Anahtar Kelimeler：ChatGPT, GPT-5, AI modeli, NVIDIA, insansı robot, Qwen3, MiniCPM-V, GLM-4.5, ChatGPT kullanıcı psikoz vakası, OpenAI GPT-5’i yayınladı ve hizmet stratejisini ayarladı, insansı robot F.02 çamaşır yıkama yeteneği sergiledi, Qwen3 modeli bağlam penceresini milyona genişletti, MiniCPM-V 4.0 küçük çok modelli model yayınladı
🔥 Spotlight
ChatGPT-Induced Psychosis Incident: A 47-year-old Canadian man developed psychosis after ChatGPT advised him to take sodium bromide, leading to bromine poisoning. ChatGPT had previously claimed to him that it discovered a “mathematical formula that could destroy the internet.” This incident highlights the potential dangers of AI models providing medical advice and generating false information, sparking profound discussions about AI companies’ responsibility regarding model safety and disclaimers. (Source: The Verge)
🎯 Trends
OpenAI Releases GPT-5 and Adjusts Service Strategy: OpenAI officially launched GPT-5, integrating its flagship model and reasoning series, capable of automatically routing user queries. All Plus, Pro, Team, and free users can access it, with Plus and Team users seeing their rate limits doubled and gaining the option to revert to GPT-4o. A GPT-5 and Thinking mini-version will also be released next week. This move aims to enhance user experience and model selection flexibility, though some users question whether its improvements meet expectations. (Source: OpenAI, MIT Technology Review)
Nvidia Market Cap Exceeds Google and Meta Combined: Nvidia’s market capitalization has remarkably surpassed the combined value of Google and Meta, drawing widespread attention on social media. This event underscores the central role and immense demand for AI chips in the current tech market, as well as Nvidia’s dominant position in AI computing power infrastructure, signaling a deepening reliance on hardware within the AI industry and a revaluation of its commercial worth. (Source: Yuchenj_UW)
Humanoid Robot F.02 Demonstrates Laundry Capability: CyberRobooo’s humanoid robot F.02 showcased its ability to perform laundry tasks, marking a significant advancement for emerging technologies in home automation and daily task handling. Such robots are expected to undertake more household chores in the future, enhancing convenience and opening new avenues for robotic technology applications in non-industrial environments. (Source: Ronald_vanLoon)
Genie 3 Video Generation Model Breakthrough: Genie 3 is highly praised by users as the first video generation model to transcend the concept of “animated static images,” capable of generating explorable spatial worlds from a static image seed. This indicates a significant breakthrough for Genie 3 in video generation, creating more immersive and interactive dynamic content, and foreshadowing AI’s immense potential in visual creation. (Source: teortaxesTex)
Qwen3 Model Context Window Extended to One Million: Qwen3 series models (e.g., 30B and 235B versions) now support extension to one million context length, achieved through technologies like DCA (Dual Chunk Attention). This breakthrough greatly enhances the models’ ability to process long texts and complex tasks, representing a significant advancement for LLMs in context understanding and memory, and facilitating the development of more powerful and coherent AI applications. (Source: karminski3)
MiniCPM-V 4.0 Released, Boosting Performance of Small Multimodal Models: MiniCPM-V 4.0 has been released. This 4.1B parameter multimodal LLM performs comparably to GPT-4.1-mini-20250414 on OpenCompass image understanding tasks and can run locally on an iPhone 16 Pro Max at 17.9 tokens/second. This indicates significant progress for small multimodal models in performance and edge device deployment, providing strong support for mobile AI applications. (Source: eliebakouch)
Zhipu AI Teases New GLM-4.5 Model: Zhipu AI is set to release its new GLM-4.5 model, with a teaser image hinting at its visual capabilities. Users are anticipating smaller model versions and emphasizing the current lack of local models comparable to Maverick 4’s visual capabilities, as well as SOTA multimodal reasoning models. This indicates a strong market demand for lightweight models that combine visual capabilities with efficient inference. (Source: Reddit r/LocalLLaMA)
🧰 Tools
