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In this blog post, we show you how agentic workflows can accelerate the processing and interpretation of genomics pipelines at scale with a natural language interface. We demonstrate a comprehensive genomic variant interpreter agent that combines automated data processing with intelligent analysis to address the entire workflow from raw VCF file ingestion to conversational query interfaces.
Our team at Amazon builds Rufus, an AI-powered shopping assistant which delivers intelligent, conversational experiences to delight our customers. More than 250 million customers have used Rufus this year. Monthly users are up 140% YoY and interactions are up 210% YoY. Additionally, customers that use Rufus during a shopping journey are 60% more likely to […]
Turning scattered information across production-line machines and systems into meaningful insights that help teams drive efficiency and competitiveness without increasing overhead costs.
ScaleOps has expanded its cloud resource management platform with a new product aimed at enterprises operating self-hosted large language models (LLMs) and GPU-based AI applications. The AI Infra Product announced today, extends the company’s existing automation capabilities to address a growing need for efficient GPU utilization, predictable performance, and reduced operational burden in large-scale AI deployments. The company said the system is already running in enterprise production environments and delivering major efficiency gains for early adopters, reducing GPU costs by between 50% and 70%, according to the company. The company does not publicly list enterprise pricing for this solution and instead invites interested customers to receive a custom quote based on their operation size and needs here.In explaining how the system behaves under heavy load, Yodar Shafrir, CEO and Co-Founder of ScaleOps, said in an email to VentureBeat that the platform uses “proactive and reactive mechanisms to handle sudden spikes without performance impact,” noting that its workload rightsizing policies “automatically manage capacity to keep resources available.” He added that minimizing GPU cold-start delays was a priority, emphasizing that the system “ensures instant response when traffic surges,” particularly for AI workloads where model load times are substantial.Expanding Resource Automation to AI InfrastructureEnterprises deploying self-hosted AI models face performance variability, long load times, and persistent underutilization of GPU resources. ScaleOps positioned the new AI Infra Product as a direct response to these issues. The platform allocates and scales GPU resources in real time and adapts to changes in traffic demand without requiring alterations to existing model deployment pipelines or application code.According to ScaleOps, the system manages production environments for organizations including Wiz, DocuSign, Rubrik, Coupa, Alkami, Vantor, Grubhub, Island, Chewy, and several Fortune 500 companies. The AI Infra Product introduces workload-aware scaling policies that proactively and reactively adjust capacity to maintain performance during demand spikes. The company stated that these policies reduce the cold-start delays associated with loading large AI models, which improves responsiveness when traffic increases.Technical Integration and Platform CompatibilityThe product is designed for compatibility with common enterprise infrastructure patterns. It works across all Kubernetes distributions, major cloud platforms, on-premises data centers, and air-gapped environments. ScaleOps emphasized that deployment does not require code changes, infrastructure rewrites, or modifications to existing manifests. Shafrir said the platform “integrates seamlessly into existing model deployment pipelines without requiring any code or infrastructure changes,” and he added that teams can begin optimizing immediately with their existing GitOps, CI/CD, monitoring, and deployment tooling.Shafrir also addressed how the automation interacts with existing systems. He said the platform operates without disrupting workflows or creating conflicts with custom scheduling or scaling logic, explaining that the system “doesn’t change manifests or deployment logic” and instead enhances schedulers, autoscalers, and custom policies by incorporating real-time operational context while respecting existing configuration boundaries.Performance, Visibility, and User ControlThe platform provides full visibility into GPU utilization, model behavior, performance metrics, and scaling decisions at multiple levels, including pods, workloads, nodes, and