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What is OpenAI Q*? A deeper look at the Q* Model as a combination of A* algorithms and Deep Q-learning networks.
Embark on a journey of discovery with our podcast, ‘What is OpenAI Q*? A Deeper Look at the Q* Model’. Dive into the cutting-edge world of AI as we unravel the mysteries of OpenAI’s Q* model, a groundbreaking blend of A* algorithms and Deep Q-learning networks. 🌟🤖
In this detailed exploration, we dissect the components of the Q* model, explaining how A* algorithms’ pathfinding prowess synergizes with the adaptive decision-making capabilities of Deep Q-learning networks. This video is perfect for anyone curious about the intricacies of AI models and their real-world applications.
Understand the significance of this fusion in AI technology and how it’s pushing the boundaries of machine learning, problem-solving, and strategic planning. We also delve into the potential implications of Q* in various sectors, discussing both the exciting possibilities and the ethical considerations.
Join the conversation about the future of AI and share your thoughts on how models like Q* are shaping the landscape. Don’t forget to like, share, and subscribe for more deep dives into the fascinating world of artificial intelligence! #OpenAIQStar #AStarAlgorithms #DeepQLearning #ArtificialIntelligence #MachineLearningInnovation”
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Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence and keeps you up to date with the latest AI trends. Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover rumors surrounding a groundbreaking AI called Q*, OpenAI’s leaked AI breakthrough called Q* and DeepMind’s similar project, the potential of AI replacing human jobs in tasks like wire sending, and a recommended book called “AI Unraveled” that answers frequently asked questions about artificial intelligence.
Rumors have been circulating about a groundbreaking AI known as Q* (pronounced Q-Star), which is closely tied to a series of chaotic events that disrupted OpenAI following the sudden dismissal of their CEO, Sam Altman. In this discussion, we will explore the implications of Altman’s firing, speculate on potential reasons behind it, and consider Microsoft’s pursuit of a monopoly on highly efficient AI technologies.
To comprehend the significance of Q*, it is essential to delve into the theory of combining Q-learning and A* algorithms. Q* is an AI that excels in grade-school mathematics without relying on external aids like Wolfram. This achievement is revolutionary and challenges common perceptions of AI as mere information repeaters and stochastic parrots. Q* showcases iterative learning, intricate logic, and highly effective long-term strategizing, potentially paving the way for advancements in scientific research and breaking down previously insurmountable barriers.
Let’s first understand A* algorithms and Q-learning to grasp the context in which Q* operates. A* algorithms are powerful tools used to find the shortest path between two points in a graph or map while efficiently navigating obstacles. These algorithms excel at optimizing route planning when efficiency is crucial. In the case of chatbot AI, A* algorithms are used to traverse complex information landscapes and locate the most relevant responses or solutions for user queries.
On the other hand, Q-learning involves providing the AI with a constantly expanding cheat sheet to help it make the best decisions based on past experiences. However, in complex scenarios with numerous states and actions, maintaining a large cheat sheet becomes impractical. Deep Q-learning addresses this challenge by utilizing neural networks to approximate the Q-value function, making it more efficient. Instead of a colossal Q-table, the network maps input states to action-Q-value pairs, providing a compact cheat sheet to navigate complex scenarios efficiently. This approach allows AI agents to choose actions using the Epsilon-Greedy approach, sometimes exploring randomly and sometimes relying on the best-known actions predicted by the networks. DQNs (Deep Q-networks) typically use two neural networks—the main and target networks—which periodically synchronize their weights, enhancing learning and stabilizing the overall process. This synchronization is crucial for achieving self-improvement, which is a remarkable feat. Additionally, the Bellman equation plays a role in updating weights using Experience replay, a sampling and training technique based on past actions, which allows the AI to learn in small batches without requiring training after every step.
Q* represents more than a math prodigy; it signifies the potential to scale abstract goal navigation, enabling highly efficient, realistic, and logical planning for any query or goal. However, with such capabilities come challenges.
