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Extremely-Deep Geothermal Drilling & The Rise Of Black Swan Dangers



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Final Up to date on: nineteenth March 2025, 06:15 pm

Deep drilling isn’t elective for enhanced geothermal programs (EGS), it’s the entire level. To know why, consider the Earth’s crust as a scorching soup. Close to the floor, it’s merely lukewarm, barely helpful past warming your home in the event you’re fortunate. Go deeper, nonetheless, and temperatures rise quickly, roughly 25 to 30 levels Celsius for each kilometer drilled, although that varies wildly relying on geology. Accessing enough warmth, ideally round 200–400 levels Celsius to economically generate electrical energy, often means reaching depths between 4 and 10 kilometers, usually in powerful, unforgiving rock.

As a word, that is one in a collection of articles on geothermal. The scope of the collection is printed within the introductory piece. In case your curiosity space or concern isn’t mirrored within the introductory piece, please go away a remark.

Traditionally, we’ve solely scratched the floor. The Kola Superdeep Borehole in Russia, drilled over two painstaking many years between 1970 and 1990, reached about 12 kilometers deep. It was a unprecedented feat, however one marred by harsh realities: drill bits continuously failed, and the deeper they went, the warmer and extra plastic the rock grew to become. At round 180 levels Celsius, the borehole began deforming like a squeezed plastic straw, marking the bounds of typical drilling.

The oil and gasoline business has surpassed this depth, at the least on paper, drilling horizontally and vertically to depths exceeding 12 kilometers. Nevertheless, these wells, like these in Qatar or Russia’s Sakhalin area, navigate softer sedimentary formations and keep away from the scorching temperatures of deep geothermal targets. Iceland’s Deep Drilling Challenge (IDDP), in contrast, plunged straight into supercritical situations at round 450 levels Celsius simply 4.5 kilometers down, proving each potential and peril. Their casings corroded swiftly, underscoring the bounds of current know-how.

Enter novel drilling approaches promising to rewrite these guidelines — every fascinating, costly, and accompanied by a wholesome dose of skepticism. Take millimeter-wave drilling, championed by Quaise Power, spun out from MIT, sitting at Know-how Readiness Stage (TRL 4 with 9 being commercialized). As a substitute of grinding rock, Quaise melts it utilizing microwaves beamed downhole via specialised waveguides. Quaise claims this could attain depths of 20 kilometers with prices scaling linearly — not exponentially.

The catches? Their largest lab take a look at noticed a 2.5 cm gap 2.5 m lengthy, which is a couple of 4,000th of their claims for the way deep they will go. As an engineering rule of thumb, you need to get to quarter-scale prototypes to be in the identical physics ballgame, so that they have a whole lot of scaling to do. Their imaginative and prescient of attaining a value of roughly a thousand {dollars} per meter sounds optimistic at greatest and fantastical at worst. Actual-world rock has fluids, fractures, and surprises, and microwaves notoriously wrestle in moist environments. And final however not least, what occurs to the melted rock? They ran compressed air to the underside of the two.5 m gap and it blew the rock out as skinny threads, however getting air to blow melted rock a number of kilometers straight up strikes me (and an terrible lot of different individuals) as deeply unlikely. It’s more likely to stay to gear and the perimeters of the opening and gum up the works. Nonetheless, if Quaise can hold the microwaves from scattering and overheating parts deep underground, it might remodel EGS economics.

GA Drilling’s PLASMABIT (TRL 4-5) follows a parallel path, utilizing plasma torches to thermally fracture rock. Their lab assessments present rock fractures superbly underneath excessive warmth, however downhole situations — pressurized water, corrosive environments, unpredictable rock compositions — are harsher. GA hedged their bets with incremental advances like their AnchorBit, basically a downhole stabilizer, already demonstrating success at boosting typical drilling charges in lab settings. However scaling plasma fracturing instruments to field-ready depths stays technically daunting. Think about igniting and sustaining a plasma torch kilometers beneath your ft — any malfunction might flip costly shortly. Individuals I do know who’ve labored with plasma torches, together with chemical processing Paul Martin, make it clear that they’re onerous to regulate.

Different strategies, corresponding to thermal spallation, make use of intense warmth jets to flake away rock, promising drilling speeds considerably quicker than typical strategies. Potter Drilling (TRL 5) and the EU ThermoDrill challenge (TRL 6-7) demonstrated promising penetration charges in lab and small area trials. But, there’s a vital caveat — this method hinges on rock sorts cracking predictably underneath thermal stress. Encountering non-cooperative geology, like softer rocks that soften somewhat than spall, might ship prices skyrocketing. And when rock is way hotter and extra plastic as it’s down deep, that is unlikely to carry out practically as effectively.

Excessive-power laser drilling additionally flirts with transformational claims. Labs have proven lasers simply slicing via shale and sandstone, however delivering a coherent, intense beam a number of kilometers underground isn’t trivial. Lasers want completely engineered optics and fiber cables immune to immense stress and warmth. Actual-world demonstrations have been restricted, and any water within the rock can scatter the laser beam, dramatically decreasing effectivity. Laser-assisted drilling is intriguing, maybe even viable in sure situations, however removed from confirmed at depth.

Conventional mechanical drilling isn’t idle. Hammer drilling applied sciences, now at TRLs round 6 or 7, are starting to reliably exhibit larger penetration charges and larger sturdiness in onerous crystalline rock at average depths. Polycrystalline diamond compact (PDC) bits, reaching TRLs of 8 or larger, have considerably elevated drilling effectivity in powerful geological situations, decreasing downtime attributable to frequent bit replacements. Directional drilling, well-established at TRL 9, permits exact focusing on of geothermal reservoirs, optimizing useful resource entry and minimizing drilling lengths. The first power of those approaches lies of their confirmed operational historical past and incremental enhancements that cut back danger relative to radically new strategies.

