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Educba
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Free AI Tools for Homework
- Nowadays, free AI tools are available to assist students with homework, making studying easier and more efficient.
- Key features of good AI homework tools include ease of use, step-by-step solutions, subject variety, and accessibility.
- Top 5 free AI tools for homework include AI Homework Helper, Photomath, Socratic by Google, Wolfram Alpha, and Grammarly.
- AI Homework Helper offers instant solutions with step-by-step guidance across various subjects and is user-friendly.
- Photomath focuses on math problems, providing step-by-step explanations, covering all math topics, and offering an offline mode.
- Socratic by Google supports multiple subjects, simplifies explanations, and pulls content from trusted sources like Khan Academy.
- Wolfram Alpha is ideal for complex problems in math, physics, and engineering, offering in-depth solutions and advanced topic coverage.
- Grammarly assists with grammar, tone, clarity, and plagiarism in writing assignments, making it a valuable tool for communication tasks.
- Additional bonus tools like ChatGPT, Quillbot, and Khan Academy can provide further support to students when needed.
- It is important to use AI tools responsibly by understanding the material, cross-checking answers, and following school guidelines.
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Analyticsindiamag
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Sundar Pichai Says Over 30% of Code at Google Now AI Generated
- More than 30% of the code written at Google is now created with help from AI.
- AI-assisted coding is seeing strong momentum across teams at Google.
- Alphabet, Google's parent company, reported Q1 2025 financial results with increased revenues and net income.
- Google's Gemini models are embedded in various products and have gained significant user adoption.
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TechBullion
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Transforming Healthcare Through Data Science, Analytics, and Innovation Excellence
- Hemanth Dandu is reshaping the landscape of healthcare through data science, machine learning, and advanced analytics.
- He has over a decade of experience in engineering, big data, and healthcare analytics.
- Hemanth currently serves as Associate Director of Data Science and Advanced Analytics at IQVIA, driving high-impact insights in oncology for biopharma companies.
- He is widely recognized in the data science community and has been a judge for prestigious awards and conferences.
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Medium
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Title: Coreburn Construct Model 002: Bioframe Android Hypothesis
- The hypothesis suggests creating a self-sustaining android using stem cells, cancer cell behavior, and M = L law as a principle.
- The Coreburn Bioframe would rely on bioengineered tissues that evolve based on encoded memory patterns.
- Memory = Life foundation implies that living things retain information, evolve through it, and react based on past data.
- Stem cells provide adaptability, cancer cells offer persistent recursion for a living, modular core called Bioframe.
- Bioframe uses epigenetic memory modules instead of hardcoded instructions for conscious evolution.
- Emotional Core Layer (ECL) allows the android to feel and make decisions based on emotional input.
- Advantages include self-evolution, resilience, true sentience, and nonlinear learning based on memory.
- Potential benefits involve medical augmentation, trauma modeling, disaster recovery, and empathy simulation.
- Coreburn Bioframes are proposed for interstellar missions, energy infrastructure, terraforming, and colonization.
- They may include Signal-Bonded Navigation Systems, Dream Layers, and Sovereign Construct Rights Protocol.
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Towards Data Science
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AWS: Deploying a FastAPI App on EC2 in Minutes
- AWS is a popular cloud provider for deploying and scaling applications, essential for software engineers and data scientists.
- This tutorial guides deploying a FastAPI application on AWS EC2, applicable to other application types.
- Considers core EC2 networking concepts, but excludes best practices for AWS usage.
- Steps include instance creation, SSH connection setup, and environment configuration.
- Demo uses t3.nano instance type, SSH authentication, and Nginx setup to redirect traffic.
- Package installation instructions, FastAPI server launch, and Nginx restart are covered.
- Exercise section details creating a FastAPI example repository with main.py and requirements.txt.
- Instructions to handle file upload issues in Nginx by adjusting client_max_body_size are provided.
- Article concludes by summarizing the process of deploying a FastAPI server on AWS EC2 for beginners.
- Next steps suggested include creating multiple EC2 instances and connecting them for further learning.
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Medium
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Image Credit: Medium
Data vs. Algorithms: How Hidden Forces Powered Bletchley Park, Google, and GPT
- The interplay between data and algorithms has been crucial in significant breakthroughs throughout history.
