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Since launching last year, the startup today has more than 8 million people using the open source or hosted version of its models, and now generates annual recurring revenue of $21 million.

Recursive’s emphasis on self-improving AI systems means much of the budget that would traditionally go toward headcount and operations is put straight into compute, as the company seeks to automate its own product development process.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. OpenAI called the Hugging Face attack unprecedented. But we’ve been here before. —Will Douglas Heaven, senior AI editor Reading OpenAI’s account last week of how some of its models broke their…

arXiv:2607.21596v1 Announce Type: new Abstract: Large language model agents increasingly solve complex tasks by constructing inference-time workflows that combine reasoning, tool use, and code execution. While such workflows enable flexible problem solving, the useful procedures discovered during execution are often transient: they help solve the current task but are not retained in a form that can systematically benefit future tasks. We present FlowEvo, a training-free framework that compiles successful traces into reusable skill records. Each record pairs a callable artifact with auxiliary structured guidance, and admission applies interface, replay, and safety checks where feasible. These skill records persist in a skill bank at inference time. FlowEvo is organized around three coupled mechanisms: (1)~workflow-to-skill compilation, which extracts reusable executable artifacts from successful traces; (2)~skill-to-workflow feedback, which retrieves accumulated skills to support future problem solving through either direct execution or structured context injection; and (3)~skill curation, which monitors downstream utility and suppresses skills that cause negative transfer. Through

arXiv:2607.21597v1 Announce Type: new Abstract: Evaluating wildfire risk systems using standard machine-learning metrics such as F1-score or IoU is fundamentally flawed: these metrics assess event prediction accuracy, not the operational coherence of a continuous risk signal. This work proposes a novel monotonic evaluation framework that measures whether increases in a predicted risk score consistently correspond to increases in observed operational load, such as number of fires, intervention time, and deployed resources. Moreover, we compare three structurally different approaches on the French Alpes-Maritimes department: the expert-based DFE index, GRU- based predictive models, and FARS, a hybrid multi-agent system combining predictive AI with LLM-based reasoning. Experimental results reveal that the DFE, despite poor classification metrics, exhibits the most balanced monotonic behavior across the full risk scale. GRU models achieve strong local monotonicity but fail to produce well-distributed risk levels. FARS inherits and reveals the structural limitations of upstream signals rather than correcting them. The central finding is a paradigm shift: a good risk model does not predi

Data ties higher AI detector scores to lower positions and to fewer pages in Google's index, though heavily flagged pages still rank in top 10. The post Google Rankings Link Higher AI Scores To Lower Positions appeared first on Search Engine Journal .

Google's DMCA case against scraper SerpApi collapsed because SearchGuard protects ad revenue, not copyright. The same reasoning weakens every site's anti-bot defense against AI crawlers. The post Google Lost Its Scraping Case – Now You Have To Pick A Side On The Open Web appeared first on Search Engine Journal .

Agentic protocols are multiplying with no consensus standard in sight. Own the layers underneath instead of chasing a winner. The post Why Data Integrity Is The New Technical SEO: From Crawling To Trust appeared first on Search Engine Journal .

Decarbonisation, sustainability and safety gains top priorities.