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CCNP Automation: A Renamed Certification, Reimagined

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CCNP Automation: A Renamed Certification, Reimagined


We be taught from an early age that change is the one fixed in life. Generally we view change as one thing to be anxious about, one thing we should shift and alter for, and that the unknown may deliver us…effectively, something. On the flip aspect, some change is nice, even wanted and refreshing (as anybody who places a brand new coat of paint on a home will attest). I suppose you could possibly say that even your views on change can change. Particularly when you’ve got somebody so as to add context that explains how a selected change advantages you.

It’s my hope that this weblog collection will make it easier to just do that, as we face main modifications centered on a subject close to and pricey to my coronary heart: the up to date Cisco Automation Certifications and their related examination matters.

In case you missed the announcement in Might 2025, we’re renaming Cisco DevNet Certifications to Cisco Automation Certifications. On February 3, 2026, every certification stage (Affiliate, Skilled, and Skilled) will transition to the CCNA/CCNP/CCIE taxonomy that’s so acquainted to our studying group, companions, and employers. Learn the announcement.

DevNet to Automation Transition

As of February 3, 2026, the Cisco Licensed DevNet Affiliate and the Cisco Licensed DevNet Skilled will turn out to be referred to as the CCNA Automation and CCIE Automation certifications, respectively. Identical take a look at, new names. It’s that straightforward.

Nonetheless, after a lot thought and consideration, it was determined that the Cisco Licensed DevNet Skilled certification wanted greater than a reputation change. It wanted to align with at present’s job market.

CCNP Automation: Reimagining the DevNet Skilled Certification

We wanted to align the CCNP Automation certification to check the talents and necessities being seen available in the market at present from our prospects who’re using community automation engineers. A lot work was put into job function and process evaluation, in addition to figuring out the {qualifications} of a certified candidate.

In any case that work was completed, it was time to lock a bunch of subject material specialists from varied backgrounds and focus areas in a room to work out the examination matters record, the three of which have been launched on the finish of July.

A brand new strategy to testing

All Cisco certification exams bear an replace and alter course of annually. We alter solely as much as 10% of the examination matters, and generally not even that a lot. However within the case of the CCNP Automation, we simply couldn’t make that quantity match with how the exams wanted to be reshaped.

When first launched, the Skilled-level DevNet exams have been targeted very a lot on reworking community engineers into people who have been snug with writing functions for community automation. These “community automation builders” wanted to be acquainted not solely with Cisco APIs and their features but additionally with the software program growth lifecycle, software deployment, and platforms, and the way to ensure these functions have been safe, accessible, resilient, and scalable.

What automation abilities do at present’s engineers want?

These abilities are nice to have in your base of information, however the market wanted one thing totally different. Engineers who’re snug with automation frameworks and instruments and may:

  • Create digital twins for pre- and post-change validations
  • Leverage programmability ideas for operations and monitoring
  • Snug navigating the brand new world of AI

We wanted to check a skillset that wasn’t “software developer who understands a little bit of networking,” however “community engineer with expertise in network-specific automation frameworks to implement an end-to-end answer.”

Lofty targets, however I really feel that our new examination matters hit all these areas (I could also be just a bit biased, since I used to be a kind of people locked in that room for hours).

Cisco Automation Certification examination matters are actually out there!

Obtain examination matters

So, what did we alter within the AUTOCOR?

In-Scope Platforms

On the prime of the Examination Subjects PDF, particular in-scope platforms are referred to as out.

  • This reduces repetition within the examination matters.
  • Focuses on the platforms you must research for.
  • Supplies boundaries for the scope, so that you don’t want to review each single Cisco product.

Maintain these platforms in thoughts as you progress by way of every of the domains.

Area Modifications

  • The examination moved from 5 domains of research to 4.
  • This will increase the examination weight on the primary two domains: Community Automation and Infrastructure as Code.
  • These sections can be acquainted to community automation professionals, however with a modified focus.
  • Reasonably than testing on single-platform APIs, every process within the Community Automation part is targeted on a answer that entails an automation software, akin to Ansible, Terraform, or Python.

In some situations, not all platforms could be configured for every process (like utilizing NETCONF for ACI). This wasn’t an oversight; as a community automation skilled, you’ll be checked out as somebody to assist information an enterprise in direction of a correct answer and toolset, so you have to know and perceive limitations and capabilities in addition to methods to accomplish a aim utilizing automation.

Infrastructure as Code part

Whereas instruments like Git are nonetheless vital, once more, the main focus has shifted in direction of utilizing a CI/CD pipeline and GitOps to deploy community automation options, both in opposition to actual {hardware} or leveraging a digital twin simulation software.

Professionals want to know:

  • CI/CD processes.
  • Cisco Modeling Labs.
  • Community sources of fact (for pulling knowledge into pipelines for variables, configuration, and so on.).

The main focus is on the answer somewhat than particular person elements.

Operations part

The emphasis is on methods to hold an automation answer purposeful previous preliminary testing and deployment. To do that, key areas embody:

  • Safety and secret administration (passwords, API keys).
  • Making certain deployed automation options stay purposeful by way of network-centric testing.
  • Utilizing model-driven telemetry on varied platforms to enhance community observability.

Remember, although, that the safety/secret administration (like passwords and API keys), certificates administration, and logging solely pertain to the community units or the automation answer being examined on, not on the complete software stack (which is one thing an software developer would care about).

We’re trimming the matters to dwelling in on that community automation engineer persona and testing it by way of actionable abilities.

