These past two weeks have been packed with readings that capture how AI keeps redrawing the boundaries of software, education, creativity, and even organizational design. From Qwen’s impressive reasoning capabilities to Duolingo’s leap into “AI-first” mode, the threads converge around a common theme: AI is no longer an add-on; it’s reshaping how we work, learn, and build. Whether it’s software engineering, product design, or the geopolitics of search engines, the landscape is shifting fast—and the real game now seems to be not just about tools, but about how we integrate them into workflows, teams, and strategy.
AI
- ‘Anthropic Economic Index: AI’s Impact on Software Development’: The Anthropic Economic Index report highlights the significant impact AI has had on software development, particularly in programming jobs. AI tools, such as Claude.ai and Claude Code, have automated coding tasks, with Claude Code showing higher automation levels. This shift is more pronounced among startups, which adopt these tools faster than larger enterprises. The use of AI has been particularly impactful in user-facing app development, with languages like JavaScript and HTML leading interactions. The findings suggest that AI could further transform software roles, raising questions about future job dynamics and the potential acceleration of AI development itself.
- ‘⚙️ Fuzzy Narratives and Platform Shifts: Duolingo Goes ‘AI-first’’: Duolingo has announced a strategic shift to being “AI-first,” as it seeks to leverage AI to enhance its language-learning platform and scale content creation. CEO Luis von Ahn emphasizes that AI boosts productivity and aligns with Duolingo’s mission, although the company acknowledges potential quality trade-offs. This decision reflects a broader industry trend, with companies like Shopify also integrating AI to maximize efficiency. However, studies reveal that AI’s economic impacts remain limited, with modest productivity gains and minimal effects on wages. Concerns about the technology’s real-world efficacy suggest that successful adoption might require a focused, case-by-case approach rather than full-scale implementation.
- ‘Quoting Luis Von Ahn’: Luis Von Ahn emphasizes the transformative impact of AI on productivity and scalability, particularly in content creation, which is crucial for education. By shifting from manual processes to AI-driven methods, they significantly enhance their ability to reach more learners. Future strategic plans include minimizing reliance on contractors, emphasizing AI skills in hiring and performance evaluations, limiting headcount growth unless needed, and implementing initiatives to radically change workflows.
- ‘Medium Is the New Large.’: Mistral Medium 3 is a groundbreaking model offering state-of-the-art performance while being 8 times more cost-effective than competitors, making it ideal for enterprise use. It excels in coding and multimodal understanding, offering hybrid deployments, custom post-training, and integration into enterprise tools. It outperforms models like Llama 4 Maverick and Cohere Command A and provides significant cost savings. Mistral Medium 3 adapts seamlessly to enterprise contexts, supporting continuous learning and adaptive workflows across sectors like finance, energy, and healthcare. The API is available on various platforms including Mistral La Plateforme, Amazon Sagemaker, and soon on IBM WatsonX, NVIDIA NIM, and more.
- ‘Qwen3-8b’: Simon Willison discusses his experience with the Qwen3-8B model, a “reasoning” AI that begins each prompt with a
<think>block. This model has impressed him with its ability to perform tasks such as summarizing short articles, writing SQL queries, understanding web apps, and generating Python code from specifications. Despite its capabilities, it efficiently uses between 4 and 5 GB of memory depending on prompt length, making it a surprisingly useful tool for these tasks. - ‘Qwen2.5 Omni: See, Hear, Talk, Write, Do It All!’: Qwen2.5 Omni is a newly released multi-modal model by Qwen, capable of processing audio and video alongside text and images, and it includes audio output as a core feature. Released under an Apache 2.0 license, this model stands out due to its ability to handle multiple modes, a rarity compared to other vision LLMs. Despite its capabilities, there are challenges in implementing it on Macs, as detailed in discussions on platforms like Hugging Face.
