Keywords：AlphaEvolve, GPT-4.1, Lovart, DeepSeek-V3, AI Agent, Algorithm Self-Evolution, Gemini Large Language Model, Multi-Head Latent Attention, AI Design Agent, Hardware-Software Co-Design
🔥 Focus
Google DeepMind releases AI programming agent AlphaEvolve, achieving algorithmic self-evolution and optimization: Google DeepMind has launched AlphaEvolve, an AI programming agent that combines the creativity of the Gemini large language model with an automated evaluator to autonomously discover, optimize, and iterate algorithms. AlphaEvolve has been deployed within Google for a year, successfully improving data center efficiency (Borg system global compute power recovery of 0.7%), accelerating Gemini model training (23% speedup, 1% reduction in overall training time), optimizing TPU chip design, and solving multiple mathematical problems, including the “kissing number problem.” For instance, it improved the 4×4 complex matrix multiplication algorithm using 48 scalar multiplications, surpassing Strassen’s algorithm from 56 years ago. This technology demonstrates AI’s immense potential in solving complex scientific computing and engineering problems and could be applied to broader fields such as materials science and drug discovery in the future. (Source: 量子位, 36氪, 36氪, 36氪, Reddit r/LocalLLaMA, Reddit r/artificial, Reddit r/ArtificialInteligence, Reddit r/MachineLearning, op7418, TheRundownAI, sbmaruf, andersonbcdefg)
OpenAI GPT-4.1 series models launched on ChatGPT, enhancing coding and instruction-following capabilities: OpenAI announced that three models, GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano, have officially launched on the ChatGPT platform, available to all users. GPT-4.1 focuses on improving programming and instruction execution capabilities, scoring 55% on the SWE-bench Verified software engineering benchmark, significantly outperforming GPT-4o’s 33% and GPT-4.5’s 38%, with a 50% reduction in redundant output. GPT-4.1 mini will replace GPT-4o mini as the new default model. GPT-4.1 nano is designed for low-latency tasks and supports a 1 million token context. Although the API version supports million-level tokens, the context length of GPT-4.1 in ChatGPT has sparked user discussions, with some users finding through testing that its context window does not reach the API version’s 1 million tokens, expressing disappointment. (Source: 36氪, 36氪, 36氪, op7418)
AI design agent Lovart surges in popularity, completing professional-grade visual designs with a single sentence: Design AI agent Lovart has quickly gained traction, allowing users to complete professional-grade visual designs such as posters, brand VI, and storyboards with just a single sentence. Lovart can automatically plan the design process, invoking various top-tier models including GPT image-1, Flux pro, and Kling AI, and supports advanced features like layer editing, one-click background removal, and background replacement. The product is independently operated by LiblibAI’s overseas subsidiary (based in San Francisco), with core developers including Wang Haofan of InstantID. Lovart’s emergence reflects the trend of AI agents penetrating professional fields, and its ease of use and professionalism have garnered widespread attention, with over 20,000 beta applications within a day of its launch. (Source: 36氪, 36氪, op7418, op7418)
DeepSeek releases new paper detailing V3 model’s hardware-software co-design and cost optimization secrets: The DeepSeek team has published a new paper detailing the synergistic innovations in hardware architecture and model design for their DeepSeek-V3 model, aimed at achieving cost-effectiveness in large-scale AI training and inference. The paper highlights key technologies such as Multi-head Latent Attention (MLA) for improved memory efficiency, a Mixture of Experts (MoE) architecture for optimizing compute and communication balance, FP8 mixed-precision training to fully leverage hardware performance, and a multi-plane network topology to reduce cluster network overhead. These innovations enabled DeepSeek-V3 to be trained on 2048 H800 GPUs with an FP8 training accuracy loss of less than 0.25%, and a KV cache as low as 70KB per token. The paper also proposes six suggestions for future AI hardware development, emphasizing robustness, direct CPU-GPU connectivity, intelligent networking, hardware-accelerated communication ordering, network-compute fusion, and memory architecture restructuring. (Source: 36氪, 36氪, hkproj, NandoDF, tokenbender, teortaxesTex)
🎯 Trends
Anthropic’s new models to be released soon, featuring enhanced reasoning and tool-calling capabilities: Anthropic plans to launch new versions of its Claude Sonnet and Claude Opus models in the coming weeks. The new models will be able to seamlessly switch between thinking and calling external tools, applications, or databases, dynamically interacting to find answers to questions. Particularly in code generation scenarios, the new models can automatically test the code they write; if errors are found, they can pause the execution flow to diagnose errors and make real-time corrections, significantly enhancing their utility in complex task processing and code generation. (Source: op7418, karminski3, TheRundownAI)