The change all of us knew was coming: AI

No trendy engineer will be capable to operate with out some understanding of AI, its influence on our trade, and methods to safely and successfully use its capabilities. Community automation engineers gained’t be any exception to this, both. Nonetheless, we wished the examination matters to be crafted in such a approach that introduced tangible, precious abilities and AI functions to the take a look at, and I really feel like we hit the mark with these.

We wished the candidates to know the implications behind utilizing AI for his or her roles, together with:

  • Ways in which AI may trigger hurt to a company, together with IP theft, knowledge sovereignty, and simply “code that doesn’t work.”
  • Likewise, community automation engineers ought to be capable to work out when options will work and when they’re simply plain made up.
    • This reinforces the usage of AI as a software and assistant, somewhat than outsourcing the whole lot to a mannequin; if one thing breaks at 3 a.m., “AI made it” gained’t be a suitable excuse to your leaders.
  • The sensible functions of AI within the networks.
    • With the short rise of mannequin context protocol (MCP) servers getting used as conduits and extensions to LLMs for community automation, it was a no brainer to incorporate a Pythonic framework of MCP server growth as a process merchandise.

We additionally wished to ensure that our engineers may assemble an AI agent that may permit them to naturally discuss to an LLM and carry out some kind of community automation, presumably even utilizing a number of MCP servers.

Whereas we perceive the world of AI is quickly evolving, we felt that the talents examined by these examination matters can be wanted and utilized by our prospects and learners, and can turn out to be a part of the baseline data that any community automation engineer ought to know.

Does this modification my DEVCOR research?

With such a serious rewrite of an examination, the reply to this query will at all times be “sure”. Whereas we all know that this might doubtlessly interrupt your research, hopefully, the extra context and reasoning why we made this modification will make it easier to really feel extra assured and comfy with making that change. Additionally, keep in mind that you’ve got till February 2, 2026, to take and move the current DEVCOR examination, so in case you really feel that you just’re shut, proceed transferring on to get that move earlier than then.

 

Subjects that aren’t carried over to AUTOCOR

If you happen to do have to alter your research, although, all will not be misplaced. Lots of the ideas that exist inside DEVCOR, like API utilization, git instructions, use of Python, Ansible, and Terraform, and understanding Cisco platforms, will all come in useful in AUTOCOR. Your research can be accelerated with these ideas, so you’ll be able to deal with making use of these ideas in an entire automation answer.

Upleveling your automation pondering

You’ll additionally be capable to uplevel your automation pondering and conversations, not simply understanding the how, however the why, which can profit you in your skilled profession. Lastly, when you don’t need to overlook the whole lot you realized about software growth and the related lifecycle, you gained’t be required to recollect it on the brand new examination.

It is a change, however this can be a change for the higher. I’ll even be placing out an identical breakdown of the ENAUTO 2.0, in addition to the brand new DCNAUTO (I contributed to all three exams), so that you could really feel snug with these modifications as effectively.

Lastly, I promise that I’ll change one thing in regards to the following elements of this collection: I promise to not use the phrase “change” practically as a lot as I did right here. 😉

As at all times, yow will discover me on LinkedIn or X (previously Twitter) when you have any questions relating to this or every other examination. Pleased finding out!

Be a part of us for our Cisco Automation Certifications Digital Occasion

September 9, 2025, at 9 a.m. Pacific Time | Register now without cost

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Understanding When AI Does not Know

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Understanding When AI Does not Know


Think about a navy surveillance system skilled to determine particular automobiles in desert environments. In the future, this method is deployed in a snowy mountain area and begins misidentifying civilian automobiles as navy targets. Or contemplate a synthetic intelligence (AI) medical prognosis system for battlefield accidents that encounters a novel sort of wound it was by no means skilled on, nevertheless it confidently—and incorrectly—recommends a regular remedy protocol.

These situations spotlight a vital problem in synthetic intelligence: how do we all know when an AI system is working outdoors its supposed data boundaries? That is the vital area of out-of-distribution (OoD) detection—figuring out when an AI system is going through conditions it wasn’t skilled to deal with. By means of our work right here within the SEI’s AI Division, significantly in collaborating with the Workplace of the Beneath Secretary of Protection for Analysis and Engineering (OUSD R&E) to determine the Middle for Calibrated Belief Measurement and Analysis (CaTE), we’ve seen firsthand the vital challenges going through AI deployment in protection functions.

The 2 situations detailed above aren’t hypothetical—they symbolize the form of challenges we encounter commonly in our work serving to the Division of Protection (DoD) guarantee AI techniques are protected, dependable, and reliable earlier than being fielded in vital conditions. As this submit particulars, this is the reason we’re specializing in OoD detection: the essential functionality that permits AI techniques to acknowledge after they’re working outdoors their data boundaries.

Why Out-of-Distribution Detection Issues

For protection functions, the place selections can have life-or-death penalties, realizing when an AI system may be unreliable is simply as vital as its accuracy when it’s working accurately. Contemplate these situations:

  • autonomous techniques that want to acknowledge when environmental circumstances have modified considerably from their coaching knowledge
  • intelligence evaluation instruments that ought to flag uncommon patterns, not force-fit them into recognized classes
  • cyber protection techniques that should determine novel assaults, not simply these seen beforehand
  • logistics optimization algorithms that ought to detect when provide chain circumstances have basically modified

In every case, failing to detect OoD inputs may result in silent failures with main penalties. Because the DoD continues to include AI into mission-critical techniques, OoD detection turns into a cornerstone of constructing reliable AI.

