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Siri Is Cooking for WWDC 2024


For years, Siri felt extra like a halfhearted try at a digital assistant than a very useful AI companion. Affected by struggles to grasp context and combine with third-party apps, Apple’s iconic assistant appeared prone to be left behind as rivals like Alexa and Google Assistant continued at a speedy tempo.

That each one modifications with iOS 18, iPadOS 18, and macOS Sequoia. Apple has given Siri an enormous shot of intelligence with the introduction of two key elements: the App Intents framework and Apple Intelligence. This highly effective mixture transforms Siri from a parlor trick right into a deeply built-in, context-aware assistant able to tapping into the information fashions and performance of your favourite apps.

On the coronary heart of this reinvention is the App Intents framework, an API that enables builders to outline “assistant schemas” — fashions that describe particular app actions and knowledge varieties. By constructing with these schemas, apps can specific their capabilities in a language that Apple’s newest AI fashions can deeply comprehend.

App Intents are simply the entry level. The true magic comes from Apple Intelligence, a model new system introduced at this 12 months’s WWDC that infuses superior generative AI instantly into Apple’s core working techniques. Combining App Intents with this new AI engine provides Siri the flexibility to intelligently function on apps’ structured knowledge fashions, perceive pure language in context, make clever ideas, and even generate content material — all whereas defending person’s privateness.

As an example the potential, this text explores how this might play out within the kitchen by imagining a hypothetical cooking app known as Chef Cooks. This app adopts a number of of Apple’s new assistant schemas.

Knowledge Modeling With App Entities

Earlier than Siri can perceive the cooking area, the cooking app should outline its knowledge entities so Apple Intelligence can comprehend them. That is carried out by creating customized structs conforming to the @AssistantEntity schema macros:

@AssistantEntity(schema: .cookbook.recipe)
struct RecipeEntity: IndexedEntity {
  let id: String
  let recipe: Recipe

  @Property(title: "Title") 
  var title: String 
    
  @Property(title: "Description") 
  var description: String? 

  @Property(title: "Delicacies") 
  var delicacies: CuisineType? 
  var substances: [IngredientEntity] 
  var directions: [InstructionEntity] 

  var displayRepresentation: DisplayRepresentation { 
    DisplayRepresentation(title: title, 
      subtitle: delicacies?.displayRepresentation) 
  } 
} 

@AssistantEntity(schema: .cookbook.ingredient) 
struct IngredientEntity: ObjectEntity { 
  let id = UUID() 
  let ingredient: Ingredient @Property(title: "Ingredient") 
  var title: String @Property(title: "Title") 
  var quantity: String? 
    
  var displayRepresentation: DisplayRepresentation { 
    DisplayRepresentation(title: title, subtitle: quantity) 
  } 
}

Adopting the .cookbook.recipe and .cookbook.ingredient schemas ensures the app’s recipes and ingredient knowledge fashions adhere to the specs that Apple Intelligence expects for the cooking area. Observe the person of the @Property property wrappers to outline titles for key attributes. With the information groundwork laid, the app can begin defining particular app intents that function this knowledge utilizing the @AssistantIntent macro.

Discovering Recipes

One of many core experiences in a cooking app is trying to find recipes. The cooking app can allow this for Siri utilizing the .cookbook.findRecipes schema.

@AssistantIntent(schema: .cookbook.findRecipes)
struct FindRecipesIntent: FindIntent {
  @Property(title: "Search Question")
  var searchQuery: String?
 
  @Dependency
  var recipeStore: RecipeStore

  @MainActor
  func carry out() async throws -> some ReturnsValue<[RecipeEntity]> {
    let outcomes = attempt await recipeStore.findRecipes(matching: searchQuery)
    return .outcome(outcomes)
  }
}

This intent accepts a searchQuery parameter and makes use of the app’s RecipeStore to seek out matching recipes from the database. Siri may then combine this app performance in quite a lot of clever methods. For instance:

“Hey Siri, discover vegetarian recipes within the Chef Cooks app.”

*Siri shows a listing of matching veggie recipes.*

Crucially, Siri can perceive the area context and even make ideas with out the person explicitly naming the app.

Viewing Recipe Particulars

With the flexibility to seek out recipes, customers seemingly will need to view the total particulars of a specific dish. The cooking app can assist this by adopting the .cookbook.openRecipe schema:

@AssistantIntent(schema: .cookbook.openRecipe)
struct OpenRecipeIntent: OpenIntent {
  var goal: RecipeEntity

  @Dependency
  var navigation: NavigationManager

  @MainActor
  func carry out() async throws -> some IntentResult {
    navigation.openRecipe(goal.recipe)
    return .outcome()
  }
}

This intent merely accepts a RecipeEntity and instructs the apps’ NavigationManager to open the corresponding full recipe element view. It permits experiences like:

“Hey Siri, present me the recipe for rooster Parmesan.”