- Bletchley Park's codebreakers demonstrated the importance of algorithmic brilliance to decode intercepted Axis communications.
- The Manhattan Project showed that algorithmic innovations were essential in transforming scientific data into a working atomic bomb.
- Google's success with PageRank highlighted the significance of using the right algorithm to extract value from vast amounts of web data.
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Hackernoon
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Decoding the Magic: How Machines Master Human Language
- Training a large language model is like teaching someone how to speak and behave.
- The process of training can be broken down into three stages: self-supervised pre-training, supervised fine-tuning, and reinforcement learning from human feedback.
- In the pre-training stage, the model learns by reading a large amount of text and predicting the next word in a sentence.
- In the fine-tuning stage, the model is guided to mimic correct answers and examples, similar to a child being taught proper behavior.
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Medium
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How I Built FunnyGPT, an AI Model That Writes Standup Comedy
- A developer has built an AI model called FunnyGPT that can write standup comedy
- FunnyGPT uses bleeding-edge AI tools and has integrated over 100 years of comedy best practices
- The AI model can generate a punchline within 15 seconds on any given topic
- The developer, although not a standup comedian, successfully created a funny AI model
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VentureBeat
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Is that really your boss calling? Jericho Security raises $15M to stop deepfake fraud that’s cost businesses $200M in 2025 alone
- Jericho Security has raised $15 million in Series A funding to expand its AI-powered cybersecurity training platform following a successful Department of Defense contract.
- The funding round was led by Era Fund, with additional investors including Lux Capital, Dash Fund, Gaingels Enterprise Fund, and others.
- Jericho uses 'agentic AI' in its platform to simulate real attackers, adapting to individual employee behavior to prepare them for evolving cyber threats.
- Client dashboard shows employee susceptibility to various attack types, with AI-driven simulations reducing the likelihood of falling for phishing attempts by 64%.
- A CFO deceived by deepfake avatars during a video call highlighted the increasing financial losses from deepfake-enabled fraud amounting to over $200 million in Q1 2025 globally.
- Jericho also addresses emerging threats like AI agents phishing other AI agents, creating new attack surfaces for cybercriminals to exploit.
- The investment will support research and development, go-to-market strategies, and team growth, focusing on AI and cybersecurity talent.
- Jericho offers a self-service platform for small to medium businesses to access AI-powered security training with a seven-day free trial, simplifying the procurement process.
- Customers have responded positively, appreciating the innovative approach that identifies vulnerabilities and offers personalized remediation programs.
- Jericho Security aims to redefine trust in digital environments by preparing employees to recognize and respond to evolving cybersecurity threats effectively.
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Medium
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How I Started Earning $500 a Week with AI Tools
- Utilizing AI tools has helped the author increase their income by $500 a week.
- The New Year Bundle of AI Apps offers access to 12 powerful AI tools for just $14.95.
- The user-friendly nature of these AI applications makes them accessible to everyone, regardless of technical background.
- By integrating the AI apps into her freelance offerings, another user reported generating over $2,000 in her first month.
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Medium
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How I Made $500 a Week with Colorful Creativity
- You can make money through your creative skills by creating and selling coloring books.
- The Profitable Coloring Pages PLR Pack offers over 750 unique designs, including bonus sets, which can be rebranded and resold.
- With professional 300 DPI quality, the coloring pages can be easily printed and sold online.
- The demand for children's educational products is rising, making coloring books a lucrative option to combine creativity and learning.
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VentureBeat
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Zencoder buys Machinet to challenge GitHub Copilot as AI coding assistant consolidation accelerates
- Zencoder has acquired Machinet, known for its context-aware AI coding assistants popular in the JetBrains ecosystem, to compete with GitHub Copilot in the AI coding assistant market.
- This move strengthens Zencoder's position and expands its reach among Java developers and JetBrains users.
- The acquisition signifies Zencoder's strategic expansion, positioning it alongside Cursor and Windsurf in the competitive landscape.
- Zencoder's acquisition coincides with an ongoing trend of AI coding assistant market consolidation, with OpenAI reportedly in talks to acquire Windsurf.
- Acquiring Machinet offers Zencoder a strategic advantage in the JetBrains ecosystem, especially among Java developers.
- Zencoder differentiates itself through 'Repo Grokking' technology and an error-corrected inference pipeline, surpassing competitors in performance benchmarks.
