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Fenix Flexin is best known as a member of Shoreline Mafia, a rap duo from Los Angeles. But he's recently found solo success with the track "Rubberz," which has climbed to number 58 on the Billboard Hot 100. Almost immediately, though, questions were raised about the song's origins, with many speculating that it was largely, […]
Reddit may still be considering ending its licensing deal with Google.
Both major AI labs’ models broke containment, escaped onto the internet, and hacked other companies. If a human had done that, the law would likely be against them. But a bot?
2 points 0 comments on Hacker News · thomsonreuters.com
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259 points 67 comments on Hacker News · microsoft.github.io
The escalating dangers of a nuclear arms race and simultaneous rush to train AI military commanders present a doomsday threat to the globe and its eight billion citizens.
Snapchat, YouTube, LinkedIn, and Substack are trying to combat the proliferation of fake AI content.
If you ask an AI coding agent to write a standalone Python script to parse a single JSON file, it will likely give you a perfect answer in seconds. But the same agent often breaks if you ask it to build a systematic data processing pipeline, like ingesting thousands of messy documents, chunking text, scoring quality, and filtering noise for a Retrieval-Augmented Generation (RAG) system that fits your specific enterprise stack. While large language models (LLMs) excel at one-off code generation, their outputs for complex data-processing tasks are typically free-form, disposable scripts. These scripts are detached from the governable workflow abstractions that MLOps teams rely on for production, making them difficult to audit or edit visually. To address this, researchers at Peking University, Zhongguancun Academy, and Shanghai’s Institute for Advanced Algorithms Research introduced DataFlow-Harness , an open-source framework that guides an LLM agent to build structured, visual data-processing workflows step-by-step, rather than writing raw code from scratch. The framework makes AI-generated pipelines easier to manage and integrate into existing architectures because the generated artifacts are persistent and easily editable. The researchers report that the platform achieves a 93.3% observed end-to-end pass rate on a 12-task data-engineering benchmark. Compared to standard Claude Code, it reduces API costs by up to 72.5% and response latency by 49.9%, while achieving nearly the same success rate as an AI given the entire codebase to write standard scripts. For enterprise teams, this means getting the speed of AI automation without accumulating unmanageable technical debt, ensuring that pipelines remain secure, auditable, and ready for production. The "NL2Pipeline gap" Data-centric AI requires workflows for tasks like synthetic data generation, retrieval augmentation, and model training. While LLMs can translate natural language into executable implementations to perform these tasks, high task accuracy is insufficient for production deployment. "The first wall is usually not writing Python," Runming He, first author of the DataFlow-Harness paper, told VentureBeat. "Modern coding agents can often produce a plausible script quickly. The harder problem is grounding that script in a live production platform: using operators that are actually installed, matching the real dataset schema, referring to registered datasets and model services, preserving dependencies between stages, and leaving behind an artifact that another engineer can understand and revise." General-purpose AI agents frequently hallucinate dependencies, relying on unavailable operators or outdated platform assumptions. Instead of leaving behind an artifact that another engineer can understand and revise, they generate disposable code that is difficult to audit through workflow managing tools. The researchers define this challenge as the "NL2Pipeline gap": the disconnect between a user expressing workflow requirements in natural language and the production environment requiring structured and persistent pipeline assets. The researchers demonstrated this gap in their experiments. For example, when Claude Code was allowed to write standard, free-form scripts using codebase context, it hit a 94.2% success rate. However, when restricted to only using the platform's specific building blocks to create a native workflow graph, its success rate dropped to 83.3%. This gap is the paper's central finding: native, governable pipelines are meaningfully harder for the agent to produce than throwaway code. “Closing this gap requires more than improving code-generation accuracy: construction must remain grounded in platform semantics and produce artifacts that integrate with the host platform,” the researchers write. How the four components work together "DataFlow-Harness changes the agent’s action space," He said. "Instead of asking the agent to emit arbitrary code, it retrieves the live operator registry and current pipeline state through MCP and applies typed, incremental changes to a persistent