Explore
Showing results for:
"Google Developers"
246 results • Page 9 of 21
Matched "Google Developers"Found in Content
Google is working on an AI-powered setup assistant that offers suggestions and allows you to ask questions.
Matched "Google Developers"Found in Content
415 points 509 comments on Hacker News · android-developers.googleblog.com
Matched "Google Developers"Found in Source
An open specification for finding and verifying tools, skills, and agents across the web.Agents are ...
Matched "Google Developers"Found in Content
1 point 0 comments on Hacker News · arxiv.org
Matched "Google Developers"Found in Content
CEO Mark Zuckerberg said the firm intends to sell its AI tools to other companies for the first time.
Matched "Google Developers"Found in Description
Few companies face higher stakes when deploying AI than Waymo , the self-driving car company under Alphabet that spun out of Google. Its models do not merely generate text or automate back-office tasks: They help vehicles navigate unpredictable streets, respond to human drivers and make split-second decisions in the physical world. But the methods Waymo uses to manage those risks — continuous evaluation, carefully curated data, human oversight and clearly defined business outcomes — offer a broader playbook for enterprises deploying AI agents in nearly any industry. Manasi Joshi, Waymo’s director of engineering for systems intelligence and machine learning, explained at VB Transform 2026 how the autonomous vehicle company trains, tests and deploys AI at scale. To date, Waymo has driven more than 220 million fully autonomous, or "rider-only," miles, with 17 times fewer serious crash injuries than human drivers over the same distance, according to the company. To achieve these impressive results, Joshi said Waymo has adopted what she called “eval-forced development” or “eval-centric development,” making evaluation a core part of engineering rather than a final check performed before deployment. “The stage at which our projects are maturing can be easily kind of transpired based on the eval maturity that they showcase,” Joshi said. In practice, Waymo assesses a project’s readiness partly by examining the maturity of the tests surrounding it. That approach has clear implications for enterprises building customer service agents, coding assistants, financial systems or other AI applications: If a company cannot reliably measure a system’s performance, it may not be ready to place that system into production. Evals must continue after launch Joshi said much of Waymo’s quality work has shifted toward evaluations, including tests conducted during model training, after training and inside open-loop and closed-loop simulations. “Eval is not a one-time task to launch a model,” she said. Waymo instead treats evaluation as a continuous process spanning driving, simulation and validation. Its methodology combines datasets, performance metrics and infrastructure capable of operating efficiently at scale. For enterprises, that means testing an agent before launch is insufficient. Teams must continue evaluating it as underlying models, business processes, user behavior and incoming data change. Those evaluations should also connect to actual business outcomes rather than relying solely on broad industry benchmarks. Joshi cautioned that model-quality measurements are only as trustworthy as the evaluation data behind them. Waymo therefore pairs its performance claims with information about the properties of the datasets used to test its systems. Testing the rare and dangerous cases Waymo’s evaluation hierarchy remains grounded in one overriding objective: safety. The company draws on first-party driving logs, some third-party data and realistic simulations that expose its systems to scenarios spanning billions of synthetic miles. Task owners choose specialized data and metrics for situations involving vulnerable road users, railroad crossings, construction zones and other complex environments. The same principle applies outside autonomous driving. Enterprises need to test not only the routine requests their agents handle successfully, but also uncommon situations where errors could create financial, legal, security or reputational damage. Joshi emphasized that Waymo does not leave release decisions entirely to automated systems. Its production-readiness reviews include extensive human oversight, while internal safety leaders approve software releases and service-area expansions. “This is not AI-driven and completely automated and zero human oversight,” she said. “Human lives are at stake.” Efficiency cannot come at the expense of reliability Waymo faces another problem familiar to enterprise AI teams: Demand for compute, storage, memory and network capacity is growing faster than the resources available. The company pursues efficiency across data extraction and storage, distributed model training, model distillation, simulation and evaluation. It also emphasizes “data efficiency,” selecting the most useful training examples instead of treating greater volume as inherently better. Waymo began using transformers in 2017 and subsequently expanded into large language models, vision-language models and vision-language-action models. Joshi said the company now uses generative multimodal models as part of its foundation-model strategy. Waymo divides its technology between onboard systems inside each vehicle and off-board infrastructure used for model development, data processing and simulation. That combination forces the company to optimize both real-time inference and the larger systems supporting it. Agents need their own evals Waymo also uses AI agents internally as productivity tools for engineers. Joshi said agents help analyze data distributions, assess data efficiency and triage problems found in vehicle telemetry, training runs and failed evaluation jobs. The goal is to accelerate investigative work so engineers can devote more time to judgment and difficult technical problems. But Waymo also evaluates those agents to ensure they produce trustworthy, accurate results rather than sending employees down unproductive paths. For enterprise leaders, Waymo’s larger lesson is that agentic AI requires more than choosing a powerful model. Organizations need a clearly defined objective, representative evaluation data, continuous testing, infrastructure that can operate efficiently and named human decision-makers who remain accountable for deployment. "Earning trust is supremely important," Joshi said.