  • App opens to the rooster Parmesan recipe.
  • The person sees an appetizing picture of Margherita pizza in Siri ideas.

“Open that recipe in Chef Cooks.”

  • App launches on to the pizza recipe.

However the place Apple Intelligence and App Intents actually shine is in additional superior clever experiences …

Clever Meal Planning

By modeling its knowledge utilizing assistant schemas, Chef Cooks can faucet into Apple Intelligence’s highly effective language mannequin to allow seamless, multi-part queries:

“Hey Siri, I need to make rooster enchiladas for dinner this week.”

Somewhat than simply trying to find and opening a rooster enchilada recipe, Siri understands the total context of this request. It first searches Chef Cooks’s knowledge for an appropriate enchilada recipe, then:

  1. Checks whether or not all substances are in inventory primarily based on the person’s semantic understanding of their kitchen stock.
  2. Provides any lacking substances to a grocery record.
  3. Provides the recipe to a brand new meal plan for the upcoming week.
  4. Supplies a time estimate for prepping and cooking the meal.

All of this occurs with out leaving the conversational Siri interface, because of the app adopting further schemas like .shoppingList.addItems and .mealPlanner.createPlan. App Intents open the door to extremely clever, multifaceted app experiences by which Siri acts as a real collaboration assistant, understanding your intent and orchestrating a number of actions throughout numerous knowledge fashions.

Interactive Widgets With WidgetKit

In fact, not each interplay should occur by voice. Chef Cooks can use its App Intents implementation to energy clever interactive widgets as effectively utilizing WidgetKit.

One instance of utilizing interactive widgets is integrating Chef Cooks’ .cookbook.findRecipe intent utilizing the Safari Internet Widget to offer a centered recipe search expertise with out leaving the browser:

struct RecipeSearchEntry: TimelineEntry {
  let date = Date()
  var searchQuery = ""

  @OpenInAppIntent(schema: .cookbook.findRecipes)   
  var findRecipesIntent: FindRecipesIntent? {
    FindRecipesIntent(searchQuery: searchQuery)
  }
}

This widget entry combines the @OpenInAppIntent property wrapper with Chef Cooks’ FindRecipeIntent implementation to permit customers to enter a search question and immediately view filtered recipe outcomes — all within the Internet Widget UI. Chef Cooks may even assemble extra superior WidgetKit experiences by combining a number of intents into wealthy, interactive widgets that drive customized flows similar to planning a meal by first discovering recipes after which including substances to a grocery record, or displaying complementary recipes and instruction movies primarily based on previous cooking periods.

With App Intents offering the structured knowledge modeling, WidgetKit can rework these clever interactions into immersive, ambient experiences throughout Apple’s platforms.

SBOM – A Software To Reverse Engineer And Examine The RPM And APT Databases To Listing All The Packages Alongside With Executables, Service And Variations

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SBOM – A Software To Reverse Engineer And Examine The RPM And APT Databases To Listing All The Packages Alongside With Executables, Service And Variations

It is a easy SBOM utility which goals to supply an insider view on which packages are getting executed.

The method and goal is easy we will get a transparent perspective view on the packages put in by APT (at the moment engaged on implementing this for RPM and different bundle managers). That is primarily wanted to verify which all packages are literally being executed.

Set up

The packages wanted are talked about within the necessities.txt file and could be put in utilizing pip:

pip3 set up -r necessities.txt

Utilization

  • Initially set up the packages.
  • Secondly , that you must arrange surroundings variables comparable to:
    • Mount the picture: At present I’m nonetheless engaged on a mechanism to routinely outline a mount level and mount several types of photos and volumes however its nonetheless fairly a process for me.
  • Lastly run the software to checklist all of the packages.
Argument Description
--analysis-mode Specifies the mode of operation. Default is static. Selections are static and chroot.
--static-type Specifies the kind of evaluation for static mode. Required for static mode solely. Selections are information and service.
--volume-path Specifies the trail to the mounted quantity. Default is /mnt.
--save-file Specifies the output file for JSON output.
--info-graphic Specifies whether or not to generate visible plots for CHROOT evaluation. Default is True.
--pkg-mgr Manually specify the bundle supervisor or dont add this feature for computerized verify.
APT:
Static Data Evaluation:
– This command runs this system in static evaluation mode, particularly utilizing the Data Listing evaluation methodology.
– It analyzes the packages put in on the mounted quantity positioned at /mnt.
– It saves the output in a JSON file named output.json.
– It generates visible plots for CHROOT evaluation.
```bash
python3 principal.py --pkg-mgr apt --analysis-mode static --static-type information --volume-path /mnt --save-file output.json
```
  • Static Service Evaluation:

  • This command runs this system in static evaluation mode, particularly utilizing the Service file evaluation methodology.