clusters. While the system applies default workload scaling policies, ScaleOps noted that engineering teams retain the ability to tune these policies as needed.In practice, the company aims to reduce or eliminate the manual tuning that DevOps and AIOps teams typically perform to manage AI workloads. Installation is intended to require minimal effort, described by ScaleOps as a two-minute process using a single helm flag, after which optimization can be enabled through a single action.Cost Savings and Enterprise Case StudiesScaleOps reported that early deployments of the AI Infra Product have achieved GPU cost reductions of 50–70% in customer environments. The company cited two examples:A major creative software company operating thousands of GPUs averaged 20% utilization before adopting ScaleOps. The product increased utilization, consolidated underused capacity, and enabled GPU nodes to scale down. These changes reduced overall GPU spending by more than half. The company also reported a 35% reduction in latency for key workloads.A global gaming company used the platform to optimize a dynamic LLM workload running on hundreds of GPUs. According to ScaleOps, the product increased utilization by a factor of seven while maintaining service-level performance. The customer projected $1.4 million in annual savings from this workload alone.ScaleOps stated that the expected GPU savings typically outweigh the cost of adopting and operating the platform, and that customers with limited infrastructure budgets have reported fast returns on investment.Industry Context and Company PerspectiveThe rapid adoption of self-hosted AI models has created new operational challenges for enterprises, particularly around GPU efficiency and the complexity of managing large-scale workloads. Shafrir described the broader landscape as one in which “cloud-native AI infrastructure is reaching a breaking point.”“Cloud-native architectures unlocked great flexibility and control, but they also introduced a new level of complexity,” he said in the announcement. “Managing GPU resources at scale has become chaotic—waste, performance issues, and skyrocketing costs are now the norm. The ScaleOps platform was built to fix this. It delivers the complete solution for managing and optimizing GPU resources in cloud-native environments, enabling enterprises to run LLMs and AI applications efficiently, cost-effectively, and while improving performance.”Shafrir added that the product brings together the full set of cloud resource management functions needed to manage diverse workloads at scale. The company positioned the platform as a holistic system for continuous, automated optimization.A Unified Approach for the FutureWith the addition of the AI Infra Product, ScaleOps aims to establish a unified approach to GPU and AI workload management that integrates with existing enterprise infrastructure. The platform’s early performance metrics and reported cost savings suggest a focus on measurable efficiency improvements within the expanding ecosystem of self-hosted AI deployments.
Here are the top 5 SQL patterns tested in FAANG data science interviews.
Learn the pros and cons of Gemini 3 Pro, from testing with both coding and console usage
The post How to Use Gemini 3 Pro Efficiently appeared first on Towards Data Science.
The new AI-DADA tool lets you create a unique Swatch design using AI prompts. You can’t make a custom MoonSwatch yet—but it’s not entirely off the table.
In this post, we demonstrate how healthcare organizations can securely implement prompt caching technology to streamline medical record processing while maintaining compliance requirements.
An explanation of time-series visualization, including in-depth code examples in Matplotlib, Plotly, and Altair.
The post Data Visualization Explained (Part 5): Visualizing Time-Series Data in Python (Matplotlib, Plotly, and Altair) appeared first on Towards Data Science.
Data cleaning doesn’t always require Python or Excel. Learn how simple command-line tools can help you clean datasets faster and more efficiently.
Nano Banana Pro is our new image generation and editing model from Google DeepMind.
Our new Gemini app feature allows you to verify Google AI images and determine whether content was created or edited by AI.
Nano Banana Pro, or Gemini 3 Pro Image, is our most advanced image generation and editing model.
Westinghouse has partnered with Google Cloud to develop a custom AI-powered platform.
Tracing the history of LLM attention: standing on the shoulders of giants
The post How Relevance Models Foreshadowed Transformers for NLP appeared first on Towards Data Science.
