# Сопутствующие статьи по теме Agent

ChatGPT Might Be Disappearing Soon

OpenAI announced at its "Intelligence at Work" event that its coding assistant, Codex, will be fully integrated into the ChatGPT app within weeks. This move marks a strategic shift from a conversational AI (Chat) towards a unified "agentic" platform capable of execution. Codex, originally launched to compete with Anthropic's Claude Code, has grown rapidly to 5 million weekly active users, with 20% being non-developers like analysts and designers. Its enterprise revenue now constitutes 40% of OpenAI's total. The integration is the first step in creating a super-app combining ChatGPT (interface), Codex (execution engine), and the Atlas browser (web access). OpenAI also unveiled new Codex features: specialized Agent plugins for six professional roles, an "Annotations" tool for direct document editing, and a "Sites" function to turn work into shareable web apps. Internally, this reflects a power shift; the Codex team now leads core product strategy. While the ChatGPT brand remains for its vast user base, the platform's future is focused on autonomous agents that perform tasks, not just chat. The article notes that competition with Claude Code pushed OpenAI's development, with Codex competing on cost-effectiveness and accessibility rather than raw coding quality. It concludes that the essence of "ChatGPT" is evolving from a chatbot into an AI agent platform, with the name potentially becoming a legacy symbol of its original function.

marsbit06/03 23:52

CPU, Quietly Returning to the Center of the AI Computing Power Stage

Over the past three years, AI computing power narratives have been dominated by GPUs. However, starting in 2026, this story began to shift. While training large models remains GPU-intensive, the rapid growth of inference and AI agent workloads, which require high levels of task orchestration, concurrency, and data flow management, has highlighted a renewed critical role for CPUs. These are tasks GPUs are not designed to handle. Intel's recent launch of the Xeon 6+ processor, built on its Intel 18A process and featuring up to 288 efficiency cores (E-cores), exemplifies this strategic pivot. It is positioned not as a mere companion to GPUs but as the essential "control plane" for AI infrastructure, optimized for high-density, energy-efficient, and high-throughput workloads characteristic of AI agents and inference. This "CPU resurgence" is not about CPUs outperforming GPUs in raw computation. It reflects a systemic bottleneck: as AI scales from training single models to deploying countless intelligent agents, the demand for coordination and data handling surges. Major cloud providers are also developing their own high-density ARM-based server CPUs for similar workloads. However, Intel's success with this strategy faces significant challenges. Competition includes NVIDIA's integrated CPU-GPU solutions, the expanding adoption of cloud vendors' in-house ARM CPUs, and the crucial market test of Intel's 18A manufacturing process against rivals like TSMC's N2. In conclusion, CPUs are indeed reclaiming a central, though redefined, role in AI compute—managing the complex orchestration that enables massive-scale AI deployment. While the trend is clear, which company will ultimately lead this CPU resurgence remains an open question to be decided in the data centers of 2027 and beyond.

marsbit06/03 10:42

From IDE to Terminal: A Practical Guide to Agent Engineering

marsbit06/03 07:01

SaaS Battle Royale: The Survivors Who Win All Share One Common Trait

**Summary** The AI revolution has triggered a "SaaS apocalypse," forcing a brutal market shakeout. The key dividing line is the pricing model. Companies like Snowflake and Datadog, which charge based on consumption (e.g., data processed or compute used), are thriving. AI workloads actively *generate* more demand for their services, fueling growth. Datadog's accelerating revenue is a prime example. Microsoft and Palantir, as platform/ecosystem players, also benefit by acting as essential channels for AI deployment. In contrast, traditional SaaS firms built on per-seat or per-task licensing (e.g., Intuit, Adobe) face direct pressure, as AI threatens to automate the very human tasks their software supports. Companies like Salesforce, a per-seat giant, are caught in the middle. While showing strong AI monetization (e.g., its Agentforce platform) and experimenting with consumption-based "Flex Credits," its stock remains under pressure, illustrating that the market rewards *completed* transitions, not just the intent. The recent Microsoft Build conference underscored key trends: AI is evolving from an assistant to an autonomous "agent," and platform providers like Microsoft are consolidating their control. The market's recovery is highly selective, focused on identifying which companies are "fed by AI" versus "eaten by AI." Future focus will be on the diffusion of this recovery to transforming companies and the real-world adoption data of AI agents like Microsoft Copilot.

marsbit06/03 02:02

After 540,000 Lines of Code, Garry Tan Realizes the Old Game of AI Programming Is Over