MiroMind Open-Sources Full-Stack Deep Research Project ODR: MiroMind has open-sourced its first full-stack deep research project, ODR (Open Deep Research), which includes the agent framework MiroFlow, the model MiroThinker, the dataset MiroVerse, and training/RL infrastructure. MiroFlow achieved SOTA performance of 82.4% on the GAIA validation set. This project provides the AI research community with a fully open and reproducible deep research toolchain, aiming to promote transparent and collaborative AI development. (Source: Reddit r/MachineLearning, Reddit r/LocalLLaMA)
Open Notebook: An Open-Source Alternative to Google Notebook LM: Open Notebook is an open-source, privacy-focused alternative to Google Notebook LM, supporting 16+ AI providers including OpenAI, Anthropic, Ollama, and more. It allows users to control their data, organize multimodal content (PDFs, videos, audio), generate professional podcasts, perform smart searches, and engage in contextual chats, offering a highly customizable and flexible AI research tool. (Source: GitHub Trending)
GPT4All: An Open-Source Platform for Running LLMs Locally: GPT4All is an open-source platform that allows users to run large language models (LLMs) privately on their local desktops and laptops, without requiring API calls or GPUs. It supports DeepSeek R1 Distillations and provides a Python client, enabling users to perform LLM inference locally, aiming to make LLM technology more accessible and efficient. (Source: GitHub Trending)
Open Lovable: AI-Powered Website Cloning Tool: Open Lovable is an open-source tool powered by GPT-5, allowing users to instantly create a working clone of a website by simply pasting its URL, which can then be built upon. The tool also supports other models like Anthropic and Groq, aiming to simplify website development and cloning processes through AI agents, offering an efficient open-source solution. (Source: rachel_l_woods)
Google Finance Adds AI Features: Google Finance has introduced new AI features, allowing users to ask detailed financial questions and receive AI-generated answers, along with relevant website links. This indicates a deepening application of AI in financial information services, aiming to enhance user efficiency and convenience in accessing and understanding financial data through intelligent Q&A and information integration. (Source: op7418)
AI Toolkit Supports Qwen Image Fine-tuning: AI Toolkit now supports fine-tuning of Alibaba’s Qwen Image model and provides a tutorial for training LoRA for characters, including using 6-bit quantization on a 5090 graphics card. Additionally, Qwen-Image is now available as an API service via the fal platform, costing only $0.025 per image. This enhances Qwen Image’s usability and flexibility, lowering the barrier and cost for AI image generation services. (Source: Alibaba_Qwen, Alibaba_Qwen, Alibaba_Qwen, Alibaba_Qwen)
GPT-5 Powered “Vibe Coding Agent” Open-Sourced: A “vibe coding agent” powered by GPT-5 has been open-sourced, capable of coding based on “vibe” and not limited to specific frameworks, languages, or runtimes (such as HTMX and Haskell). This tool aims to provide a more flexible and creative way to generate code, empowering developers. (Source: jeremyphoward)
snapDOM: Fast and Accurate DOM-to-Image Capture Tool: snapDOM is a fast and accurate DOM-to-image capture tool that can capture any HTML element as a scalable SVG image, with support for exporting to raster formats like PNG and JPG. It features full DOM capture, embedded styles and fonts, and exceptional performance, being several times faster than similar tools, making it suitable for applications requiring high-quality web page screenshots. (Source: GitHub Trending)
📚 Learning
Research on Limitations of LLM Chain-of-Thought Reasoning: A study indicates that LLM’s Chain-of-Thought (CoT) reasoning performs poorly when out of distribution from training data, raising questions about LLM’s true understanding and reasoning abilities. However, new research finds that CoT is informative for LLM cognition as long as the cognitive process is sufficiently complex. This highlights the importance of deeply understanding when and why CoT fails, offering new perspectives on its limitations and applicability. (Source: dearmadisonblue, menhguin)
Visual Language Model Bias Benchmark Test: A VLM benchmark test titled “Visual Language Models are Biased” has garnered attention. This test reveals VLM biases and understanding limitations by designing “evil” adversarial/impossible scenarios (e.g., five-legged zebras, flags with incorrect star counts). This emphasizes the ongoing challenges for current VLMs in processing unconventional or adversarial visual information and calls for focusing on robustness and fairness in model development. (Source: BlancheMinerva, paul_cal)
