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AWS cuts LLM latency 77%, NVIDIA accelerates biology and Anthropic flags 200M exchanges.Your AI Models and Databricks Are Missing Your Best DataThe regulated, high-value data your AI initiatives need most usually can't leave on-prem systems. AIStor gives Databricks live access to it in place, so your models train and query on real, current data—not a stale copy. Get the guide for AI and data leaders.Get the GuideSponsoredAI Distilled #152: AI Agents Are Scaling Faster Than the Systems We TrustWelcome to AI Distilled 152AI Can Do More. Can We Trust What It Does?Before we dive in, a quick opportunity for founders in our community. Fundraising often means weeks of cold outreach without reaching the right investors. Through our partnership with VC Pitch Conference, selected founders can pitch to 20 matched investors in one day on September 17.🎟️ Want to see if it is right for you? Register for a free ticket to explore the opportunity. Packt readers can also get 10% off with PACKT10.Now, onto the bigger question shaping this issue.AI is moving from answering questions to taking action. Agents can run workflows, coordinate other agents, write code, and operate with increasing autonomy. But greater capability creates a harder problem: trust.We lead with Robert Hawker, Microsoft Fabric and Power BI consultant, trainer, and author, who shows why trust starts with the foundation. Fragmented data, conflicting metrics, and restrictive governance can undermine even the best analytics platform. His solution: start with the business outcome, fix the data underneath it, and build governance that helps people move faster.That same tension runs through this week’s developments. OpenAI and Google are expanding agent infrastructure, AWS is cutting LLM latency, NVIDIA is accelerating AI for biology, and Samsung is bringing Mistral into its chip fabs. At the same time, Anthropic is uncovering model extraction and weapons-related misuse.AI capability is accelerating. Building the trust, governance, and infrastructure around it is now the harder problem. This week’s highlightsOpenAI launches the Agents API → Build and run long-running cloud agents on the Codex harness.Anthropic reports nearly 200M distillation exchanges → Including 151M attributed to an Alibaba campaign targeting Claude capabilities.Claude linked to weapons-development activity → A Yemen-based cell reportedly used it for guidance software, simulation, and troubleshooting.Google launches its Developer Plugin → Bundling Agent Skills, MCP tools, documentation, and guardrails for coding agents.Google expands Antigravity with its SDK → A toolkit for building and monitoring custom multi-agent systems.AWS cuts LLM latency by up to 77% → SageMaker’s prefix-aware routing keeps shared prompt context cached.Samsung brings Mistral into its chip fabs → On-premises AI for defect detection, manufacturing precision, and yield stabilization.NVIDIA launches BioNeMo Inference Runtime → GPU acceleration built specifically for biomolecular structure models.AI creates “agentic flooding” → Public services face surging applications as AI makes complex claims and complaints easier to file.Meta’s Muse crosses 83K U.S. iOS downloads → Meta’s latest bet on consumer AI agents enters a rapidly growing race.Let's get into it.Cheers,Merlyn Shelley,Growth Lead, Packt.Live LLM Engineering Masterclass📅 Saturday, September 12 | 09:30 AM–1:30 PM EDTBuild reliable LLM systems withproduction evals, RAG, resilient agents, regression testing, observability and cost control.Join Bruno Live This Weekend →Your Power BI Problem May Not Be Power BIA company can invest heavily in Microsoft Fabric and Power BI, migrate data, build semantic models, publish dashboards, and train users, yet still watch employees return to their Excel trackers.It is tempting to call this resistance to change. Often, it is something more fundamental: the analytics environment has not earned users’ trust.Reports may be slow, incomplete, duplicated, or disconnected from the decisions people actually need to make. Governance may arrive too late, after conflicting metrics have already spread, or too forcefully, turning every improvement into a lengthy approval process. In both cases, the technology is visible, but the business value is not.In a conversation hosted byApeksha Shetty, Portfolio Manager at Packt, Microsoft Fabric and Power BI consultant, trainer, author, and experienced data and analytics leaderRobert Hawkershared a more practical way to approach the problem. His central argument is that successful analytics transformation does not begin with a platform feature. It begins with the outcome an organization needs, the data problems