YC President Garry Tan's project "Garry's List" involved over 540,000 lines of Rails code, but he concluded this approach is outdated. He argues the software industry is stuck in a "Foxconn factory" mindset: building excessive tests, validators, and control logic to constrain LLMs, which have become cheap and capable enough to work autonomously. The old paradigm treated LLM calls as expensive and code as cheap, leading to complex systems to "manage" the AI. This has now reversed. The future lies in "just-in-time software"—using natural language instructions (Markdown-based "skill packs") and minimal code, letting the AI handle the work. Tan advocates for "skillifying" workflows: after an agent completes a task, it packages the process into a reusable, tested skill pack. This shifts value from writing code to designing capabilities. For example, his agent reviewed 85 hackathon submissions in 30 minutes, a task that previously took days. He emphasizes "tokenmaxxing"—willingly spending on LLM tokens to gain a years-long competitive advantage, as costs are plummeting. The core shift is from measuring output in lines of code to focusing on clarity, taste, and judgment. The best future engineers won't write the most code, but will know what to build and how to unlock the most intelligence with the least code.

marsbit06/02 21:38

Chatbot has been burning money for three years, is it still the 'New Continent' of the AI era?

For years, the AI industry has been guided by a singular "map" — the belief that the AI era's "new continent" would be found in the Chatbot, a super-app akin to the mobile internet's super-apps. This belief was fueled by ChatGPT's explosive 2022 debut. However, three years of heavy investment reveal a different reality: the Chatbot-as-ultimate-entry-point model is struggling. The core issue is economic. Chatbots defy traditional internet economics. Unlike apps with near-zero marginal cost, each AI query consumes significant, expensive compute. More users mean higher costs, not profits. OpenAI, despite ~900M weekly active users, reportedly loses money. The expected network effects and data flywheels that power internet giants are weak in Chatbots, as one user's interactions don't improve another's experience. Monetization is a major hurdle. The subscription model faces low conversion rates, especially in China where users expect AI to be free. The "free + ads" model also struggles. Chatbot interactions often lack commercial intent, and inserting ads compromises the trust essential for an answer engine. Perplexity's minimal ad revenue and subsequent pivot away from ads highlight this difficulty. Switching between Chatbots is easy, making user loyalty low and competition a potential race to the bottom on price. Data suggests the standalone Chatbot's growth is slowing, and user engagement (avg. ~6 mins/day) pales compared to apps like TikTok. The product form itself is limiting; studies show nearly half of interactions are simple Q&A, trapping AI's potential in a passive, single-turn "cage." A contrasting, more successful path is emerging, exemplified by Anthropic. With over 85% of its ~$30B annualized revenue from enterprises, it focuses on AI as a productivity tool, not a companion. The rise of AI Agents (like OpenClaw) and the integration of AI into existing workflows (e.g., Google's AI Overviews, Apple Intelligence in OS) signal a shift. The future may not be a dominant Chatbot app, but AI embedded seamlessly into social apps, operating systems, and hardware — a capability-layer revolution, not a new distribution container. The conclusion is clear: the old "map" centered on a standalone Chatbot super-app is leading to a dead end. To find the true valuable "continent" of the AI era, the industry must update its navigation to prioritize deep integration, practical utility, and sustainable economics over a generic conversation window.

marsbit06/02 10:35

Huang Renxun and Marvell CEO Discuss on Stage: The Future of AI Competition is Not Computing Power but Connectivity, 'Use Copper Where You Can, Use Optics Only Where You Must'

Summary: At Computex 2024, NVIDIA CEO Jensen Huang joined Marvell CEO Matt Murphy on stage, highlighting the strategic partnership between their companies. The core theme was that the next decisive battleground for AI infrastructure is not compute or memory, but connectivity. As AI models evolve into vast agent-based systems, the ability to connect millions of processors efficiently is becoming the critical bottleneck. Huang announced NVIDIA's strategic $20 billion investment in Marvell, reflecting the deep integration between their technologies for AI data centers. A key discussion point was the transition from copper to optical interconnects within racks. The guiding principle, articulated by Huang, is: "You use optics wherever you must, you use copper wherever you can." While copper remains cost-effective for short distances, its physical limits are being reached as bandwidth demands double. When moving to 400Gbps, copper can no longer fully connect an entire rack. This shift necessitates innovations like Co-Packaged Optics (CPO), which integrates optical engines directly into the chip package to solve density and power challenges. Marvell demonstrated its 51.2T CPO-based switch, eliminating copper traces on the PCB. The future vision is a "distance-free data center," where optical connectivity removes physical constraints. This allows for fully disaggregated, dynamic architectures where compute, memory, and storage pools can be combined on-demand based on workload requirements, rather than being limited by connection boundaries. Marvell, positioned as a neutral "Switzerland" in the ecosystem with a comprehensive portfolio across all connectivity distances, is central to enabling this next era of AI infrastructure.

marsbit06/02 09:41

Silicon Valley's New FDE Role Gains Popularity: What Kind of AI Talent Do Enterprises Need?

