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Google’s Agent2Agent protocol finds new residence on the Linux Basis

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Google’s Agent2Agent protocol finds new residence on the Linux Basis


On the Open Supply Summit North America, it was introduced that Google donated its Agent2Agent (A2A) protocol to the Linux Basis.

The A2A protocol affords a normal approach for connecting brokers to one another. On this approach, it enhances Anthropic’s Mannequin Context Protocol (MCP), which offers a solution to join brokers to totally different knowledge sources and functions.

“Drawing on Google’s inner experience in scaling agentic techniques, we designed the A2A protocol to deal with the challenges we recognized in deploying large-scale, multi-agent techniques for our prospects. A2A empowers builders to construct brokers able to connecting with some other agent constructed utilizing the protocol and affords customers the flexibleness to mix brokers from numerous suppliers,” Google wrote in a weblog publish when it first launched A2A in April. 

When it was first introduced, Google revealed that there have been over 50 companions contributing to the undertaking, together with Atlassian, Cohere, Datadog, Deloitte, Elastic, Oracle, and Salesforce. Based on the Linux Basis, there at the moment are over 100 know-how companions concerned within the undertaking. 

By becoming a member of a company just like the Linux Basis, the undertaking will profit from vendor neutrality and inclusive contributions. The group may even proceed the protocol’s deal with extensibility, safety, and real-world usability. 

“The Agent2Agent protocol establishes a significant open normal for communication, enabling the trade to construct really interoperable AI brokers throughout various platforms and techniques,” stated Rao Surapaneni, vp and GM of Enterprise Functions Platform at Google Cloud. “By collaborating with the Linux Basis and main know-how suppliers, we’ll allow extra modern and useful AI capabilities underneath a trusted, open-governance framework.”

Agentic AI takes Gemini in Android Studio to the following stage

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Agentic AI takes Gemini in Android Studio to the following stage



Agentic AI takes Gemini in Android Studio to the following stage

Posted by Sandhya Mohan – Product Supervisor, and Jose Alcérreca – Developer Relations Engineer

Software program growth is present process a big evolution, transferring past reactive assistants to clever brokers. These brokers do not simply supply solutions; they’ll create execution plans, make the most of exterior instruments, and make advanced, multi-file modifications. This leads to a extra succesful AI that may iteratively resolve difficult issues, basically altering how builders work.

At Google I/O 2025, we supplied a glimpse into our work on agentic AI in Android Studio, the built-in growth setting (IDE) targeted on Android growth. We showcased that by combining agentic AI with the built-in portfolio of instruments within Android Studio, the IDE is ready to help you in creating Android apps in ways in which have been by no means attainable earlier than. We are actually extremely excited to announce the following frontier in Android growth with the provision of ‘Agent Mode’ for Gemini in Android Studio.

These options can be found within the newest Android Studio Narwhal Characteristic Drop Canary launch, and will likely be rolled out to enterprise tier subscribers within the coming days. As with all new Android Studio options, we invite builders to present suggestions to direct our growth efforts and guarantee we’re creating the instruments it is advisable construct higher apps, sooner.

Agent Mode

Gemini in Android Studio’s Agent Mode is a brand new experimental functionality designed to deal with advanced growth duties that transcend what you may expertise by simply chatting with Gemini.

With Agent Mode, you may describe a posh aim in pure language — from producing unit assessments to advanced refactors — and the agent formulates an execution plan that may span a number of recordsdata in your venture and executes underneath your path. Agent Mode makes use of a variety of IDE instruments for studying and modifying code, constructing the venture, looking the codebase and extra to assist Gemini full advanced duties from begin to end with minimal oversight from you.

To make use of Agent Mode, click on Gemini within the sidebar, then choose the Agent tab, and describe a activity you’d just like the agent to carry out. Some examples of duties you may attempt in Agent Mode embrace:

    • Construct my venture and repair any errors
    • Extract any hardcoded strings used throughout my venture and migrate to strings.xml
    • Add assist for darkish mode to my utility
    • Given an connected screenshot, implement a brand new display in my utility utilizing Materials 3

The agent then suggests edits and iteratively fixes bugs to finish duties. You possibly can evaluation, settle for, or reject the proposed modifications alongside the best way, and ask the agent to iterate in your suggestions.

moving image showing Gemini breaking tasks down into a plan with simple steps, and the list of IDE tools it needs to complete each step

Gemini breaks duties down right into a plan with easy steps. It additionally exhibits the listing of IDE instruments it wants to finish every step.