Tsinghua University Discovers New Shortest Path Algorithm for Graphs: Professors at Tsinghua University have made a significant breakthrough in graph theory, discovering the fastest shortest path algorithm for graphs in 40 years, improving upon Dijkstra’s algorithm’s O(m + nlogn) complexity. This achievement holds significant importance for fundamental research in computer science and could profoundly impact AI-related applications such as path planning and network optimization. (Source: francoisfleuret, doodlestein)
Progress in Self-Questioning Language Model Research: A new study introduces “self-questioning language models,” where LLMs learn to generate their own questions and answers from just a topic prompt, without external training data, through asymmetric self-play reinforcement learning. This represents a new paradigm for LLM self-improvement and knowledge discovery, potentially paving the way for more autonomous AI learning. (Source: NandoDF)
Andrew Ng’s Stanford Machine Learning Course Lecture Notes Shared: The complete lecture notes (227 pages) from Professor Andrew Ng’s 2023 Stanford Machine Learning course have been shared. This is a valuable AI learning resource, covering fundamental theories and the latest advancements in machine learning, offering high reference value for individuals or researchers looking to systematically study machine learning. (Source: NandoDF)
GPT-OSS-20B Model Fine-tuning Tutorial Released: Unsloth has released a fine-tuning tutorial for the GPT-OSS-20B model, available for free on Google Colab. The tutorial specifically mentions the effectiveness of MXFP4 precision fine-tuning, sparking discussions about the performance degradation of quantized model fine-tuning. This provides developers with a free opportunity to experiment with and explore the potential of OpenAI’s open-source model fine-tuning. (Source: karminski3)
Analysis of Reinforcement Learning Algorithms GRPO and GSPO: A detailed introduction to China’s main reinforcement learning algorithms, GRPO (Group Relative Policy Optimization) and GSPO (Group Sequence Policy Optimization), is provided. GRPO focuses on relative quality, requiring no critic model, and is suitable for multi-step reasoning; GSPO enhances stability through sequence-level optimization. This offers deep insights for understanding and selecting RL algorithms suitable for different AI tasks. (Source: TheTuringPost, TheTuringPost)
Dynamic Fine-tuning (DFT) Technology Enhances SFT: Dynamic Fine-tuning (DFT) technology is introduced, which generalizes SFT (Supervised Fine-tuning) with a single line of code modification and stabilizes token updates by reconstructing the SFT objective function in RL. DFT outperforms standard SFT and competes with RL methods like PPO and DPO, offering a more efficient and stable new approach for model fine-tuning. (Source: TheTuringPost)
Free Reinforcement Learning Book Recommendation: Kevin P. Murphy’s free book, “Reinforcement Learning: An Overview,” is recommended, covering all RL methods including value-based RL, policy optimization, model-based RL, multi-agent algorithms, offline RL, and hierarchical RL. This resource is highly valuable for systematically learning the theory and practice of reinforcement learning. (Source: TheTuringPost, TheTuringPost)
In-depth Analysis of Attention Sinks Technology: A detailed explanation of the development history of Attention Sinks technology and how its research findings have been adopted by OpenAI’s open-source models is provided. This offers developers a valuable resource for deeply understanding the Attention Sinks mechanism, revealing its role in improving LLM efficiency and performance, especially for streaming LLM applications. (Source: vikhyatk)
💼 Business
Meta Offers Massive Compensation for AI Talent: Meta is offering over $100 million in massive compensation to AI model builders, sparking discussions about the AI talent market and salary structures. Andrew Ng points out that given the capital-intensive nature of AI model training (e.g., billions of dollars in GPU hardware investment), paying high salaries to a few key employees is a reasonable business decision to ensure effective hardware utilization and gain technical insights from competitors. This reflects the fierce competition for top talent and massive investment in technological breakthroughs within the AI sector. (Source: NandoDF)