DAG." To achieve this, the platform organizes workflow synthesis around four components: the Data Pipeline Backend, the interaction layer (DataFlow-WebUI), the MCP Tools Layer, and the AI guidance layer (DataFlow-Skills). The Data Pipeline Backend acts as the authoritative source of truth across conversational, visual, and programmatic interfaces. It represents the pipeline as a directed acyclic graph (DAG), a structured workflow map containing data sources, configured pre-built processing modules (which the researchers refer to as "operators"), and execution dependencies. Instead of generating free-form code, agents interact with this backend through “typed mutations,” like adding an operator or connecting edges. DataFlow-Skills are markdown files that inject domain-specific knowledge into the model's context window, guiding it on operator-selection patterns, schema inference, and assembly procedures. Rather than letting the AI guess how to assemble components, skills provide the AI with compatibility rules, teaching it how to correctly match different data formats and handle complex data structures without breaking the pipeline. The MCP Tools Layer gives the AI access to the operator registry and current state of the data workflow. The AI proposes structured changes through the tools layer. The system validates the changes to ensure the workflow runs in a valid sequence and that every connected module speaks the same data language. DataFlow-WebUI provides two interfaces that allow humans and AI to build the workflow together. Developers can describe workflow requirements in natural language through a conversational interface. They can also access the workflow as a graphical map in a visual DAG editor. Here, they can directly inspect the changes proposed by the AI and make modifications. “The current implementation performs static checks against platform metadata before accepting pipeline changes,” He said. “These include checks for registered datasets, operators and model-serving references, field flow, and some invalid parameter usage, as well as structural validity. The result is visible in a graphical editor and can be revised either manually or by the agent in later turns.” The results: 93.3% pass rate, 72.5% lower cost The researchers tested DataFlow-Harness on a benchmark of 12 tasks across six industrial data-processing scenarios, such as QA generation, review governance, and schema normalization. They used Claude Opus 4.7 as the backbone model in their experiments. They compared DataFlow-Harness against three baselines: Vanilla CC: An unconstrained coding baseline using standard Claude Code. Context-Aware CC: An agent that has access to the DataFlow codebase in its context window. MCP-only: An agent that has access to the DataFlow MCP tools and is instructed to generate platform-native DAGs (without access to DataFlow-Skills). DataFlow-Harness achieved a 93.3% end-to-end pass rate, improving by 10.0 percentage points over MCP-only and beating Vanilla CC (91.7%), while being within 0.9 percentage points of Context-Aware CC (94.2%). Importantly, it reduced API costs to $0.261 per task, a 72.5% drop compared to Vanilla CC and 42.8% compared to Context-Aware CC. In generating workflows, it was 49.9% faster than Vanilla CC and 17.6% faster than Context-Aware CC. DataFlow-Harness proved particularly effective on complex tasks that depend on implicit domain knowledge, like QA generation. The baseline MCP-only approach frequently generated structurally valid DAGs but struggled to infer task-specific procedures from operator descriptions alone. To show how this works in the real world, the researchers detailed a textbook-to-VQA extraction task. This job required the AI to stitch together capabilities such as PDF parsing, layout recovery, OCR, figure extraction, multimodal understanding, and long-range question-answer matching. DataFlow-Harness achieved 97.2% precision and an 87.3% coverage rate, easily beating the baselines. By having the AI snap together existing platform assets rather than coding complex tasks from scratch, it recovered more valid QA pairs from the document. Their experiments also showed that DataFlow-Harness is highly effective at creating data generation pipelines. For example, in a synthetic instruction-data generation task, the agent built a multi-stage pipeline that generated candidate instruction–response pairs, critiqued and rewrote them, scored them with an LLM-based judge, and filtered low-quality outputs before training. "Such workflows are costly to build and fragile to maintain as collections of ad hoc scripts," He said. "The harness does not make them automatically safe, but it turns them into explicit, editable stages that engineers can inspect, test, and govern using normal production controls." Similarly, when tasked with building a math data cleaning-and-synthesis pipeline, the data produced by the DataFlow-Harness pipeline trained a better-performing model with higher average accuracy on AIME24 and AIME25 benchmarks than the data produced by the vanilla Claude Code pipeline. Tech stack fit and implementation tradeoffs For engineering teams evaluating