blocking it, and a governance model designed to produce lasting improvement.Start governance where the business already feels the painThere is no universal first step for enterprise data governance. The right starting point depends on the organization’s industry, strategy, and immediate pressures.In financial services, a regulatory return may set the priority. During a major transformation, attention may need to shift to new processes, new systems, and the quality of migrated data. Sometimes the trigger is an operational emergency, such as suppliers threatening action because data failures have prevented timely payment.When no urgent driver is obvious, Hawker recommends starting with the organization’s strategy. Ask leaders what is preventing them from delivering their objectives, without limiting the discussion to issues already labelled as data problems. A deeper analysis often reveals that apparently operational or commercial blockers have data at their root.That connection matters. Stakeholders are more likely to support governance when they can see how it improves the probability of achieving an outcome they already care about.Prioritization can then follow a familiar benefit-versus-effort assessment. Quantitative and qualitative value should be weighed against the cost and complexity of resolving each issue, with high-benefit, lower-effort opportunities rising to the top. But the calculation is not purely mechanical. An early initiative backed by a supportive stakeholder can create proof, credibility, and an internal advocate for the wider programme.The important distinction is between a quick fix and a quick win. Hawker recalls a proposal to pay a supplier for a one-off cleanse of supplier data using generic industry rules. Instead, he redirected the investment into the foundations of a repeatable data quality process and a tool containing rules specific to the organization. The initiative delivered near-term improvement while building a capability that could sustain it.The lesson is simple: even early wins should point towards the long-term operating model.When dashboards are ignored, diagnose the experience before blaming the usersLow Power BI adoption can look like a technology problem, a data problem, a governance failure, or a cultural issue. Frequently, several are present at once.Hawker says the most common cause is a gap between report developers and business users. Developers understand the data and may have built the semantic model, but they are not close enough to the operational decision to create a useful product. Business users understand what the analysis needs to accomplish, yet may lack access to governed data or the Power BI skills required to build it themselves.The diagnosis should therefore begin with wide-ranging conversations, especially with the people whose roles should benefit most from the reporting.What users say provides clues about where to look:“Reports are slow or out of date.”Poor semantic modelling, many-to-many relationships, failed refreshes, insufficient operational oversight, or a throttled Fabric capacity may be involved. What appears to be a performance complaint can expose gaps in skills and governance.How Modern AI Systems Really Find Answers: Build GraphRAG Applications📅 Saturday, September 19 | 09:30 AM–1:30 PM EDTMove beyond vector search and build explainable AI withNeo4j, knowledge graphs, Cypher and LLM agents.Join Alessandro Live →Partner Spotlight | Building a startup? Get in front of investors.Start FREE. Pitch when you’re ready.Fundraising shouldn’t mean weeks of cold outreach. On September 17, join the VC Pitch Conference and connect with a network of 290+ VCs and angel investors.🎟️ Register for a FREE ticket →Ready to pitch? Selected founders can get 20 guaranteed 1:1 investor meetings in one day, matched by sector, stage, and geography.🚀 Pitch to 20 investors and save 10% with PACKT10 →“The figures are wrong or important data is missing.”The source data may be unreliable, but governance is also implicated. Releasing a report should include an explicit assessment of whether its data quality is known and adequate for the intended decision.“There are five reports for the same topic, and I do not know which one to trust.”This is a sign of weak analytical governance. Decentralized development without shared definitions and endorsement creates competing versions of the truth.