Whereas highly effective, you’re firmly in management, with the power to evaluation, refine and information the agent’s output at each step. When the agent proposes code modifications, you may select to simply accept or reject them.

screenshot of Gemini in Android Studio showing the Agent prompting the user to accept or reject a change

The Agent waits for the developer to approve or reject a change.

Moreover, you may allow “Auto-approve” in case you are feeling fortunate 😎 — particularly helpful if you need to iterate on concepts as quickly as attainable.

You possibly can delegate routine, time-consuming work to the agent, releasing up your time for extra artistic, high-value work. Check out Agent Mode within the newest preview model of Android Studio – we look ahead to seeing what you construct! We’re investing in constructing extra agentic experiences for Gemini in Android Studio to make your growth much more intuitive, so you may count on to see extra agentic performance over the following a number of releases.

moving image showing that Gemini understanding the context of an app

Gemini is able to understanding the context of your app

Supercharge Agent Mode along with your Gemini API key

screenshot of Gemini API key prompt in Android Studio

The default Gemini mannequin has a beneficiant no-cost every day quota with a restricted context window. Nevertheless, now you can add your individual Gemini API key to develop Agent Mode’s context window to an enormous 1 million tokens with Gemini 2.5 Professional.

A bigger context window enables you to ship extra directions, code and attachments to Gemini, resulting in even increased high quality responses. That is particularly helpful when working with brokers, because the bigger context gives Gemini 2.5 Professional with the power to cause about advanced or long-running duties.

screenshot of how to add your API Key in the Gemini settings

Add your API key within the Gemini settings

To allow this function, get a Gemini API key by navigating to Google AI Studio. Sign up and get a key by clicking on the “Get API key” button. Then, again in Android Studio, navigate to the settings by going to File (Android Studio on macOS) > Settings > Instruments > Gemini to enter your Gemini API key. Relaunch Gemini in Android Studio and get even higher responses from Agent Mode.

Remember to safeguard your Gemini API key, as further prices apply for Gemini API utilization related to a private API key. You possibly can monitor your Gemini API key utilization by navigating to AI Studio and choosing Get API key > Utilization & Billing.

Be aware that enterprise tier subscribers already get entry to Gemini 2.5 Professional and the expanded context window robotically with their Gemini Code Help license, so these builders is not going to see an API key choice.

Mannequin Context Protocol (MCP)

Gemini in Android Studio’s Agent Mode can now work together with exterior instruments through the Mannequin Context Protocol (MCP). This function gives a standardized approach for Agent Mode to make use of instruments and lengthen data and capabilities with the exterior setting.

There are a lot of instruments you may hook up with the MCP Host in Android Studio. For instance you possibly can combine with the Github MCP Server to create pull requests instantly from Android Studio. Listed here are some further use instances to contemplate.

On this preliminary launch of MCP assist within the IDE you’ll configure your MCP servers by means of a mcp.json file positioned within the configuration listing of Studio, utilizing the next format:

{
  "mcpServers": {
    "reminiscence": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-memory"
      ]
    },
    "sequential-thinking": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sequential-thinking"
      ]
    },
    "github": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "GITHUB_PERSONAL_ACCESS_TOKEN",
        "ghcr.io/github/github-mcp-server"
      ],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": ""
      }
    }
  }  
}
Instance configuration with two MCP servers

For this preliminary launch, we assist interacting with exterior instruments through the stdio transport as outlined within the MCP specification. We plan to assist the total suite of MCP options in upcoming Android Studio releases, together with the Streamable HTTP transport, exterior context assets, and immediate templates.

For extra info on tips on how to use MCP in Studio, together with the mcp.json configuration file format, please confer with the Android Studio MCP Host documentation.

By delegating routine duties to Gemini by means of Agent Mode, you’ll be capable of deal with extra modern and gratifying points of app growth. Obtain the most recent preview model of Android Studio on the canary launch channel right this moment to attempt it out, and tell us how a lot sooner app growth is for you!

As all the time, your suggestions is necessary to us – verify recognized points, report bugs, counsel enhancements, and be a part of our vibrant group on LinkedIn, Medium, YouTube, or X. Let’s construct the way forward for Android apps collectively!



