
TypeSafe AI, founded by OpenAI co-founder Diogo Almeida, has released Jev, a new transformer-based model designed specifically for software automation rather than natural language generation. Unlike traditional LLMs, Jev outputs calibrated probabilities and does not produce text, allowing it to run significantly faster and cheaper while eliminating hallucinations. Early adopters like Vercel report 5 to 18 times faster performance in safety classification tasks compared to OpenAI’s models, with input costs metered by the billion rather than the million. The company trains Jev exclusively on synthetic data using a technique called reinforcement learning from calibrated decisions, aiming to make intelligent decision-making ubiquitous and inexpensive.
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TypeSafe Launches Jev Judgment Model
7 developments
© TechCrunch AIGoogle is acquiring an unfinished data center from Crusoe in Wyoming, signaling a strategic pivot to direct infrastructure control amid scaling pressures. The $17 billion investment highlights the immense capital required for AI compute, as independent developers struggle with cost and timeline overruns. This move illustrates the growing vertical integration of tech giants into physical infrastructure, reducing reliance on third-party providers like Crusoe for massive deployments. By taking ownership, Google bypasses the bottlenecks that often plague external development teams. The deal reflects a broader industry trend where hyperscalers reclaim control over their most critical assets. Independent builders now face stiffer competition from companies with deeper pockets and direct operational oversight.
© TechCrunch AIOpenAI is securing an additional $11 billion in a funding round that values the company at $1.25 trillion, falling short of its initial $30 billion target but still signaling massive investor confidence. This latest capital injection comes as the broader AI market faces headwinds, with Anthropic’s potential IPO valuation also cooling from earlier highs. The move highlights OpenAI's continued ability to attract deep-pocketed support despite regulatory uncertainties and political shifts affecting data center expansion. While the round hasn't fully closed, the sheer scale of this valuation reinforces OpenAI's dominant position in the race for AI supremacy. Investors are betting big on the lab's ability to maintain its lead in compute and model capabilities. The delay in IPO plans suggests a strategic pause to navigate these complex market conditions. This funding ensures OpenAI remains the financial heavyweight in the sector.
© TechCrunch AICensys uncovered over 800 publicly accessible instances of Artex, an autonomous penetration testing tool linked to major breaches in Korean finance and ConnectWise. These exposed deployments reveal a critical security gap: powerful AI agents running without authentication turn offensive capabilities into open targets for misuse. The developers recently shifted to closed-source distribution, yet the code remains widely available on public repositories, leaving the threat landscape largely unchanged. This situation demonstrates how easily sophisticated AI-driven attacks can be launched by anyone with basic configuration skills. The widespread exposure underscores significant risks associated with misconfigured AI security tools.
This release quietly closes the hardware gap for local inference by adding default support for CUDA 13.5 and ROCm 10.0 alongside existing CUDA 12 builds. AMD GPU users finally get first-class parity with NVIDIA, while Snapdragon mobile chips gain dedicated NPU acceleration paths. Apple Silicon KleidiAI is explicitly disabled here, a notable regression for Mac performance that suggests ongoing tuning issues. The inclusion of OpenVINO and SYCL backends further cements llama.cpp as the universal runtime for inference across nearly every major silicon architecture.
Kimi’s new K3.1-Pro model joins the llama.cpp ecosystem, bringing a distinct architectural twist to local inference. By replacing full-attention MLA layers with DSA (Dynamic Sparse Attention) and linear-attention KDA layers, this release tests how well sparse caching handles hybrid attention patterns. The implementation keeps indexer precision high to avoid quality loss during top-k selection, ensuring the model runs accurately on consumer hardware. This expands the library’s reach into newer Chinese open-weight models that rely on complex memory management.
This release quietly cements llama.cpp as the universal inference runtime by adding default builds for CUDA 13.5 and ROCm 10.0 across Linux and Windows. The inclusion of ROCm 10 means AMD GPU users no longer need custom compilation to access modern acceleration, effectively closing the gap with NVIDIA's ecosystem support. While KleidiAI on Apple Silicon is disabled in this build, the broader platform coverage ensures that local inference remains accessible on virtually any hardware configuration without manual setup.