“My Excel tracker is more reliable.”This may indicate a cultural barrier, but dismissing the user would be a mistake. If an established process provides dependable information, the new solution must offer a compelling improvement, such as a better outcome, stronger security, or less manual work.Adoption is not achieved by asking people to stop using familiar tools. It is achieved by giving them something demonstrably more useful and trustworthy.What happens when poor data becomes an operational emergencyHawker has seen the consequences of low-quality data move far beyond reporting.At one organization formed through acquisition, hurried migrations had left critical records incomplete. More than 90% of the field used to store suppliers’ remittance-advice email addresses was blank, even though a different field for purchase-order emails had been populated.From the operators’ perspective, remittance advices were being created. In practice, they were not reaching suppliers. Suppliers could not easily match incoming payments to outstanding invoices, so queries flooded the payments team. Staff became consumed by resolving those queries, a payment backlog developed, and some suppliers considered stopping services because they had not been paid. The risk extended as far as the power supply to a large UK manufacturing plant.Read the full article on Packt’s Medium and explore the key ideas through the infographic.Build Production-Ready AI Agents : From Workflow to Deployment📅 Saturday, September 26–Sunday, September 27 | 10:00 AM–2:00 PM EDTBuild production-ready AI agents withn8n, from real-world workflows and tool integrations to testing, security, governance, and deployment.Join Ashley Live →AI Pulse: This Week✅ Introducing the Agents API: OpenAI is turning its Codex infrastructure into something developers can build on directly. The new Agents API, now in public beta, lets teams launch production-ready cloud agents with a single API call, while OpenAI manages the underlying harness. Agents can run for days, work with files and code, use tools, coordinate parallel subagents, and operate in hosted or self-managed environments.✅Houthis used Claude AI for weapons development and guided-rocket test? What Anthropic threat report claimed: Anthropic says a Yemen-based weapons engineering cell used Claude for something far beyond ordinary coding: developing software tied to guided rockets and missile systems. Claude Code reportedly helped with guidance software, simulations and troubleshooting, with multiple AI instances working in parallel. After one guided-rocket field test failed, the group even returned to Claude to investigate what went wrong.✅ Samsung taps Mistral AI models for semiconductor manufacturing: Samsung is bringing AI directly onto the semiconductor factory floor. Through a new partnership with Mistral AI, Samsung will deploy private, on-premises models, including Mistral Large, across its chip operations. The models will help detect defects, tune fab equipment and analyse sensitive engineering data locally, with the goal of shortening development cycles, improving manufacturing precision and stabilising yields across memory, logic and foundry production.✅ AI agents are flooding public services with new requests: AI is not just changing how people work. It is changing how they interact with government. Researchers call it “agentic flooding”: AI is making complex forms, complaints and petitions dramatically easier to file. UK housing complaints have more than doubled since 2022, while US consumer-finance complaints grew 5x, raising a new challenge: public services must handle surging demand without shutting out legitimate claims✅ NVIDIA BioNeMo Inference Runtime: NVIDIA is tuning GPU inference specifically for biology. BioNeMo Inference Runtime accelerates biomolecular structure models such as AlphaFold2, OpenFold3 and Boltz-2 with biology-aware PyTorch modules, fused GPU kernels and graph optimizations. It boosts throughput while reducing memory pressure, and can scale independent predictions across multiple GPUs with Ray, targeting protein design, synthetic data generation, structural scoring and large-scale structure prediction.✅ Anthropic details distillation campaigns from Alibaba, Moonshot AI, and DeepSeek: Anthropic says Chinese AI labs are escalating efforts to extract Claude’s capabilities for their own models. Its latest threat report links nearly 200 million exchanges to five distillation campaigns. The largest, attributed to Alibaba, generated 151 million exchanges in three months, allegedly targeting reasoning traces for Qwen, while a separate Moonshot AI campaign reportedly routed requests linked to Chinese military use cases. ✅ Introducing the Google Cloud Developer Plugin for AI Coding Agents: Google is making AI coding agents easier to equip for real cloud work. Its new Google Cloud Developer Plugin bundles related Agent Skills and MCP tools into one installable package, so agents can handle tasks like authentication, project setup and gcloud operations with official documentation grounding and built-in guardrails. It is also based on an open, vendor-neutral plugin standard for portability across coding environments.