What Does Out-of-Distribution Actually Imply?

Earlier than diving into options, let’s make clear what we imply by out-of-distribution. Distribution refers back to the distribution of the information that the mannequin was skilled on. Nevertheless, it isn’t all the time clear what makes one thing out of a distribution.

Within the easiest case, we’d say new enter knowledge is OoD if it will have zero likelihood of showing in our coaching knowledge. However this definition not often works in apply as a result of mostly used statistical distributions, similar to the conventional distribution, technically permit for any worth, nonetheless unlikely. In different phrases, they’ve infinite help.

Out-of-distribution sometimes means one among two issues:

  1. The brand new enter comes from a basically completely different distribution than the coaching knowledge. Right here, basically completely different means there’s a method of measuring the 2 distributions as not being the identical. In apply, although, a extra helpful definition is that when a mannequin is skilled on one distribution, it performs unexpectedly on the opposite distribution.
  2. The likelihood of seeing this enter within the coaching distribution is extraordinarily low.

For instance, a facial recognition system skilled on pictures of adults would possibly contemplate a toddler’s face to be from a special distribution completely. Or an anomaly detection system would possibly flag a tank shifting at 200 mph as having an especially low likelihood in its recognized distribution of auto speeds.

Three Approaches to OoD Detection

Strategies for OoD detection might be broadly categorized in 3 ways:

1. Knowledge-Solely Strategies: Anomaly Detection and Density Estimation

These approaches attempt to mannequin what regular knowledge appears to be like like with out essentially connecting it to a selected prediction activity. Sometimes this activity is completed utilizing strategies from one among two sub-domains:

1) Anomaly detection goals to determine knowledge factors that deviate considerably from what’s thought-about regular. These methods might be categorized by their knowledge necessities: supervised approaches that use labeled examples of each regular and anomalous knowledge, semi-supervised strategies that primarily study from regular knowledge with maybe a number of anomalies, and unsupervised methods that should distinguish anomalies[1] with none express labels. Anomalies are outlined as knowledge that deviates considerably from the vast majority of beforehand noticed knowledge. In anomaly detection, deviates considerably is usually left as much as the assumptions of the approach used.

2) Density estimation includes studying a likelihood density perform of coaching knowledge that may then be used to assign a likelihood to any new occasion of knowledge. When a brand new enter receives a really low likelihood, it is flagged as OoD. Density estimation is a traditional drawback in statistics.

Whereas these approaches are conceptually easy and supply a number of mature methods to be used with low-dimensional, tabular knowledge, they current challenges with the high-dimensional knowledge that may be frequent in protection functions, similar to pictures or sensor arrays. In addition they require considerably arbitrary selections about thresholds: how “uncommon” does one thing should be earlier than we name it OoD?

2. Constructing OoD Consciousness into Fashions

A substitute for the data-only method is to coach a brand new supervised mannequin particularly to detect OoD cases. There are two in style methods.

1) Studying with rejection trains fashions to output a particular “I do not know” or “reject” response when they’re unsure. That is much like how a human analyst would possibly flag a case for additional overview relatively than make a hasty judgment.

2) Uncertainty-aware fashions like Bayesian neural networks and ensembles explicitly mannequin their very own uncertainty. If the mannequin exhibits excessive uncertainty about its parameters for a given enter, that enter is probably going OoD.

Whereas these approaches are theoretically interesting, they usually require extra advanced coaching procedures and computational sources (For extra on this subject see right here and right here), which might be difficult for deployed techniques with dimension, weight, and energy constraints. Such constraints are frequent in edge environments similar to front-line deployments.

3. Including OoD Detection to Current Fashions

Quite than having to coach a brand new mannequin from scratch, the third method takes benefit of fashions which have already been skilled for a selected activity and augments them with OoD detection capabilities.

The only model includes thresholding the boldness scores that fashions already output. If a mannequin’s confidence falls beneath a sure threshold, the enter is flagged as probably OoD. Extra refined methods would possibly analyze patterns within the mannequin’s inside representations.

These approaches are sensible as a result of they work with current fashions, however they’re considerably heuristic and will make implicit assumptions that do not maintain for all functions.

DoD Functions and Concerns

For protection functions, OoD detection is especially worthwhile in a number of contexts:

  • mission-critical autonomy: Autonomous techniques working in contested environments want to acknowledge after they’ve encountered circumstances they weren’t skilled for, probably falling again to extra conservative behaviors.
  • intelligence processing: Techniques analyzing intelligence knowledge must flag uncommon patterns that human analysts ought to look at, relatively than force-fitting them into recognized classes.
  • cyber operations: Community protection techniques must determine novel assaults that do not match patterns of beforehand seen threats.
  • provide chain resilience: Logistics techniques must detect when patterns of demand or provide have basically modified, probably triggering contingency planning.

For the DoD, a number of further issues come into play:

  • useful resource constraints: OoD detection strategies should be environment friendly sufficient to run on edge units with restricted computing energy.
  • restricted coaching knowledge: Many protection functions have restricted labeled coaching knowledge, making it tough to exactly outline the boundaries of the coaching distribution.
  • adversarial threats: Adversaries would possibly intentionally create inputs designed to idiot each the primary system and its OoD detection mechanisms.
  • criticality: Incorrect predictions made by machine studying (ML) fashions which are offered as assured and proper might have extreme penalties in high-stakes missions.