Lightfield, a customer relationship management platform built entirely around artificial intelligence, officially launched to the public this week after a year of quiet development — a bold pivot by a startup that once had 20 million users and $43 million in the bank building something completely different.The San Francisco-based company is positioning itself as a fundamental reimagining of how businesses track and manage customer relationships, abandoning the manual data entry that has defined CRMs for decades in favor of a system that automatically captures, organizes, and acts on customer interactions. With more than 100 early customers already using the platform daily — over half spending more than an hour per day in the system — Lightfield is a direct challenge to the legacy business models of Salesforce and HubSpot, both of which generate billions in annual revenue."The CRM, categorically, is perhaps the most complex and lowest satisfaction piece of software on Earth," said Keith Peiris, Lightfield's co-founder and CEO, in an exclusive interview with VentureBeat. "CRM companies have tens of millions of users, and you'd be hard-pressed to find a single one who actually loves the product. That problem is our opportunity."The general availability announcement marks an unusual inflection point in enterprise software: a company betting that large language models have advanced enough to replace structured databases as the foundation of business-critical systems. It's a wager that has attracted backing from Coatue Management, which led the company's Series A when it was still building presentation software under the name Tome.How Tome's founders abandoned 20 million users to build a CRM from scratchThe story behind Lightfield's creation reflects both conviction and pragmatism. Tome had achieved significant viral success as an AI-powered presentation platform, gaining millions of users who appreciated its visual design and ease of use. But Peiris said the team concluded that building lasting differentiation in the general-purpose presentation market would prove difficult, even with a working product and real user traction."Tome went viral as an AI slides product, and it was visually delightful and easy to use—the first real generative AI-based presentation platform," Peiris explained. "But, the more people used it, the more I realized that to really help people communicate something—anything—we needed more context."That realization led to a fundamental rethinking. The team observed that the most effective communication requires deep understanding of relationships, company dynamics, and ongoing conversations — context that exists most richly in sales and customer-facing roles. Rather than building a horizontal tool for everyone, they decided to build vertically for go-to-market teams."We chose this lane, 'sales,' because so many people in these roles used Tome, and it seemed like the most logical place to go vertical," Peiris said. The team reduced headcount to a core group of engineers and spent a year building in stealth.Dan Rose, a senior advisor at Coatue who led the original investment in Tome, said the pivot validated his conviction in the founding team. "It takes real guts to pivot, and even more so when the original product is working," Rose said. "They shrunk the team down to a core group of engineers and got to work building Lightfield. This was not an easy product to build, it is extremely complex under the hood."Why Lightfield stores complete conversations instead of forcing data into fieldsWhat distinguishes Lightfield from traditional CRMs is architectural, not cosmetic. While Salesforce, HubSpot, and their competitors require users to define rigid data schemas upfront — dropdown menus, custom fields, checkbox categories — and then manually populate those fields after every interaction, Lightfield stores the complete, unstructured record of what customers actually say and do."Traditional CRMs force every interaction through predefined fields — they're compressing rich, nuanced customer conversations into structured database entries," Peiris said. "We store customer data in its raw, lossless form. That means we're capturing significantly more detail and context than a traditional CRM ever could."In practice, this means the system automatically records and transcribes sales calls, ingests emails, monitors product usage, and maintains what the company calls a "relationship timeline" — a complete chronological record of every touchpoint between a company and its customers. AI models then extract structured information from this raw data on demand, allowing companies to reorganize their data model without manual rework."If you realize you need different fields or want to reorganize your schema entirely, the system can remap and refill itself automatically," Peiris explained. "You're not locked into decisions you made on day one when you barely understood your sales process."The system also generates meeting preparation briefs, drafts follow-up emails based on conversation context, and can be queried in natural language — capabilities that represent a departure from the passive database model that has defined CRMs since the category's inception in the 1980s.Sales