Amplifying AI Readiness within the DoD Workforce

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Amplifying AI Readiness within the DoD Workforce


AI readiness is a longtime precedence for the Division of Protection workforce, together with preparation of the workforce to make use of and combine knowledge applied sciences and synthetic intelligence capabilities into skilled and warfighting practices. One problem with figuring out staff skilled in knowledge/AI areas is the dearth of formal certifications held by staff. Staff can develop related information and abilities utilizing non-traditional studying paths, and consequently civilian and federal organizations can overlook certified candidates. Staff could select to domesticate experience on their very own time with on-line assets, private tasks, books, and so forth., in order that they’re ready for open positions even once they lack a level or different conventional certification.

The SEI’s Synthetic Intelligence Division is working to handle this problem. We not too long ago partnered with the Division of the Air Power Chief Knowledge and AI Workplace (DAF CDAO) to develop a technique to determine and assess hidden workforce expertise for knowledge and AI work roles. The collaboration has had some important outcomes, together with (1) a Knowledge/AI Cyber Workforce Rubric (DACWR) for evaluation of abilities recognized throughout the DoD Cyberworkforce Framework, (2) prototype assessments that seize an information science pipeline (knowledge processing, mannequin creation, and reporting), and (3) a proof-of-concept platform, SkillsGrowth, for staff to construct profiles of their experience and evaluation efficiency and for managers to determine the info/AI expertise they want. We element beneath the advantages of those outcomes.

A Knowledge/AI Cyber Workforce Rubric to Enhance Usability of the DoD Cyber Workforce Growth Framework

The DoD Cyber Workforce Framework (DCWF) defines knowledge and AI work roles and “establishes the DoD’s authoritative lexicon based mostly on the work a person is performing, not their place titles, occupational sequence, or designator.” The DCWF gives consistency when defining job positions since totally different language could also be used for a similar knowledge and AI tutorial and business practices. There are 11 knowledge/AI work roles, and the DCWF covers a variety of AI disciplines (AI adoption, knowledge analytics, knowledge science, analysis, ethics, and so forth.), together with the information, abilities, talents, and duties (KSATs) for every work position. There are 296 distinctive KSATs throughout knowledge and AI work roles, and the variety of KSATs per work position varies from 40 (knowledge analyst) to 75 (AI check & analysis specialist), the place most KSATs (about 62 %) seem in a single work position. The KSAT descriptions, nonetheless, don’t distinguish ranges of efficiency or proficiency.

The information/AI cyber workforce rubric that we created builds on the DCWF, including ranges of proficiency, defining fundamental, intermediate, superior, and skilled proficiency ranges for every KSAT.

figure1_06242025

Determine 1: An Excerpt from the Rubric

Determine 1 illustrates how the rubric defines acceptable efficiency ranges in assessments for one of many KSATs. These proficiency-level definitions assist the creation of knowledge/AI work role-related assessments starting from conventional paper-and-pencil assessments to multimodal, simulation-based assessments. The rubric helps the DCWF to supply measurement choices {of professional} apply in these work roles whereas offering flexibility for future adjustments in applied sciences, disciplines, and so forth. Measurement towards the proficiency ranges may give staff perception into what they will do to enhance their preparation for present and future jobs aligned with particular work roles. The proficiency-level definitions may also assist managers consider job seekers extra constantly. To determine hidden expertise, it is very important characterize the state of proficiency of candidates with some affordable precision.

Addressing Challenges: Confirming What AI Staff Know

Potential challenges emerged because the rubric was developed. Staff want a method to exhibit the power to use their information, no matter the way it was acquired, together with via non-traditional studying paths comparable to on-line programs and on-the-job talent improvement. The evaluation course of and knowledge assortment platform that helps the evaluation should respect privateness and, certainly, anonymity of candidates – till they’re able to share info relating to their assessed proficiency. The platform ought to, nonetheless, additionally give managers the power to find wanted expertise based mostly on demonstrated experience and profession pursuits.

This led to the creation of prototype assessments, utilizing the rubric as their basis, and a proof-of-concept platform, SkillsGrowth, to supply a imaginative and prescient for future knowledge/AI expertise discovery. Every evaluation is given on-line in a studying administration system (LMS), and every evaluation teams units of KSATs into no less than one competency that displays every day skilled apply. The aim of the competency groupings is pragmatic, enabling built-in testing of a associated assortment of KSATs fairly than fragmenting the method into particular person KSAT testing, which could possibly be much less environment friendly and require extra assets. Assessments are meant for basic-to-intermediate stage proficiency.

