Anahtar Kelimeler：AI matematik problemleri, AGI, LLM, Pekiştirmeli öğrenme, AI balonu, GLM 4.6, MobileLLM-Pro, QeRL, GPT-5 matematiksel akıl yürütme, Andrej Karpathy röportajı, AI yatırım çılgınlığı, Basetenco performans optimizasyonu, Claude Skills kurumsal uygulamalar
AI Industry News Roundup
🔥 Spotlight
AI Math Problem “Discovery” Controversy: OpenAI vs. Academic Giants : OpenAI researchers claimed GPT-5 “discovered” solutions to 10 bounty math problems, sparking public anticipation for breakthroughs in AI’s mathematical reasoning capabilities. However, mathematician Thomas Bloom clarified that these “solutions” were merely GPT-5 efficiently retrieving already published literature, not independent problem-solving by the model. Google DeepMind CEO Demis Hassabis called it “embarrassing,” and Lecun sarcastically remarked that OpenAI “got burned by its own GPT hype.” This incident has ignited widespread discussion about the rigor of AI promotion, AI’s role in scientific research (efficient retrieval vs. independent creation), and the path to AGI. Terence Tao also pointed out that AI’s immediate potential in mathematics lies in accelerating “mundane tasks” like literature search, rather than solving the hardest open problems, emphasizing that human experts still need to review AI results.
(Source: Yuchenj_UW, ns123abc, ylecun, gfodor, jonst0kes, YejinChoinka, timsoret, karpathy, bookwormengr)
Andrej Karpathy Interview Sparks Deep Reflection on AGI, LLM, and RL : In an interview with Dwarkesh Patel, Andrej Karpathy shared profound insights on AI development, the AGI timeline, LLM cognitive flaws, and the limitations of Reinforcement Learning (RL). He believes AGI is still a decade away and criticized RL as “sipping supervised data with a straw,” calling it inefficient and noisy, leading to models “collapsing” and lacking diversity. He proposed that human learning isn’t through RL but through “synthetic data generation” and “synthesis,” and that human “forgetting” promotes generalization rather than being a flaw. Karpathy also called for AI tools to collaborate more realistically with humans, rather than pursuing fully autonomous Agents, to avoid the proliferation of “slop” code. This interview sparked widespread discussion and reflection within the community on the current state and future direction of AI technology.
(Source: gfodor, jonst0kes, YejinChoinka, timsoret, gfodor, karpathy, farguney, farguney, natolambert, bookwormengr, iScienceLuvr, yacinelearning)
AI Bubble Debate: Boom or Overvaluation? : The debate surrounding whether AI is in a bubble is intensifying. While some argue that the current AI investment frenzy resembles historical tech bubbles, with phenomena like overvaluation and irrational investment, others suggest that AI’s revenue growth, hyperscale cloud providers’ cash flow, and endless enterprise demand make it more akin to a demand-driven, capital-intensive boom. The risk lies in the possibility of a bubble bursting if investment as a percentage of GDP becomes too high, revenue growth slows, or financing structures become fragile. Currently, most believe that AI technology itself holds immense potential, but market valuations might be inflated.
(Source: Reddit r/ArtificialInteligence, Reddit r/ArtificialInteligence, EigenGender)
🎯 Trends
GLM 4.6 Model Achieves Performance Breakthrough, Basetenco Becomes Fastest Provider : The GLM 4.6 model has demonstrated outstanding performance in AI analysis, with Basetenco emerging as its fastest service provider, achieving 114 TPS (tokens per second) and a TTFT (Time to First Token) of less than 0.18 seconds, twice as fast as the second-place provider. This indicates significant progress in GLM 4.6’s processing speed and efficiency, foreshadowing further performance enhancements for LLMs in practical applications.
(Source: cline)
Hugging Face Platform Trends: Open Models and Datasets : The Hugging Face platform shows an increasing diversity of open models and datasets, including the continued activity of the Qwen series models, the repair and positive reception of GPT-OSS, and the emergence of numerous high-quality open datasets (such as Fineweb, Webscale-RL, SVQ audio dataset, etc.). OCR models are becoming popular, with PaddleOCR-VL quickly topping the trending list after its release. Furthermore, the appearance of the model router Arch-Router-1.5B suggests that future AI systems might dynamically route between different models to select the optimal solution.
(Source: huggingface, huggingface, huggingface, huggingface, huggingface, ben_burtenshaw, QuixiAI, mervenoyann)
Meta Releases MobileLLM-Pro Model, Advancing Long-Context Processing on Edge Devices : Meta has launched the MobileLLM-Pro model, which outperforms Gemma 3 1B and Llama 3.2 1B in pre-training performance and boasts an ultra-long context capability of 128k. Its hybrid use of local and global attention mechanisms effectively reduces memory consumption and accelerates long-context inference on edge devices. The release of MobileLLM-Pro signals Meta’s continued efforts in developing efficient, lightweight models for wearables and mobile scenarios, promising significant advancements for mobile AI applications.
(Source: Reddit r/deeplearning)
NVIDIA Introduces New QeRL Reinforcement Learning Method for More Efficient AI Training : NVIDIA has introduced a new Reinforcement Learning (RL) method called QeRL, which combines quantization (NVFP4) and Low-Rank Adaptation (LoRA) to achieve faster, more compute-resource-efficient RL training. Its key innovation lies in Adaptive Quantization Noise (AQN), which transforms quantization noise into an exploration tool and dynamically adjusts it during the RL process. This technology is expected to significantly boost RL training efficiency, reduce computational demands, and promote the application of AI models in a wider range of scenarios.
(Source: TheTuringPost, TheTuringPost)
Claude Skills: Transforming Enterprise Knowledge into Reusable AI Processes : Anthropic’s Claude Skills feature allows users to convert their team’s “tribal knowledge” into repeatable AI operational processes. By defining skill packages through conversation, Claude can automatically invoke them when needed, without requiring manual prompt writing. This helps address inefficiencies in enterprise AI applications, solidifying best practices into AI capabilities, thereby increasing productivity and reducing reliance on employees manually copying and pasting prompts.
(Source: alexalbert__, BlackHC)