✅ Meta's AI agent Muse is now the No. 2 app in the US: Meta’s new Muse AI agent is climbing the App Store, but its debut shows how difficult the consumer-agent race could be. Muse has passed 83,000 U.S. iOS downloads, reaching No. 2, yet trails Meta AI’s 108,000 launch-day installs and ChatGPT’s early pace. The bigger contest is emerging between Muse and Instinct, as both compete to make autonomous agents part of everyday life.✅ Power agent hubs or custom harnesses with the Antigravity SDK: Google is giving developers more control over how fleets of AI agents run. The Antigravity SDK brings the runtime behind Antigravity 2.0 into custom agent hubs, combining multi-agent execution with real-time telemetry, persistent session state, skills and safety policies. Instead of piecing together logs and tool traces, teams can monitor agent activity, performance and failures from a centralized control plane.✅ Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference: AWS is tackling a hidden source of LLM latency: repeatedly processing the same prompt context. SageMaker Inference’s new prefix-aware routing sends requests sharing a prompt prefix to the same instance, allowing cached computation to be reused. On Llama 3.1 70B, AWS reports up to 77% lower median time-to-first-token, 16% higher throughput, and cache hit rates above 80%, particularly benefiting RAG and long-context workloads.Designing Data Infrastructure for AI Agents & LLMs📅 Saturday, September 26 | 9:30 AM–1:30 PM EDTDesign production-ready data infrastructure for AI agents & LLMs, from safe write paths and state management to provenance, tracing, recovery, and production architecture.Join Sandipan Live →📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us.If you have any comments or feedback, just reply back to this email.Thanks for reading and have a great day!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;display:none;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}.social_block .social-table{display:inline-block!important}}

Open models, autonomous agents, and billions flowing into the infrastructure behind them.Query On-Prem Iceberg and Delta Tables Live From DatabricksImport a share into Unity Catalog and query on-prem data in place—no copying, no downsampling, results back in seconds. Watch the 7-minute walkthrough of MinIO AIStor Table Sharing connected live to Databricks.Watch the DemoSponsoredAI Distilled #151: AI is crossing an important thresholdWelcome to AI Distilled 151AI Is Becoming Autonomous. Your Product Metrics Haven’t Caught Up.AI is crossing an important threshold.OpenAI’s GPT-6 Astra is pushing further into autonomous computer use and professional workflows. AWS is putting agents inside the software development lifecycle and everyday email workflows. Google Cloud is rebuilding infrastructure around agent swarms, real-time AI, and orchestration. Meanwhile, billions are pouring into the foundations underneath it all, from NVIDIA’s $12.93 billion Hugging Face acquisition to Crusoe’s reported $30 billion valuation.The common thread is clear: AI is moving from generating outputs to taking actions.But as these systems become more autonomous, a harder question emerges: how do we know they are actually making products and experiences better?That is where our expert-led conversation begins.Lorenzo Toni, Lead Data Scientist — AI Product Analytics at Amazon, explains why the metrics AI teams traditionally trust can tell the wrong story. An AI feature can increase engagement, session duration, and interactions while quietly hurting conversion and user trust. Lorenzo explores how causal inference, better experimentation, and measuring downstream behaviour can reveal what conventional A/B tests miss.Because building more capable AI is only half the challenge. Knowing whether it creates real value is becoming just as important.This week’s highlightsNVIDIA to acquire Hugging Face for $12.93B — Can NVIDIA scale Hugging Face without compromising the openness that made it central to the AI ecosystem?Crusoe reportedly raises $3B at a $30B valuation — The former crypto miner’s valuation has tripled in 10 months as demand for AI compute explodes.OpenAI unveils GPT-6 Astra — OpenAI’s latest frontier model pushes AI further from answering prompts toward autonomously executing professional work.GPT-6 Astra vs Gemini vs Claude — As models become agents, which one is best positioned to reliably take action?AWS puts AI deeper into the development lifecycle with AgentCore — Agents generate engineering artifacts and automate security analysis while