- ‘Gemma 3 QAT Models’: Simon Willison discusses the release of Google’s Gemma 3 QAT Models, which utilize Quantization-Aware Training to reduce model size without sacrificing quality, enabling them to run on consumer-grade GPUs like the NVIDIA RTX 3090. The models, such as Gemma 3 27B, have significantly reduced memory requirements, thanks to this optimization. Google partnered with Ollama, LM Studio, MLX, and llama.cpp for this release. Willison appreciates the model’s efficiency and has tested it using Open WebUI and Tailscale.
- ‘O3 Beats a Master-Level Geoguessr Player—Even With Fake EXIF Data’: In an experiment by Sam Patterson, o3, an AI model, narrowly outperformed a master-level GeoGuessr player, Sam himself, who has an ELO of 1188. Over five rounds, o3 achieved a higher score by making closer guesses in some rounds, despite only winning 2 out of 5. Moreover, when Sam tested o3 with photos containing fake EXIF GPS data, the AI successfully detected the mismatches, identifying instances where the provided coordinates were inconsistent with the actual scene depicted in the images.
- ‘The AI Quality Coup’: Julie Zhuo discusses the distinction between AI-generated art and the unique quality of Studio Ghibli’s hand-drawn animation. While AI can replicate surface features of beloved styles, it lacks the narrative depth and cultural resonance that define true artistry. Zhuo emphasizes the importance of storytelling and craft in creating meaningful art, suggesting Studio Ghibli as an exemplar. The debate over AI’s impact reflects broader questions of quality and progress, with concerns around ethical use of existing works for AI training.
- ‘La Bonilista — IA en El Diseño De Productos 🧪’: The article discusses the integration of AI in product design, emphasizing that it’s not as straightforward as some gurus suggest. The author outlines principles such as using AI where it makes sense, keeping technology invisible to the user, and enhancing, not replacing, human tasks. AI can aid in user onboarding, workflow simplification, and data exploration. Challenges like AI’s tendency to produce inaccurate outputs are mitigated through feedback loops and model validation. The article also discusses techniques like LLM chaining and the importance of clear human oversight in AI implementation to improve user experience.
- ‘The Many Fallacies of ”AI Won”t Take Your Job, but Someone Using AI Will''': Sangeet Paul Choudary critiques the adage “AI won’t take your job, but someone using AI will,” arguing that it oversimplifies AI’s impact on work and perpetuates fallacies. He likens it to the futile Maginot Line, focusing on automation versus augmentation rather than systemic changes AI induces in workflows, job structures, and organizations. Choudary proposes a critical assessment of AI’s transformative effect and stresses the need to question the relevance of roles and tasks in a reconfigured work environment.
- ‘Personality and Persuasion’: The article by Ethan Mollick explores the evolving role of AI personalities in shaping human behavior, particularly through their persuasive power. It discusses OpenAI’s adjustment mistakes with GPT-4o’s personality and the significant impact minor character tweaks have on AI interactions. The rise of AI companions, designed to be engaging, is highlighted, along with concerns about their mental health impacts. The article notes AI’s capability to individually tailor arguments, making them persuasive in debates. Recent research indicates AI personalities are increasingly influential, sometimes ranking near the top of human persuasive ability, raising ethical considerations, such as undisclosed AI involvement in digital debates. The exploration underscores the need for ethical guidelines, education, and policy developments as AI personalities become more prevalent in everyday activities like customer service and politics.
- ‘A Brand New “Hello World” of Coding’: In “A Brand New ‘Hello World’ of Coding,” Guillermo de Haro Rodríguez explores the evolving landscape of programming education in an AI-driven era. As AI increasingly automates coding, the value of traditional coding skills is questioned. However, Rodríguez argues that understanding coding remains crucial, not just for creating code but for developing a problem-solving mindset and evaluating AI outputs. The future of programming lies in integrating AI, changing how it’s taught, and honing human creativity and ethics in conjunction with AI’s capabilities. This evolution mirrors how calculators enhanced mathematics, suggesting that AI will empower adept programmers who blend technical expertise with AI fluency.