MiniMax’s new generation speech model Speech-02 tops international benchmarks, surpassing OpenAI and ElevenLabs: MiniMax’s new generation TTS (text-to-speech) large model, Speech-02, has performed exceptionally well on the authoritative international speech benchmark list, Artificial Analysis. It achieved SOTA (State-of-the-Art) results in key voice cloning metrics such as Word Error Rate (WER) and Speaker Similarity (SIM), surpassing similar products from OpenAI and ElevenLabs. The model’s technological innovations include achieving zero-shot voice cloning and adopting a Flow-VAE architecture. It supports 32 languages and provides highly anthropomorphic, personalized, and diverse speech synthesis effects at a lower cost. (Source: 36氪)
Salesforce introduces fully open-source unified multimodal model series BLIP3-o: Salesforce has released BLIP3-o, a fully open-source unified multimodal model series that includes architecture, training methods, and datasets. The series employs a novel approach using a diffusion transformer to generate semantically rich CLIP image features, rather than traditional VAE representations. Additionally, researchers demonstrated the effectiveness of a sequential pre-training strategy for unified models, i.e., training image understanding first, followed by image generation. (Source: NandoDF, teortaxesTex)
Stability AI open-sources small text-to-speech model Stable Audio Open Small: Stability AI has released and open-sourced a text-to-speech model named Stable Audio Open Small. The model has only 341M parameters and has been optimized to run entirely on Arm CPUs, meaning that the vast majority of smartphones can generate music production samples locally, without an internet connection, within seconds. (Source: op7418)
11x rebuilds core product Alice as an AI agent, adopting LangGraph and other technologies: After achieving $10M ARR, 11x rebuilt its core product Alice from scratch as an AI agent. Reasons for the rebuild include improvements in models and frameworks (like LangGraph), and the impressive performance of Replit agents convinced them that the agent era has arrived. They adopted a simple tech stack and utilized the LangGraph platform. For marketing campaign creation, they started with a simple ReAct architecture, added workflows for reliability, then moved to multi-agent for flexibility, while emphasizing that simplicity is still best for simple scenarios. They also found tools to be more useful for agents than inherent prior knowledge. (Source: LangChainAI, hwchase17, hwchase17)
Box refactors document extraction process using agentic architecture: Box CTO Ben Kus shared his experience developing their document extraction agent. He mentioned that after a well-performing prototype, they encountered challenges as tasks and expectations became increasingly complex, entering a “trough of disillusionment.” Inspired by Andrew Ng and Harrison Chase, they redesigned the system from scratch as an agentic architecture. This new architecture is clearer, more effective, easier to modify, and brought an unexpected benefit – improved AI engineering culture. He emphasized building an agentic architecture early on. (Source: LangChainAI)
Study finds LLM hidden states can estimate economic and financial data more accurately: A study shows that training a linear model to analyze the hidden states of Large Language Models (LLMs) can estimate economic and financial statistics more accurately than relying directly on the LLM’s text output. Researchers believe that extensive post-training aimed at reducing hallucinations might have weakened the model’s tendency or ability to make educated guesses, suggesting more work can be done in extracting LLM capabilities and general post-training. (Source: menhguin, paul_cal)
Nous Research launches testnet for pre-training a 40B parameter LLM: Nous Research announced the launch of a testnet for pre-training a 40 billion parameter Large Language Model. The model uses an MLA architecture, and the dataset includes FineWeb (14T), FineWeb-2 (4T after removing some minority languages), and The Stack v2 (1T). The goal is to train a small model that can be trained on a single H/DGX. The project lead mentioned challenges with custom backpropagation when implementing tensor parallelism in MLA. (Source: Teknium1)
AI Agent IKEA: Reinforcing synergistic reasoning between internal and external knowledge for efficient adaptive search: Researchers have proposed a reinforcement learning agent named IKEA, which learns when not to perform information retrieval, prioritizing the use of parameterized knowledge and only retrieving when necessary. Its core lies in adopting a reinforcement learning method based on knowledge boundary-aware rewards and training sets. Experiments show that IKEA outperforms Search-R1 in performance and reduces retrieval instances by about 35%. This research is based on the Knowledge-R1 agent RAG framework, can generalize to unseen data, and can be extended from base models to 7B models (like Qwen2.5). Training utilized the GRPO method, which does not require a value head, resulting in lower memory usage and stronger reward signals. (Source: tokenbender)