  • It analyzes the packages put in on the mounted quantity positioned at /custom_mount.
  • It saves the output in a JSON file named output.json.
  • It doesn’t generate visible plots for CHROOT evaluation. bash python3 principal.py --pkg-mgr apt --analysis-mode static --static-type service --volume-path /custom_mount --save-file output.json --info-graphic False

  • Chroot evaluation with or with out Graphic output:

  • This command runs this system in chroot evaluation mode.
  • It analyzes the packages put in on the mounted quantity positioned at /mnt.
  • It saves the output in a JSON file named output.json.
  • It generates visible plots for CHROOT evaluation.
  • For graphical output maintain --info-graphic as True else False bash python3 principal.py --pkg-mgr apt --analysis-mode chroot --volume-path /mnt --save-file output.json --info-graphic True/False

RPMStatic Evaluation: – Much like how its accomplished on apt however there is just one sort of static scan avaialable for now. bash python3 principal.py --pkg-mgr rpm --analysis-mode static --volume-path /mnt --save-file output.json

  • Chroot evaluation with or with out Graphic output:
  • Precisely how its accomplished on apt. bash python3 principal.py --pkg-mgr rpm --analysis-mode chroot --volume-path /mnt --save-file output.json --info-graphic True/False

Supporting Photos

At present the software works on Debian and Purple Hat primarily based photos I can guarentee the debian outputs however the Purple-Hat onces nonetheless wants work to be accomplished its not good.

I’m engaged on the pacman facet of issues I’m looking for a relaiable approach of accessing the pacman db for static evaluation.

Graphical Output Photos (Chroot)

APT Chroot

RPM Chroot

SBOM – A Software To Reverse Engineer And Examine The RPM And APT Databases To Listing All The Packages Alongside With Executables, Service And Variations

Internal Workings

For the workings and course of associated documentation please learn the wiki web page: Hyperlink

TODO

  • [x] Assist for RPM
  • [x] Assist for APT
  • [x] Assist for Chroot Evaluation
  • [x] Assist for Variations
  • [x] Assist for Chroot Graphical output
  • [x] Assist for organized graphical output
  • [ ] Assist for Pacman

Concepts and Discussions

Concepts concerning this subject are welcome within the discussions web page.



Is the Google Pixel Watch 3 waterproof?

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Is the Google Pixel Watch 3 waterproof?

Like most smartwatches, the Pixel Watch 3 is waterproof, not waterproof. You may swim in pool or ocean water, and it could stand up to rain, sweat, or regular showerhead stress. However scorching or high-pressure water can bypass its protections; do not use it throughout actions like water snowboarding or a spa day.

As well as, your Pixel Watch 3 band might not be waterproof. Particularly, whereas the default Lively band handles water, the official leather-based and steel bands aren’t rated to deal with water and needs to be dried off instantly after a moist or sweaty exercise.

Holding your Pixel Watch 3 protected against water

Pixel Watch 3 41mm and 45mm with Porcelain bands

(Picture credit score: Andrew Myrick / Android Central)

Formally, the Pixel Watch 3 has water safety rankings of 5ATM and IP68, like many of the finest Android smartwatches right this moment. Which means it is examined to final at depths of fifty meters for as much as 10 minutes or 1.5 meters for as much as half-hour. 

It is also mud resistant, that means salt water or seashore sand should not get contained in the case.



finder – Methods to take away a file named “.”?

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Overview

Incorrect specification of an argument in a commandline device (particulars under) has led to the creation of a file actually named “.”. I’ve already discovered that instantly making an attempt to delete in Finder will set off deletion of all content material within the containing folder (fortuitously this can be a short-term folder), however nonetheless not the “.” file itself. Furthermore, no folders containing this file may be efficiently deleted, neither with Finder nor from bash/zsh shell.

Makes an attempt to delete from zsh

For instance:

mytmp % ls -hal
whole 65
drwx------  1 consumer  employees    16K Aug 21 11:20 .
-rwx------  1 consumer  employees     0B Aug 21 09:51 .
drwx------  1 consumer  employees    16K Aug 16 16:30 ..

mytmp % ls -aB
.   .   ..

mytmp % rm -rf '.'                   
rm: "." and ".." will not be eliminated

mytmp % cd ..

scratch % rm -rf mytmp
rm: mytmp: Permission denied

scratch % sudo rm -rf mytmp
rm: mytmp: Permission denied

I’ve additionally tried variations with out -r, since actually it’s the non-directory model that’s to be deleted.