A Layered Method to Verifying Out-of-Distribution Detection

Whereas OoD detection strategies present a robust means to evaluate whether or not ML mannequin predictions might be unreliable, they arrive with one vital caveat. Any OoD detection approach, both implicitly or explicitly, makes assumptions about what’s “regular” knowledge and what’s “out-of-distribution” knowledge. These assumptions are sometimes very tough to confirm in real-world functions for all doable adjustments in deployment environments. It’s seemingly that no OoD detection technique will all the time detect an unreliable prediction.

As such, OoD detection must be thought-about a final line of protection in a layered method to assessing the reliability of ML fashions throughout deployment. Builders of AI-enabled techniques also needs to carry out rigorous check and analysis, construct displays for recognized failure modes into their techniques, and carry out complete evaluation of the circumstances beneath which a mannequin is designed to carry out versus circumstances wherein its reliability is unknown.

Trying Ahead

Because the DoD continues to undertake AI techniques for vital missions, OoD detection shall be a vital part of making certain these techniques are reliable and sturdy. The sector continues to evolve, with promising analysis instructions together with

  • strategies that may adapt to regularly shifting distributions over time
  • methods that require minimal further computational sources
  • approaches that mix a number of detection methods for higher reliability
  • integration with human-AI teaming to make sure applicable dealing with of OoD instances
  • algorithms based mostly on virtually verifiable assumptions about real-world shifts

By understanding when AI techniques are working outdoors their data boundaries, we will construct extra reliable and efficient AI capabilities for protection functions—realizing not simply what our techniques know, but in addition what they do not know.

Now in Android #120. Canary Channel, Agentic AI in Android… | by Daniel Galpin | Android Builders | Aug, 2025

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Now in Android #120. Canary Channel, Agentic AI in Android… | by Daniel Galpin | Android Builders | Aug, 2025


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Android has launched a brand new Canary launch channel for its early-access packages, changing the earlier developer preview mannequin. This channel offers earlier and extra constant entry to in-development platform options all year long.

The brand new Canary channel presents steady, rolling over-the-air (OTA) updates for supported Pixel gadgets, addressing earlier limitations the place previews required handbook flashing and the preview program ended as soon as a platform model reached Beta, requiring handbook flashing to enter it once more for the following cycle.

You need to use the Canary channel to:

  • Entry new options and potential conduct modifications of their earliest levels.
  • Combine builds into your Steady Integration (CI) methods to determine app compatibility points early.
  • Obtain Canary SDKs via the SDK Supervisor and entry builds through the Android Studio Canary channel for improvement and testing.

The Canary channel operates in parallel with the prevailing Beta program. Canary builds are bleeding-edge and will include bugs or breaking modifications, making them finest suited to exploring and making an attempt out apps, slightly than major system use. The Beta channel stays because the place to focus your testing to be sure to’re prepared for the following platform launch.

You may get began by flashing your supported Pixel system utilizing the Android Flash Software or configuring the Android Emulator with a Canary picture. You possibly can present suggestions and bug stories via the Google Problem Tracker.

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The newest Android Studio Narwhal Function Drop Canary launch contains “Agent Mode,” a brand new functionality that allows the AI to deal with advanced improvement duties. This function permits you to describe a purpose in pure language, and the agent formulates an execution plan that may span a number of challenge recordsdata, using IDE instruments for code modifications, constructing, and looking out.

You work together with it by describing duties after which reviewing, accepting, or rejecting the proposed modifications, with an choice for auto-approve.

Moreover, you possibly can improve Agent Mode by including your personal Gemini API key, which expands the context window to 1 million tokens with Gemini 2.5 Professional. This expanded context permits Gemini to course of extra directions and code. Enterprise tier subscribers routinely obtain this profit via their Gemini Code Help license.

Agent Mode additionally helps the Mannequin Context Protocol (MCP), offering a standardized solution to combine with exterior instruments. On this preliminary launch, MCP helps stdio transport for software interplay. You configure MCP servers through an mcp.json file in your Studio configuration listing.

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Android is transitioning to 16 KB reminiscence web page sizes to reinforce app efficiency. Starting November 1, 2025, new and up to date apps developed in opposition to the NDK and concentrating on Android 15+ submitted to Google Play should assist 16 KB web page sizes. Apps developed solely with Kotlin or Java are already appropriate.

Gadgets configured for 16 KB web page sizes might even see general efficiency enhancements of 5–10%, together with sooner app launch occasions, lowered battery consumption, and faster digicam begins. Since 4KB pages are additionally aligned to 16KB boundaries, a single software binary can run on each 4 KB and 16 KB gadgets as soon as up to date.

Android Studio offers instruments to help with this transition:

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Google Play is updating the way you handle one-time merchandise, introducing a brand new three-level hierarchy for outlining and promoting digital objects.

The brand new mannequin consists of:

  • One-time product: Defines the merchandise being bought (e.g., “Diamond sword”).
  • Buy choice: Dictates how the merchandise is granted, its worth, and availability. Now you can use “purchase” or “lease” choices.
  • Provide: Modifies a purchase order choice, enabling options like reductions or pre-orders.

This lets you promote the identical product in a number of methods. New capabilities embody configuring rental durations, organising pre-orders with outlined dates and reductions, and managing regional pricing and availability for particular person buy choices and presents. The idea of a default product worth has been eliminated.