Nevertheless, mechanical drilling stays challenged at depths past 7 kilometers attributable to growing temperatures that degrade device integrity and rock changing into much less brittle and extra plastic, making environment friendly drilling more and more troublesome. The important thing technical dangers embody managing excessive warmth, minimizing bit put on, and avoiding catastrophic device failures that may shortly escalate challenge prices. Even incremental enhancements right here may yield higher returns than betting all the pieces on completely novel strategies.

This brings us again to why ultra-deep drilling is hard. Beneath sure temperatures, rock turns into ductile — much less susceptible to fracturing and extra prone to deform and seal any induced fractures. Fracking can briefly induce fractures, however sustaining long-term permeability stays unproven. Furthermore, ultra-deep drilling means working on the extremes of fabric capabilities: casing steels weaken, electronics fail, and surprising geologic surprises, corresponding to overpressured fluids and even magma, can flip a promising challenge right into a expensive dead-end in a single day.

Given this, deep geothermal drilling epitomizes what’s referred to as a ‘long-tail danger,’ or as Bent Flyvbjerg vividly frames it — a basic breeding floor for ‘black swan’ occasions. These unpredictable, uncommon, and high-impact outcomes aren’t merely theoretical—they stack up alarmingly when combining excessive depths, first-of-a-kind (FOAK) applied sciences, and unprecedented geological situations. Every added kilometer doesn’t simply improve capital prices; it exponentially multiplies uncertainties, creating layers of technical, geological, and financial dangers. Novel drilling strategies enlarge this uncertainty: applied sciences that perform superbly in managed laboratory settings can falter disastrously underneath harsh, real-world situations deep underground. Flyvbjerg’s insights warn us that optimism bias continuously underestimates the complexity and potential for catastrophic failure in such modern ventures, making deep geothermal drilling a compelling however perilously unsure endeavor.

Fly too near the Earth’s molten warmth, and your funding can evaporate — fairly actually — in the event you hit supercritical situations unprepared. Thus, novel drilling applied sciences, whereas alluring, should navigate a deadly path: proving they will really decrease prices, reliably handle surprises, and obtain constant financial efficiency at industrial scale.

The uncomfortable fact is that deep geothermal drilling — notably utilizing cutting-edge, largely untested strategies — embodies precisely the kind of long-tail, black-swan-rich endeavor that Bent Flyvbjerg has proven is most prone to huge delays, value overruns, and outright failures. Betting closely on these formidable however immature applied sciences may yield revolutionary breakthroughs, or simply as possible, turn out to be one other cautionary story of costly hubris chasing desires far under floor. My opinion that geothermal for electrical era would stay a rounding error globally hasn’t modified after going deep on superior drilling applied sciences.

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Watch This Webinar to Study Methods to Get rid of Id-Primarily based Assaults—Earlier than They Occur

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Mar 19, 2025The Hacker InformationId Safety / Webinar

Watch This Webinar to Study Methods to Get rid of Id-Primarily based Assaults—Earlier than They Occur

In right this moment’s digital world, safety breaches are all too widespread. Regardless of the various safety instruments and coaching applications obtainable, identity-based assaults—like phishing, adversary-in-the-middle, and MFA bypass—stay a serious problem. As an alternative of accepting these dangers and pouring assets into fixing issues after they happen, why not forestall assaults from occurring within the first place?

Our upcoming webinar, Methods to Get rid of Id-Primarily based Threats,” will present you the way, that includes Past Id consultants Jing Reyhan (Director of Product Advertising) and Louis Marascio (Sr. Product Architect). Be a part of them to find how a secure-by-design entry answer can block phishing, adversary-in-the-middle assaults, and extra—earlier than they ever attain your community.

What You Will Study

  1. Cease Assaults on the Supply: Study to proactively block threats like phishing—earlier than they’ll goal your techniques.
  2. Grasp Key Safety Strategies: Uncover how secure-by-design options allow phishing resistance, verifier impersonation resistance, machine compliance, and steady, risk-based entry management.
  3. Sensible, Actionable Recommendation: Acquire clear, easy-to-implement steps to safeguard your group with out requiring superior technical expertise.
  4. Actual-World Success Tales: See how these confirmed methods work in real-life situations that spotlight their effectiveness.
  5. Acquire a Aggressive Edge: Forestall breaches to scale back prices and construct belief together with your prospects and companions.

Even should you’re not a tech knowledgeable, you may study invaluable insights about how identity-based threats function—and methods to cease them.

It is time to rethink conventional safety approaches. As an alternative of reacting to assaults, uncover methods to forestall them altogether. By becoming a member of our webinar, you may take a serious step towards securing your group’s future.

Register now and discover ways to eradicate whole courses of identity-based assaults out of your risk panorama. Do not miss this chance to rework your safety technique and defend what issues most.

Watch this Professional Webinar

Be happy to share this invitation with colleagues and anybody who values proactive safety. We stay up for seeing you on the webinar!

Discovered this text attention-grabbing? This text is a contributed piece from one in every of our valued companions. Comply with us on Twitter and LinkedIn to learn extra unique content material we submit.



Past Retrieval: NVIDIA Charts Course for the Generative Computing Period

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NVIDIA CEO Jensen Huang introduced a collection of groundbreaking developments in AI computing capabilities on the firm’s GTC March 2025 keynote, describing what he known as a “$1 trillion computing inflection level.” The keynote revealed the manufacturing readiness of the Blackwell GPU structure, a multi-year roadmap for future architectures, main breakthroughs in AI networking, new enterprise AI options, and vital developments in robotics and bodily AI.