I moreover tried the suggestion of @bmike to use the inode. Whereas we will determine the inode, the deletion doesn’t seem to work:

scratch % ls -ila mytmp
whole 65
8056451580272514705 drwx------  1 consumer  employees  16384 Aug 21 11:20 .
8652239633868421122 -rwx------  1 consumer  employees      0 Aug 21 09:51 .
                  2 drwx------  1 consumer  employees  16384 Aug 21 11:43 ..

scratch % discover mytmp -inum 8652239633868421122 -delete

## no change
scratch % ls -ila mytmp
whole 65
8056451580272514705 drwx------  1 consumer  employees  16384 Aug 21 11:20 .
8652239633868421122 -rwx------  1 consumer  employees      0 Aug 21 09:51 .
                  2 drwx------  1 consumer  employees  16384 Aug 21 11:43 ..

How can one truly delete (or rename) such a file?


Extra Background

The device that resulted on this was haplogrep, a Java-based CLI. One can set up it by means of Conda by way of

conda set up -c conda-forge -c bioconda haplogrep

The subcommand used was haplogrep classify, which has the next choices:

mytmp % haplogrep classify

mtDNA Haplogroup Classifiction v2.4.0
https://github.com/seppinho/haplogrep-cmd
(c) Sebastian Schönherr, Hansi Weissensteiner, Lukas Forer, Dominic Pacher
[email protected]

[classify]
Lacking required choices: '--input=', '--output=', '--format='
Utilization: haplogrep classify [--chip] [--extend-report] [--rsrs]
                          [--skip-alignment-rules] [--write-fasta]
                          [--write-fasta-msa] --format=
                          [--hetLevel=] [--hits=] --in=
                          [--lineage=] [--metric=] --out=
                          [--phylotree=]
      --chip                VCF information from a genotype chip
                              Default: false
      --extend-report       Add flag for a prolonged closing output
                              Default: false
      --format=     Specify enter file format: vcf, fasta or hsd
      --hetLevel= Add heteroplasmies with a stage > X from the VCF
                              file to the profile (default: 0.9)
      --hits=         Calculate greatest n hits
      --in, --input=    Enter VCF, fasta or hsd file
      --lineage=   Export lineage data as dot file, n0=no
                              tree, 1=with SNPs, 2=solely construction, no SNPs
      --metric=     Specifiy different metrics (hamming or jaccard) than
                              default (kulczynski)
      --out, --output= Output file location
      --phylotree=    Specify phylotree model
      --rsrs                Use RSRS Model
                              Default: false
      --skip-alignment-rules
                            Skip mtDNA nomenclature fixes primarily based on guidelines for
                              FASTA import
                              Default: false
      --write-fasta         Write ends in fasta format
                              Default: false
      --write-fasta-msa     Write a number of sequence alignment (_MSA.fasta)
                              Default: false

I misinterpreted the “Output file location” description of the --out argument as asking for a path, main me to make use of --out . and thus leading to making a file named “.”.

Renaming

The file itself can’t be renamed in Finder or with mv, nevertheless, the containing folder may be renamed.

Deepfakes of Prince William Lure Social Media Customers into an Funding Rip-off


Deepfakes of Prince William and the UK Prime Minister are pushing funding scams on Fb and Instagram.

Uncovered by Fenimore Harper Communications, a media analysis group, the deepfakes take the type of advertisements that result in a phony cryptocurrency platform.[i] In accordance with Fenimore Harper, the equal of $27,000 U.S. {dollars} has been spent on these advertisements and so they have reached greater than 890,000 individuals.

In all, scammers fueled the advertisements with 14 totally different currencies as various as Columbian Pesos, Thai Bahm, Uruguayan Peso, Bangladeshi Taka, and United Arab Emirates Dirham.

In one of many advertisements, a faux Prime Minister Sir Keir Starmer broadcasts a “nationwide make investments platform,” and “to generate profits on this official platform, all you want is a cellphone or pc.” One other advert claims that 45 people have been specifically chosen to earn “life-changing cash” via a mysterious challenge.

One other advert contains a faux Prince William saying, “I’m happy to announce that I, Prince William, and the complete royal household totally assist Prime Minister Keir Starmer’s initiative and his new platform.”