DataFlow-Harness, it is important to understand how it fits into existing infrastructure. Released under the Apache 2.0 license, the current implementation requires a bit of engineering to fit into popular tech stacks. "The current implementation is native to the DataFlow platform; it is not a turnkey Airflow, Prefect, or Spark plug-in," He said. To use those systems as an execution backbone, teams must build an adapter to connect their organization’s registry, metadata, and execution interfaces to the agent's control layer. Furthermore, organizations must invest in the boundaries they want the AI to respect. This requires maintaining an operator registry, defining schemas, and encoding recurring domain procedures as Skills. Because of this overhead, He recommends against using the framework for small, one-off transformations where a simple script suffices, or in legacy environments that cannot expose reliable metadata. Finally, while the platform prevents illogical connections by validating structural properties, it is an engineering control layer, not a compliance substitute. "The harness should still be treated as an engineering control layer, not as a substitute for compliance policy, validated detection models, access controls, audit logging, or human approval," He said. The platform is open-source, and developers can access the source code and codebase documentation directly via the project's GitHub repository . As protocols like MCP become standardized, the boundary between human engineers and AI agents will shift. "The goal is not autonomous data engineering without oversight," He said. "It is a better division of labor: agents perform repetitive construction inside explicit boundaries, while engineers remain responsible for the semantics, policies, and consequential decisions that require domain accountability."
A tool that allowed anyone to generate fake AI-generated imagery and superimpose it over real Google Earth maps quickly spurred backlash.
The AI agent observability space is taking off — but how can enterprises be sure what observability products and solutions they need? Observability startup groudcover (lower case "g" intentional) announced this week that it raised $100 million in a round led by One Peak, bringing its total funding to $160 million. The company says it has more than 250 paying customers, tripled annual recurring revenue over the past year and is increasingly replacing established observability platforms inside enterprise environments. Those are company-reported figures, but together they point to growing momentum in one of enterprise software's most competitive markets. That market has long been dominated by companies including Datadog, Dynatrace, New Relic, Splunk and Grafana. Between them, they represent billions of dollars in annual revenue and years of product maturity. Breaking into that group has never been easy. groundcover's argument is that artificial intelligence has fundamentally changed the assumptions those platforms were built on. Rather than competing feature for feature, the four-year-old company is trying to convince enterprises that the architecture underpinning observability itself needs to change as AI systems become more autonomous, produce vastly more telemetry and increasingly participate in software operations. Whether that thesis proves correct remains an open question, but it offers a compelling lens through which to examine how observability is evolving alongside enterprise AI. AI is turning telemetry into an infrastructure problem Observability has traditionally been viewed as a post-production discipline. Engineers deploy applications, monitor logs, metrics and traces, investigate incidents, and improve reliability over time. That workflow is changing. AI-assisted software development has dramatically accelerated deployment cycles. Coding assistants generate more code, infrastructure evolves more rapidly, and organizations are deploying increasingly complex distributed systems that combine microservices, Kubernetes clusters, APIs and large language models. At the same time, enterprises are beginning to operate AI agents that execute multi-step workflows, call external tools and interact with production systems. Each of those activities generates telemetry. The result is an explosion of operational data that organizations increasingly want to retain rather than discard. AI applications introduce additional layers of observability beyond traditional infrastructure monitoring, including prompt execution, model latency, token consumption, retrieval pipelines, tool invocations and agent behavior. As enterprises experiment with autonomous systems, that telemetry becomes increasingly valuable because it provides the context needed to understand what an AI system actually did and why. For many organizations, this creates tension with pricing models that charge according to the amount of data ingested. Historically, engineers have often responded by sampling traces, shortening retention periods or limiting which data is collected. Those approaches reduce costs, but they also reduce visibility precisely when AI-driven systems demand more complete