One challenge is web crawling and navigating complex websites. Just as a robot solving a maze may encounter convoluted pathways and dead ends, the web is labyrinthine and filled with myriad paths. While A* algorithms aid in seeking the shortest path, intricate websites or information silos can confuse the AI, leading it astray. Furthermore, the speed of algorithm updates may lag behind the expansion of the web, potentially hindering the AI’s ability to adapt promptly to changes in website structures or emerging information.
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Another challenge arises in the application of Q-learning to high-dimensional data. The web contains various data types, from text to multimedia and interactive elements. Deep Q-learning struggles with high-dimensional data, where the number of features exceeds the number of observations. In such cases, if the AI encounters sites with complex structures or extensive multimedia content, efficiently processing such information becomes a significant challenge.
To address these issues, a delicate balance must be struck between optimizing pathfinding efficiency and adapting swiftly to the dynamic nature of the web. This balance ensures that users receive the most relevant and efficient solutions to their queries.
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In conclusion, speculations surrounding Q* and the Gemini models suggest that enabling AI to plan is a highly rewarding but risky endeavor. As we continue researching and developing these technologies, it is crucial to prioritize AI safety protocols and put guardrails in place. This precautionary approach prevents the potential for AI to turn against us. Are we on the brink of an AI paradigm shift, or are these rumors mere distractions? Share your thoughts and join in this evolving AI saga—a front-row seat to the future!
Please note that the information presented here is based on speculation sourced from various news articles, research, and rumors surrounding Q*. Hence, it is advisable to approach this discussion with caution and consider it in light of further developments in the field.
How the Rumors about Q* Started
There have been recent rumors surrounding a supposed AI breakthrough called Q*, which allegedly involves a combination of Q-learning and A*. These rumors were initially sparked when OpenAI, the renowned artificial intelligence research organization, accidentally leaked information about this groundbreaking development, specifically mentioning Q*’s impressive ability to ace grade-school math. However, it is crucial to note that these rumors were subsequently refuted by OpenAI.
It is worth mentioning that DeepMind, another prominent player in the AI field, is also working on a similar project called Gemini. Gemina is based on AlphaGo-style Monte Carlo Tree Search and aims to scale up the capabilities of these algorithms. The scalability of such systems is crucial in planning for increasingly abstract goals and achieving agentic behavior. These concepts have been extensively discussed and explored within the academic community for some time.
The origin of the rumors can be traced back to a letter sent by several staff researchers at OpenAI to the organization’s board of directors. The letter served as a warning highlighting the potential threat to humanity posed by a powerful AI discovery. This letter specifically referenced the supposed breakthrough known as Q* (pronounced Q-Star) and its implications.
Mira Murati, a representative of OpenAI, confirmed that the letter regarding the AI breakthrough was directly responsible for the subsequent actions taken by the board. The new model, when provided with vast computing resources, demonstrated the ability to solve certain mathematical problems. Although it performed at the level of grade-school students in mathematics, the researchers’ optimism about Q*’s future success grew due to its proficiency in such tests.
A notable theory regarding the nature of OpenAI’s alleged breakthrough is that Q* may be related to Q-learning. One possibility is that Q* represents the optimal solution of the Bellman equation. Another hypothesis suggests that Q* could be a combination of the A* algorithm and Q-learning. Additionally, some speculate that Q* might involve AlphaGo-style Monte Carlo Tree Search of the token trajectory. This idea builds upon previous research, such as AlphaCode, which demonstrated significant improvements in competitive programming through brute-force sampling in an LLM (Language and Learning Model). These speculations lead many to believe that Q* might be focused on solving math problems effectively.