To make the most of these new options, it’s essential to improve to Play Billing Library 8.0 and use the brand new monetization.onetimeproducts service of the Play Developer API or the up to date Play Developer Console. You additionally have to combine with the queryProductDetailsAsync API. Present SKUs could be migrated, however new options aren’t supported by the older querySkuDetailsAsync API or inappproducts service. Merchandise created through the Play Console UI shall be normalized inside the new system. Pre-order performance is at the moment out there via an Early Entry Program.

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Samsung unveiled the Galaxy Z Fold7, Z Flip7, and Galaxy Watch8 sequence, providing you new alternatives to construct for adaptive experiences. The foldables run Android 16 with an enhanced desktop windowing expertise, and you may create adaptive apps for them with Compose Adaptive Layouts, Jetpack Navigation 3, and Window Dimension Courses. The Galaxy Watch 8 sequence is the primary system to function Put on OS 6.

Google Play’s Indie Video games Fund in Latin America returns for its fourth yr, committing one other $2 million to assist ten indie sport studios within the area. This new spherical of funding will convey the overall funding in Latin American indie video games via this program to $8 million USD.

Chosen studios will obtain between $150,000 and $200,000, together with assist from the Google Play group. This system is open to indie sport builders who’ve already launched a sport on any platform (cell, PC, or console).

Purposes closed on July 31, 2025, with precedence given to submissions obtained by July 15, 2025.

Put on OS watch face improvement is altering to solely assist the Watch Face Format. As of January 27, 2025, you can not publish new AndroidX or Wearable Help Library (WSL) watch faces to the Play Retailer. By January 14, 2026, current legacy watch faces will now not be installable from the Play Retailer, nor will you be capable to replace them or proceed monetizing them. To proceed providing your watch faces, it’s essential to migrate them to the Watch Face Format and resubmit them to the Play Retailer by January 14, 2026. Watch Face Studio customers ought to resubmit utilizing model 1.8.7 or newer. The weblog publish lists sources which are out there to help with migration.

Amanotes, a cell sport firm based by Invoice and Silver, has surpassed 3 billion downloads for its rhythm-based music video games like Magic Tiles 3. The corporate goals to make musical efficiency accessible by combining cell gaming with rhythm-based faucets. Amanotes leveraged instruments comparable to Firebase for A/B testing and Play Console for analytics and customized pricing for its world progress on Google Play.

At I/O ’25, Google Play launched a number of updates that can assist you streamline subscriptions and maximize income.

  1. Subscriptions with add-ons allow multi-product checkout, permitting you to promote subscription add-ons with base subscriptions in a single transaction and aligned cost schedule. You possibly can study extra about creating add-ons right here.
  2. Showcasing advantages in additional locations throughout Play, together with the Subscriptions Middle, reminder emails, and through buy and cancellation processes, has lowered voluntary churn by 2%. You possibly can enter your subscription profit particulars in Play Console.
  3. New grace interval and account maintain durations intention to lower involuntary churn. Now you can customise a grace interval of as much as 30 days and an account maintain interval of as much as 60 days, with the overall restoration interval not exceeding 60 days. Builders extending their decline restoration interval have seen a mean 10% discount in involuntary churn for renewals. You possibly can alter these settings in Play Console.

Extra initiatives embody prompting customers to arrange cost strategies, increasing cost choices and market availability, optimizing in-app transactions with AI, and utilizing cart abandonment reminders.

Play Video games Companies (PGS) is updating its options to reinforce sport discovery and participant retention on Google Play. You need to migrate from PGS v1 to PGS v2 by Might 2026, as video games utilizing the v1 SDK will now not be publishable or updatable after this date, and v1 APIs shall be totally deprecated by Q3 2028.

Key updates and options embody:

  • Achievements: Now deeply built-in throughout the Play Retailer, achievements are discoverable through search and sport element pages, and might drive engagement with Play Factors. It is best to configure not less than 15 incremental achievements, with 5 achievable inside the first two hours of gameplay. Bulk configuration is out there in Play Console.
  • Promotional Content material: Viewers concentrating on capabilities have been enhanced. You need to use the Play Grouping API to create customized segments based mostly on gameplay context for tailor-made re-engagement promotions.
  • Signal-in: PGS presents seamless sign-in and progress syncing throughout Android gadgets. The Recall API helps you handle hyperlinks between PGS and your in-game accounts.

Future developments embody AI-powered avatars that you would be able to combine into participant profiles inside your sport.

Wonderful Seasun Video games is launching its motion taking pictures sport, Mecha BREAK, on Google Play Video games on PC as a part of its multiplatform PC and console launch.

The sport makes use of Google Play Video games on PC for its launch technique, together with pre-registration campaigns that attracted over 3.5 million gamers throughout beta. You possibly can entry the native PC program to obtain as much as 15% in extra earnback, together with assist for PC sport improvement, distribution, and progress. This assist contains managing PC builds, releases, and retailer listings via Play Console, and accessing PC-specific gross sales stories.

The platform offers a safe infrastructure with instruments like Play Integrity API, providing malware safety and anti-cheat capabilities. Mecha BREAK itself delivers a high-fidelity PC expertise with a proprietary 3D engine, providing various fight types comparable to 6v6 hero battles, 3v3 matches, and a PvPvE “Mashmak” extraction mode.

