Latest updates from the AI industry
New data released by OpenAI under its OpenAI Signals initiative shows that Indian users are leaning heavily into technical and skill-driven use cases, particularly coding and data analysis. With ChatGPT crossing 100 million weekly active users in India, the country is now its largest market outside the US and the fastest-growing market for Codex. Weekly Codex usage in India has jumped four times in just the last two weeks.Coding And Data Analysis Dominate UsageThe strongest signal from the report is clear. Coding and analytical tasks are leading adoption in India.Indian Plus and Pro users are using ChatGPT’s data analysis tools at roughly four times the global average. Codex usage for coding tasks stands at nearly three times the global average. Even at a broader level, Indian users are almost three times more likely than the global median to ask coding-related questions.Education-linked queries also remain strong, with Indian users nearly twice as likely to use ChatGPT for learning and study-related prompts compared to global averages.The highest concentration of coding usage comes from India’s established tech hubs. Telangana leads the chart, followed by Karnataka and Tamil Nadu. The data suggests that developers, engineers, and early-career tech professionals are embedding AI directly into programming and debugging workflows.Work-Focused AI, Not Just Casual UseProfessional use cases are another defining pattern.Around 35 percent of consumer messages in India are work-related, compared to about 30 percent globally. Within the workplace, users are primarily relying on ChatGPT for drafting and editing documents, technical troubleshooting, debugging code, and speeding up project execution.The data shows that AI is not being used only for experimentation or entertainment. It is becoming part of daily productivity systems.Ronnie Chatterji, Chief Economist, OpenAI, said “AI adoption is moving faster than our ability to measure it - and that’s a challenge for anyone trying to make smart decisions. Signals is our way of putting real-world evidence on the table, so India’s AI debate can be grounded in facts, not hype.”Young Users Driving Technical AdoptionThe surge is being powered largely by younger Indians.Users aged 18 to 24 account for just under half of all messages sent from India. Those between 18 and 34 make up around 80 per cent of total consumer messages. This demographic skew helps explain the dominance of coding, education, and early-career professional use cases.Outside work, nearly 35 per cent of usage revolves around practical guidance, while around 20 per cent each relates to general information and writing tasks.The overall trend is unmistakable. In India, ChatGPT is evolving into a coding assistant, debugging partner, research aide, and workflow accelerator. Technical adoption is not trailing global benchmarks. It is outpacing them.
OpenAI revealed its plans to expand beyond Delhi at the AI IMpact Summit by setting up new offices in Bengaluru & Mumbai, and partnering with companies like TCS, Pine Labs, JioHotStar, MakeMyTripThe post OpenAI Announces Partnerships With TCS, JioHotstar, MakeMyTrip at the India AI Impact Summit 2026 appeared first on MEDIANAMA.
Vonage verified Postman workspace extends reach by 40+ million developers; MCP servers and Startup Program among resources designed to further accelerate time-to-value for developers and enterprises Vonage , part of Ericsson (NASDAQ: ERIC), today announced a number of new initiatives, to furt...
Keeping up with the world of Java programming can feel like a full-time job itself. New versions drop...The post Stay Ahead with the Latest Java Programming News and Trends appeared first on TechAnnouncer.
Temporal is a tool for building distributed systems. Instead of relying on complex distributed setups, it gives teams a simpler path forward. Developers gain room to focus on logic, using workflow creation without extra hurdles.
With AI agents increasingly acting as digital concierges for shoppers, verifying bot identities, securing the APIs they rely on, and detecting anomalous behaviour will be key to safeguarding automated transactions, according to Akamai
Gemini 3.1 Pro 🤖, OpenAI's strategic issues 💡, building AI eng culture 👨💻
Stay abreast of the growing selection of artificially generated media that businesses can use to produce text, images, music and code.
Vibe coding relies on large AI models trained on vast codebases. You give a prompt like “Create an e-commerce site” and the AI attempts to autocomplete a solution, often generating hundreds or thousands of lines of code. Companies offer this as “dev on autopilot”: Replit advertises “No-code needed — tell Replit Agent your app idea, and it will build it for you. It’s like having an entire team of software engineers on demand.” Base44 likewise claims you can “build fully-functional apps in minutes with just your words. No coding necessary.” The pitch is: anyone — non-coder or coder alike — can just imagine an app and have the AI spit out a prototype.