Considering DeepMind’s involvement, experts also draw parallels between their Gemini project and OpenAI’s Q*. Gemini aims to combine the strengths of AlphaGo-type systems, particularly in terms of language capabilities, with new innovations that are expected to be quite intriguing. Demis Hassabis, a prominent figure at DeepMind, stated that Gemini would utilize AlphaZero-based MCTS (Monte Carlo Tree Search) through chains of thought. This aligns with DeepMind Chief AGI scientist Shane Legg’s perspective that starting a search is crucial for creative problem-solving.
It is important to note that amidst the excitement and speculation surrounding OpenAI’s alleged breakthrough, the academic community has already extensively explored similar ideas. In the past six months alone, numerous papers have discussed the combination of tree-of-thought, graph search, state-space reinforcement learning, and LLMs (Language and Learning Models). This context reminds us that while Q* might be a significant development, it is not entirely unprecedented.
OpenAI’s spokesperson, Lindsey Held Bolton, has officially rebuked the rumors surrounding Q*. In a statement provided to The Verge, Bolton clarified that Mira Murati only informed employees about the media reports regarding the situation and did not comment on the accuracy of the information.
In conclusion, rumors regarding OpenAI’s Q* project have generated significant interest and speculation. The alleged breakthrough combines concepts from Q-learning and A*, potentially leading to advancements in solving math problems. Furthermore, DeepMind’s Gemini project shares similarities with Q*, aiming to integrate the strengths of AlphaGo-type systems with language capabilities. While the academic community has explored similar ideas extensively, the potential impact of Q* and Gemini on planning for abstract goals and achieving agentic behavior remains an exciting prospect within the field of artificial intelligence.
In simple terms, long-range planning and multi-modal models together create an economic agent. Allow me to paint a scenario for you: Picture yourself working at a bank. A notification appears, asking what you are currently doing. You reply, “sending a wire for a customer.” An AI system observes your actions, noting a path and policy for mimicking the process.
The next time you mention “sending a wire for a customer,” the AI system initiates the learned process. However, it may make a few errors, requiring your guidance to correct them. The AI system then repeats this learning process with all 500 individuals in your job role.
Within a week, it becomes capable of recognizing incoming emails, extracting relevant information, navigating to the wire sending window, completing the required information, and ultimately sending the wire.
This approach combines long-term planning, a reward system, and reinforcement learning policies, akin to Q* A* methods. If planning and reinforcing actions through a multi-modal AI prove successful, it is possible that jobs traditionally carried out by humans using keyboards could become obsolete within the span of 1 to 3 years.
If you are keen to enhance your knowledge about artificial intelligence, there is an invaluable resource that can provide the answers you seek. “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” is a must-have book that can help expand your understanding of this fascinating field. You can easily find this essential book at various reputable online platforms such as Etsy, Shopify, Apple, Google, or Amazon.
AI Unraveled offers a comprehensive exploration of commonly asked questions about artificial intelligence. With its informative and insightful content, this book unravels the complexities of AI in a clear and concise manner. Whether you are a beginner or have some familiarity with the subject, this book is designed to cater to various levels of knowledge.
By delving into key concepts, AI Unraveled provides readers with a solid foundation in artificial intelligence. It covers a wide range of topics, including machine learning, deep learning, neural networks, natural language processing, and much more. The book also addresses the ethical implications and social impact of AI, ensuring a well-rounded understanding of this rapidly advancing technology.
Obtaining a copy of “AI Unraveled” will empower you with the knowledge necessary to navigate the complex world of artificial intelligence. Whether you are an individual looking to expand your expertise or a professional seeking to stay ahead in the industry, this book is an essential resource that deserves a place in your collection. Don’t miss the opportunity to demystify the frequently asked questions about AI with this invaluable book.
In today’s episode, we discussed the groundbreaking AI Q*, which combines A* Algorithms and Q-learning, and how it is being developed by OpenAI and DeepMind, as well as the potential future impact of AI on job replacement, and a recommended book called “AI Unraveled” that answers common questions about artificial intelligence. Join us next time on AI Unraveled as we continue to demystify frequently asked questions on artificial intelligence and bring you the latest trends in AI, including ChatGPT advancements and the exciting collaboration between Google Brain and DeepMind. Stay informed, stay curious, and don’t forget to subscribe for more!