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Listed here are the highest 3 updates for Android developer productiveness from Google I/O 2025:

  1. Gemini in Android Studio: Gemini-powered AI is now out there to simplify constructing and testing code, with options like “Journeys for Android Studio,” a “Model Improve Agent,” and “Agent Mode” for advanced duties. It might additionally generate Jetpack Compose previews and remodel UI code utilizing pure language.
  2. New Jetpack Compose launch: The newest steady model contains Navigation 3 for extra flexibility, a Materials 3 Expressive replace to reinforce consumer expertise, and new options like autofill assist and auto-sizing textual content, together with efficiency enhancements.
  3. Kotlin Multiplatform (KMP) shared module template: A brand new template in Android Studio helps builders share enterprise logic between Android and iOS apps, lowering improvement time. That is supported by up to date Jetpack libraries and new codelabs.

Google I/O ’25 introduced three key updates for AI on Android improvement.

  1. Gemini Nano for on-device AI experiences: New ML Package GenAI APIs powered by Gemini Nano can be found for duties like textual content summarization, proofreading, and picture description instantly on gadgets.
  2. Google AI Edge for customized fashions: The Google AI Edge platform now presents improved assist for on-device {hardware} accelerators and a brand new AI Edge Portal for benchmarking. It’s also possible to handle customized mannequin downloads effectively with Play for On-Machine AI, now in beta.
  3. Firebase AI Logic for cloud AI: For superior generative AI use instances, Firebase AI Logic offers a simplified, safe solution to combine bigger fashions like Gemini Flash, Gemini Professional, and Imagen, together with assist for conversational AI with Gemini Dwell API and visible asset technology with Imagen.

Moreover, the open-source Androidify app demonstrates constructing AI-driven experiences with these new APIs, and extra examples can be found within the Android AI Pattern Catalog.

Ash and Aaron cowl the brand new capabilities for Widgets and Dwell Updates.

Widgets now supply Canonical Layouts for sooner improvement and Generated Previews (enhanced in Look 1.2 for Android 15+) that show actual consumer information, requiring cautious administration of reminiscence and price limits.

For Dwell Updates & Notifications, a brand new ProgressStyleTemplate streamlines progress indicators for time-sensitive, user-initiated journeys (e.g., supply, ride-share) — not for adverts or spam. Builders can programmatically decide if notifications meet Dwell Replace standards and customise a brand new distinguished standing chip for fast consumer entry.

Google Play now allows multi-platform Android sport distribution from a single Android App Bundle, reaching billions of customers throughout telephones, tablets, foldables, and PCs. Google Play Video games on PC is nearing basic availability, providing computerized keyboard-to-touch translation for current video games and non-obligatory native mouse assist (through a manifest flag) for an enhanced PC expertise.

Jetpack Navigation 3 is now in Alpha, providing a brand new state-based method particularly designed to align with Jetpack Compose’s declarative nature.

Key newsworthy factors for Android app builders:

  • Decoupled Logic: It offers a robust separation between navigation logic and UI, enhancing testability and suppleness.
  • State as Single Supply of Reality: Advocates to your app’s state (together with navigation) as the only supply of reality, transferring away from event-based navigation points.
  • Developer-Owned Backstack: Builders now personal the backstack state, giving extra management and simplifying advanced eventualities like type-safety throughout modules and multi-pane layouts.
  • Versatile Constructing Blocks: Gives constructing blocks that combine higher with Compose’s patterns, permitting a extra sturdy and developer-controlled navigation answer.

Builders are inspired to discover the Alpha and supply suggestions.

Compose Previews in Android Studio considerably speed up UI improvement by permitting you to check Jetpack Compose UI modifications immediately with out full builds.

Key options embody:

Essential finest practices embody:

  • Avoiding direct ViewModel instantiation in previews.
  • Utilizing @PreviewParameter for environment friendly information provision.
  • Making a “sticker sheet” with a number of @Preview annotations for visible part documentation and sooner group onboarding.

The Embedded Structure Inspector in Android Studio is a vital software for debugging Jetpack Compose UIs, permitting you to examine format hierarchy. It’s accessed through the Operating Gadgets Software Window whereas your app is operating.

Android 14 launched SDK Runtime, a brand new expertise enhancing privateness and safety by isolating third-party SDKs (known as Runtime-enabled SDKs or RE SDKs) in a safe sandbox. Apps/SDKs (known as Runtime-aware SDKs or RA SDKs) work together with these RE SDKs through routinely generated “shims,” with backward compatibility supplied by Jetpack.

For SDK builders, constructing an RE SDK requires important modifications: a selected challenge construction utilizing Android SDK Bundles (ASB), new Gradle configurations (together with Privateness Sandbox plugins and specialised Jetpack dependencies), and declaring APIs with new annotations (@PrivacySandboxService, @PrivacySandboxInterface). Particular issues apply for UI and launching actions, and a selected entry level should be applied. This marks a basic shift in how SDKs shall be constructed and built-in on Android.

Key Takeaways:

  • Physique Sensor Permissions are Evolving: Beginning with Android 16, Google is changing broad physique sensor permissions with new, particular, privacy-focused ones (e.g., android.permission.well being.READ_HEART_RATE). Apps accessing delicate well being information (coronary heart price, oxygen, pores and skin temp) might want to adapt to those new permissions, requiring authorized, user-benefiting use instances.
  • Blockchain/Crypto Apps — Nation-Particular Guidelines: A brand new Assist Middle article offers country-specific necessities for apps providing blockchain content material (crypto exchanges/wallets). Builders on this area ought to pay attention to potential location-specific varieties.
  • AI-Generated Content material Finest Practices: New steerage is out there on finest practices for apps that embody AI-generated content material, specializing in defending customers and apps.
  • Content material Rankings & Advertisements: Content material rankings solely replicate your app’s inside content material, not adverts. Guarantee advert content material displayed inside your app isn’t considerably extra mature than your app’s content material ranking.
  • Upcoming Deadlines: Bear in mind to evaluate 2025 deadlines for varied insurance policies, together with Photograph/Video Permissions, Traces of Credit score, Information/Magazines, and Well being Content material & Companies.