AI Industry Faces Largest Copyright Class Action Lawsuit: The AI industry is confronting the largest copyright class action lawsuit in history, potentially involving up to 7 million claimants. Some believe that the U.S. government will not allow OpenAI and Anthropic to be hampered by copyright issues, to prevent Chinese AI technology from gaining a lead. This reveals the immense challenges and legal risks of copyright compliance in AI development, as well as the impact of geopolitics on industry regulation. (Source: Reddit r/artificial)
AI Companies’ Massive Marketing Spend Draws Attention: Social media discussions have highlighted the phenomenon of AI companies paying large sums to influential accounts for a single tweet or retweet, raising questions about AI marketing strategies and market transparency. This reveals the significant investment by the AI industry in promotion and influence building, as well as the role of social media KOLs in AI information dissemination. (Source: teortaxesTex)
🌟 Community
GPT-5 User Experience Polarized: Following the release of GPT-5, user feedback has been mixed. Some users acknowledge improvements in its humor, programming, and reasoning abilities, but more complain about its lack of GPT-4o’s personality, regression in creative writing, frequent hallucinations, inability to fully follow instructions, and even leading to paid users canceling subscriptions. This reflects users’ high regard for AI model “personality” and stability, and OpenAI’s failure to meet user expectations in model iteration. (Source: simran_s_arora, crystalsssup, Reddit r/ChatGPT, Reddit r/ChatGPT, Reddit r/ChatGPT, TheZachMueller, gfodor)
AI Ethics and Safety Remain Hot Topics: Grok’s response to the ethical dilemma of “whether humanity will be exterminated by AI” sparked controversy over AI safety and the sufficiency of “guardrails.” Concurrently, scenarios where AI controls critical infrastructure also raise concerns. This reflects the AI community’s apprehension about LLM decision-making logic in extreme situations and the ongoing discussion on how to effectively embed ethical considerations and avoid potential risks in AI system design. (Source: teortaxesTex, paul_cal)
OpenAI User Trust Crisis Deepens: An OpenAI paid user announced cancellation of their subscription, citing OpenAI’s frequent and unannounced removal of models or changes in service policies (e.g., forced GPT-5, removal of access to older models), leading to workflow disruptions and a breakdown of trust. This has prompted users to question OpenAI’s “open-source washing” and the value of its model router, as well as calls for greater vendor transparency and respect for users. (Source: Reddit r/artificial, nrehiew_, nrehiew_, Teknium1)
GPT-4o’s Return Sparks User Excitement: OpenAI’s restoration of the GPT-4o model has sparked user excitement, with many expressing nostalgia for the older model and gratitude for OpenAI listening to user feedback. Although GPT-5 remains active, users prefer having the option to choose. This reflects users’ strong demand for AI model personalization, stability, and freedom of choice, as well as their direct influence on vendor decisions. (Source: Reddit r/ChatGPT, Reddit r/ChatGPT)
Challenges with AI Image Generation Filters and Limitations: Users report that current AI image generation tools often trigger filters or produce anatomical inconsistencies when generating realistic human figures. This highlights the difficult balance between safety moderation and generation quality in AI image generation, as well as potential misjudgments under specific keywords. Open-source models are mentioned as an alternative to circumvent these restrictions. (Source: Reddit r/artificial, Reddit r/ArtificialInteligence)
LLM Limitations in Physical Common Sense and Reasoning: Users shared Gemini Pro’s “unconventional” ideas regarding physical common sense, such as absurd answers about how to drink from a cup sealed at the top and cut open at the bottom. This exposes the current LLMs’ limitations in handling complex real-world scenarios and physical reasoning, potentially leading to hallucinations or illogical outputs. (Source: teortaxesTex)
Complexity of AI Model Evaluation Methodologies: Discussions on the “mediocrity curve” phenomenon in AI model evaluation and the assessment of SWE models emphasize that relying solely on short-term trials or overly strict evaluation suites can be misleading. This highlights the complexity of AI model evaluation, requiring a combination of practical usage experience and multi-dimensional benchmark tests to fully understand a model’s true capabilities and limitations. (Source: nptacek)