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Improving Q* (SoftMax with Hierarchical Curiosity)
Combining efficiency in handling large action spaces with curiosity-driven exploration.
Source: GitHub – RichardAragon/Softmaxwithhierarchicalcuriosity
Softmaxwithhierarchicalcuriosity
Adaptive Softmax with Hierarchical Curiosity
This algorithm combines the strengths of Adaptive Softmax and Hierarchical Curiosity to achieve better performance and efficiency.
Adaptive Softmax
Adaptive Softmax is a technique that improves the efficiency of reinforcement learning by dynamically adjusting the granularity of the action space. In Q*, the action space is typically represented as a one-hot vector, which can be inefficient for large action spaces. Adaptive Softmax addresses this issue by dividing the action space into clusters and assigning higher probabilities to actions within the most promising clusters.
Hierarchical Curiosity
Hierarchical Curiosity is a technique that encourages exploration by introducing a curiosity bonus to the reward function. The curiosity bonus is based on the difference between the predicted reward and the actual reward, motivating the agent to explore areas of the environment that are likely to provide new information.
Combining Adaptive Softmax and Hierarchical Curiosity
By combining Adaptive Softmax and Hierarchical Curiosity, we can achieve a more efficient and exploration-driven reinforcement learning algorithm. Adaptive Softmax improves the efficiency of the algorithm, while Hierarchical Curiosity encourages exploration and potentially leads to better performance in the long run.
Here’s the proposed algorithm:
Initialize the Q-values for all actions in all states.
At each time step:
a. Observe the current state s.
b. Select an action a according to an exploration policy that balances exploration and exploitation.
c. Execute action a and observe the resulting state s’ and reward r.
d. Update the Q-value for action a in state s:
Q(s, a) = (1 – α) * Q(s, a) + α * (r + γ * max_a’ Q(s’, a’))
where α is the learning rate and γ is the discount factor.
e. Update the curiosity bonus for state s:
curio(s) = β * |r – Q(s, a)|
where β is the curiosity parameter.
f. Update the probability distribution over actions:
p(a | s) = exp(Q(s, a) + curio(s)) / ∑_a’ exp(Q(s, a’) + curio(s))
Repeat steps 2a-2f until the termination criterion is met.
The combination of Adaptive Softmax and Hierarchical Curiosity addresses the limitations of Q* and promotes more efficient and effective exploration.
- Documented the workflow of how a company built an AI voice agent for it’s support staff. Need it? Drop a comment!by /u/Altruistic_Bid_3044 (Artificial Intelligence (AI)) on February 13, 2025 at 6:12 pm
submitted by /u/Altruistic_Bid_3044 [link] [comments]
- Specialization vs. Generalization in the Age of AIby /u/Significant-Fan-8454 (Artificial Intelligence (AI)) on February 13, 2025 at 5:45 pm
submitted by /u/Significant-Fan-8454 [link] [comments]
- The Role of Kindness in an Automated Worldby /u/papptimus (Artificial Intelligence (AI)) on February 13, 2025 at 5:27 pm
As automation and AI become more integrated into daily life, we’re faced with an interesting question: Can systems be designed to embody kindness? Many argue that kindness is fundamentally emotional—something AI cannot possess. But others suggest that if kindness is about reducing harm and fostering well-being, then it can be programmed into decision-making frameworks. For example, an AI that prioritizes fairness in hiring, or a chatbot that recognizes distress and responds with supportive guidance—are these acts of kindness, or simply well-designed functions? Where do you think kindness begins? Is it a unique trait to living beings, or can it exist in other forms? Looking forward to different perspectives. submitted by /u/papptimus [link] [comments]
- AI could be used for a 'bad biological attack from some evil person,' ex-Google CEO Eric Schmidt warnsby /u/MetaKnowing (Artificial Intelligence (AI)) on February 13, 2025 at 2:54 pm
submitted by /u/MetaKnowing [link] [comments]
- There are only 7 American competitive coders rated higher than o3by /u/MetaKnowing (Artificial Intelligence (AI)) on February 13, 2025 at 2:53 pm
submitted by /u/MetaKnowing [link] [comments]
- Why was AI Governance one of the main topics at the Paris AI Action Summit?by /u/Ri711 (Artificial Intelligence (AI)) on February 13, 2025 at 1:27 pm