teams report reviving dead deals and cutting response times from months to daysCustomer testimonials suggest the automation delivers measurable impact, particularly for small teams without dedicated sales operations staff. Tyler Postle, co-founder of Voker.ai, said Lightfield's AI agent helped him revive more than 40 stalled opportunities in a single two-hour session — leads he had neglected for six months while using HubSpot."Within 2 days, 10 of those were revived and became active opps that moved to poc," Postle said. "The problem was, instead of being a tool of action and autotracking—HubSpot was a tool where I had to do the work to record customer convos. Using HubSpot I was a data hygienist. Using Lighfield, I’m a closer."Postle reported that his response times to prospects improved from weeks or months to one or two days, a change noticeable enough that customers commented on it. "Our prospects and customers have even noticed it," he said.Radu Spineanu, co-founder of Humble Ops, highlighted a specific feature that addresses what he views as the primary cause of lost deals: simple neglect. "The killer feature is asking 'who haven't I followed up with?'" Spineanu said. "Most deals die from neglect, not rejection. Lightfield catches these dropped threads and can draft and send the follow-up immediately. That's prevented at least three deals from going cold this quarter."Spineanu had evaluated competing modern CRMs including Attio and Clay before selecting Lightfield, dismissing Salesforce and HubSpot as "built for a different era." He said those platforms assume companies have dedicated operations teams to configure workflows and maintain data quality — resources most early-stage companies lack.Why Y Combinator startups are rejecting Salesforce and starting with AI-native toolsPeiris claims that the current batch of Y Combinator startups — widely viewed as a bellwether for early-stage company behavior — have largely rejected both Salesforce and HubSpot. "If you were to poll a random sampling of current YC startups and ask whether they're using Salesforce or HubSpot, the overwhelming answer would be 'no,'" he said. "Salesforce is too expensive, too complex to set up, and frankly doesn't do enough to justify the investment for an early-stage company."According to Peiris, most startups begin with spreadsheets and eventually graduate to a first CRM — a transition point where Lightfield aims to intercede. "Increasingly, they're choosing Lightfield instead and skipping that intermediate step entirely," he said.This represents a familiar pattern in enterprise software disruption: a new generation of companies forming habits around different tools, creating an opening for challengers to establish themselves before businesses grow large enough to face pressure toward industry-standard platforms. The company's strategy appears to deliberately target this window, aiming to grow alongside early customers and become embedded in their processes as they scale.Can Salesforce and HubSpot retrofit their legacy systems for AI, or is the architecture too old?Both Salesforce and HubSpot have announced AI features in recent quarters, adding capabilities like conversation intelligence and automated data entry to their existing platforms. The question facing Lightfield is whether established vendors can incorporate similar capabilities—leveraging their existing customer bases and integrations — or whether fundamental architectural differences create a genuine moat.Peiris argues the latter. "The fundamental difference is in how we store data," he said. "Because we have access to that complete context, the analysis we provide and the work we generate tends to be substantially higher quality than tools built on top of traditional database structures."Existing conversation intelligence tools like Gong and Revenue.io, which analyze sales calls and provide coaching insights, already serve similar functions but require Salesforce instances to operate. Peiris said Lightfield's advantage comes from unifying the entire data model rather than layering analysis on top of fragmented systems."We have a more complete picture of each customer because we integrate company knowledge, communication sync, product analytics, and full CRM detail all in one place," he said. "That unified context means the work being generated in Lightfield—whether it's analysis, follow-ups, or insights—tends to be significantly higher quality."The privacy and accuracy concerns that come with AI-automated customer interactionsThe architecture creates obvious risks. Storing complete conversation histories raises privacy concerns, and relying on large language models to extract and interpret information introduces the possibility of errors—what AI researchers call hallucinations.Peiris acknowledged both issues directly. On privacy, the company maintains that call recording follows standard practices, with visible notifications that recording is in progress, and that storing sales correspondence mirrors what CRM vendors have done for decades. The company has achieved SOC 2 Type I certification and is pursuing both SOC 2 Type II and HIPAA compliance. "We don't train models on customer data, period," Peiris said.On accuracy, he