Matched "Google Developers"Found in Content
814 points 310 comments on Hacker News · christianselig.com
Matched "Google Developers"Found in Content
Google's new API relies on parents to set age ranges in Family Link.
Matched "Google Developers"Found in Description
Nimble , a New York City-based tech startup VentureBeat previously covered for its efforts to re-invent web search for enterprises by using multiple AI agents to improve accuracy and depth, is taking another step toward its vision of a world in which agents do most of the web searching instead of us typing and reviewing the results manually. Nimble today launched Web Search Agents , a new retrieval system designed to help AI agents perform more 21% more accurate web research while using significantly fewer tokens — 51% less compared with leading AI search alternatives on comparable, according to the firm. While Nimble did not disclose its specific benchmarking methodology or competitors evaluated, the results underscore a growing trend in enterprise AI: optimizing retrieval has become as important as improving the underlying language models themselves. Nimble's leadership says the product combines self-learning retrieval strategies, proprietary web indexes, and live web access to deliver domain-specific search capabilities that outperform general-purpose web search services for enterprise workloads. "Our research team built self-learning retrieval algorithms that learn a customer's domain," said Nimble CEO and co-founder Uri Knorovich in an interview with VentureBeat. "They find the exact information more efficiently, reduce the amount of multi-hop reasoning required, and lower token usage while improving accuracy." Rather than positioning itself as another general search engine, Nimble is targeting developers building autonomous agents that require continuously updated information from the public web for research, lead generation, competitive intelligence, compliance, and other business-critical workflows. It's also designed to slot in seamlessly to an enterprise's existing systems and workflows. "You can run the agent directly through the Nimble API with zero infrastructure," Knorovich said. "For large enterprises, we're partnering with Microsoft, Oracle, Snowflake, and others so customers can deploy these agent systems inside their own infrastructure." How does it work and stack up to other, existing AI-powered search and agentic systems? Read on to find out. Moving beyond generic AI web search into specialized search agents that fit your enterprise's needs Most AI applications today rely on general-purpose search application programming interfaces (APIs) for search engines and public knowledge bases that return broad collections of files, leaving the language model responsible for determining which sources are relevant. That process often requires multiple retrieval steps, additional reasoning, and significant token expenditure before an agent produces an answer. This is obviously inefficient and raises the cost spent to run AI search looking through irrelevant sources. Nimble argues that before long, every enterprise will need its own methods for searching, retrieving, and validating external information since each enterprise relies on its own distinct preferred sources, signals, and standards of trust. As such, instead of applying one search strategy to every workload, Nimble's Web Search Agents are designed to learn the characteristics of a specific domain and adapt how information is retrieved, providing agents with structured, relevant context rather than forcing them to sift through large amounts of generic search results. "Instead of one generic retrieval model, we build specialized retrieval models for each customer's domain, making them faster, cheaper, and more accurate," Knorovich explained. "A single enterprise can run hundreds of different agents. Each one has its own domain expertise, guardrails, goals, and search algorithm. The optimization starts with the second search, without requiring any setup from the customer." Its goal is not only to reduce redundant retrieval, but also to shorten multi-step research paths and avoid repeatedly sending raw pages through a language model for parsing, resulting in the 51% reduced token figure the company cites. The distinction is particularly relevant for long-running enterprise agents performing research over hours or days rather than answering simple consumer questions. In those scenarios, reducing unnecessary tool calls can significantly lower operating costs while improving answer consistency. That emphasis reflects a broader shift occurring across the AI tooling ecosystem. As foundation models become increasingly capable, infrastructure vendors are competing on everything surrounding the model—including retrieval, orchestration, memory, observability, and governance. Optimizing retrieval for production AI The launch builds on Nimble’s broader strategy of becoming an enterprise web intelligence platform rather than simply a web scraping provider. Earlier this year, the company introduced its broader Agentic Search Platform following a $47 million Series B financing , positioning itself as infrastructure that transforms the live web into structured, machine-readable data for AI systems. The company’s latest release extends that vision with a concept it calls “Harness as a Tool,” which powers its new domain-specialized Web Search Agents. Rather than requiring engineering teams to assemble separate search APIs, browser automation, extraction pipelines, validation