Fenimore Harper says that a few of the advertisements are nonetheless operating. The group mentioned that it recognized these advertisements utilizing Meta’s personal AI mannequin, Llama 3.1 70B.

Prince William and UK Prime Minister deepfakes result in a phony funding website

Fenimore Harper’s report then discovered that a few of the advertisements directed individuals to a bogus cryptocurrency platform known as “Instant Edge.”

As soon as on the location, individuals had been requested to supply fundamental contact information, adopted by encouragement to make investments.

Fenimore Cooper discovered a number of unfavourable opinions for the platform on Trustpilot, “principally from victims complaining they misplaced their cash or had been hounded by scammers over the cellphone.”

Most of the hyperlinks to the bogus platform look like lifeless now, whilst some advertisements nonetheless seem to flow into.

As reported by The Impartial, a spokesperson for Meta mentioned, “Our methods detected and eliminated the overwhelming majority of those adverts earlier than this report was printed. As a part of our advertisements evaluation course of—which might embody each automated and human opinions — we now have a number of layers of research and detection, each earlier than and after an advert goes reside. It’s in opposition to our insurance policies to run advertisements that improperly use pictures of public or political figures for misleading functions, and we take away these advertisements when detected.”[ii]

A complicated rip-off hides behind low-quality deepfakes

A faux advert that includes Prime Minister Sir Keir Starmer in Fenimore Harper’s report reveals that the deepfakes are low-grade. Of their instance, the scammers use beforehand aired footage of the Prime Minister dubbed over with AI voice-cloned audio. As within the case of many cheaper deepfakes, the lip-synching matches poorly.

With that, this rip-off echoes the Taylor Swift cookware deepfake rip-off we reported on earlier this 12 months. It additionally used poorly dubbed AI voice-cloned audio atop clips of beforehand aired footage.

Nonetheless, regardless of the low-quality deepfake, this rip-off units itself aside with the best way the scammers manipulated Google search outcomes. On condition that many individuals use search to analysis potential investments, the scammers made positive to provide themselves favorable opinions.

In accordance with Fenimore Harper, the scammers used Web optimization-hacking strategies in order that the scammers may “place their very own copy in Google’s ‘featured snippets’ … [making the] high consequence a glowing endorsement for the rip-off.”

Fenimore Harper says that the scammers additional duped Google’s AI overview function, which summarizes search outcomes. Of their instance, individuals should scroll via a number of outcomes that comprise disinformation earlier than they get to a reputable supply for opinions.

In all, it seems the scammers put additional thought and care into their rip-off. They did greater than financial institution on a deepfake and a bogus website to lure in victims. They anticipated the subsequent transfer for a lot of victims, which was to hop on a search engine and see if the chance was legit.

Defending your self from on-line funding scams

Scammers have more and more turned to AI deepfakes of celebrities and different public figures to push their scams. What’s new right here is that we now have a primary minister and a member of the royal household falling sufferer to a deepfake as a part of the rip-off.

Nonetheless, you’ll be able to avoid on-line funding scams like these, whether or not they use AI deepfakes or not. Take into account the next as obvious “alternatives” crop up on-line:

Go together with a professional.

Working with an accredited monetary adviser is all the time a sound step with any funding you select to make, as is just investing funds you’ll be able to afford to lose if the funding falls via.

Be careful for brand spanking new, untried platforms.

Keep away from investments that ask you to contribute cash instantly from one among your individual accounts somewhat than by way of a dependable, verified platform.

Search trusted analysis sources.

As we noticed above, the highest ends in a search won’t be probably the most credible supply of information. When researching monetary alternatives, search for established, reliable sources of evaluation. Seek the advice of a number of sources as effectively.

Be cautious of superstar and popular culture tie-ins.

Regard any funding based mostly on a popular culture reference like films, memes, and reveals with a extremely essential eye. The identical goes for public figures. It would very effectively be a rip-off constructed round buzz somewhat than a legit funding, such because it was with the Squid Recreation cryptocurrency rip-off we noticed in 2021 and the more moderen AI deepfake scams that includes a faux Elon Musk selling bogus investments.

Use on-line safety software program.

AI-powered on-line safety such as you’ll discover in our McAfee+ plans sniffs out hyperlinks to suspicious websites that promote scams and comprise malware. Scams like these take you to shady corners of the web, and our safety will warn you earlier than you faucet or click on — and block these websites in case you faucet or click on by mistake.

[i] https://www.fenimoreharper.com/analysis/starmer-disinformation-meta-deepfakes

[ii] https://www.unbiased.co.uk/information/uk/home-news/starmer-prince-william-ai-deepfake-crypto-scam-b2595554.html

 

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