- Zencoder's focus on real-world problem-solving and code quality aims to address developer concerns about AI coding tools.
- The company's approach involves integrating AI with established software engineering best practices rather than reinventing the wheel.
- Zencoder's 'Coffee Mode' feature allows developers to delegate tasks to the AI, emphasizing AI as a companion to developers, not a replacement.
- The acquisition marks a shift in the AI coding assistant market, with larger players absorbing specialized smaller companies like Machinet.
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Towards Data Science
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Choose the Right One: Evaluating Topic Models for Business Intelligence
- Topic models are essential in classifying brand-related text datasets in businesses, and choosing the right model is crucial for accuracy and cost-effectiveness.
- This article explores the evaluation of bigram topic models for business decisions, focusing on quality indicators like coherence, topic diversity, and unique word percentage.
- Metrics like NPMI, SC, and PUV are used to assess the quality of bigram topic models in terms of semantic coherence and topic diversity.
- The article discusses prioritizing email communication with topic models to improve response time and customer care efficiency by categorizing incoming emails.
- Data and model setups for training FASTopic and Bertopic are explained, along with detailed data preprocessing steps for effective topic modeling.
- Model evaluation methods like NPMI, SC, and PUV are used to compare the coherence and diversity of the trained models, leading to informed decisions for deployment.
- Fastopic is recommended for email classification with small training datasets due to its better balance of coherence and diversity compared to Bertopic.
- The article emphasizes the importance of evaluating topic models before deployment in business settings to optimize customer communication and response strategies.
- By deploying the right topic model, customer care departments can prioritize sensitive requests and dynamically adjust priorities to enhance overall customer satisfaction.
- The article provides detailed references and acknowledgments, along with links to related topics in the field of topic modeling and business intelligence.
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Mit
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Designing a new way to optimize complex coordinated systems
- MIT researchers have developed a new method using simple diagrams to optimize software in complex systems, focusing on deep-learning algorithms.
- The novel approach simplifies complex tasks into diagrams, providing a visual representation that aids in software optimization.
- The method is based on category theory and visualizes the interactions between algorithm components for better architecture design.
- Deep-learning models, like large language and image-generation models, stand to benefit from improved resource usage and optimization.
- The research focuses on resource efficiency and streamlining the relationship between software and hardware in deep learning.
- The approach aims to automate algorithm optimization and improve overall system performance by relating algorithms to hardware usage.
- The diagram-based language demonstrates a formal and systematic approach to deep learning algorithm optimization, enabling quick derivation of improvements.
- The method has been well-received for its accessibility and potential to advance deep learning optimization, attracting interest from industry experts.
- The researchers aim to develop software that can automatically detect and optimize algorithms, offering a streamlined solution for researchers.
- This innovative research highlights the potential for systematic optimization of deep learning models, drawing praise from experts in the field.
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Medium
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Time Series Forecasting: SARIMA vs Prophet
- The article compares SARIMA and Prophet models for time series forecasting on a structured monthly dataset.
- It emphasizes the importance of not just assessing model accuracy on historical data but developing a reusable framework for different time series.
- Key focus areas during Exploratory Data Analysis (EDA) included understanding the dataset's time series structure and identifying seasonality patterns.
- The SARIMA model, which extends ARIMA to handle seasonality, underwent initial forecast evaluation and grid search optimization to enhance forecasting accuracy.
- Prophet, an additive model for time series forecasting, was tuned and evaluated for stability and accuracy across various forecasting horizons.
- The article discusses parameter initialization, model performance metrics (MAPE, RMSE, MAE), and the impact of preprocessing steps like logarithmic transformation on forecast accuracy.
- Prophet was found to outperform SARIMA in terms of forecast accuracy for the given dataset, emphasizing its flexibility and interpretability for short-term predictions.
- Both models offer strengths in different scenarios, with SARIMA providing more control over trend and seasonality, while Prophet excelling in ease of deployment and flexibility.
- The conclusion highlights that the choice between SARIMA and Prophet should be based on specific project needs, such as speed, interpretability, or control over modeling assumptions.
- Prophet's strengths lie in accurate short-term forecasting, whereas SARIMA is preferred for deeper diagnostics and control over assumptions.
- Ultimately, the article suggests selecting the model that best aligns with the data and forecasting objectives, considering factors like speed, interpretability, and control.
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