logic, memory systems, and orchestration code, Nimble packages those capabilities behind a managed interface. The harness can determine what to search, navigate pages when conventional indexes are insufficient, extract relevant information, validate the results, and return the final context in a form designed for downstream agents. Nimble also says the system retains domain-specific memory and builds proprietary indexes that improve as customers run more searches. "The biggest research breakthrough is adding semantic memory and a caching layer to the agent," Knorovich told VentureBeat. "The agent learns usage patterns and domain expertise over time, so every subsequent search becomes faster and more efficient." As for what domains Nimble can tackle, the company says it can address virtually any knowledge work domain. "We've seen customers build investment banking analysts, competitive intelligence agents for product managers, go-to-market research agents, newsroom monitoring, insurance applications, life sciences research, and supply chain optimization," Knorovich said. "Our customers surprise us every day with new agent use cases." However, for enterprises concerned about data privacy and retention, Knorovich assured VentureBeat that: "Nimble is zero-data-retention by design. Customer queries are never stored in our environment, and when customers deploy semantic memory and self-learning models, that knowledge stays in their own tenant—not ours." Customer deployments point to operational gains Nimble supported the announcement with early customer examples from AI-native software vendors and enterprise users. AI-native CRM company Rox reported achieving a 20× reduction in token costs after adopting Nimble’s retrieval infrastructure while simultaneously improving the quality and completeness of information available to its AI agents. Although the company did not disclose detailed workload measurements or a reproducible baseline, the example illustrates the operational savings retrieval optimization can provide for high-volume agent deployments. Nimble says its infrastructure currently supports more than 90 million searches each day across Fortune 500 enterprises and AI-native companies operating mission-critical workflows where accuracy, completeness, and enterprise control are essential. API, SDK and MCP support target AI builders The platform is immediately available through an API, SDK, and Model Context Protocol (MCP) integration, allowing developers to connect Nimble directly into AI agents regardless of the orchestration framework they use. Developers can use the platform for several categories of web intelligence, including: Low-latency live web search Deep multi-step web research Web crawling Structured dataset generation Domain-specific information retrieval The company also provides documentation and pre-built agents for common web extraction tasks while allowing developers to build custom retrieval agents using natural-language descriptions instead of manually maintaining scraping logic. Nimble is offering two notably different consumption models. Developers can begin with a pay-as-you-go Agent API priced from $0.025 per Web Search Agent request at the listed low-effort setting. Companies that want Nimble to configure and manage custom data delivery can instead buy annual managed plans beginning at $2,500 per month. Where Nimble fits in the emerging agentic search stack Nimble enters a market that has rapidly expanded beyond traditional web search into autonomous research agents capable of planning, browsing, reasoning, and synthesizing information. Products such as ChatGPT Deep Research , Google Gemini Deep Research , Alibaba’s Tongyi DeepResearch , Perplexity , and Sakana Marlin all seek to automate knowledge work that previously required hours—or, in Marlin’s case, potentially weeks—of human research. Rather than competing head-to-head as another end-user research assistant, however, Nimble is positioning itself one layer lower in the AI stack—as the web intelligence infrastructure that powers those agents or custom enterprise applications built on leading foundation models. That distinction reflects an increasingly important architectural shift in enterprise AI. Most “Deep Research” systems optimize the overall research workflow, generating search plans, iteratively gathering information, and producing synthesized reports. Nimble instead argues that the retrieval layer itself has become the primary bottleneck for enterprise AI deployments. If an agent retrieves too many irrelevant pages or performs unnecessary search iterations, token consumption, latency, and operating costs all increase before the model even begins its main reasoning process. "Customers across life sciences, insurance, healthcare, pharma, retail, and digital-native companies are all telling us the same thing: we need to feed our agents with more accurate context, and we need to reduce the amount of tokens every task consumes," Knorovich said. The launch blog makes that argument more concrete by describing how teams frequently rebuild the same retrieval stack themselves. A production agent may start with a search API, then accumulate browser controls, parsers, extraction components, validation steps, memory, caching, evaluations, and custom workflow logic. Nimble is positioning its harness as a managed alternative to that growing engineering burden. In Nimble’s view, improving retrieval before reasoning begins is more valuable than simply giving a language model more documents to analyze. The company’s