The “Token Economic system” and AI Factories

Central to Huang’s imaginative and prescient is the idea of “tokens” as the elemental constructing blocks of AI and the emergence of “AI factories” as specialised knowledge facilities designed for generative computing.

“That is how intelligence is made, a brand new type of manufacturing unit generator of tokens, the constructing blocks of AI. Tokens have opened a brand new frontier,” Huang informed the viewers. He emphasised that tokens can “rework pictures into scientific knowledge charting alien atmospheres,” “decode the legal guidelines of physics,” and “see illness earlier than it takes maintain.”

This imaginative and prescient represents a shift from conventional “retrieval computing” to “generative computing,” the place AI understands context and generates solutions fairly than simply fetching pre-stored knowledge. In accordance with Huang, this transition necessitates a brand new type of knowledge middle structure the place “the pc has change into a generator of tokens, not a retrieval of information.”

Blackwell Structure Delivers Large Efficiency Positive factors

The NVIDIA Blackwell GPU structure, now in “full manufacturing,” delivers what the corporate claims is “40x the efficiency of Hopper” for reasoning fashions beneath equivalent energy situations. The structure contains assist for FP4 precision, resulting in vital power effectivity enhancements.

“ISO energy, Blackwell is 25 occasions,” Huang acknowledged, highlighting the dramatic effectivity positive aspects of the brand new platform.

The Blackwell structure additionally helps excessive scale-up by way of applied sciences like NVLink 72, enabling the creation of huge, unified GPU programs. Huang predicted that Blackwell’s efficiency will make earlier era GPUs considerably much less fascinating for demanding AI workloads.

(Supply: NVIDIA)

Predictable Roadmap for AI Infrastructure

NVIDIA outlined a daily annual cadence for its AI infrastructure improvements, permitting prospects to plan their investments with larger certainty:

  • Blackwell Extremely (Second half of 2025): An improve to the Blackwell platform with elevated FLOPs, reminiscence, and bandwidth.
  • Vera Rubin (Second half of 2026): A brand new structure that includes a CPU with doubled efficiency, a brand new GPU, and next-generation NVLink and reminiscence applied sciences.
  • Rubin Extremely (Second half of 2027): An excessive scale-up structure aiming for 15 exaflops of compute per rack.

Democratizing AI: From Networking to Fashions

To appreciate the imaginative and prescient of widespread AI adoption, NVIDIA introduced complete options spanning networking, {hardware}, and software program. On the infrastructure degree, the corporate is addressing the problem of connecting lots of of 1000’s and even hundreds of thousands of GPUs in AI factories by way of vital investments in silicon photonics expertise. Their first co-packaged optics (CPO) silicon photonic system, a 1.6 terabit per second CPO based mostly on micro ring resonator modulator (MRM) expertise, guarantees substantial energy financial savings and elevated density in comparison with conventional transceivers, enabling extra environment friendly connections between huge numbers of GPUs throughout totally different websites.

Whereas constructing the inspiration for large-scale AI factories, NVIDIA is concurrently bringing AI computing energy to people and smaller groups. The corporate launched a brand new line of DGX private AI supercomputers powered by the Grace Blackwell platform, geared toward empowering AI builders, researchers, and knowledge scientists. The lineup contains DGX Spark, a compact growth platform, and DGX Station, a high-performance desktop workstation with liquid cooling and a formidable 20 petaflops of compute.

NVIDIA DGX Spark (Supply: NVIDIA)

Complementing these {hardware} developments, NVIDIA introduced the open Llama Nemotron household of fashions with reasoning capabilities, designed to be enterprise-ready for constructing superior AI brokers. These fashions are built-in into NVIDIA NIM (NVIDIA Inference Microservices), permitting builders to deploy them throughout varied platforms from native workstations to the cloud. The strategy represents a full-stack resolution for enterprise AI adoption.

Huang emphasised that these initiatives are being enhanced by way of intensive collaborations with main corporations throughout a number of industries who’re integrating NVIDIA fashions, NIM, and libraries into their AI methods. This ecosystem strategy goals to speed up adoption whereas offering flexibility for various enterprise wants and use circumstances.

Bodily AI and Robotics: A $50 Trillion Alternative

NVIDIA sees bodily AI and robotics as a “$50 trillion alternative,” based on Huang. The corporate introduced the open-source NVIDIA Isaac GR00T N1, described as a “generalist basis mannequin for humanoid robots.”

Important updates to the NVIDIA Cosmos world basis fashions present unprecedented management over artificial knowledge era for robotic coaching utilizing NVIDIA Omniverse. As Huang defined, “Utilizing Omniverse to situation Cosmos, and Cosmos to generate an infinite variety of environments, permits us to create knowledge that’s grounded, managed by us and but systematically infinite on the identical time.”

The corporate additionally unveiled a brand new open-source physics engine known as “Newton,” developed in collaboration with Google DeepMind and Disney Analysis. The engine is designed for high-fidelity robotics simulation, together with inflexible and delicate our bodies, tactile suggestions, and GPU acceleration.

Isaac GR00T N1 (Supply: NVIDIA)

Agentic AI and Trade Transformation

Huang outlined “agentic AI” as AI with “company” that may “understand and perceive the context,” “motive,” and “plan and take motion,” even utilizing instruments and studying from multimodal info.