humans retain oversight.Google Cloud rethinks infrastructure for the agentic era — Agent swarms, stateless MCP, sandboxed workloads, and session-aware networking are changing the infrastructure underneath AI.Meta offers steep AI discounts in exchange for training data — A roughly 95% discount puts an unusually explicit price on the value of prompts and model outputs.AWS connects Amazon Quick with Outlook — AI moves beyond summarizing your inbox to drafting replies, scheduling meetings, and triggering workflows.Let's get into it.Cheers,Merlyn Shelley,Growth Lead, Packt.Live LLM Engineering Masterclass📅 Saturday, September 12 | 09:30 AM–1:30 PM EDTBuild reliable LLM systems withproduction evals, RAG, resilient agents, regression testing, observability and cost control.Save 45% with PRODLLM45Your AI Experiment Says the Feature Is Working. Your Users May Be Telling You OtherwiseAn AI feature can pass an experiment and still make the product worse.Engagement can rise. Session duration can increase. Users can interact with the feature more frequently. An A/B test can even produce statistically convincing results.And yet, a few weeks later, conversion starts falling.Users stop returning.Trust begins to erode.That is the measurement problem AI product teams increasingly need to confront.Traditional digital products generally give users a relatively deterministic experience. Change a button, ranking algorithm, checkout flow, or interface and teams can establish a reasonably clean treatment and control.Generative AI changes that assumption.The same feature can produce different answers for different users, and even different answers for the same user. A seemingly insignificant prompt adjustment can alter the length, tone, relevance, accuracy, and usefulness of an interaction.So what exactly should teams measure?And more importantly,how do you know whether an AI feature actually caused the outcome you are seeing?That question was at the centre of a conversation betweenNilesh Kowadkar, Relationship Lead at Packt, andLorenzo Toni, Lead Data Scientist — AI Product Analytics at Amazon, focused on experimentation and causal inference for AI products. Drawing on a generalized e-commerce case study, Lorenzo unpacked why apparently positive metrics can be misleading and why AI teams need to look beyond conventional A/B testing.Partner Spotlight | Building a startup? Pitch live to 20 investors in one day.Fundraising shouldn’t mean spending weeks cold-emailing VCs and waiting for replies.We’re partnering withVC Pitch Conferenceto give founders a more direct route to investors. OnSeptember 17, selected startups can join20 guaranteed 1:1 investor calls in a single day, with meetings matched bysector, stage, and geography.With290+ VCs and angel investorslooking across AI, fintech, B2B, and other sectors, it’s an opportunity to put your startup directly in front of investors actively looking for their next deal.Packt readers get an additional 10% off the early-bird ticket with codePACKT10.Pitch Your Startup to Investors →When More Engagement Is Actually a Warning SignConsider an e-commerce platform introducing an AI chatbot designed to help customers discover products.Instead of navigating through search results, users could ask questions, compare prices, describe what they wanted, and receive recommendations conversationally.A change was made to the prompt powering the chatbot.Initially, the numbers looked promising.Engagement increased. Users performed more actions. They also spent longer interacting with the chatbot.Under a conventional product analytics framework, those signals could easily be interpreted as evidence that the new experience was working.Then, roughly two weeks later, another pattern emerged.Chatbot interactions began declining.More importantly, overall website conversion started falling.The metric that initially appeared particularly encouraging,time spent with the chatbot, turned out to be negatively correlated with conversion.Users were not necessarily spending longer because the experience was valuable.Some were spending longer because the chatbot was taking too long to help them reach a decision.A ten-minute conversation might look like excellent engagement on a dashboard. To the customer, it might mean:I could have found this myself in thirty seconds.That distinction matters.Because when AI introduces friction instead of removing it, the damage may extend beyond a single failed interaction. Users learn from that experience. The next time they need something, they may bypass the chatbot entirely.The apparent engagement win can therefore become atrust problem.Dive into the full article on Packt’s Medium, with a visual infographic to bring the key ideas together.How