Web Search Agents therefore adapt retrieval strategies to a particular workload, combining proprietary indexes with real-time web retrieval and task-specific search policies rather than applying the same search algorithm across every domain. That makes Nimble less of a direct competitor to OpenAI’s or Google’s research assistants than to developer-focused retrieval infrastructure such as Exa and Tavily. Those platforms also provide AI-native search APIs and research capabilities, but Nimble differentiates itself by emphasizing self-learning retrieval strategies, proprietary indexing, enterprise governance, managed delivery, and token efficiency for production agents. For organizations building their own AI systems, the distinction could become increasingly important. Foundation models are becoming more capable across the industry, shifting competitive differentiation toward the infrastructure surrounding them—including retrieval, orchestration, memory, observability, and governance. Nimble’s strategy reflects that broader trend, betting that better web intelligence can deliver larger operational gains than incremental improvements in model reasoning alone. Enterprise infrastructure versus AI research assistants The different positioning is also reflected in pricing.While consumer-facing AI research assistants are generally sold as productivity subscriptions for individual users or teams, Nimble is pricing its managed service as enterprise infrastructure designed to power production applications. Its pay-as-you-go API, however, gives developers a lower-cost path to test the underlying agent technology before committing to a managed deployment. Platform Primary audience Primary focus Lowest publicly available price (USD) Nimble Developers and enterprises Managed web retrieval and orchestration infrastructure combining specialized search, browsing, extraction, validation, proprietary indexing, and memory $0.025 per Agent API request (low-effort setting). Managed service starts at $2,500/month (Startup plan, billed annually). ChatGPT Deep Research Professionals, enterprises, and knowledge workers Autonomous multi-step research with iterative browsing, synthesis, and citations $20/month (ChatGPT Plus). Higher limits are available with Pro, Team, Enterprise, and Edu plans. Google Gemini Deep Research Consumers and enterprises Research planning integrated with Gemini, Google Search, and Google's productivity ecosystem $19.99/month (Google AI Pro, U.S.). Higher-capacity AI Ultra and enterprise Workspace offerings are also available. Tongyi DeepResearch Developers and AI researchers Open research model for long-horizon information-seeking and agentic search Free (open source). Users are responsible for their own infrastructure and cloud compute costs. Perplexity Consumers, professionals, and enterprise teams AI-powered web search and cited research Free entry tier. Perplexity Pro starts at $20/month with Enterprise Pro available separately. Exa Developers and AI platform builders AI-native search, content retrieval, and asynchronous research agents Free developer tier (includes monthly credits). Paid Search API pricing starts at approximately $7 per 1,000 requests while Agent runs range from $0.012 to $1.00 per run depending on effort level. Tavily Developers building AI agents Search, extraction, crawling, and research APIs for agents and RAG workflows Free developer tier (1,000 monthly credits). Pay-as-you-go usage starts at approximately $ 0.008 per credit. Sakana Marlin Enterprises, strategy teams, financial institutions, and research organizations Ultra Deep Research for hours-long strategic reasoning and executive-grade reports Pay-as-you-go from approximately $0.61 per credit (¥98/credit) with with 100 credits required per research run (approx $61 per run). The first subscription tier is Pro at approximately $936/month (¥150,000/month) followed by Team at approximately $2,495/month (¥400,000/month) with Enterprise pricing available by quote. The comparison reveals three increasingly distinct markets. ChatGPT Deep Research, Gemini Deep Research, and Perplexity operate primarily as user-facing research assistants. Exa and Tavily provide developer-facing retrieval and research APIs. Nimble and Sakana Marlin occupy more enterprise-oriented territory, but at different layers: Nimble supplies retrieval infrastructure, while Marlin performs long-horizon strategic analysis. Sakana Marlin is particularly useful as a counterpoint. It is positioned as a "Virtual CSO" rather than a search API, running autonomous research loops for as long as eight hours and producing executive-ready reports, references, and supporting materials. Nimble, by contrast, is designed to sit beneath those kinds of systems, supplying the specialized retrieval, browsing, extraction, validation, and orchestration that enterprise agents need to gather reliable external information before reasoning begins. The comparison therefore should not be read as a direct price-to-price evaluation. A $20/month ChatGPT Plus or $19.99/month Google AI Pro subscription buys an individual AI workspace with Deep Research capabilities. Nimble's $2,500/month managed plan funds concurrent production agents, managed ETL, MCP integration, web-page capacity, storage, and hands-free data delivery. Sakana Marlin's approximately $936/month (¥150,000/month) Pro plan pays for extended, compute-intensive strategic research workflows. Each price reflects a fundamentally different product boundary and deployment model rather than simply a different level