“Agentic AI principally means that you’ve an AI that has company. It will possibly understand and perceive the context of the circumstance. It will possibly motive, very importantly can motive about the best way to reply or the best way to clear up an issue, and it might plan and motion. It will possibly plan and take motion. It will possibly use instruments,” Huang defined.

This functionality is driving a surge in computational calls for: “The quantity of computation requirement, the scaling legislation of AI is extra resilient and actually hyper accelerated. The quantity of computation we want at this level on account of agentic AI, on account of reasoning, is definitely 100 occasions greater than we thought we would have liked this time final yr,” he added.

The Backside Line

Jensen Huang’s GTC 2025 keynote introduced a complete imaginative and prescient of an AI-driven future characterised by clever brokers, autonomous robots, and purpose-built AI factories. NVIDIA’s bulletins throughout {hardware} structure, networking, software program, and open-source fashions sign the corporate’s willpower to energy and speed up the subsequent period of computing.

As computing continues its shift from retrieval-based to generative fashions, NVIDIA’s give attention to tokens because the core foreign money of AI and on scaling capabilities throughout cloud, enterprise, and robotics platforms offers a roadmap for the way forward for expertise, with far-reaching implications for industries worldwide.

A Revolutionary CNAPP For Preventive Cybersecurity

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Transferring Past Detection to Actual-Time, Automated Safety Throughout Workloads, Cloud, and Infrastructure 

SecPod, a worldwide cybersecurity supplier, has introduced the Basic Availability of Saner Cloud, a Cloud-Native Utility Safety Platform designed to offer automated remediation and workload safety throughout multi-cloud environments.

Not like typical safety options that focus totally on detection, Saner Cloud integrates safety utilizing AI-driven automation to remediate threats in real-time.

Safety groups have lengthy confronted challenges managing disparate safety options throughout cloud workloads, IT infrastructure, and endpoints, usually leading to fragmented operations and a excessive quantity of unresolved alerts.

Saner Cloud is designed to deal with these challenges by offering a unified safety platform that constantly detects, prioritizes, and remediates vulnerabilities, misconfigurations, id dangers, and compliance violations—robotically and in actual time.

In the course of the launch, Chandrashekhar Basavanna, Founder & CEO of SecPod, emphasised the pressing must rethink cloud safety and shift in direction of an AI-powered, prevention-first mannequin. 

“Cloud safety has lengthy been reactive—detecting dangers however failing to repair them. Safety is barely efficient when threats are eradicated earlier than they are often exploited.

The business has spent years chasing alerts however has didn’t bridge the hole between consciousness and motion. AI now offers us the facility to maneuver past detection and automate remediation at scale.

Saner Cloud ensures safety is enforced in actual time, throughout each workload, each piece of infrastructure, and each cloud atmosphere.” – Chandrashekhar Basavanna, Founder and CEO of SecPod.

Not like legacy cloud safety options that focus solely on cloud misconfigurations or id dangers, Saner Cloud secures the complete assault floor, masking endpoints, servers, community infrastructure, cloud environments, and cloud workloads – all from a single platform. 

Saner Cloud leverages superior AI-driven automation to remove handbook safety operations.

Saner Cloud applies clever danger prioritization and executes remediation robotically, guaranteeing that safety gaps are fastened earlier than they are often exploited by attackers.

By constantly studying from new assault patterns and safety incidents, Saner Cloud dynamically adapts its safety enforcement, delivering self-healing cloud infrastructure that continues to be protected towards even probably the most refined threats. 

Most Cloud-Native Utility Safety Platform (CNAPP) options concentrate on visibility, requiring safety groups to manually tackle giant volumes of unresolved dangers.

Saner Cloud is designed to bridge this hole by not solely figuring out safety points but in addition automating their remediation in real-time and at scale.

By using Infrastructure-as-Code remediation, automated patching, and safety coverage enforcement, Saner Cloud helps mitigate compliance drift, cut back misconfiguration dangers, and decrease delays in risk response.

The platform constantly enforces safety throughout cloud and IT belongings whereas embedding compliance measures straight into cloud operations, aiding organizations in sustaining ongoing adherence to regulatory frameworks.

The launch of Saner Cloud displays a shift towards a prevention-first method in cloud safety, specializing in lowering danger publicity relatively than solely figuring out threats.

As cyber threats evolve, organizations require safety measures which can be fast, automated, and steady, relatively than counting on handbook alert response.

Saner Cloud is now accessible for enterprises worldwide. For extra data or to schedule a demo, customers can go to www.secpod.com

About SecPod 

SecPod is a number one cybersecurity know-how firm dedicated to stopping cyberattacks by proactive safety. Its mission is to safe computing infrastructure by enabling preventive safety posture. 

On the core of SecPod’s choices is the Saner Platform – a set of options that assist organizations set up a powerful safety posture to preempt cyber threats towards endpoints, servers, networks, and cloud infrastructure, in addition to cloud workloads.

With its cutting-edge and complete options, SecPod empowers organizations to remain forward of evolving threats and construct a resilient safety framework. 

Contact

Head – Advertising
Supriya Bhaskar Rao
SecPod Applied sciences
supriya.kasibhatta@secpod.com

ios – GTMSessionFetcher.h -A number of Errors Duplicate interface definition for sophistication ‘GTMSessionFetcherUserDefaultsFactory’


I am faceing an issue vor over every week now.

Throughout constructing my app in xCode 16.2 it’s failing with this set of errors:

Error Messages

Exhibiting All Errors Solely
/Customers/apagtxschindelboeck/StudioProjects/WardrobeStylist/ios/Pods/GTMSessionFetcher/Sources/Core/Public/GTMSessionFetcher/GTMSessionFetcher.h:420:1: Duplicate interface definition for sophistication 'GTMSessionFetcherUserDefaultsFactory'


Exhibiting All Errors Solely
/Customers/apagtxschindelboeck/StudioProjects/WardrobeStylist/ios/Pods/GTMSessionFetcher/Sources/Core/Public/GTMSessionFetcher/GTMSessionFetcher.h:430:28: Redefinition of 'GTMSessionFetcherError'

...