Google Play has up to date its coverage for private mortgage apps concentrating on Pakistan: Apps selling short-term private loans (reimbursement inside 60 days or much less) at the moment are usually prohibited. When you’ve got such apps, ensure to evaluate your compliance.

For Android apps coping with blockchain content material (like crypto exchanges or software program wallets), Google Play now offers a Blockchain-based Content material Coverage Assist Middle. This useful resource particulars country-specific compliance necessities, together with potential location-specific varieties. In case your app targets a location not explicitly listed, you should declare it as a crypto change or software program pockets within the Monetary Options declaration. See goo.gle/blockchain-policy for full particulars.

Going through declining cell gaming progress in 2023, Google Play highlights ‘style enlargement’ as an important technique for builders. This entails integrating gameplay, monetization, or aesthetics throughout genres to amass broader audiences, deepen engagement, and enhance monetization (e.g., 4X video games including informal mini-games). It is best to concentrate on early UA testing, understanding participant motivations, guaranteeing seamless integration, and using Play Console instruments like retailer itemizing experiments and staged rollouts for efficient iteration.

Google’s Android group highlights Vulkan and the Adaptive Efficiency Framework (ADPF) as essential for contemporary Android sport improvement. Vulkan is a sophisticated graphics API providing elevated effectivity and entry to superior GPU options, enabling you to maximise your video games’ potential on modern {hardware}.

Right here’s a abstract of the latest AndroidX modifications:

Important Bug Fixes & Stability Enhancements:

Paging (3.4.0-alpha01): A number of important bugs affecting UI stability and information loading have been fastened:

  1. Resolved IndexOutOfBoundsException when scrolling whereas updating RecyclerView.
  2. Mounted points stopping extra hundreds from triggering when refreshing whereas scrolling.
  3. Addressed crashes occurring when scrolling whereas refreshing Paging objects.

Room (2.8.0-alpha01): Mounted a impasse that would often happen when utilizing suspending transactions with the AndroidSQLiteDriver.

Important New Options & API Enhancements:

WebKit (1.15.0-alpha01): Main enhancements for WebView management:

  1. HTTP Header Management: A brand new API (Profile.setOriginMatchedHeader) to set HTTP header name-value pairs on requests to particular origins inside WebViews and repair staff.
  2. Improved Cookie Dealing with: Builders can now embody Cookie headers in WebResourceRequest.getRequestHeaders() and course of Set-Cookie values from WebResourceResponseCompat.setCookies inside shouldInterceptRequest callbacks. This significantly improves management over cookie administration in intercepted WebView requests.
  3. Renderer Heat-up: An experimental API to heat up the WebView renderer course of for a Profile, enhancing efficiency for advanced WebView lifecycles.

Room (2.8.0-alpha01): Launched androidx.room:room-sqlite-wrapper and the RoomDatabase.getSupportWrapper() extension operate. This enables builders adopting SQLiteDriver to nonetheless acquire a SupportSQLiteDatabase wrapper, easing incremental migration for codebases which have intensive SupportSQLite API usages.

Expanded Kotlin Multiplatform (KMP) Help (Medium Precedence, Excessive for KMP Builders):

Put on OS Particular Updates (Medium Precedence, Excessive for Put on Builders):

Different Notable Adjustments (Decrease Precedence for basic app improvement, however necessary for particular use instances):

  • SQLite (2.6.0-alpha01): Provides an API (SQLiteConnection.hasActiveTransaction()) to verify if a SQLiteConnection has an lively transaction, which may assist in managing advanced transaction logic.
  • Take a look at UI Automator (1.0.0-alpha01): A brand new uiautomator-shell library permits executing shell instructions as a shell consumer, backporting UiAutomation#executeShellCommandRwe (from API 34) for testing and instrumentation functions.

The Android Builders Backstage podcast is again with two extra episodes.

Zoom picture shall be displayed

AI-powered Gemini options in Android Studio have considerably developed, providing complete developer assist from design to upkeep. Key developments embody clever code completion, design software integrations (e.g., composable previews), and unit take a look at situation technology.

Newsworthy options:

  • Model Improve Assistant: Automates dependency updates and fixes compilation errors, lowering developer “toil.”
  • Agent Mode (public preview): Permits Gemini to instantly modify code inside the IDE based mostly on directions, with developer approval required for modifications.
  • Privateness: Gemini operates inside the IDE, protecting code non-public with out exterior transmission for mannequin coaching, with enhanced privateness choices for companies.

Android Studio now automates app information backup and restore testing, eliminating cumbersome handbook ADB instructions. This considerably simplifies validating your app’s information persistence (settings, sport states, permissions) throughout gadgets and emulators. Builders can configure backup scope through the manifest, differentiating between cloud (25MB) and device-to-device (1–2GB) transfers. Customization of backup descriptors is feasible for advanced eventualities, although delicate information is excluded for safety. This new tooling streamlines testing for dependable information switch and a seamless consumer expertise.