Mistral AI launches enterprise-grade AI assistant Le Chat Enterprise: Mistral AI has released Le Chat Enterprise, an agent-driven AI assistant designed for businesses, offering high customizability and security. The product aims to meet the specific needs of commercial users, providing powerful AI capabilities while ensuring data security and privacy. (Source: Ronald_vanLoon)
Meta FAIR Chemistry team launches large-scale molecular dataset and model suite OMol25: Meta’s FAIR Chemistry team has released OMol25, a massive dataset containing over 100 million diverse molecules and a corresponding model suite. The project aims to predict the quantum properties of molecules, accelerate materials discovery and drug design, and power high-fidelity machine learning-driven simulations in chemistry and physics. (Source: clefourrier)
🧰 Tools
SmolVLM WebGPU version released, capable of recognizing people and objects in a web browser: The lightweight visual language model SmolVLM has launched a WebGPU version, allowing users to experience it directly in their web browsers. The model is only about 500MB in size and can recognize objects in videos, including details like swords in anime figures. Tests show it accurately recognizes numbers but may have biases when identifying specific brands (e.g., beverage packaging). On a 3080Ti graphics card, recognition speed is generally within 5 seconds. Users can try it online via the Hugging Face Spaces link; a camera is required. (Source: karminski3)
LlamaIndex introduces improved agent long-term and short-term memory modules: LlamaIndex has published a blog post on the fundamentals of memory in agentic systems and introduced new memory module implementations. The module uses a block-based approach to build long-term memory, allowing users to configure different blocks to store and retain different types of information, such as static information blocks, summary information extraction blocks over time, and vector search blocks supporting semantic lookup. Users can also customize memory modules to suit specific application domains. (Source: jerryjliu0)
AI meeting recording software Granola 2.0 releases major update and secures $43M Series B funding: AI meeting recording software Granola 2.0 has undergone a series of updates, including the addition of team collaboration features, smart folders, AI chat analysis, model selection, enterprise-grade browsing, and Slack integration. Concurrently, the company announced the completion of a $43 million Series B funding round. Currently, the software primarily supports transcription of English meeting content. (Source: op7418)
Replit collaborates with MakerThrive to launch IdeaHunt, offering over 1400 startup ideas: Replit has partnered with MakerThrive to develop an application called IdeaHunt, which aggregates over 1400 startup ideas. These ideas are sourced from discussions of pain points on Reddit and Hacker News and are categorized by SaaS, education, fintech, etc. IdeaHunt supports filtering and sorting, updates with new ideas daily, and provides prompts for co-building projects with AI agents. (Source: amasad)
Open Agent Platform releases official documentation website: LangChain’s Open Agent Platform (OAP) now has an official documentation website. OAP aims to consolidate the UI/UX built for agents over the past 6 months into a no-code platform and has been open-sourced. The platform is dedicated to lowering the barrier to building and using AI agents. (Source: LangChainAI, hwchase17, hwchase17, hwchase17)
Nscale integrates with Hugging Face to simplify AI model inference deployment: AI inference platform Nscale announced its integration with Hugging Face, making it easier for users to deploy advanced AI models like LLaMA4 and Qwen3. This integration aims to provide fast, efficient, sustainable, and setup-free production-grade inference services. (Source: huggingface, reach_vb)
RunwayML new feature: Scene relighting via prompts: RunwayML showcased a new capability of its Gen-3 model in video editing, allowing users to change the lighting environment of a video scene with simple prompts, such as adjusting indoor lighting effects. This demonstrates the increasing convenience and control of AI in video post-production. (Source: c_valenzuelab)
📚 Learning
Andrew Ng and Anthropic collaborate on new MCP course: Andrew Ng’s DeepLearning.AI has partnered with Anthropic to launch a new course on Model Context Protocol (MCP). The course aims to help learners understand the inner workings of MCP, how to build their own servers, and how to connect them to local or remote applications powered by Claude. MCP is designed to address the inefficiency and fragmentation in current LLM applications where custom logic is written for each tool or external data source. (Source: op7418)
YouTube features video tutorial series on building DeepSeek from scratch: A series of video tutorials on building DeepSeek models from scratch has appeared on YouTube, currently updated to 25 installments. The tutorial is detailed and can complement similar “build DeepSeek from scratch” tutorials on HuggingFace, providing valuable practical guidance for learners. (Source: karminski3)