4 Assessments for Knowledge/AI Job Expertise Identification

The assessments comply with a fundamental knowledge science pipeline seen in knowledge/AI job positions: knowledge processing, machine studying (ML) modeling and analysis, and outcomes reporting. These assessments are related for job positions aligned with the info analyst, knowledge scientist, or AI/ML specialist work roles. The assessments additionally present the vary of evaluation approaches that the DACWR can assist. They embody the equal of a paper-and-pencil check, two work pattern assessments, and a multimodal, simulation expertise for staff who might not be comfy with conventional testing strategies.

On this subsequent part, we define a number of of the assessments for knowledge/AI job expertise identification:

  • The Technical Abilities Evaluation assesses Python scripting, querying, and knowledge ingestion. It accomplishes this utilizing a piece pattern check in a digital sandbox. The check taker should test and edit simulated personnel and tools knowledge, create a database, and ingest the info into tables with particular necessities. As soon as the info is ingested, the check taker should validate the database. An automatic grader gives suggestions (e.g., if a desk identify is inaccurate, if knowledge will not be correctly formatted for a given column, and so forth.). As proven in Determine 2 beneath, the evaluation content material mirrors real-world duties which are related to the first work duties of a DAF knowledge analyst or AI specialist.

figure2_06242025

Determine 2: Making a Database within the Technical Abilities Evaluation

  • The Modeling and Simulation Evaluation assesses KSATs associated to knowledge evaluation, machine studying, and AI implementation. Just like the Technical Abilities Evaluation, it makes use of a digital sandbox surroundings (Determine 3). The primary job within the Modeling and Simulation Evaluation is to create a predictive upkeep mannequin utilizing simulated upkeep knowledge. Check takers use Python to construct and consider machine studying fashions utilizing the scikit-learn library. Check takers could use no matter fashions they need, however they have to obtain particular efficiency thresholds to obtain the very best rating. Automated grading gives suggestions upon answer submission. This evaluation displays fundamental modeling and analysis that might be carried out by staff in knowledge science, AI/ML specialist, and probably knowledge analyst-aligned job positions.

figure3_06242025

Determine 3: Making ready Mannequin Creation within the Modeling and Simulation Evaluation

  • The Technical Communication Evaluation focuses on reporting outcomes and visualizing knowledge, concentrating on each technical and non-technical audiences. Additionally it is aligned with knowledge analyst, knowledge scientist, and different associated work roles and job positions (Determine 4). There are 25 questions, and these are framed utilizing three query sorts – a number of selection, assertion choice to create a paragraph report, and matching. The query content material displays frequent knowledge analytic and knowledge science practices like explaining a time period or end in a non-technical means, choosing an applicable option to visualize knowledge, and making a small story from knowledge and outcomes.

figure4_06242025

Determine 4: Making a Paragraph Report within the Technical Communications Evaluation

  • EnGauge, a multimodal expertise, is another method to the Technical Abilities and Technical Communication assessments that gives analysis in an immersive surroundings. Check takers are evaluated utilizing practical duties in contexts the place staff should make selections about each the technical and interpersonal necessities of the office. Staff work together with simulated coworkers in an workplace surroundings the place they interpret and current knowledge, consider outcomes, and current info to coworkers with totally different experience (Determine 5). The check taker should assist the simulated coworkers with their analytics wants. This evaluation method permits staff to point out their experience in a piece context.

figure5_06232025

Determine 5: Working with a Simulated Coworker within the EnGauge Multimodal Evaluation

A Platform for Showcasing and Figuring out Knowledge/AI Job Expertise

We developed the SkillsGrowth platform to additional help each staff in showcasing their expertise and managers in figuring out staff who’ve obligatory abilities. SkillsGrowth is a proof-of-concept system, constructing on open-source software program, that gives a imaginative and prescient for the way these wants may be met. Staff can construct a resume, take assessments to doc their proficiencies, and price their diploma of curiosity in particular abilities, competencies, and KSATs. They will seek for roles on websites like USAJOBS.

SkillsGrowth is designed to exhibit instruments for monitoring the KSAT proficiency ranges of staff in real-time and for evaluating these KSAT proficiency ranges towards the KSAT proficiencies required for jobs of curiosity. SkillsGrowth can also be designed to assist use circumstances comparable to managers looking out resumes for particular abilities and KSAT proficiencies. Managers may also assess their groups’ knowledge/AI readiness by viewing present KSAT proficiency ranges. Staff may also entry assessments, which might then be reported on a resume.