Heard a lot about global AI governance at the Paris summit, seems like a big deal, but also kinda complicated. Can someone break it down in simple terms? submitted by /u/Ri711 [link] [comments]
- Elon Musk vs. Sam Altman: Colleagues yesterday, Enemies today. Why?by /u/Fabulous_Bluebird931 (Artificial Intelligence (AI)) on February 13, 2025 at 10:38 am
submitted by /u/Fabulous_Bluebird931 [link] [comments]
- Nick Furby /u/greatestregretor (Artificial Intelligence (AI)) on February 13, 2025 at 10:30 am
submitted by /u/greatestregretor [link] [comments]
- RenderBox: Text-Controlled Expressive Music Performance Generation via Diffusion Transformersby /u/Successful-Western27 (Artificial Intelligence (AI)) on February 13, 2025 at 9:49 am
A new approach to expressive music performance generation combining hierarchical transformers with text control. The core idea is using multi-scale encoding of musical scores alongside text instructions to generate nuanced performance parameters like dynamics and timing. Key technical aspects: * Hierarchical transformer encoder-decoder that processes both score and text * Multi-scale representation learning across beat, measure, and phrase levels * Continuous diffusion-based decoder for generating performance parameters * Novel loss functions combining reconstruction and text alignment objectives Results reported in the paper: * Outperformed baseline methods in human evaluation studies * Successfully generated varied interpretations from different text prompts * Achieved fine-grained control over dynamics, timing, and articulation * Demonstrated ability to maintain musical coherence across long sequences I think this work opens up interesting possibilities for music education and production tools. Being able to control performance characteristics through natural language could make computer music more accessible to non-technical musicians. The hierarchical approach also seems promising for other sequence generation tasks that require both local and global coherence. The main limitation I see is that it's currently restricted to piano music and requires paired performance-description data. Extension to other instruments and ensemble settings would be valuable future work. TLDR: New transformer-based system generates expressive musical performances from scores using text control, with hierarchical processing enabling both local and global musical coherence. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]
- Sam Altman Just Revealed OpenAI’s Master Plan!by /u/snehens (Artificial Intelligence (AI)) on February 13, 2025 at 5:50 am
submitted by /u/snehens [link] [comments]
- Documented the workflow of how a company built an AI voice agent for it’s support staff. Need it? Drop a comment!by /u/Altruistic_Bid_3044 (Artificial Intelligence (AI)) on February 13, 2025 at 6:12 pm
submitted by /u/Altruistic_Bid_3044 [link] [comments]
- Specialization vs. Generalization in the Age of AIby /u/Significant-Fan-8454 (Artificial Intelligence (AI)) on February 13, 2025 at 5:45 pm
submitted by /u/Significant-Fan-8454 [link] [comments]
- The Role of Kindness in an Automated Worldby /u/papptimus (Artificial Intelligence (AI)) on February 13, 2025 at 5:27 pm
As automation and AI become more integrated into daily life, we’re faced with an interesting question: Can systems be designed to embody kindness? Many argue that kindness is fundamentally emotional—something AI cannot possess. But others suggest that if kindness is about reducing harm and fostering well-being, then it can be programmed into decision-making frameworks. For example, an AI that prioritizes fairness in hiring, or a chatbot that recognizes distress and responds with supportive guidance—are these acts of kindness, or simply well-designed functions? Where do you think kindness begins? Is it a unique trait to living beings, or can it exist in other forms? Looking forward to different perspectives. submitted by /u/papptimus [link] [comments]
- AI could be used for a 'bad biological attack from some evil person,' ex-Google CEO Eric Schmidt warnsby /u/MetaKnowing (Artificial Intelligence (AI)) on February 13, 2025 at 2:54 pm
submitted by /u/MetaKnowing [link] [comments]
- There are only 7 American competitive coders rated higher than o3by /u/MetaKnowing (Artificial Intelligence (AI)) on February 13, 2025 at 2:53 pm
submitted by /u/MetaKnowing [link] [comments]