was similarly forthright. "Of course it happens," Peiris said when asked about misinterpretations. "It's impossible to completely eliminate hallucinations when working with large language models."The company's approach is to require human approval before sending customer communications or updating critical fields — positioning the system as augmentation rather than full automation. "We're building a tool that amplifies human judgment, not one that pretends to replace it entirely," Peiris said.This is a more cautious stance than some AI-native software companies have taken, reflecting both technical realism about current model capabilities and potential liability concerns around customer-facing mistakes.How Lightfield plans to consolidate ten different sales tools into one platformLightfield's pricing strategy reflects a broader thesis about enterprise software economics. Rather than charging per-seat fees for a point solution, the company is positioning itself as a consolidated platform that can replace multiple specialized tools — sales engagement platforms, conversation intelligence systems, meeting assistants, and the CRM itself."The real problem is that running a modern go-to-market function requires cobbling together 10 different independent point solutions," Peiris said. "When you pay for 10 separate seat licenses, you're essentially paying 10 different companies to solve the same foundational problems over and over again."The company operates primarily through self-service signup rather than enterprise sales teams, which Peiris argues allows for lower pricing while maintaining margins. This is a common playbook among modern SaaS companies but represents a fundamental difference from Salesforce's model, which relies heavily on direct sales and customer success teams.Whether this approach can support a sustainable business at scale remains unproven. The company's current customer base skews heavily toward early-stage startups—more than 100 Y Combinator companies, according to the company — a segment with limited budgets and high failure rates.But Lightfield is betting it can become the system of record for a cohort of fast-growing companies, eventually creating an installed base comparable to how Salesforce established itself decades ago. The company's trajectory will likely depend on whether AI capabilities alone provide sufficient differentiation—or whether incumbents can adapt quickly enough to defend their positions.The real test: whether sales teams will trust AI enough to let it run their businessThe company has outlined several areas for expansion, including an open platform for workflows and webhooks that would allow third-party integrations. Early customers have specifically requested connections with tools like Apollo for prospecting and Slack for team communication — gaps that Postle, the Voker.ai founder, acknowledged but dismissed as temporary."The fact that HS and Salesforce have these integrations already isn't a moat," Postle said. "HS and Salesforce are going to lose to lightfield because they aren't AI native, no matter how much they try to pretend to be."Rose highlighted an unusual use case that emerged during Lightfield's own development: the company's product team used the CRM itself to analyze customer conversations and identify feature requests. "In this sense, Lightfield more than just a sales database, it's a customer intelligence layer," Rose said.This suggests potential applications beyond traditional sales workflows, positioning the system as infrastructure for any function that requires understanding customer needs—product development, customer success, even marketing strategy.For now, the company is focused on proving the core value proposition with early-stage companies. But the broader question Lightfield raises extends beyond CRM software specifically: whether AI capabilities have advanced sufficiently to replace structured databases as the foundation of enterprise systems, or whether the current generation of large language models remains too unreliable for business-critical functions.The answer will likely emerge not from technical benchmarks but from customer behavior—whether sales teams actually trust AI-generated insights enough to base decisions on them, and whether the efficiency gains justify the inherent unpredictability of working with systems that approximate rather than calculate.Lightfield is betting that the trade-off has already shifted in favor of approximation, at least for the millions of salespeople who currently view their CRM as an obstacle rather than an asset. Whether that bet proves correct will help define the next generation of enterprise software.
Check out three use cases for NotebookLM that go beyond the expected functionality of generating FAQs, study guides, or basic summaries.
A fresh way to think about computational notebooks
The post Why I’m Making the Switch to marimo Notebooks appeared first on Towards Data Science.
In 1948, Claude Shannon published a paper that changed how we think about information forever.
Generative AI is making it even easier for attackers to exploit old and often forgotten network equipment. Replacing it takes investment, but Cisco is making the case that it’s worth it.
