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Data Science News
Medium
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281
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The Silent Struggle of Students No One Talks About
- Success and achievements in student life often come with silent struggles like sleepless nights, self-doubt, and pressure to prove oneself.
- The constant pressure to achieve more can be suffocating, and even when goals are reached, there is little time to appreciate them.
- The fear of an uncertain future, loneliness, and the hidden moments of burnout are often not discussed or acknowledged.
- It is important to break the silence, normalize discussions about struggles, celebrate progress, and support each other in the journey.
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Analyticsindiamag
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109
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New Consumer App ‘Together Chat’ Offers Access to DeepSeek R1 and More
- California-based Together.ai introduced Together Chat, a consumer AI platform that offers access to leading open-source models.
- The platform integrates multiple AI models, including DeepSeek R1 for complex queries, Llama 3.3 70B and Qwen 2.5 72B for intermediate inquiries, Qwen Coder 32B for coding assistance, and Flux Schnell for image generation.
- Together Chat operates as a Progressive Web App and provides users with 10 daily credits for DeepSeek R1 and 50 for other models.
- Together AI recently raised $305 million in Series B funding, with investors including General Catalyst, Prosperity7, Salesforce Ventures, NVIDIA, and Kleiner Perkins.
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Medium
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184
The Dual Sheet Model incorporates real world data: Updated code
- The code snippet demonstrates the Dual Sheet Model (DSM) incorporating real-world data and equations for dark energy studies.
- Real-world data for dark energy values and Hubble parameters are provided for analysis and comparison.
- The code includes interpolations, optimizations, and calculations of observables like effective w(z) and H(z).
- Functions for potentials, densities, Hubble parameters, derivatives, and chi-squared objective are defined and utilized.
- The code optimizes parameters for the DSM using the Nelder-Mead method and presents best-fit parameters lambda, m, and m_tilde.
- Simulations are run with the optimized parameters to compute w(z) and H(z) values for comparison with observational data.
- Plots are generated to show the results of w(z) and H(z) from the DSM simulation against observational data from DESI, CMB, Planck, and Union3.
- The plots illustrate the Dark Energy Equation of State and the Hubble Parameter comparison between DSM and observations.
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VentureBeat
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52
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‘Studio Ghibli’ AI image trend overwhelms OpenAI’s new GPT-4o feature, delaying free tier
- The internet is currently flooded with colorful, smooth anime-style images of famous photographs rendered in the style of Studio Ghibli.
- OpenAI's new update to the GPT-4o model allowed users to create these images using the 'native image generation' feature.
- Users can upload pre-existing images to GPT-4o and receive convincing and amusing results in the style of Studio Ghibli animation.
- OpenAI initially planned to enable this feature for free users but the overwhelming demand delayed its availability for paying subscribers.
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Medium
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242
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ALERT: Manus Just Dropped, and It’s Coming for Your Job
- Manus is an AI system that can perform various tasks, from finding apartments to coding websites.
- It cross-references data and provides tailored reports when searching for apartments.
- It can screen resumes, rank candidates, and generate spreadsheets for job recruiters.
- Using a multi-agent architecture, Manus combines different AI models and a custom task decomposition engine to perform its tasks.
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Medium
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114
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10 Developer Habits That Made Me a 6-Figure Coder
- Developers should use meaningful variable and function names, write docstrings for every function, keep functions small and single-responsibility, and avoid unnecessary complexity.
- Automation is crucial in saving time and improving efficiency in development tasks.
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Towards Data Science
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68
Automate Supply Chain Analytics Workflows with AI Agents using n8n
- The article discusses the use of n8n, a low-code platform, to automate supply chain analytics workflows and build AI agents.
- The author initially explored frameworks like LangChain and LangGraph before discovering the power of n8n.
- n8n is an open-source workflow automation tool that allows easy connection between apps, APIs, and AI model frameworks.
- The example provided in the article demonstrates the ease of replicating an AI agent built with Python using n8n with minimal code.
- The AI Supply Chain Control Tower workflow in n8n includes sub-workflows, chat interfaces, AI agents, and SQL querying.
- The process is smooth and requires limited configuration, making it accessible even for individuals with limited Python skills.
- The article highlights that while n8n simplifies automation, Python is not obsolete and can still be valuable for supply chain analytics.
- n8n offers easy connectivity to various services and the flexibility to enhance analytics products by adding additional connections.
- The author, Samir Saci, is a Supply Chain Engineer who focuses on using data analytics to enhance logistics operations and reduce costs.
- For consulting or advice on analytics and sustainable Supply Chain transformation, the author can be contacted via Logigreen Consulting.
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Medium
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132
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Choosing ML Algorithms: Navigating Complex Data with Confidence
- Choosing the right machine learning algorithm can be a complex journey.
- A project requiring a predictive model led to a transformation in the author's approach.
- The data was complex, messy, and required an algorithm that could handle its intricacies.
- The author realized that understanding and making sense of the data was as important as the prediction itself.
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Towards Data Science
3w
341
AI Agents from Zero to Hero — Part 2
- AI Agents are autonomous programs that perform tasks, make decisions, and communicate with others.