Developers say generative AI is compressing timelines from months to minutes, but the technology requires careful human oversight
Agents built on top of today's models often break with simple changes — a new library, a workflow modification — and require a human engineer to fix it. That's one of the most persistent challenges in deploying AI for the enterprise: creating agents that can adapt to dynamic environments without constant hand-holding. While today's models are powerful, they are largely static.To address this, researchers at the University of California, Santa Barbara have developed Group-Evolving Agents (GEA), a new framework that enables groups of AI agents to evolve together, sharing experiences and reusing their innovations to autonomously improve over time.In experiments on complex coding and software engineering tasks, GEA substantially outperformed existing self-improving frameworks. Perhaps most notably for enterprise decision-makers, the system autonomously evolved agents that matched or exceeded the performance of frameworks painstakingly designed by human experts.The limitations of 'lone wolf' evolutionMost existing agentic AI systems rely on fixed architectures designed by engineers. These systems often struggle to move beyond the capability boundaries imposed by their initial designs. To solve this, researchers have long sought to create self-evolving agents that can autonomously modify their own code and structure to overcome their initial limits. This capability is essential for handling open-ended environments where the agent must continuously explore new solutions.However, current approaches to self-evolution have a major structural flaw. As the researchers note in their paper, most systems are inspired by biological evolution and are designed around "individual-centric" processes. These methods typically use a tree-structured approach: a single "parent" agent is selected to produce offspring, creating distinct evolutionary branches that remain strictly isolated from one another.This isolation creates a silo effect. An agent in one branch cannot access the data, tools, or workflows discovered by an agent in a parallel branch. If a specific lineage fails to be selected for the next generation, any valuable discovery made by that agent, such as a novel debugging tool or a more efficient testing workflow, dies out with it.In their paper, the researchers question the necessity of adhering to this biological metaphor. "AI agents are not biological individuals," they argue. "Why should their evolution remain constrained by biological paradigms?"The collective intelligence of Group-Evolving AgentsGEA shifts the paradigm by treating a group of agents, rather than an individual, as the fundamental unit of evolution.The process begins by selecting a group of parent agents from an existing archive. To ensure a healthy mix of stability and innovation, GEA selects these agents based on a combined score of performance (competence in solving tasks) and novelty (how distinct their capabilities are from others).Unlike traditional systems where an agent only learns from its direct parent, GEA creates a shared pool of collective experience. This pool contains the evolutionary traces from all members of the parent group, including code modifications, successful solutions to tasks, and tool invocation histories. Every agent in the group gains access to this collective history, allowing them to learn from the breakthroughs and mistakes of their peers.A “Reflection Module,” powered by a large language model, analyzes this collective history to identify group-wide patterns. For instance, if one agent discovers a high-performing debugging tool while another perfects a testing workflow, the system extracts both insights. Based on this analysis, the system generates high-level "evolution directives" that guide the creation of the child group. This ensures the next generation possesses the combined strengths of all their parents, rather than just the traits of a single lineage.However, this hive-mind approach works best when success is objective, such as in coding tasks. "For less deterministic domains (e.g., creative generation), evaluation signals are weaker," Zhaotian Weng and Xin Eric Wang, co-authors of the paper, told VentureBeat in written comments. "Blindly sharing outputs and experiences may introduce low-quality experiences that act as noise. This suggests the need for stronger experience filtering mechanisms" for subjective tasks.GEA in actionThe researchers tested GEA against the current state-of-the-art self-evolving baseline, the Darwin Godel Machine (DGM), on two rigorous benchmarks. The results demonstrated a massive leap in capability without increasing the number of agents used.This collaborative approach also makes the system more robust against failure. In their experiments, the researchers intentionally broke agents by manually injecting bugs into their implementations. GEA was able to repair these critical bugs in an average of 1.4 iterations, while the baseline took 5 iterations. The system effectively leverages the "healthy" members of the group to diagnose and patch the compromised ones.On SWE-bench Verified, a benchmark consisting of real GitHub issues including bugs and feature requests, GEA achieved a 71.0% success rate, compared to the baseline's 56.7%. This translates to a significant boost in autonomous engineering throughput, meaning the agents are far more capable of handling real-world software maintenance. Similarly, on Polyglot, which tests code generation across diverse programming languages, GEA achieved 88.3% against the baseline's 68.3%, indicating high adaptability to different tech stacks.For enterprise R&D teams, the most critical finding is that GEA allows AI to design itself as effectively as human engineers. On SWE-bench, GEA’s 71.0% success rate effectively matches the performance of OpenHands, the top human-designed open-source framework. On Polyglot, GEA significantly outperformed Aider, a popular