Briefly, we suggest to assist the DCWF via the Knowledge/AI Cyber Workforce Rubric and its operationalization via the SkillsGrowth platform. Staff can present what they know and ensure what they know via assessments, with the info managed in a means that respects privateness issues. Managers can discover the hidden knowledge/AI expertise they want, gauge the info/AI talent stage of their groups and extra broadly throughout DoD.

SkillsGrowth thus demonstrates how a sensible profiling and evaluative system may be created utilizing the DCWF as a basis and the CWR as an operationalization technique. Assessments throughout the DACWR are based mostly on present skilled practices, and operationalized via SkillsGrowth, which is designed to be an accessible, easy-to-use system.

figure6_06242025

Determine 6: Checking Private and Job KSAT Proficiency Alignment in SkillsGrowth

Looking for Mission Companions for Knowledge/AI Job Expertise Identification

We at the moment are at a stage of readiness the place we’re in search of mission companions to iterate, validate, and broaden this effort. We wish to work with staff and managers to enhance the rubric, evaluation prototypes, and the SkillsGrowth platform. There’s additionally alternative to construct out the set of assessments throughout the info/AI roles in addition to to create superior variations of the present evaluation prototypes.

There’s a lot potential to make figuring out and creating job candidates more practical and environment friendly to assist AI and mission readiness. If you’re considering our work or partnering with us, please ship an e-mail to information@sei.cmu.edu.

Measuring information, abilities, capacity, and job success for knowledge/AI work roles is difficult. It is very important take away obstacles in order that the DoD can discover the info/AI expertise it wants for its AI readiness objectives. This work creates alternatives for evaluating and supporting AI workforce readiness to attain these objectives.

The UK’s Industrial Technique: Individuals and Expertise on the Coronary heart of Financial Progress

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The UK’s Industrial Technique: Individuals and Expertise on the Coronary heart of Financial Progress


The UK’s new Industrial Technique charts a daring and essential path towards long-term financial renewal—anchored in innovation, digital infrastructure, and the transformative potential of Synthetic Intelligence (AI). At Cisco, we’re energised by this imaginative and prescient and proud to assist the UK authorities’s ambition to future-proof the nation’s international competitiveness.

Throughout three core pillars—accelerating AI, driving productiveness, and constructing digital expertise at scale—the technique aligns carefully with Cisco’s priorities and capabilities.

Maximising the AI Alternative

AI is quickly changing into the defining expertise of our time, remodeling industries, labour markets, and international worth chains. By inserting AI on the coronary heart of its Industrial Technique, the UK is making a strategic wager on the longer term—and it’s the fitting one. Constructing on this yr’s AI Alternatives Motion Plan is each pragmatic and visionary.

AI has the ability to turbocharge productiveness, speed up breakthroughs, and reimagine how we ship public companies. From predictive insights and clever automation to adaptive, safe infrastructure, Cisco sees AI as a catalyst for long-term transformation—not simply incremental positive factors.

However realising AI’s full worth calls for greater than adoption. It requires belief, coordination, and readiness. We want cross-sector partnerships to make sure that AI is deployed ethically and securely. Which means establishing governance frameworks, defending information, and embedding belief into each layer of the digital stack.

Infrastructure issues too. Cisco’s AI Readiness Index reveals that 60% of UK organisations say their networks aren’t scalable or versatile sufficient to assist AI at pace. To remain globally aggressive, the UK should modernise its connectivity spine.

Digital Infrastructure as a Driver of Inclusive Progress

Expertise is a development engine, however its advantages should lengthen past conventional tech hubs and to each nook of the nation. The Industrial Technique’s concentrate on wide-spread development is spot on; Cisco has lengthy championed the ability of connectivity to create alternative, shut divides, and supercharge native economies.

Actually, Cisco’s “GM One Community” partnership with Larger Manchester Mixed Authority is a superb instance of how good metropolis initiatives that mix next-gen infrastructure, IoT, and information insights can enhance public companies, scale back prices, and assist native economies.

With the UK doubling down on cloud, 5G, and digital public companies, there’s a clear alternative to unlock productiveness positive factors and regional innovation.

A Nationwide Expertise Technique for a Digital Financial system

Digital expertise are the connective tissue linking innovation, productiveness, and long-term financial resilience, and no industrial technique can succeed with no parallel funding within the individuals who will make it a actuality.

Within the UK, Cisco’s Networking Academy trains almost 100,000 learners yearly and is aiming to achieve 1 million individuals since inception by 2030. However assembly the rising demand for digital expertise would require wider systemic change.