The Silicon Valley job market is seeing a resurgence of the Forward Deployed Engineer (FDE) role, originally pioneered by Palantir, now focused on AI. Companies like OpenAI and Anthropic are deploying AI FDEs to client sites to customize general-purpose large language models into specific Agent workflows for business processes. However, the article argues that the broader and more significant trend is the rise of the in-house "AI Engineer." These professionals, needed in far greater numbers than external FDEs, integrate AI capabilities—like prompt engineering, Agent frameworks, and evaluation systems—directly into a company's own software and operations, often using AI coding tools. This evolution suggests AI's impact on employment is not simply about replacement but about creating new, generalist roles that will later specialize, much like software engineering split into front-end, back-end, and DevOps. The future is likely to see a proliferation of specialized AI engineering roles (e.g., LLMOps, Evals Engineer), but the current and critical demand is for engineers who blend technical skill with business understanding to embed AI effectively, ensuring companies retain flexibility in their technology choices.

marsbit06/02 04:40

What's New in Jensen Huang's 'Agent Factory'?

In a keynote at COMPUTEX 2026, NVIDIA CEO Jensen Huang shifted the company's focus from hardware "full-stack" solutions to the era of AI Agents. The centerpiece is the Vera Rubin platform, now in production, which is designed specifically for Agent workloads and offers 10x the efficiency of its predecessor. The platform features the new Vera CPU, built for AI, and incorporates Spectrum-X Ethernet Photonics with CPO technology for improved networking and energy efficiency. NVIDIA introduced DSX, an integrated toolkit for designing, simulating, and operating AI data centers, aiming to streamline "AI factory" deployment and management. For end-user deployment, the company unveiled DGX Station for Windows, a desktop AI supercomputer for running Agents locally, and the RTX Spark SoC for AI PCs. On the software front, NVIDIA launched the 550B-parameter Nemotron 3 Ultra model for enterprise Agents and the Cosmos 3 foundation model for physical AI, unifying visual reasoning and action prediction. In robotics, a partnership with Unitree yielded the H2 Plus, a reference humanoid robot built on the Isaac GR00T platform to lower development barriers. Security was emphasized with enhanced confidential computing for Vera Rubin and new data path security features for the BlueField-4 STX storage platform. The presentation highlighted a strategic pivot: NVIDIA is reorganizing its entire technology stack—from chips and data centers to models, software, and robots—around the emerging ecosystem of autonomous, practical AI Agents.

marsbit06/01 12:04

$26 Billion: An 'All-Chinese Team' Backs the World's Highest-Valued AI Programming Company

Cognition AI, the company behind the AI programmer "Devin," has raised over $1 billion in new funding at a valuation of $26 billion, just eight months after reaching a $10.2 billion valuation. The round was led by Lux Capital, General Catalyst, and 8VC. Founded by three young Chinese entrepreneurs with strong competitive programming backgrounds, Cognition initially gained fame with Devin, marketed as the world's first AI software engineer capable of handling tasks from start to finish. While its early demos were impressive, real-world usage revealed reliability and cost-effectiveness issues, leading to a significant price cut for Devin in 2025. A pivotal moment came when Cognition acquired the assets of AI IDE company Windsurf after a failed acquisition by OpenAI. This move gave Cognition a crucial developer-facing tool, allowing it to pursue a two-pronged strategy: Devin for autonomous task execution and Windsurf for integrated, collaborative coding within an IDE. This shift helped the company move away from the controversial "AI replacement" narrative towards a model of augmenting human engineers, particularly for repetitive or maintenance tasks. This strategic pivot is backed by strong commercial metrics. The company reports a 10x increase in enterprise usage this year, with an annual revenue run-rate of $492 million and a 50% month-over-month growth in enterprise Devin usage over the past six months. Its client list now includes major corporations like Goldman Sachs and Mercedes-Benz, as well as government agencies like NASA and the U.S. Army. Investors are betting on Cognition becoming a foundational piece of next-generation software engineering infrastructure, positioning it at the center of a hybrid future where AI agents and human developers work in tandem.

marsbit05/31 10:22