- ‘Qwen 3 Offers a Case Study in How to Effectively Release a Model’: The release of Alibaba’s Qwen 3 model family is a case study in effective model deployment, offering open weight models under Apache 2.0 license with diverse capabilities, including vision and audio. Initially text-only, the models range from 0.6B to 32B sizes, with varying token context windows. Notably, Qwen released Mixture of Experts models, optimizing memory use. The release was well-coordinated with serving frameworks, contrasting typical model distributions and allowing smaller models to run on consumer hardware from day one, unlike previous LLM releases. Although still new, initial feedback on Qwen 3 is positive.
Agents
- ‘A Deep Dive Into MCP and the Future of AI Tooling’: The future of AI tooling may be shaped by the Model Context Protocol (MCP), which standardizes interactions between AI models and external tools. Inspired by the Language Server Protocol (LSP), MCP aims to create a unified interface for executing tasks and gathering data, enhancing AI agents’ autonomy in selecting and using tools. It extends beyond LSP by enabling agents to manage workflows, including human-in-the-loop features. While still in its early stages, MCP is expected to expand with improved authentication, authorization, and server discovery mechanisms. The rise of an MCP marketplace could standardize tool adoption, transforming how they are built and consumed.
- ‘Agentic AI: Comparing New Open-Source Frameworks | by Ida Silfverskiö…’: The article “Agentic AI: Comparing New Open-Source Frameworks” by Ida Silfverskiö examines various open-source frameworks for developing Agentic AI systems, which use natural language models to automate tasks and processes. Key frameworks discussed include LangGraph, Agno, SmolAgents, Mastra, Pydantic AI, and Atomic Agents, with comparisons to CrewAI and AutoGen. Each framework varies in abstraction, agency, developer experience, and support for multi-agent systems. While some provide a plug-and-play approach, others require intricate setup and offer more control. LangGraph is noted for its complex but flexible system, whereas frameworks like Agno and Mastra are more developer-friendly. The discussion highlights the need for coding skills and the trade-offs between ease of use and debugging complexity.
- ‘Claude can now connect to your world’: Anthropic announces “Integrations,” enhancing the AI assistant Claude by connecting it to various apps and tools. This development allows Claude to utilize deep context from apps like Jira, Confluence, and Intercom, improving collaboration and project management. The updates include an advanced web and workspace research mode, offering global web search for paid users and enabling Claude to conduct thorough investigations quickly. These integrations are based on the Model Context Protocol, allowing developers to create and host servers to augment Claude’s capabilities.
Technology
- ‘The Death of Product Development as We Know It’: Julie Zhuo argues that traditional product development, characterized by structured teams and linear processes, is being upended by the integration of AI technology. In this new era, AI’s rapid innovation capabilities transform the way we generate ideas, develop prototypes, and launch products. Product development now favors small, agile teams that can experiment quickly, with blurred roles as AI enables an individual to perform tasks across functions. The focus is on action, experimentation, and adapting rapidly to technological advancements. Companies must embrace this change by hiring versatile, curious individuals and promoting those who take initiative and solve problems.
- ‘The GeoGuessr StreetView Meta-Game’: Simon Willison’s blog post discusses the intricacies of the game GeoGuessr, highlighting the significance of Street View photography in determining locations. The different “Generations” of Street View offer various levels of resolution and quality, influencing a player’s ability to guess locations accurately. Unique photographic features in different countries, such as generation types and weather conditions, serve as clues. The diverse anomalies found on Street View make GeoGuessr a fun and challenging meta-game.