of AI capability. Why retrieval is becoming the next AI battleground As enterprise AI systems mature, the industry is increasingly recognizing that model quality alone does not determine application performance. Large language models frequently fail not because they cannot reason, but because they lack timely, trustworthy external information. That reality has fueled rapid investment across retrieval-augmented generation, AI-native search, web intelligence platforms, knowledge graphs, browser automation, and agent infrastructure. Nimble’s launch reflects this evolution by focusing less on building another frontier model and more on improving the quality of information flowing into existing ones. Whether the company’s reported 21-point improvement in answer quality and 51% reduction in token usage hold up across a broad range of enterprise deployments remains to be independently validated. The larger strategic bet is that, as frontier models become more interchangeable, companies will differentiate themselves through the data, retrieval policies, trusted-source rules, memory systems, and orchestration layers surrounding those models. Nimble is not trying to build the researcher that sits in front of the user. It is trying to become part of the infrastructure that determines what the researcher can find, how efficiently it can find it, and whether the resulting evidence is complete enough to support production decisions. Web Search Agents are available through Nimble’s API, SDK, and MCP integrations, with a free trial available for developers evaluating the platform.
Matched "Google Developers"Found in Content
Google Play is getting a privacy-preserving way to let apps know how old users are.
Matched "Google Developers"Found in Title
Google is expanding its Play Age Signals API, giving Android developers a privacy-preserving way to tailor experiences based on users’ age ranges.
Matched "Google Developers"Found in Description
Posted by Paul Feng, VP of Product Management, Google Play Providing a safe online experience and protecting users from harm is a top priority at Google Play. We take this responsibility seriously and have been investing continuously to offer baseline protections on our platform while also empowering parents with the tools they need to make decisions for their families. Importantly, we also want to empower Play developers with the capabilities to deliver age-appropriate experiences based on their app's content. To support this, today, we are taking another big step in our ongoing partnership with parents and developers by announcing the expansion of the Google Play Age Signals API to all Play developers globally. Building on current availability in Brazil, we will expand this experience first to users in Australia and Canada by mid-August, with a full global rollout to all users later this year. Empowering developers to create age-appropriate experiences The Play Age Signals API is a privacy-preserving tool that puts parents in the driver's seat allowing them to share their child's age range (e.g. 16-17) directly with apps. It also enables adults to easily share their age when prompted by the app developer. In turn, developers receive the signals they need to tailor their own in-app safety experiences and content for users in an age-appropriate way. We want to give developers the ability to choose the right protections for the nature of their app. A weather app, for example, shouldn't need the same safety settings as entertainment or media apps. Rather than enforcing one-size-fits-all rules, we give developers the flexibility to choose how they integrate safety signals. With this reliable signal, you retain complete agency to tailor your app's content, features, and settings to match your audience. Users have a choice to share their age range in a privacy-friendly way Simplifying controls for parents Parents shouldn't have to manage complex safety settings across dozens of different apps to keep their children safe. The Play Age Signals API simplifies this by putting age-sharing controls in one place, directly inside the Google Family Link app . Parents have a choice to share their child’s age range, and if they choose to share, all Play apps that use Play Age Signals API can receive age signals. This lets children jump straight into age-appropriate content without parents having to manually configure settings inside these apps. Age ranges are never shared by default, and parents can update or turn off these settings at any time. Centralized and easy way to manage age sharing settings for parents via Family Link App Building on our broader safety tools The Play Age Signals API builds upon a strong foundation of established safety features and strict policies we have long enforced on Google Play. Today, we already mandate that apps designed for families meet rigorous safety standards , and we continuously review and scan applications to ensure they are safe for children. For developers, we also offer built-in tools like Restrict Minor Access in the Play Console to help them manage who can discover their apps. For parents, Google Family Link remains a trusted, central dashboard where they can manage screen-time limits, PIN-based content filters , and app download approvals. Expanding the Play Age Signals API globally adds a powerful new tool to our existing safety suite, helping parents and developers work together to make Google Play an even safer, more trustworthy place for families.
Hacker News
“GPT-5.5's API pricing is reshaping how startups build AI products”