I discovered this https://github.com/firebase/firebase-ios-sdk/points/10726#issuecomment-1408807696 attainable answer, but it surely was not working.

That is my present Consumer Header Search Path:

Consumer Header Search Path

I came upon, that my Dependencies require GTMSessionFetcher Core and Full. So perhaps that is inflicting the error:

Podfile.lock:

PODS:
...
  - AppAuth (1.7.6):
    - AppAuth/Core (= 1.7.6)
    - AppAuth/ExternalUserAgent (= 1.7.6)
  - AppAuth/Core (1.7.6)
  - AppAuth/ExternalUserAgent (1.7.6):
    - AppAuth/Core
  - BoringSSL-GRPC (0.0.36):
    - BoringSSL-GRPC/Implementation (= 0.0.36)
    - BoringSSL-GRPC/Interface (= 0.0.36)
  - BoringSSL-GRPC/Implementation (0.0.36):
    - BoringSSL-GRPC/Interface (= 0.0.36)
  - BoringSSL-GRPC/Interface (0.0.36)
  - cloud_firestore (5.6.5):
    - Firebase/Firestore (= 11.8.0)
    - firebase_core
    - Flutter
  - Firebase/Auth (11.8.0):
    - Firebase/CoreOnly
    - FirebaseAuth (~> 11.8.0)
  - Firebase/Core (11.8.0):
    - Firebase/CoreOnly
    - FirebaseAnalytics (~> 11.8.0)
  - Firebase/CoreOnly (11.8.0):
    - FirebaseCore (~> 11.8.0)
  - Firebase/Firestore (11.8.0):
    - Firebase/CoreOnly
    - FirebaseFirestore (~> 11.8.0)
  - firebase_auth (5.5.1):
    - Firebase/Auth (= 11.8.0)
    - firebase_core
    - Flutter
  - firebase_core (3.12.1):
    - Firebase/CoreOnly (= 11.8.0)
    - Flutter
  - FirebaseAnalytics (11.8.0):
    - FirebaseAnalytics/AdIdSupport (= 11.8.0)
    - FirebaseCore (~> 11.8.0)
    - FirebaseInstallations (~> 11.0)
    - GoogleUtilities/AppDelegateSwizzler (~> 8.0)
    - GoogleUtilities/MethodSwizzler (~> 8.0)
    - GoogleUtilities/Community (~> 8.0)
    - "GoogleUtilities/NSData+zlib (~> 8.0)"
    - nanopb (~> 3.30910.0)
  - FirebaseAnalytics/AdIdSupport (11.8.0):
    - FirebaseCore (~> 11.8.0)
    - FirebaseInstallations (~> 11.0)
    - GoogleAppMeasurement (= 11.8.0)
    - GoogleUtilities/AppDelegateSwizzler (~> 8.0)
    - GoogleUtilities/MethodSwizzler (~> 8.0)
    - GoogleUtilities/Community (~> 8.0)
    - "GoogleUtilities/NSData+zlib (~> 8.0)"
    - nanopb (~> 3.30910.0)
  - FirebaseAppCheckInterop (11.10.0)
  - FirebaseAuth (11.8.1):
    - FirebaseAppCheckInterop (~> 11.0)
    - FirebaseAuthInterop (~> 11.0)
    - FirebaseCore (~> 11.8.0)
    - FirebaseCoreExtension (~> 11.8.0)
    - GoogleUtilities/AppDelegateSwizzler (~> 8.0)
    - GoogleUtilities/Atmosphere (~> 8.0)
    - GTMSessionFetcher/Core (< 5.0, >= 3.4)
    - RecaptchaInterop (~> 100.0)
  - FirebaseAuthInterop (11.10.0)
  - FirebaseCore (11.8.1):
    - FirebaseCoreInternal (~> 11.8.0)
    - GoogleUtilities/Atmosphere (~> 8.0)
    - GoogleUtilities/Logger (~> 8.0)
  - FirebaseCoreExtension (11.8.0):
    - FirebaseCore (~> 11.8.0)
  - FirebaseCoreInternal (11.8.0):
    - "GoogleUtilities/NSData+zlib (~> 8.0)"
  - FirebaseFirestore (11.8.0):
    - FirebaseCore (~> 11.8.0)
    - FirebaseCoreExtension (~> 11.8.0)
    - FirebaseFirestoreInternal (= 11.8.0)
    - FirebaseSharedSwift (~> 11.0)
  - FirebaseFirestoreInternal (11.8.0):
    - abseil/algorithm (~> 1.20240116.1)
    - abseil/base (~> 1.20240116.1)
    - abseil/container/flat_hash_map (~> 1.20240116.1)
    - abseil/reminiscence (~> 1.20240116.1)
    - abseil/meta (~> 1.20240116.1)
    - abseil/strings/strings (~> 1.20240116.1)
    - abseil/time (~> 1.20240116.1)
    - abseil/sorts (~> 1.20240116.1)
    - FirebaseAppCheckInterop (~> 11.0)
    - FirebaseCore (~> 11.8.0)
    - "gRPC-C++ (~> 1.65.0)"
    - gRPC-Core (~> 1.65.0)
    - leveldb-library (~> 1.22)
    - nanopb (~> 3.30910.0)
  - FirebaseInstallations (11.8.0):