Modern AI Systems Really Find Answers: Build GraphRAG Applications📅 Saturday, September 19 | 09:30 AM–1:30 PM EDTMove beyond vector search and build explainable AI withNeo4j, knowledge graphs, Cypher and LLM agents.Save 35% with MODERNAI35AI Pulse: This Week✅ NVIDIA to acquire Hugging Face for $12.93B: NVIDIA is buying Hugging Face for $12.93 billion, but the bigger story may be what it promises not to change. Hugging Face will remain hardware-neutral, multi-cloud, and open to competing models and accelerators, while NVIDIA pours resources into its infrastructure. With 18 million developers already on the platform, the deal raises a much bigger question: can this alliance keep open-source AI competitive as closed labs race ahead?✅ Crusoe reportedly raises $3B at a $30B valuation: Crusoe has reportedly raised $3 billion at a $30 billion valuation, tripling its valuation in just 10 months. But the bigger signal is where that money is flowing. Once a crypto miner, Crusoe is now building AI infrastructure for Meta, Microsoft, and OpenAI, and has landed a $13 billion deal with Jane Street. With IPO talks reportedly underway, Crusoe’s rapid rise shows just how valuable the infrastructure powering the AI boom has become.✅ OpenAI’s GPT-6 Astra: A new generation of intelligence. OpenAI has unveiled GPT-6 Astra, and the headline numbers are hard to ignore: 99.9% on ARC-AGI-3, 98% on FrontierMath Tier 4, and 100% on ExploitBench. But Astra’s bigger leap may be beyond benchmarks. OpenAI says it can navigate computers, browsers, code, research, and professional workflows with greater autonomy while staying within its authorized scope. Are we moving from AI that assists with work to AI we can actually delegate work to?✅ GPT-6 Astra vs Gemini vs Claude: Which AI agent is best? GPT-6 Astra has arrived, but does it actually put OpenAI ahead of Gemini and Claude? The new model pushes deeper into coding, reasoning, computer use, and autonomous workflows, intensifying a race that is increasingly about more than raw intelligence. As AI shifts from answering prompts to executing real work, the bigger question is which model can become the most capable and reliable agent.✅ AI-driven development lifecycle using Amazon Bedrock AgentCore: AI can write code, but can it take on the development lifecycle without losing human control? AWS is putting that idea into practice with Amazon Bedrock AgentCore, showing how agents can automatically turn SQL schemas into ER diagrams and scan code for vulnerabilities, CVEs, and policy violations. The bigger shift is toward AI handling routine engineering execution while humans stay in control of critical decisions.✅ What’s new in AI infrastructure this month: AI agents are getting more capable, but is today’s infrastructure actually ready to run them at scale? Google Cloud’s August updates tackle the bottlenecks emerging underneath agentic AI, from Filestore supporting agent swarms and sandboxed Ray clusters to always-on Cloud Run instances, stateless MCP, and session-aware load balancing. The bigger shift: AI infrastructure is being redesigned around agents, not traditional applications.✅ Meta is paying to peek at how you use their latest AI model: What if the cheapest AI model came with a hidden currency: your data? Meta is offering roughly 95% discounts on its new Muse Spark model to users willing to share prompts and outputs for future model training. The move turns training data into an explicit economic trade-off and raises a bigger question for enterprises: how much is your data actually worth when AI providers are willing to pay for it?✅ Integrating Outlook with Amazon Quick for AI-powered email automation: How much of your workday disappears into reading threads, chasing action items, and rebuilding context from email? AWS is bringing Amazon Quick into Outlook to automate that overhead, from summarizing conversations and drafting contextual replies to scheduling meetings and triggering multi-step workflows. The bigger idea is simple: what if your inbox stopped being a task list and started completing the tasks for you?Build Production-Ready AI Agents : From Workflow to Deployment📅 Saturday, September 26–Sunday, September 27 | 10:00 AM–2:00 PM EDTBuild production-ready AI agents withn8n, from real-world workflows and tool integrations to testing, security, governance, and deployment.Save 35% with PACKTAI35📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us.If you have any comments or feedback, just reply back to this email.Thanks for reading and have a great day!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;display:none;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}.social_block .social-table{display:inline-block!important}}