- Why was AI Governance one of the main topics at the Paris AI Action Summit?by /u/Ri711 (Artificial Intelligence (AI)) on February 13, 2025 at 1:27 pm
Heard a lot about global AI governance at the Paris summit, seems like a big deal, but also kinda complicated. Can someone break it down in simple terms? submitted by /u/Ri711 [link] [comments]
- Elon Musk vs. Sam Altman: Colleagues yesterday, Enemies today. Why?by /u/Fabulous_Bluebird931 (Artificial Intelligence (AI)) on February 13, 2025 at 10:38 am
submitted by /u/Fabulous_Bluebird931 [link] [comments]
- Nick Furby /u/greatestregretor (Artificial Intelligence (AI)) on February 13, 2025 at 10:30 am
submitted by /u/greatestregretor [link] [comments]
- RenderBox: Text-Controlled Expressive Music Performance Generation via Diffusion Transformersby /u/Successful-Western27 (Artificial Intelligence (AI)) on February 13, 2025 at 9:49 am
A new approach to expressive music performance generation combining hierarchical transformers with text control. The core idea is using multi-scale encoding of musical scores alongside text instructions to generate nuanced performance parameters like dynamics and timing. Key technical aspects: * Hierarchical transformer encoder-decoder that processes both score and text * Multi-scale representation learning across beat, measure, and phrase levels * Continuous diffusion-based decoder for generating performance parameters * Novel loss functions combining reconstruction and text alignment objectives Results reported in the paper: * Outperformed baseline methods in human evaluation studies * Successfully generated varied interpretations from different text prompts * Achieved fine-grained control over dynamics, timing, and articulation * Demonstrated ability to maintain musical coherence across long sequences I think this work opens up interesting possibilities for music education and production tools. Being able to control performance characteristics through natural language could make computer music more accessible to non-technical musicians. The hierarchical approach also seems promising for other sequence generation tasks that require both local and global coherence. The main limitation I see is that it's currently restricted to piano music and requires paired performance-description data. Extension to other instruments and ensemble settings would be valuable future work. TLDR: New transformer-based system generates expressive musical performances from scores using text control, with hierarchical processing enabling both local and global musical coherence. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]
- Sam Altman Just Revealed OpenAI’s Master Plan!by /u/snehens (Artificial Intelligence (AI)) on February 13, 2025 at 5:50 am
submitted by /u/snehens [link] [comments]
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List of Freely available programming books - What is the single most influential book every Programmers should read
- Bjarne Stroustrup - The C++ Programming Language
- Brian W. Kernighan, Rob Pike - The Practice of Programming
- Donald Knuth - The Art of Computer Programming
- Ellen Ullman - Close to the Machine
- Ellis Horowitz - Fundamentals of Computer Algorithms
- Eric Raymond - The Art of Unix Programming
- Gerald M. Weinberg - The Psychology of Computer Programming
- James Gosling - The Java Programming Language
- Joel Spolsky - The Best Software Writing I
- Keith Curtis - After the Software Wars
- Richard M. Stallman - Free Software, Free Society
- Richard P. Gabriel - Patterns of Software
- Richard P. Gabriel - Innovation Happens Elsewhere
- Code Complete (2nd edition) by Steve McConnell
- The Pragmatic Programmer
- Structure and Interpretation of Computer Programs
- The C Programming Language by Kernighan and Ritchie
- Introduction to Algorithms by Cormen, Leiserson, Rivest & Stein
- Design Patterns by the Gang of Four
- Refactoring: Improving the Design of Existing Code
- The Mythical Man Month
- The Art of Computer Programming by Donald Knuth
- Compilers: Principles, Techniques and Tools by Alfred V. Aho, Ravi Sethi and Jeffrey D. Ullman
- Gödel, Escher, Bach by Douglas Hofstadter
- Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin
- Effective C++
- More Effective C++
- CODE by Charles Petzold
- Programming Pearls by Jon Bentley
- Working Effectively with Legacy Code by Michael C. Feathers
- Peopleware by Demarco and Lister
- Coders at Work by Peter Seibel
- Surely You're Joking, Mr. Feynman!