    - FirebaseCore (~> 11.8.0)
    - GoogleUtilities/Atmosphere (~> 8.0)
    - GoogleUtilities/UserDefaults (~> 8.0)
    - PromisesObjC (~> 2.4)
  - FirebaseSharedSwift (11.10.0)
  - Flutter (1.0.0)
  - google_sign_in_ios (0.0.1):
    - AppAuth (>= 1.7.4)
    - Flutter
    - FlutterMacOS
    - GoogleSignIn (~> 7.1)
    - GTMSessionFetcher (>= 3.4.0)
  - GoogleAPIClientForREST/Core (4.1.0):
    - GTMSessionFetcher/Full (< 5.0, >= 1.6.1)
  - GoogleAppMeasurement (11.8.0):
    - GoogleAppMeasurement/AdIdSupport (= 11.8.0)
    - GoogleUtilities/AppDelegateSwizzler (~> 8.0)
    - GoogleUtilities/MethodSwizzler (~> 8.0)
    - GoogleUtilities/Community (~> 8.0)
    - "GoogleUtilities/NSData+zlib (~> 8.0)"
    - nanopb (~> 3.30910.0)
  - GoogleAppMeasurement/AdIdSupport (11.8.0):
    - GoogleAppMeasurement/WithoutAdIdSupport (= 11.8.0)
    - GoogleUtilities/AppDelegateSwizzler (~> 8.0)
    - GoogleUtilities/MethodSwizzler (~> 8.0)
    - GoogleUtilities/Community (~> 8.0)
    - "GoogleUtilities/NSData+zlib (~> 8.0)"
    - nanopb (~> 3.30910.0)
  - GoogleAppMeasurement/WithoutAdIdSupport (11.8.0):
    - GoogleUtilities/AppDelegateSwizzler (~> 8.0)
    - GoogleUtilities/MethodSwizzler (~> 8.0)
    - GoogleUtilities/Community (~> 8.0)
    - "GoogleUtilities/NSData+zlib (~> 8.0)"
    - nanopb (~> 3.30910.0)
  - GoogleSignIn (7.1.0):
    - AppAuth (< 2.0, >= 1.7.3)
    - GTMAppAuth (< 5.0, >= 4.1.1)
    - GTMSessionFetcher/Core (~> 3.3)
  - GoogleUtilities/AppDelegateSwizzler (8.0.2):
    - GoogleUtilities/Atmosphere
    - GoogleUtilities/Logger
    - GoogleUtilities/Community
    - GoogleUtilities/Privateness
  - GoogleUtilities/Atmosphere (8.0.2):
    - GoogleUtilities/Privateness
  - GoogleUtilities/Logger (8.0.2):
    - GoogleUtilities/Atmosphere
    - GoogleUtilities/Privateness
  - GoogleUtilities/MethodSwizzler (8.0.2):
    - GoogleUtilities/Logger
    - GoogleUtilities/Privateness
  - GoogleUtilities/Community (8.0.2):
    - GoogleUtilities/Logger
    - "GoogleUtilities/NSData+zlib"
    - GoogleUtilities/Privateness
    - GoogleUtilities/Reachability
  - "GoogleUtilities/NSData+zlib (8.0.2)":
    - GoogleUtilities/Privateness
  - GoogleUtilities/Privateness (8.0.2)
  - GoogleUtilities/Reachability (8.0.2):
    - GoogleUtilities/Logger
    - GoogleUtilities/Privateness
  - GoogleUtilities/UserDefaults (8.0.2):
    - GoogleUtilities/Logger
    - GoogleUtilities/Privateness
  - "gRPC-C++ (1.65.5)":
    - "gRPC-C++/Implementation (= 1.65.5)"
    - "gRPC-C++/Interface (= 1.65.5)"
  - "gRPC-C++/Implementation (1.65.5)":
    - abseil/algorithm/container (~> 1.20240116.2)
    - abseil/base/base (~> 1.20240116.2)
    - abseil/base/config (~> 1.20240116.2)
    - abseil/base/core_headers (~> 1.20240116.2)
    - abseil/base/log_severity (~> 1.20240116.2)
    - abseil/base/no_destructor (~> 1.20240116.2)
    - abseil/cleanup/cleanup (~> 1.20240116.2)
    - abseil/container/flat_hash_map (~> 1.20240116.2)
    - abseil/container/flat_hash_set (~> 1.20240116.2)
    - abseil/container/inlined_vector (~> 1.20240116.2)
    - abseil/flags/flag (~> 1.20240116.2)
    - abseil/flags/marshalling (~> 1.20240116.2)
    - abseil/useful/any_invocable (~> 1.20240116.2)
    - abseil/useful/bind_front (~> 1.20240116.2)
    - abseil/useful/function_ref (~> 1.20240116.2)
    - abseil/hash/hash (~> 1.20240116.2)
    - abseil/log/absl_check (~> 1.20240116.2)
    - abseil/log/absl_log (~> 1.20240116.2)
    - abseil/log/test (~> 1.20240116.2)
    - abseil/log/globals (~> 1.20240116.2)
    - abseil/log/log (~> 1.20240116.2)
    - abseil/reminiscence/reminiscence (~> 1.20240116.2)