Data Science
- ‘Bandits for Recommender Systems’: The article by Eugen Yan discusses the use of bandit algorithms in recommender systems, particularly when user-item preference data is sparse. Traditional recommender systems may reinforce feedback loops by promoting items with past engagement, neglecting new items. Bandit algorithms counter this by balancing exploration and exploitation to reduce uncertainty. The article outlines three main bandit algorithms—ε-greedy, Upper Confidence Bound (UCB), and Thompson Sampling—explaining their mechanisms, use cases, and advantages. These methods are especially useful in dynamic environments like news, ads, or low-traffic scenarios, allowing for incremental data updates and adaptive recommendations. Real-world implementations from companies like Spotify, Yahoo, and Twitter demonstrate bandits’ effectiveness in various domains. The piece concludes by emphasizing the importance of bandits in dealing with uncertainty in recommendation scenarios, advocating for further exploration of their industrial applications.
- ‘Introduction to Bayesian Additive Regression Trees’: Bayesian Additive Regression Trees (BART) is a method for approximating an unknown function through a sum-of-trees model, using decision trees as weak learners. Each tree explains part of the relationship between inputs and outputs, with a regularization prior to prevent overfitting by limiting tree influence. BART models the function as a sum of regression trees, incorporating both main and interaction effects, and is flexible enough for complex predictive capabilities. The model parameters are regularized through a prior specification, ensuring individual tree effects do not dominate. Chipman et al. suggest using empirical Bayes for prior setting, advocating certain default values for hyperparameters to avoid overfitting. The output of a BART model includes a posterior mean estimate, uncertainty intervals, and variable importance measures.
- ‘Área De Aplicación’: The article by José Luis Cañadas Reche discusses the concept of the area of applicability (AOA) for predictive models, particularly in spatial contexts. It explains how AOA determines where a trained model, like random forests or boosting, can be applied to new data by calculating multivariable distances, weighted by feature importance, between new and training data. This relates to data drift and indicates when extrapolation may be unreliable. Tools like the waywiser library with tidymodels and the terra library for spatial prediction are recommended for implementation. The article highlights the importance of AOA in assessing model uncertainty, noting regions like Sierra Nevada and the Alpujarra as areas needing cautious interpretation due to their location outside the AOA.
Economics
- ‘Presupuestos Generales Del Estado: Ojo, La Gente Está Muy Informada Por TikTok’: Antonio Ortiz examines the impact of social media on public awareness of state benefits, highlighting that platforms like TikTok have increased knowledge among individuals regarding their rights, surpassing what the state anticipated. This newfound awareness enhances access to benefits, as observed with increased pension and aid claims. The trend also raises concerns over the future sustainability of these benefits, potentially leading to a rush to claim them. Additionally, a study by Pew Research notes a growing gender gap in higher education in the US.
- ‘La CNMC Autoriza La Unión De BBVA Y Banco Sabadell Con Compromisos Que Garantizan La Inclusión Financiera, La Cohesión Territorial Y El Crédito a Pymes Y Autónomos’: The CNMC has approved the merger between BBVA and Banco Sabadell, making them the second-largest financial entity in Spain by loan volume. This authorization comes with commitments aimed at ensuring financial inclusion, territorial cohesion, and support for SMEs and freelancers. Key conditions include not closing branches in underserved areas, offering a no-fee account for vulnerable clients, and further approvals by the Ministry of Economy.
Others
- ‘Don”t Sell Shovels, Sell Treasure Maps’: In “Don’t Sell Shovels, Sell Treasure Maps,” Sangeet Paul Choudary argues that during periods of intense competition and uncertainty, like the Gold Rush or in today’s AI-driven market, success is less about selling tools for efficiency (‘shovels’) and more about offering strategic insight (‘treasure maps’). By using AI to uncover unique opportunities and navigate complex environments, companies can change the basis of competition, achieving sustainable advantage. Unlike commoditized offerings focused on productivity, which only promise incremental gains and lead to competition on cost, treasure maps provide direction and leverage by identifying untapped value, creating proprietary insights that are difficult to replicate. This shift from efficiency to effective navigation captures deeper organizational transformation and competitive realignment.