Which means embedding digital literacy into each layer of the training and workforce improvement system—from main colleges all the way in which as much as grownup reskilling programmes. It means larger flexibility with apprenticeships, rethinking vocational pathways, advocating for lifelong studying alternatives, and constructing deeper collaboration between employers, educators, and authorities through Expertise England and different establishments.

The UK’s eight strategic sectors from life sciences and vitality to manufacturing and inventive industries will more and more rely upon staff fluent in AI, cybersecurity, and sophisticated community environments. By prioritising entry to those expertise and investing in individuals as a lot as we do platforms, the UK can assist be sure that a future-ready economic system leaves nobody out.

From Imaginative and prescient to Velocity

The UK’s Industrial Technique presents a compelling blueprint for a extra modern and aggressive economic system. However its success will hinge on tempo and execution. Delivering on its promise—and quick—will entice funding, construct investor confidence, and generate real-world impression for companies and residents alike.

At Cisco, our a long time of expertise connecting something to make every thing potential runs deep, and we’re prepared to assist the UK carry this technique to life. Collectively, we will construct a UK economic system, by powering safe AI deployment and shutting the talents hole, that’s digitally linked and globally revered.

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Prime 3 Updates for Android Developer Productiveness @ Google I/O ‘25

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Prime 3 Updates for Android Developer Productiveness @ Google I/O ‘25



Prime 3 Updates for Android Developer Productiveness @ Google I/O ‘25

Posted by Meghan Mehta – Android Developer Relations Engineer

#1 Agentic AI is out there for Gemini in Android Studio

Gemini in Android Studio is the AI-powered coding companion that makes you extra productive at each stage of the dev lifecycle. At Google I/O 2025 we previewed new agentic AI experiences: Journeys for Android Studio and Model Improve Agent. These improvements make it simpler so that you can construct and check code. We additionally introduced Agent Mode, which was designed to deal with complicated, multi-stage improvement duties that transcend typical AI assistant capabilities, invoking a number of instruments to perform duties in your behalf. We’re excited to see the way you leverage these agentic AI experiences which at the moment are accessible within the newest preview model of Android Studio on the canary launch channel.

It’s also possible to use Gemini to robotically generate Jetpack Compose previews, in addition to rework UI code utilizing pure language, saving you effort and time. Give Gemini extra context by attaching pictures and challenge recordsdata to your prompts, so you may get extra related responses. And for those who’re searching for enterprise-grade privateness and security measures backed by Google Cloud, Gemini in Android Studio for companies is now accessible. Builders and admins can unlock these options and advantages by subscribing to Gemini Code Help Commonplace or Enterprise editions.

#2 Construct higher apps sooner with the most recent steady launch of Jetpack Compose

Compose is our really helpful UI toolkit for Android improvement, utilized by over 60% of the highest 1K apps on Google Play. We launched a new model of our Jetpack Navigation library: Navigation 3, which has been rebuilt from the bottom as much as provide you with extra flexibility and management over your implementation. We unveiled the brand new Materials 3 Expressive replace which offers instruments to boost your product’s attraction by harnessing emotional UX, making it extra partaking, intuitive, and fascinating in your customers. The newest steady Invoice of Supplies (BOM) launch for Compose provides new options reminiscent of autofill assist, auto-sizing textual content, visibility monitoring, animate bounds modifier, accessibility checks in assessments, and extra! This launch additionally consists of important rewrites and enhancements to a number of sub-systems together with semantics, focus and textual content optimizations.

These optimizations can be found to you with no code modifications apart from upgrading your Compose dependency. In case you’re trying to check out new Compose performance, the alpha BOM provides new options that we’re engaged on together with pausable composition, updates to LazyLayout prefetch, context menus, and others. Lastly, we have added Compose assist to CameraX and Media3, making it simpler to combine digicam seize and video playback into your UI with Compose idiomatic parts.

#3 The brand new Kotlin Multiplatform (KMP) shared module template helps you share enterprise logic

KMP allows groups to ship high quality Android and iOS apps with much less improvement time. The KMP ecosystem continues to develop: final yr alone, over 900 new KMP libraries had been printed. At Google I/O we launched a brand new Android Studio KMP shared module template that will help you craft and handle enterprise logic, up to date Jetpack libraries and new codelabs (Getting began with Kotlin Multiplatform and Migrating your Room database to KMP) that will help you get began with KMP. We additionally shared further bulletins at KotlinConf.

Be taught extra about what we introduced at Google I/O 2025 that will help you construct higher apps, sooner.

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