    - abseil/meta/type_traits (~> 1.20240116.2)
    - abseil/random/bit_gen_ref (~> 1.20240116.2)
    - abseil/random/distributions (~> 1.20240116.2)
    - abseil/random/random (~> 1.20240116.2)
    - abseil/standing/standing (~> 1.20240116.2)
    - abseil/standing/statusor (~> 1.20240116.2)
    - abseil/strings/wire (~> 1.20240116.2)
    - abseil/strings/str_format (~> 1.20240116.2)
    - abseil/strings/strings (~> 1.20240116.2)
    - abseil/synchronization/synchronization (~> 1.20240116.2)
    - abseil/time/time (~> 1.20240116.2)
    - abseil/sorts/non-obligatory (~> 1.20240116.2)
    - abseil/sorts/span (~> 1.20240116.2)
    - abseil/sorts/variant (~> 1.20240116.2)
    - abseil/utility/utility (~> 1.20240116.2)
    - "gRPC-C++/Interface (= 1.65.5)"
    - "gRPC-C++/Privateness (= 1.65.5)"
    - gRPC-Core (= 1.65.5)
  - "gRPC-C++/Interface (1.65.5)"
  - "gRPC-C++/Privateness (1.65.5)"
  - gRPC-Core (1.65.5):
    - gRPC-Core/Implementation (= 1.65.5)
    - gRPC-Core/Interface (= 1.65.5)
  - gRPC-Core/Implementation (1.65.5):
    - abseil/algorithm/container (~> 1.20240116.2)
    - abseil/base/base (~> 1.20240116.2)
    - abseil/base/config (~> 1.20240116.2)
    - abseil/base/core_headers (~> 1.20240116.2)
    - abseil/base/log_severity (~> 1.20240116.2)
    - abseil/base/no_destructor (~> 1.20240116.2)
    - abseil/cleanup/cleanup (~> 1.20240116.2)
    - abseil/container/flat_hash_map (~> 1.20240116.2)
    - abseil/container/flat_hash_set (~> 1.20240116.2)
    - abseil/container/inlined_vector (~> 1.20240116.2)
    - abseil/flags/flag (~> 1.20240116.2)
    - abseil/flags/marshalling (~> 1.20240116.2)
    - abseil/useful/any_invocable (~> 1.20240116.2)
    - abseil/useful/bind_front (~> 1.20240116.2)
    - abseil/useful/function_ref (~> 1.20240116.2)
    - abseil/hash/hash (~> 1.20240116.2)
    - abseil/log/test (~> 1.20240116.2)
    - abseil/log/globals (~> 1.20240116.2)
    - abseil/log/log (~> 1.20240116.2)
    - abseil/reminiscence/reminiscence (~> 1.20240116.2)
    - abseil/meta/type_traits (~> 1.20240116.2)
    - abseil/random/bit_gen_ref (~> 1.20240116.2)
    - abseil/random/distributions (~> 1.20240116.2)
    - abseil/random/random (~> 1.20240116.2)
    - abseil/standing/standing (~> 1.20240116.2)
    - abseil/standing/statusor (~> 1.20240116.2)
    - abseil/strings/wire (~> 1.20240116.2)
    - abseil/strings/str_format (~> 1.20240116.2)
    - abseil/strings/strings (~> 1.20240116.2)
    - abseil/synchronization/synchronization (~> 1.20240116.2)
    - abseil/time/time (~> 1.20240116.2)
    - abseil/sorts/non-obligatory (~> 1.20240116.2)
    - abseil/sorts/span (~> 1.20240116.2)
    - abseil/sorts/variant (~> 1.20240116.2)
    - abseil/utility/utility (~> 1.20240116.2)
    - BoringSSL-GRPC (= 0.0.36)
    - gRPC-Core/Interface (= 1.65.5)
    - gRPC-Core/Privateness (= 1.65.5)
  - gRPC-Core/Interface (1.65.5)
  - gRPC-Core/Privateness (1.65.5)
  - GTMAppAuth (4.1.1):
    - AppAuth/Core (~> 1.7)
    - GTMSessionFetcher/Core (< 4.0, >= 3.3)
  - GTMSessionFetcher (3.5.0):
    - GTMSessionFetcher/Full (= 3.5.0)
  - GTMSessionFetcher/Core (3.5.0)
  - GTMSessionFetcher/Full (3.5.0):
    - GTMSessionFetcher/Core
  - image_picker_ios (0.0.1):
    - Flutter
  - leveldb-library (1.22.6)
  - nanopb (3.30910.0):
    - nanopb/decode (= 3.30910.0)
    - nanopb/encode (= 3.30910.0)
  - nanopb/decode (3.30910.0)
  - nanopb/encode (3.30910.0)
  - path_provider_foundation (0.0.1):
    - Flutter
    - FlutterMacOS
  - PromisesObjC (2.4.0)
  - RecaptchaInterop (100.0.0)
  - shared_preferences_foundation (0.0.1):
    - Flutter
    - FlutterMacOS