coding assistant, which achieved 52.0%. This suggests that organizations may eventually reduce their reliance on large teams of prompt engineers to tweak agent frameworks, as the agents can meta-learn these optimizations autonomously.This efficiency extends to cost management. "GEA is explicitly a two-stage system: (1) agent evolution, then (2) inference/deployment," the researchers said. "After evolution, you deploy a single evolved agent... so enterprise inference cost is essentially unchanged versus a standard single-agent setup."The success of GEA stems largely from its ability to consolidate improvements. The researchers tracked specific innovations invented by the agents during the evolutionary process. In the baseline approach, valuable tools often appeared in isolated branches but failed to propagate because those specific lineages ended. In GEA, the shared experience model ensured these tools were adopted by the best-performing agents. The top GEA agent integrated traits from 17 unique ancestors (representing 28% of the population) whereas the best baseline agent integrated traits from only 9. In effect, GEA creates a "super-employee" that possesses the combined best practices of the entire group."A GEA-inspired workflow in production would allow agents to first attempt a few independent fixes when failures occur," the researchers explained regarding this self-healing capability. "A reflection agent (typically powered by a strong foundation model) can then summarize the outcomes... and guide a more comprehensive system update."Furthermore, the improvements discovered by GEA are not tied to a specific underlying model. Agents evolved using one model, such as Claude, maintained their performance gains even when the underlying engine was swapped to another model family, such as GPT-5.1 or GPT-o3-mini. This transferability offers enterprises the flexibility to switch model providers without losing the custom architectural optimizations their agents have learned.For industries with strict compliance requirements, the idea of self-modifying code might sound risky. To address this, the authors said: "We expect enterprise deployments to include non-evolvable guardrails, such as sandboxed execution, policy constraints, and verification layers."While the researchers plan to release the official code soon, developers can already begin implementing the GEA architecture conceptually on top of existing agent frameworks. The system requires three key additions to a standard agent stack: an “experience archive” to store evolutionary traces, a “reflection module” to analyze group patterns, and an “updating module” that allows the agent to modify its own code based on those insights.Looking ahead, the framework could democratize advanced agent development. "One promising direction is hybrid evolution pipelines," the researchers said, "where smaller models explore early to accumulate diverse experiences, and stronger models later guide evolution using those experiences."
Martin Fowler warns that replacing developers with AI fundamentally misunderstands software development. His essay argues that code generation is trivial compared to architectural judgment, domain understanding, and maintenance — and that organizations deskilling their teams risk long-term fragility.
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Both models are already outshining their competition in key tests
Efficient Computer Co. says it’s going to make the dream of low-energy artificial intelligence computing a reality after raising $60 million in early-stage funding today. The Series A round was led by Triatomic Capital and saw participation from Eclipse, Overlap Holdings, Union Square Ventures, RTX Ventures, Toyota Ventures, Overmatch Ventures and others, bringing its total [...]The post Efficient Computer raises $60M to keep AI devices running for months on end appeared first on SiliconANGLE.
I spoke with Sam Bright, VP and GM of Google Play and Developer Ecosystem, about how Gemini's expansion in Android Studio can help human devs do more faster - and better.
Amazon tech lead Anni Chen's evolution from vibe coding skeptic to daily practitioner reflects a sweeping industry shift toward AI-generated code, raising critical questions about productivity, quality, career paths, and the changing role of software engineers.
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Anthropic launches Claude Sonnet 4, featuring anti-sycophancy training and extended thinking capabilities that challenge OpenAI and Google. The mid-tier model promises AI that prioritizes honesty over agreeableness, targeting enterprise customers demanding reliability in production environments.
The AI models are American, but the food that feeds the beast is being enriched in India.
The improvements are looking very good.
Anthropic's Claude Sonnet 4.6 offers enhanced AI capabilities, bridging gaps in computer automation and setting new benchmarks in user preference.
Vibe coding and AI agents are upending software development — and the implications for jobs are profound, says iqbusiness's Morgan Goddard.
Anthropic upgrades its Claude chatbot with Sonnet 4.6, promising sharper coding, stronger reasoning, and a massive 1M token context window.
Staying up-to-date with information is a big deal these days, right? Well, Anthropic has come out with something...The post Unlocking Real-Time Data: Integrating the Anthropic API with Web Search Capabilities appeared first on TechAnnouncer.
Shadow AI is creating an identity crisis that most organisations don't yet realise they have, says Reghardt Van Der Rijst, practice lead: identity at Altron Security.
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The foundation artificial intelligence (AI) models industry is rapidly emerging as a transformative force across various sectors, driven by continuous technological advancements and expanding applications. As businesses and organizations increasingly harness AI capabilities, this market is poised for substantial growth