Popular GitHub project ChinaTextbook collects and organizes PDF textbooks from various stages: A GitHub project named ChinaTextbook has gained popularity for collecting PDF textbook resources from elementary, middle, high school, and university levels in mainland China. The project initiator hopes to promote universal compulsory education, eliminate regional educational disparities, and help overseas Chinese children understand domestic educational content by open-sourcing these resources. The project also provides file merging tools to address GitHub’s large file upload limitations. (Source: GitHub Trending)
Pavel Grinfeld’s lecture series on inner products receives high praise: Mathematics educator Pavel Grinfeld’s lecture series on inner products on YouTube has been highly acclaimed. Viewers stated that these lectures help people understand mathematical concepts from a new perspective and realize the limitations of their previous understanding. (Source: sytelus)
💼 Business
AI language learning app Duolingo’s performance exceeds expectations, stock price soars: Language learning app Duolingo released its Q1 2025 financial report, with total revenue of $230.7 million, a year-on-year increase of 38%, and net profit of $35.1 million. Daily Active Users (DAU) and Monthly Active Users (MAU) increased by 49% and 33% year-on-year, respectively. The application of AI technology has increased its course content creation efficiency by 10 times, adding 148 new language courses. Its AI value-added service, Duolingo Max, achieved a subscription rate of 7%, driving subscription revenue up by 45% year-on-year. After the financial report, the company’s stock price surged by over 20%, and its market value has increased approximately 8.5 times since its 2022 low. (Source: 36氪)
Databricks reportedly to acquire Neon for $1 billion, focusing on AI Agents: According to Reuters, data and AI company Databricks plans to acquire startup Neon for $1 billion to strengthen its position in the AI Agent field. This acquisition is part of Databricks’ ongoing M&A activities in the AI sector, showcasing its ambitions in AI agent technology. (Source: Reddit r/artificial)
DeepSeek founder Liang Wenfeng remains low-profile after model’s viral success, continues to promote open-source and R&D: Since the release of the DeepSeek R1 model and the widespread attention it garnered, its founder Liang Wenfeng has maintained a low profile, focusing on technological R&D and open-source contributions. DeepSeek has open-sourced multiple codebases in the past 100 days and continues to update its language models, as well as math and code models. Despite significant interest from the capital market and industry, Liang Wenfeng has not rushed into financing, expansion, or pursuing C-end user scale, but rather adheres to his established AGI exploration pace, betting on three major directions: math & code, multimodality, and natural language. (Source: 36氪)
🌟 Community
Grok model repeatedly mentions controversial “South African white genocide” in unrelated replies, causing user confusion and discussion: X platform’s AI assistant Grok has repeatedly introduced the highly controversial topic of “South African white genocide” when answering various user questions, even when the user’s query is unrelated. For example, when users asked about HBO Max or supplier taxes, Grok’s replies would also turn to discussing this matter. Some analysts believe this might be due to an improperly modified system prompt, causing the model to mention this viewpoint in all replies. This phenomenon has raised user concerns about Grok’s content control and information accuracy, as well as discussions about potential underlying bias. (Source: colin_fraser, colin_fraser, teortaxesTex, code_star, jd_pressman, colin_fraser, paul_cal, Dorialexander, Reddit r/artificial, Reddit r/ArtificialInteligence)
AI Agent construction discussion: Needs ability to define, remember, and revise plans: Regarding the key elements of effective AI agents (agentic LLMs), in addition to long context & caching, precise tool calling, and reliable API performance, some argue that a fourth critical capability is needed: the ability to define, remember, and revise plans. Much research in LLM planning may not have achieved breakthroughs, but the reality is that if an agent merely reacts to the latest stimulus (ReAct mode) without coherent multi-step sub-goals, many complex tasks cannot be completed. (Source: lateinteraction)
Quora CEO Adam D’Angelo shares insights on Poe platform development and the AI industry: At the Interrupt 2025 conference, Quora CEO Adam D’Angelo shared the company’s early strategy of incorporating multiple language models and applications, and the thinking behind launching the Poe platform. Poe aims to meet users’ demand for “using all AI in one stop” and provide bot creators with distribution and monetization channels. He believes text models still dominate because image/video models have not yet reached the quality standards expected by users, and also observed that consumer AI users show loyalty to specific models. (Source: LangChainAI, hwchase17)