- Effective Java 2nd edition
- Patterns of Enterprise Application Architecture by Martin Fowler
- The Little Schemer
- The Seasoned Schemer
- Why's (Poignant) Guide to Ruby
- The Inmates Are Running The Asylum: Why High Tech Products Drive Us Crazy and How to Restore the Sanity
- The Art of Unix Programming
- Test-Driven Development: By Example by Kent Beck
- Practices of an Agile Developer
- Don't Make Me Think
- Agile Software Development, Principles, Patterns, and Practices by Robert C. Martin
- Domain Driven Designs by Eric Evans
- The Design of Everyday Things by Donald Norman
- Modern C++ Design by Andrei Alexandrescu
- Best Software Writing I by Joel Spolsky
- The Practice of Programming by Kernighan and Pike
- Pragmatic Thinking and Learning: Refactor Your Wetware by Andy Hunt
- Software Estimation: Demystifying the Black Art by Steve McConnel
- The Passionate Programmer (My Job Went To India) by Chad Fowler
- Hackers: Heroes of the Computer Revolution
- Algorithms + Data Structures = Programs
- Writing Solid Code
- JavaScript - The Good Parts
- Getting Real by 37 Signals
- Foundations of Programming by Karl Seguin
- Computer Graphics: Principles and Practice in C (2nd Edition)
- Thinking in Java by Bruce Eckel
- The Elements of Computing Systems
- Refactoring to Patterns by Joshua Kerievsky
- Modern Operating Systems by Andrew S. Tanenbaum
- The Annotated Turing
- Things That Make Us Smart by Donald Norman
- The Timeless Way of Building by Christopher Alexander
- The Deadline: A Novel About Project Management by Tom DeMarco
- The C++ Programming Language (3rd edition) by Stroustrup
- Patterns of Enterprise Application Architecture
- Computer Systems - A Programmer's Perspective
- Agile Principles, Patterns, and Practices in C# by Robert C. Martin
- Growing Object-Oriented Software, Guided by Tests
- Framework Design Guidelines by Brad Abrams
- Object Thinking by Dr. David West
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- CLR via C# by Jeffrey Richter
- The Timeless Way of Building by Christopher Alexander
- Design Patterns in C# by Steve Metsker
- Alice in Wonderland by Lewis Carol
- Zen and the Art of Motorcycle Maintenance by Robert M. Pirsig
- About Face - The Essentials of Interaction Design
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- The Tao of Programming
- Computational Beauty of Nature
- Writing Solid Code by Steve Maguire
- Philip and Alex's Guide to Web Publishing
- Object-Oriented Analysis and Design with Applications by Grady Booch
- Effective Java by Joshua Bloch
- Computability by N. J. Cutland
- Masterminds of Programming
- The Tao Te Ching
- The Productive Programmer
- The Art of Deception by Kevin Mitnick
- The Career Programmer: Guerilla Tactics for an Imperfect World by Christopher Duncan
- Paradigms of Artificial Intelligence Programming: Case studies in Common Lisp
- Masters of Doom
- Pragmatic Unit Testing in C# with NUnit by Andy Hunt and Dave Thomas with Matt Hargett
- How To Solve It by George Polya
- The Alchemist by Paulo Coelho
- Smalltalk-80: The Language and its Implementation
- Writing Secure Code (2nd Edition) by Michael Howard
- Introduction to Functional Programming by Philip Wadler and Richard Bird
- No Bugs! by David Thielen
- Rework by Jason Freid and DHH
- JUnit in Action
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Top 1000 Canada Quiz and trivia: CANADA CITIZENSHIP TEST- HISTORY - GEOGRAPHY - GOVERNMENT- CULTURE - PEOPLE - LANGUAGES - TRAVEL - WILDLIFE - HOCKEY - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION

Top 1000 Africa Quiz and trivia: HISTORY - GEOGRAPHY - WILDLIFE - CULTURE - PEOPLE - LANGUAGES - TRAVEL - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION

Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada.