DEPENDENCIES:
  - cloud_firestore (from `.symlinks/plugins/cloud_firestore/ios`)
  - Firebase/Auth
  - Firebase/Core
  - Firebase/Firestore
  - firebase_auth (from `.symlinks/plugins/firebase_auth/ios`)
  - firebase_core (from `.symlinks/plugins/firebase_core/ios`)
  - Flutter (from `Flutter`)
  - google_sign_in_ios (from `.symlinks/plugins/google_sign_in_ios/darwin`)
  - GoogleAPIClientForREST/Core
  - GoogleSignIn (~> 7.1.0)
  - GTMSessionFetcher/Full (~> 3.5.0)
  - image_picker_ios (from `.symlinks/plugins/image_picker_ios/ios`)
  - path_provider_foundation (from `.symlinks/plugins/path_provider_foundation/darwin`)
  - shared_preferences_foundation (from `.symlinks/plugins/shared_preferences_foundation/darwin`)

SPEC REPOS:
  trunk:
    - abseil
    - AppAuth
    - BoringSSL-GRPC
    - Firebase
    - FirebaseAnalytics
    - FirebaseAppCheckInterop
    - FirebaseAuth
    - FirebaseAuthInterop
    - FirebaseCore
    - FirebaseCoreExtension
    - FirebaseCoreInternal
    - FirebaseFirestore
    - FirebaseFirestoreInternal
    - FirebaseInstallations
    - FirebaseSharedSwift
    - GoogleAPIClientForREST
    - GoogleAppMeasurement
    - GoogleSignIn
    - GoogleUtilities
    - "gRPC-C++"
    - gRPC-Core
    - GTMAppAuth
    - GTMSessionFetcher
    - leveldb-library
    - nanopb
    - PromisesObjC
    - RecaptchaInterop

EXTERNAL SOURCES:
  cloud_firestore:
    :path: ".symlinks/plugins/cloud_firestore/ios"
  firebase_auth:
    :path: ".symlinks/plugins/firebase_auth/ios"
  firebase_core:
    :path: ".symlinks/plugins/firebase_core/ios"
  Flutter:
    :path: Flutter
  google_sign_in_ios:
    :path: ".symlinks/plugins/google_sign_in_ios/darwin"
  image_picker_ios:
    :path: ".symlinks/plugins/image_picker_ios/ios"
  path_provider_foundation:
    :path: ".symlinks/plugins/path_provider_foundation/darwin"
  shared_preferences_foundation:
    :path: ".symlinks/plugins/shared_preferences_foundation/darwin"

SPEC CHECKSUMS:
  abseil: d121da9ef7e2ff4cab7666e76c5a3e0915ae08c3
  AppAuth: d4f13a8fe0baf391b2108511793e4b479691fb73
  BoringSSL-GRPC: ca6a8e5d04812fce8ffd6437810c2d46f925eaeb
  cloud_firestore: 56e7bb3888f09698dc061d38d02d87d4fd80e2cb
  Firebase: d80354ed7f6df5f9aca55e9eb47cc4b634735eaf
  firebase_auth: 3d848b9b866b201e5c8e0c06d8b2cec272fd8825
  firebase_core: ac395f994af4e28f6a38b59e05a88ca57abeb874
  FirebaseAnalytics: 4fd42def128146e24e480e89f310e3d8534ea42b
  FirebaseAppCheckInterop: 9664c858489710f682766ef54e2b6741d3b62070
  FirebaseAuth: ad59a1a7b161e75f74c39f70179d2482d40e2737
  FirebaseAuthInterop: 01a804fb074424fd58b92dd50dd0272277199356
  FirebaseCore: 99fe0c4b44a39f37d99e6404e02009d2db5d718d
  FirebaseCoreExtension: 3d3f2017a00d06e09ab4ebe065391b0bb642565e
  FirebaseCoreInternal: df24ce5af28864660ecbd13596fc8dd3a8c34629
  FirebaseFirestore: 563a4ab1a65e2858f05e150bb4c31b0f8f79248b
  FirebaseFirestoreInternal: 8c5921c360a70e447bfeefb245f450e8b50e750b
  FirebaseInstallations: 6c963bd2a86aca0481eef4f48f5a4df783ae5917
  FirebaseSharedSwift: 1baacae75939499b5def867cbe34129464536a38
  Flutter: e0871f40cf51350855a761d2e70bf5af5b9b5de7
  google_sign_in_ios: 4111e87aa5e24a4404f00ea13479f35e571969cc
  GoogleAPIClientForREST: 9483c112c80ffcac161766f508c1167d301accfd
  GoogleAppMeasurement: fc0817122bd4d4189164f85374e06773b9561896
  GoogleSignIn: d4281ab6cf21542b1cfaff85c191f230b399d2db
  GoogleUtilities: 26a3abef001b6533cf678d3eb38fd3f614b7872d
  "gRPC-C++": 2fa52b3141e7789a28a737f251e0c45b4cb20a87
  gRPC-Core: a27c294d6149e1c39a7d173527119cfbc3375ce4
  GTMAppAuth: f69bd07d68cd3b766125f7e072c45d7340dea0de
  GTMSessionFetcher: 5aea5ba6bd522a239e236100971f10cb71b96ab6
  image_picker_ios: c560581cceedb403a6ff17f2f816d7fea1421fc1
  leveldb-library: cc8b8f8e013647a295ad3f8cd2ddf49a6f19be19
  nanopb: fad817b59e0457d11a5dfbde799381cd727c1275
  path_provider_foundation: 2b6b4c569c0fb62ec74538f866245ac84301af46
  PromisesObjC: f5707f49cb48b9636751c5b2e7d227e43fba9f47
  RecaptchaInterop: 7d1a4a01a6b2cb1610a47ef3f85f0c411434cb21
  shared_preferences_foundation: fcdcbc04712aee1108ac7fda236f363274528f78

PODFILE CHECKSUM: dd4b2f1ee2bf91b0d0cbbb3ce4edb791a9659030

COCOAPODS: 1.16.2

Podfile:

platform :ios, '17.0'
ENV['COCOAPODS_DISABLE_STATS'] = 'true'

undertaking 'Runner', {
  'Debug' => :debug,
  'Profile' => :launch,
  'Launch' => :launch,
}

use_frameworks! :linkage => :static

pod 'Firebase/Core'
pod 'Firebase/Auth'
pod 'Firebase/Firestore'
pod 'GoogleSignIn', '~> 7.1.0'
pod 'GTMSessionFetcher/Full', '~> 3.5.0'
pod 'GoogleAPIClientForREST/Core'

...

Do someone has any concepts?

Thanks!