The Allen Institute for AI (Ai2) hopes to take advantage of an increased demand for customized models and enterprises seeking more transparency from AI models with its latest release.Ai2 made the latest addition to its Olmo family of large language models available to organizations, continuing to focus on openness and customization. Olmo 3 has a longer context window, more reasoning traces and is better at coding than its previous iteration. This latest version, like the other Olmo releases, is open-sourced under the Apache 2.0 license. Enterprises will have complete transparency into and control over the training data and checkpointing. Ai2 will release three versions of Olmo 3:Olmo 3- Think in both 7B and 32B are considered the flagship reasoning models for advanced researchOlmo 3- Base also in both parameters, which is ideal for programming, comprehension, math and long-context reasoning. Ai2 said this version is “ideal for continued pre-training or fine-tuningOlmo 3-Instruct in 7B that is optimized for instruction following, multi-turn dialogue and tool useThe company said Olmo 3- Think is the “first-ever fully open 32B thinking model that generates explicit reasoning-chain-style content.” Olmo-3 Think also has a long context window of 65,000 tokens, perfect for longer-running agentic projects or reasoning over longer documents. Noah Smith, Ai2’s senior director of NLP research, told VentureBeat in an interview that many of its customers, from regulated enterprises to research institutions, want to use models that give them assurance about what went into the training. “The releases from our friends in the tech world are very cool and super exciting, but there are a lot of people for whom data privacy control over what goes into the model, how the models train and other constraints on how the model can be used as front of mind,” said Smith. Developers can access the models on Hugging Face and the Ai2 Playground. Transparency and customizationSmith said models like Olmo 3, which the company believes any organization using its models has to have control over and mold in the way that best works for them.“We don't believe in one-size-fits-all solutions,” Smith said. It's a known thing in the world of machine learning that if you try and build a model that solves all the problems, it ends up not being really the best model for any one problem. There aren't formal proofs of that, but it's a thing that old timers like me have kind of observed.”He added that models with the ability to specialize “are maybe not as flash as getting high scores on math exams” but offer more flexibility for enterprises. Olmo 3 allows enterprises to essentially retrain the model by adding to the data mix it learns from. The idea is that businesses can bring in their proprietary sources to guide the model in answering specific company queries. To help enterprises during this process, Ai2 added checkpoints from every major training phase. Demand for model customization has grown as enterprises that cannot build their own LLMs want to create company-specific or industry-focused models. Startups like Arcee have begun offering enterprise-focused, customizable small models. Models like Olmo 3, Smith said, also give enterprises more confidence in the technology. Since Olmo 3 provides the training data, Smith said enterprises can trust that the model did not ingest anything it shouldn’t have.Ai2 has always claimed to be committed to greater transparency, even launching a tool called OlmoTrace in April that can track a model’s output directly back to the original training data. The company releases open-sourced models and posts its code to repositories like GitHub for anyone to use. Competitors like Google and OpenAI have faced criticism from developers over moves that hid raw reasoning tokens and chose to summarize reasoning, claiming that they now resort to “debugging blind” without transparency. Ai2 pretrained Olmo 3 on the six-trillion-token OpenAI dataset, Dolma 3. The dataset encompasses web data, scientific literature and code. Smith said they optimized Olmo 3 for code, compared to the focus on math for Olmo 2. How it stacks upAi2 claims that the Olmo 3 family of models represents a significant leap for truly open-source models, at least for open-source LLMs developed outside China. The base Olmo 3 model trained “with roughly 2.5x greater compute efficiency as measured by GPU-hours per token,” meaning it consumed less energy during pre-training and costs less.The company said the Olmo 3 models outperformed other open models, such as Marin from Stanford, LLM360’s K2, and Apertus, though Ai2 did not provide figures for the benchmark testing. “Of note, Olmo 3-Think (32B) is the strongest fully open reasoning model, narrowing the gap to the best open-weight models of similar scale, such as the Qwen 3-32B-Thinking series of models across our suite of reasoning benchmarks, all while being trained on 6x fewer tokens,” Ai2 said in a press release. The company added that Olmo 3-Instruct performed better than Qwen 2.5, Gemma 3 and Llama 3.1.
AI filters arrive, LeCun on LLMs, Grok 4.1 Fast, Create more how-tos, and more...
The draft order, obtained by WIRED, instructs the US Justice Department to sue states that pass laws regulating AI.
Record sales, a strong financial forecast, and CEO Jensen Huang’s impassioned arguments on his company’s earnings call weren’t enough to push Nvidia shares back to their October high.
In this post, we explore deployment patterns and best practices for Claude Code with Amazon Bedrock, covering authentication methods, infrastructure decisions, and monitoring strategies to help enterprises deploy securely at scale. We recommend using Direct IdP integration for authentication, a dedicated AWS account for infrastructure, and OpenTelemetry with CloudWatch dashboards for comprehensive monitoring to ensure secure access, capacity management, and visibility into costs and developer productivity .
DeepMind’s chief says he envisions Gemini as an operating system for physical robots. The company has hired Aaron Saunders to help make that a reality.
Brisk It’s Zelos-450 boasts gimmicky generative AI. However, it’s a stellar piece of hardware, especially at this price.