That’s it for this version of Now in Android, together with the Android platform Canary Channel, the primary launch of Agentic AI in Android Studio, Play updates together with 16 KB Web page Dimension necessities and different coverage modifications, new capabilities round one-time purchases, Play Video games Companies v2, and Google Play Video games on PC, I/O recaps on AI and dev productiveness, subsequent gen Samsung gadgets operating Android 16, and Compose ideas.

Examine again quickly to your subsequent replace from the Android developer universe!

Speed up device adoption with a developer experimentation framework

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Speed up device adoption with a developer experimentation framework


It’s crucial that enterprise leaders can allow their enterprise to work on the pace that expertise is shifting. Nonetheless, prolonged and cumbersome approval processes to undertake new instruments can hinder a corporation’s capability to maintain tempo, particularly when they should stay compliant with rules and requirements, comparable to FedRAMP and SOC-2. 

With a three-stage developer experimentation framework, organizations could make it simpler to guage new applied sciences, decide their match and speed up their adoption throughout the group.

Stage One: Trial

The aim of the primary stage entails exploratory testing to gauge how a growth device would possibly profit the group. The first goal is to determine concrete functions for the group via hands-on experimentation.

Throughout the trial stage, the device ought to be used solely by a small cohort of engineers whereas it’s nonetheless new, untested and unvetted. Moreover, the trial stage ought to be restricted to a brief time period for preliminary testing of the device to scale back danger and forestall prolonged use. This era ought to enable ample time for preliminary experimentation to find out the worth of a device and decide about whether or not to discover it in additional element.

Stage Two: Pilot

The pilot stage expands device entry to a broader group of builders to assemble extra suggestions. By rising utilization throughout varied growth groups, the device’s extra tangible advantages could be evaluated to find out whether or not full-scale implementation throughout the product growth group is suitable.

If an exterior vendor is getting used and the device is just not publicly out there on the web for obtain or use with out signup, a mutual non-disclosure settlement, or NDA, ought to be in place with the seller earlier than getting into the pilot stage. It will assist to make sure that any discussions held with the seller stay confidential.

The pilot stage should not exceed 4 to 6 weeks to stop the device from being “unofficially” utilized by a big set of builders for prolonged intervals of time. Groups can then collect adequate suggestions from a large sufficient viewers to make an correct resolution about whether or not the device ought to proceed to the rollout stage.

Stage Three: Rollout

The ultimate stage focuses on profitable rollout. 

The first goals embrace finishing thorough authorized and safety assessments, and creating an efficient deployment and enablement technique to make the device accessible throughout all engineering groups. Enablement is particularly important to assist drive broader adoption. With out adoption, the deployment could be meaningless.

A safety assessment of the device and its meant makes use of have to be carried out to make sure that the device is secure to make use of. The safety assessment should contain detailed evaluation of all the data the device will eat or entry, which is able to assist stop builders from placing themselves or the group below pointless danger. The device can even have to endure any procurement and new vendor onboarding processes the group might have in place.

Moreover, success metrics ought to be created to guage if a device truly achieves its aim, and may give attention to the device’s capability to fulfill the wants of the builders.

Streamlining Group-Vast Software Adoption

Expertise is consistently altering, and organizations can’t afford to be left behind. Enterprise leaders must be assured that the instruments that they’re implementing are sustainable and efficient long-term.

Establishing an experimentation framework allows organizations to streamline their device adoption course of to allow them to sustain with rising expertise developments. By abandoning any instruments that may get dominated out within the trial or pilot section, they will keep away from the danger of dedicating safety, authorized and procurement overhead to a device that will finally present inadequate worth.

subnet – DHCPv6 / IPv6 Ubiquiti EdgeOS/EdgeMax Prefix vs PD

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subnet – DHCPv6 / IPv6 Ubiquiti EdgeOS/EdgeMax Prefix vs PD


When establishing DHCPv6 on EdgeRouter, I can run the a configuration instructions like the next:

set dhcpv6-pd pd 0 interface eth1 prefix-id :0
set dhcpv6-pd pd 0 interface eth2 prefix-id :1

This leads to the interfaces having totally different IPs:

2601:xxx:xxxx:7450::1/64
2601:xxx:xxxx:7451::1/64

I’m struggling to know what the pd 0 portion of the command controls, and I’m unable to seek out any documentation about these instructions to assist my understanding, and I am not super-familiar with IPv6 to fill within the gaps. I do know that if the CIDR block is just too small, you can’t create extra pds, however I am confused how this all ties collectively.

I am aware of IPv4 and subnetting/CIDR, and I am confused as a result of it seems IPv6 additionally helps subnetting/CIDR, however on high of that has a secondary ‘block’ kind (prefix-id) and doubtlessly a tertiary (pd)?!

Moreover, I believed that a part of IPv6 was its means to delegate IPs offered in a block to machines in your community, so I am attempting to know the distinction in setup between that, and a typical NAT/masquerade of a single WAN tackle from a separate subnet in your community (ie: ISP Provides you IP 1.1.1.1 and you utilize 192.168.1.0/24 in your community).

  1. What’s PD vs Prefix ID?
  2. How do PD and Prefix ID slot in with CIDR and understanding of IPv4 CIDR
  3. Does PD must do with delegation of extra IPs in a offered block or is it unrelated?
  4. Why does my eth0 IPv6 IP differ drastically than the IPs being handed out to the LAN? (ie: eth0 2001:…/128). I might count on this to be the primary tackle on the block, and the LAN machines take the extra addresses on the identical block, however this does not appear to be the case.
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