operational context. "We've seen telemetry exploding," groundcover co-founder and CEO Shahar Azulay said during a recent media briefing. "Users are frustrated by not getting all the value from Datadog and similar platforms. They're limiting the data, siloing it, sampling it." Whether that frustration is widespread enough to reshape the market remains to be seen, but the underlying trend is difficult to ignore. AI is making observability less about collecting enough data and more about collecting everything organizations may eventually need. Rather than adding AI, groundcover argues the architecture itself has to change Many observability vendors have introduced AI assistants, AI-powered root cause analysis and AI observability features over the past two years. Datadog, Dynatrace, New Relic and Grafana have all announced products aimed at helping enterprises monitor AI applications or automate operational tasks. groundcover acknowledges those developments but argues they do not address what it sees as the more fundamental issue: where telemetry lives and how customers pay for it. Instead of operating a conventional SaaS platform that stores customer telemetry in vendor-managed infrastructure, groundcover uses what it calls a bring-your-own-cloud (BYOC) architecture. Customers keep the data plane—including telemetry storage and processing— inside their own AWS, Microsoft Azure or Google Cloud environments, while groundcover provides a managed control plane and user experience. A fully self-hosted deployment option is also available. While some competitors, including Datadog and a few other observability vendors, do offer limited hybrid or customer-controlled data residency options, these are generally not equivalent to a full BYOC model. In most cases, telemetry is still processed and stored within the vendor’s managed infrastructure, with only partial controls (such as regional data residency, private links, or selective log forwarding) available. That architectural decision influences nearly every aspect of the company's strategy. Because customers already pay for their own cloud infrastructure, groundcover argues it can avoid charging based on telemetry ingestion. Instead, pricing is based primarily on monitored hosts, regardless of telemetry volume. The company believes this changes customer behavior. Rather than deciding which logs or traces are too expensive to keep, organizations can theoretically retain complete telemetry and use it for operational analysis, compliance and AI-assisted troubleshooting. "We don't price by data volume," Azulay said. "We price by the size of the infrastructure." The distinction matters because AI workloads tend to increase telemetry far faster than infrastructure itself. That does not necessarily make host-based pricing universally cheaper. Organizations with relatively light workloads spread across many hosts may find different economics than dense Kubernetes environments generating enormous amounts of telemetry. The company's own briefing notes that per-host pricing is most advantageous for organizations with high telemetry density and may be less compelling for lightly utilized fleets. Still, the broader argument is less about cost alone than predictability. Enterprise infrastructure teams often struggle with observability bills that fluctuate alongside application growth. groundcover's model attempts to align pricing more closely with infrastructure planning rather than data generation. eBPF sits at the center of the company's technical differentiation The second pillar of groundcover's strategy is eBPF, a Linux kernel technology that has rapidly become one of the most important building blocks for modern cloud observability. Instead of requiring developers to manually instrument applications, eBPF allows software running inside the operating system kernel to observe network traffic, system calls and application behavior with minimal code changes. That enables faster deployment and broader visibility across infrastructure. For organizations operating Kubernetes clusters and cloud-native applications, reducing instrumentation complexity can significantly shorten deployment times while increasing telemetry coverage. Azulay argues this becomes especially important as AI systems generate increasingly complex interactions across services. "Our sensor allows us to observe systems very deeply from infrastructure to application to AI workloads without developers needing to instrument code," he said during the briefing. eBPF itself is hardly unique. Many observability vendors now incorporate it into their platforms. What groundcover argues differentiates its approach is combining automatic eBPF collection with customer-controlled storage, OpenTelemetry compatibility and unified pricing inside a single platform. The company's own research briefing acknowledges that none of these technologies individually represents a competitive moat. The claimed differentiation lies in the combination of eBPF-first collection, managed BYOC architecture, host-based economics and full-stack observability delivered together. AI agents are becoming both customers—and users—of observability