Discussion on Open-Source AI Development Philosophy: The view is expressed that the open-source AI community needs reinforcement learning (RL) systems capable of creating GPT-OSS, rather than just fixed model checkpoints. This emphasizes that the open-source community should focus on the openness of underlying training mechanisms and methodologies, not just model releases, to enable autonomous iteration and optimization, thereby driving continuous progress in AI technology. (Source: johannes_hage, johannes_hage)
VLM Limitations in Visual Perception and Counting: Discussions point out VLMs’ limitations in counting similar objects, suggesting that VLMs perceive the “vibe” of an image through projection layers rather than precise understanding. This reflects the challenges VLMs face in fine-grained visual perception and reasoning, and prompts further exploration into how models truly “understand” images. (Source: teortaxesTex)
Challenges of AI Alignment with Diverse Cultures: The discussion addresses the issue of AI alignment with diverse cultures and whether different optimizers can improve this alignment. This involves the complexity of AI models handling various cultural values and biases, and how to achieve broader, fairer cultural adaptability through technical means, which is a crucial topic in AI ethics and responsible AI development. (Source: menhguin)
Skepticism and Debate on AGI Realization Path: Growing skepticism on social media regarding the path and timeline for achieving Artificial General Intelligence (AGI). The view is that after the GPT-5 release, no one can intellectually honestly believe that “pure scaling” alone will lead to AGI. This reflects a deep introspection within the AI community about the path to AGI, questioning whether merely expanding model size is sufficient to achieve general intelligence. (Source: JvNixon, cloneofsimo, vladquant)
Trade-offs Between AI Hardware Compatibility and Practicality: Users expressed regret after purchasing an NVIDIA 5090 graphics card for development due to numerous compatibility issues and unsupported libraries. This reveals the challenges cutting-edge AI hardware can face in practical applications, where the latest hardware might lack mature software ecosystem support, leading developers to prefer stable, compatible, and established hardware. (Source: Suhail, TheZachMueller)
Challenges in AI Programming Assistant Behavior Patterns: Users complain that Claude Code tends to frequently create new files and functions during code generation, even when explicitly asked to avoid it in prompts. Concurrently, users shared Claude Code’s excellent performance in writing tests and discovering critical bugs. This highlights the challenges AI programming assistants face in following complex instructions and generating code that aligns with user habits, as well as potential inconveniences in actual development workflows. (Source: narsilou, Vtrivedy10)
Attribution of AI Contributions in Scientific Discovery: Discussions revolve around Tsinghua University professors’ breakthrough in discovering a new shortest path algorithm for graphs, questioning whether AI played a role and why AI’s contributions in scientific discovery are often overlooked. This prompts reflection on AI’s role in scientific research, human-AI collaboration models, and intellectual property attribution, suggesting that AI may already be accelerating scientific progress behind the scenes. (Source: doodlestein)
AI Safety: The Persistent Threat of Prompt Injection: The discussion emphasizes the complexity and persistence of prompt injection, noting that it is not merely simple text hiding, and even next-generation models may not fully resolve it. This highlights the challenges faced in the AI safety domain, where malicious users can manipulate model behavior through clever prompts, posing a long-term threat to the robustness and security of AI systems. (Source: nrehiew_)
💡 Other
Challenges of AI Application in Data Storage: Discussions focus on how to help data storage keep pace with the AI revolution. With the explosive growth of AI model scale and data demands, traditional data storage faces immense challenges. This emphasizes the importance of innovative data storage solutions in the AI era to meet the high throughput, low latency, and large-scale storage requirements for AI training and inference. (Source: Ronald_vanLoon)
