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According to Forrester, 88% of engineering, product, and design leaders think prototyping needs to happen earlier. Wait til late in the lifecycle to surface changes and you'll get months of delays.
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Meta has acquired Limitless, a startup known for its AI pendant that records conversations. Limitless will cease hardware sales but continue to offer support for a year. Its team will focus on supporting Meta's existing AR and AI wearables rather than launching new products. The acquisition comes amid rising competition in AI hardware as major players like Meta expand their device offerings.
Google plans to launch the Gemini Nano Banana 2 Flash model, which matches the performance of the Nano Banana 2 Pro, at a lower cost. The move targets a broader user base by providing high-quality outputs suitable for large-scale deployments. Code references suggest an announcement is imminent, aligning with Google's strategy of model tiering for cost optimization.
OpenAI could be releasing GPT-5.2 as early as tomorrow. The update will focus on speed, reliability, and customizability. It is aimed at closing the gap that Google created with the release of Gemini 3 last month. OpenAI had declared a 'code red' in response to Gemini 3's release.
The ARC Prize 2025 Score and Paper winners have been announced. 1,455 teams submitted 15,154 entries for the ARC Prize 2025. All ARC Prize 2025 winning solutions and papers were open-source. This post takes a look at the winners and how ARC-AGI will push AI reasoning further in 2026.
Poetiq has established a new state-of-the-art on the ARC-AGI-1 and 2 benchmarks. Its approach of building intelligence on top of any model allows it to integrate new models within hours of their release. The company's core meta-systems produce optimized agents to automate the extraction of knowledge for hard tasks that require complex reasoning. This article looks at how Poetiq's team built its systems.
Rnj-1 is a world-class pair of base and instruction-tuned large language models. The 8B model roughly follows the open-source Gemma 3 architecture. The Rnj-1 Base and Instruct models compare favorably against similarly sized open weight models. This post summarizes the key capabilities of the models and covers the background behind their development.
Google Research has released two new papers introducing an architecture (Titans) and a theoretical blueprint (MIRAS) that combine the speed of recurrent neural networks with the accuracy of transformers. Together, Titans and MIRAS advance the concept of test-time memorization, the ability of an AI model to maintain long-term memory. They introduce a meaningful shift toward real-time adaptation. The architecture actively learns and updates its own parameters as data streams in, enabling the model to incorporate new, specific details into its core knowledge instantly.
cuTile Python is a programming language for writing parallel kernels for NVIDIA GPUs. It automatically leverages advanced hardware capabilities while providing portability across different NVIDIA GPU architectures. cuTile enables the latest hardware features without requiring code changes.
In reinforcement learning, the reward is part of the agent, not part of the environment. Once we start thinking in terms of the reward translation as being part of the agent, we can also start thinking of ways in which the agent can affect the reward translation mechanism. It could be dynamic and change across time, be tied to policy, or even learned. The shift in perspective creates many possible options, just by changing the notation - because notation matters.
Google's Gemini 3 Pro set new benchmarks in multimodal vision tasks, excelling at document, screen, spatial, and video understanding with enhanced visual reasoning capabilities.
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Google is exploring integrating Opal's agent-based automation into Gemini, enhancing direct access to advanced AI tools.
A concise breakdown of how tensor parallelism splits Multi-Head Attention and Feed-Forward layers across GPUs to enable large-scale training of transformer models.
Reformist reinforcement learning seems to have found a strong niche in fine-tuning language models, but the techniques you get from reinforcement learning are almost always bad.