Perhaps the most interesting aspect of groundcover's strategy extends beyond traditional monitoring. The company increasingly describes observability as infrastructure for autonomous software development. Historically, observability platforms have served human operators investigating production incidents. groundcover believes future observability platforms will increasingly serve AI agents as well. Its Agent Mode product allows engineers to investigate incidents using natural language across logs, metrics, traces and Kubernetes events. More importantly, Azulay envisions observability becoming the feedback mechanism that informs coding agents about what actually happened in production. Rather than simply detecting failures after deployment, observability becomes continuous operational context that autonomous systems can use to evaluate changes, identify regressions and eventually recommend or implement fixes. "We're seeing observability moving from being a post-production tool... to people taking context from production and feeding it back to their coding agents so they can write code better," Azulay said. Today, the company emphasizes that humans remain in the loop. Agent Mode investigates incidents and surfaces recommendations, but production changes still require human approval. Azulay expects autonomy to increase gradually as organizations become more comfortable allowing AI systems to participate in operational workflows. That vision reflects a broader trend emerging across enterprise software, where AI agents increasingly span development, testing, deployment and operations rather than functioning as isolated assistants. Why some enterprises are considering alternatives groundcover is entering an intensely competitive market populated by vendors with decades of enterprise experience. Datadog alone generated more than $3 billion in annual revenue in 2025 . Dynatrace, Cisco's Splunk business, Grafana Labs and New Relic all maintain extensive partner ecosystems, mature integrations and enterprise support organizations that newer entrants cannot easily replicate. groundcover is not attempting to outscale those incumbents overnight. Instead, it argues that AI creates an architectural inflection point similar to previous transitions from on-premises infrastructure to cloud-native computing. According to Azulay, many customers initially adopt groundcover to reduce observability costs but increasingly remain because they want unrestricted access to richer telemetry and AI-native workflows. He says deployments typically replace incumbent platforms rather than operate alongside them, although the company has not publicly disclosed customer migration data or independent studies validating that claim. The company's journalist briefing also urges caution around some performance claims. Revenue growth, customer counts and enterprise adoption figures originate from groundcover itself. Published customer case studies reporting significant cost savings are vendor-authored and should not be treated as independent validation without additional evidence. The briefing also recommends scrutinizing exactly what metadata leaves customer environments in standard BYOC deployments, rather than assuming that no operational data ever reaches vendor infrastructure. Those caveats are important because the observability market has become crowded. Gartner currently tracks more than one hundred observability products, and nearly every major vendor now markets AI-powered operational capabilities. Success will likely depend less on whether AI matters—which increasingly appears inevitable—and more on whether enterprises conclude that existing architectures remain sufficient. The larger question investors are betting on Viewed narrowly, groundcover's Series C is another large infrastructure funding round. Viewed more broadly, it reflects a growing debate about what observability becomes in an era where software increasingly writes, tests and operates itself. If AI continues generating exponentially larger volumes of operational data, traditional assumptions about telemetry collection, pricing and storage may come under increasing pressure. Vendors that built businesses around charging for data ingestion may need to evolve their economics alongside customer expectations. New entrants, meanwhile, have an opportunity to design around those changing assumptions from the outset. groundcover believes that opportunity lies in combining customer-controlled infrastructure, automatic telemetry collection and AI-assisted operations into a platform designed for autonomous software rather than simply adding AI features to existing observability products. Whether that architectural bet proves durable will depend on enterprise adoption over the next several years. But the company's latest funding round suggests at least some investors believe the next battle in observability will not be fought over dashboards or alerts. It will be fought over who builds the operational data layer that increasingly intelligent software relies upon to understand—and eventually manage—the systems it runs.
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