- Agents organize tasks into a logical progression and adapt their strategies in dynamic environments.
- Iterations involve repeating actions with slight changes or improvements in each cycle to refine output or reach optimal solutions.
- Chains are sequences of linked actions where each step depends on the previous one, forming a continuous flow of operations.
- The tutorial demonstrates using iterations and chains for AI Agents, with Python code examples for practical application.
- Tools like YahooFinance APIs and matplotlib are utilized to download and visualize financial data.
- The article introduces a function to process model responses and handle the usage of tools by the Agent.
- By incorporating iterations, the Agent can retry actions multiple times to solve problems and adapt.
- Chains allow for a linear sequence of actions, with outputs of one step used as inputs for subsequent steps, enhancing problem-solving capabilities.
- The structured approach using iterations and chains enhances the reliability of AI Agents, enabling developers to create customized solutions for various scenarios.
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Towards Data Science
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286
Uncertainty Quantification in Machine Learning with an Easy Python Interface
- Uncertainty quantification in machine learning (ML) models is crucial for understanding the precision of predictions, especially in real-world applications.
- ML Uncertainty is a Python package that offers an intuitive interface to estimate uncertainties in ML predictions and model parameters using powerful mathematical methods.
- The package supports linear regression, LASSO regression, ridge regression, elastic net, regression splines, and non-linear regression.
- ML Uncertainty allows for uncertainty estimation in random forests by leveraging bootstrapping techniques to estimate prediction intervals.
- The ErrorPropagation class in ML Uncertainty computes uncertainty in response variables due to uncertainties in input variables and model parameters.
- ML Uncertainty distinguishes itself by enabling uncertainty propagation and catering to various models beyond linear regression.
- The package optimizes computational efficiency by separating model training from uncertainty quantification to offer reliable estimates.
- Future work involves expanding ML Uncertainty to other ML frameworks like PyTorch and TensorFlow, adding support for more models, and improving documentation.
- ML Uncertainty aims to enhance the reliability and interpretability of machine learning models by providing efficient uncertainty quantification tools.
- Contributions from the open-source community are encouraged to enhance the capabilities and applicability of the ML Uncertainty package.
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Medium
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360
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Why I Don’t Recommend Bootcamps or Computer Science Degree Programs
- The writer was unable to find a job as a software engineer despite having some available opportunities.
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21 Likes
Medium
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52
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Jobs in High Demand in 2025
- AI professionals skilled in building, testing, and maintaining AI models are in high demand.
- Cloud computing is widely sought after, providing various internet-based computer services.
- Translation jobs are still in high demand as AI struggles to understand deep feelings and cultural differences.
- Creating mobile apps remains a rewarding and enjoyable skill in high demand.
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Analyticsindiamag
3w
211
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Perplexity Crosses $100 Million in Annualised Revenue
- Perplexity, the AI search engine startup, has achieved over $100 million in annualized revenue.
- Perplexity reached this milestone 20 months after the launch of Perplexity Pro and has achieved 6.3x year-over-year growth.
- Perplexity offers a free plan, a $20 monthly Pro plan, and an enterprise plan for companies. Developers can also integrate its Sonar AI models with APIs.
- Perplexity is in talks to raise funds between $500 million and $1 billion, valuing the company at $18 billion.
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12 Likes
Medium
3w
400
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How Airbnb Measures Listing Lifetime Value
- Airbnb calculates listing lifetime value to understand valuable accommodation listings for guests and help hosts increase listing value.
- Their framework estimates baseline LTV, incremental LTV, and marketing-induced incremental LTV, segmented to inform supply expansion strategy.
- Challenges in lifetime value estimation arise in multi-sided platforms like Airbnb, where transactions of one listing can impact another.
- Incremental LTV is estimated by subtracting cannibalized value from baseline LTV, addressing the challenge of accurately measuring value added by each listing.
- Marketing-induced incremental LTV measures the additional value created by internal initiatives like marketing campaigns for listing improvement tips.
- The estimation framework focuses on accurate baseline LTV, leveraging machine learning and listing features to predict future bookings.
- During the pandemic, Airbnb faced challenges due to changes in travel dynamics, with strategies implemented to improve model accuracy.
- To handle uncertainty, daily adjustments are made to expected values based on accrued values and updated listing features.
- Estimating lifetime value helps Airbnb provide a better user experience and informs decision-making for listings and marketing campaigns.
- This measurement framework may also extend to other applications, such as Airbnb Experiences listings, to determine the value of experiences based on travel trends.
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VentureBeat
3w
79
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Groq and PlayAI just made voice AI sound way more human — here’s how
- Groq and PlayAI have partnered to bring Dialog, an advanced text-to-speech model, to market using Groq's high-speed inference platform.
- The partnership offers one of the most natural-sounding and responsive text-to-speech systems available, addressing shortcomings in existing voice AI technologies.
- Dialog is available in English and Arabic, making it the first voice AI designed for the Middle East region.
- Groq's specialized processing infrastructure allows Dialog to generate text up to 10 times faster than real-time, providing a significant advantage in latency for conversational AI applications.
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