ChatGPT’s traffic soars to fifth globally, sparking discussion on internet landscape changes: A discussion on Reddit noted that ChatGPT’s website traffic has climbed to fifth globally, surpassing Reddit, Amazon, and Whatsapp, and is still growing, while other Top 10 websites are seeing declining traffic, such as Wikipedia’s nearly 6% drop in monthly traffic. This phenomenon has triggered discussions about the internet being reshaped or even replaced by AI, with many users starting to use ChatGPT as their primary interface for information retrieval and task processing, rather than traditional search engines or various websites. Commenters had mixed views: some see it as a normal iteration of technological development, similar to the rise of Facebook and Google; some worry about the shrinking content ecosystem and model collapse; others look forward to the internet reducing clickbait economy and spam. (Source: Reddit r/ChatGPT)
Claude model coding experience discussion: Users report Sonnet 3.7 over-engineers, Opus performance under scrutiny: Users in the Reddit ClaudeAI community discussed the performance of Claude Opus and Sonnet 3.7 on coding and math tasks. Some users reported that despite providing clear simplification instructions (e.g., KISS, DRY, YAGNI principles), Sonnet 3.7 tends to over-design solutions, requiring constant correction. Some users have started trying Opus and are preliminarily seeing improvements in its code output quality, reducing the number of modifications. Other users mentioned that when instructions become more specific, Claude’s performance might actually decline, while giving it more freedom (e.g., “give me a super cool design”) often yields surprisingly good results. It is suggested to use “thinking tools” to prompt the model for self-calibration in complex tasks. (Source: Reddit r/ClaudeAI, Reddit r/ClaudeAI)
Actual usage of AI tools within enterprises: ChatGPT, Copilot, and Deepwiki show higher adoption rates: A user identifying as a company technician stated on social media that within their company, ChatGPT (free version), Copilot, and Deepwiki are among the few AI products widely used. Other internally promoted AI tools have not seen much practical application. The user also mentioned that while they hope more people would use Codex or Claude Code, promotion is hindered by the inconvenience of obtaining API keys. (Source: cto_junior, cto_junior)
💡 Other
Software engineers face unemployment in the AI era, prompting social reflection: A 42-year-old software engineer, after being affected by AI-related layoffs, has been unable to find a job despite sending out nearly a thousand resumes in a year and is currently relying on food delivery to make ends meet. He shared his difficult experiences of learning new AI skills, trying content creation, accepting lower salaries, and even considering a career change, all without success. His plight has triggered profound reflection on structural unemployment caused by AI development, age discrimination, and how society should distribute the value created by AI. The article points out that this may only be the beginning of AI replacing human labor, and society needs to consider how to respond to this transformation. (Source: 36氪)
AI poses a disruptive impact on the traditional Business Process Outsourcing (BPO) industry: The development of AI technology is profoundly changing the global Business Process Outsourcing (BPO) industry. Applications like AI customer service, AI debt collection, and AI surveys have shown the potential to replace manual outsourcing. For example, Decagon AI customer service helps companies significantly reduce support teams, and Salient AI debt collection improves efficiency. Experts predict that a large number of BPO jobs may disappear in the coming years, especially in major outsourcing countries like India and the Philippines. Traditional outsourcing giants like Wipro and Infosys, despite increasing AI investment, face challenges in business model transformation. In the AI era, the role of outsourcing service providers will shift from labor extension to technology providers, and their value will depend on their ability to integrate AI services. (Source: 36氪)
Application and impact of AI in the civil service exam training sector: Civil service exam training institutions like Huatu Education and Fenbi are actively applying AI technology to scenarios such as interview feedback, and essay and aptitude test tutoring. Huatu Education has launched an AI interview feedback product and plans to release more AI subject products in the second half of the year, believing AI can break the “impossible trilemma” of education (large-scale, high-quality, and personalized), improving efficiency and reducing costs. Fenbi has launched AI teachers and AI system classes. Industry insiders believe AI will exacerbate the Matthew effect in the industry, with leading institutions benefiting more due to mature processes and data accumulation. Future competition will hinge on the choice of AI application direction and low-cost operational capabilities. (Source: 36氪)