- optim_sgd(web$parameters, lr=0.1)
scaler <- cuda_amp_grad_scaler()

for (epoch in seq_len(epochs)) {
  for (i in seq_along(knowledge)) {
    with_autocast(device_type = "cuda", {
      output <- web(knowledge[[i]])
      loss <- loss_fn(output, targets[[i]])  
    })
    
    scaler$scale(loss)$backward()
    scaler$step(choose)
    scaler$replace()
    choose$zero_grad()
  }
}

On this instance, utilizing combined precision led to a speedup of round 40%. This speedup is
even greater in case you are simply operating inference, i.e., don’t have to scale the loss.

Pre-built binaries

With pre-built binaries, putting in torch will get lots simpler and sooner, particularly if
you might be on Linux and use the CUDA-enabled builds. The pre-built binaries embrace
LibLantern and LibTorch, each exterior dependencies essential to run torch. Moreover,
when you set up the CUDA-enabled builds, the CUDA and
cuDNN libraries are already included..

To put in the pre-built binaries, you should utilize:

difficulty opened by @egillax, we might discover and repair a bug that brought on
torch features returning an inventory of tensors to be very gradual. The perform in case
was torch_split().

This difficulty has been mounted in v0.10.0, and counting on this conduct ought to be a lot
sooner now. Right here’s a minimal benchmark evaluating each v0.9.1 with v0.10.0:

lately introduced e book ‘Deep Studying and Scientific Computing with R torch’.

If you wish to begin contributing to torch, be happy to achieve out on GitHub and see our contributing information.

The total changelog for this launch might be discovered right here.

Swift command design sample – The.Swift.Dev.

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Swift command design sample – The.Swift.Dev.



· 1 min learn

This time I’ll present you a behavioral sample. Here’s a little instance of the command design patten written in Swift.

The command sample will be useful should you’d like to supply a typical interface for various actions that will probably be executed later in time. Normally it’s an object that encapsulates all the knowledge wanted to run the underlying motion correctly.

Instructions are sometimes used to deal with person interface actions, create undo managers, or handle transactions. Let’s see a command sample implementation in Swift by making a command line argument handler with emojis. 💾

#!/usr/bin/env swift

import Basis

protocol Command {
    func execute()
}

class HelpCommand: Command {

    func execute() {
        Assist().data()
    }
}

class Assist {

    func data() {
        print("""

             🤖 Commander 🤖
                  v1.0

        Accessible instructions:

            👉 assist      This command
            👉 ls        Record paperwork

        Bye! 👋

        """)
    }
}

class ListCommand: Command {

    func execute() {
        Record().homeDirectoryContents()
    }
}

class Record {

    func homeDirectoryContents() {
        let fileManager = FileManager.default
        guard let documentsURL = fileManager.urls(for: .documentDirectory, in: .userDomainMask).first else {
            print("Couldn't open paperwork listing")
            exit(-1)
        }
        do {
            let fileURLs = strive fileManager.contentsOfDirectory(at: documentsURL, includingPropertiesForKeys: nil)
            print("nt📁 Itemizing paperwork listing:n")
            print(fileURLs.map { "tt💾 " + $0.lastPathComponent }.joined(separator: "nn") + "n" )
        }
        catch {
            print(error.localizedDescription)
            exit(-1)
        }

    }
}

class App {

    var instructions: [String:Command] = [:]

    init() {
        self.instructions["help"] = HelpCommand()
        self.instructions["ls"] = ListCommand()
    }

    func run() {
        let arguments = CommandLine.arguments[1...]

        guard let key = arguments.first, self.instructions[key] != nil else "))]")
            exit(-1)
        

        self.instructions[key]!.execute()
    }
}

App().run()

For those who save this file, can run it by merely typing ./file-name.swift from a terminal window. The Swift compiler will deal with the remainder.

Actual world use instances for the command design sample:

+ varied button actions
+ assortment / desk view choice actions
+ navigating between controllers
+ historical past administration / undo supervisor
+ transactional conduct
+ progress administration
+ wizards

As you may see this sample will be utilized in a number of areas. Apple even made a selected class for this function referred to as NSInvocation, however sadly it’s not obtainable in Swift, on account of it’s dynamic conduct. That’s not an enormous deal, you may all the time make your personal protocol & implementation, generally you simply want one additional class that wraps the underlying command logic. 😛

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Discover ways to use lazy properties in Swift to enhance efficiency, keep away from optionals or simply to make the init course of extra clear.

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