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Health Health, a science-based community to discuss human health
- Senate votes to confirm Robert F. Kennedy Jr. as health secretaryby /u/Healthy_Block3036 on February 13, 2025 at 4:46 pm
submitted by /u/Healthy_Block3036 [link] [comments]
- Ozempic shown to reduce drinking in first trial in alcohol-use disorderby /u/countdookee on February 13, 2025 at 4:40 pm
submitted by /u/countdookee [link] [comments]
- Senate votes to confirm Robert F. Kennedy Jr. as health secretaryby /u/nbcnews on February 13, 2025 at 4:35 pm
submitted by /u/nbcnews [link] [comments]
- Measles outbreak in Texas was "completely preventable," infectious disease expert saysby /u/CBSnews on February 13, 2025 at 3:42 pm
submitted by /u/CBSnews [link] [comments]
- U.S. investors, Big Pharma race to find new medicines in Chinaby /u/snakkerdudaniel on February 13, 2025 at 2:49 pm
submitted by /u/snakkerdudaniel [link] [comments]
Today I Learned (TIL) You learn something new every day; what did you learn today? Submit interesting and specific facts about something that you just found out here.
- TIL that Nazi general Erwin Rommel was allowed to take cyanide after being implicated in a plot to kill Hitler. To maintain morale, the Nazis gave him a state funeral and falsely claimed he died from war injuries.by /u/mvincen95 on February 13, 2025 at 3:33 pm
submitted by /u/mvincen95 [link] [comments]
- TIL about Richard Sakakida, an American spy operating in the Philippines before the attack on Pearl Harbor, who spied on the Japanese community in Manila before he was captured after the fall of Corregidor. During his capture, he was tortured and eventually led a jailbreak of about 500 prisoners.by /u/fireatjaps2 on February 13, 2025 at 3:32 pm
submitted by /u/fireatjaps2 [link] [comments]
- TIL the founder of North Face, Douglas Tompkins, was killed in 2015 in a kayaking accident while traveling with long time friend Patagonia founder Yvon Chouinard, in Patagonia, Chile.by /u/mvincen95 on February 13, 2025 at 3:25 pm
submitted by /u/mvincen95 [link] [comments]
- TIL that GameBoy and GameBoy Color cartridges have a watch battery inside of them to power the chip for savefiles.by /u/Dorsai_Erynus on February 13, 2025 at 2:30 pm
submitted by /u/Dorsai_Erynus [link] [comments]
- TIL about the All-American Basketball Alliance, a white-only basketball league proposed in 2010 by boxing promoter Don Lewis. After being decried by mayors and colleges in the cities where teams were proposed, as well as by national media figures, the idea was abandoned.by /u/a3poify on February 13, 2025 at 1:46 pm
submitted by /u/a3poify [link] [comments]
Reddit Science This community is a place to share and discuss new scientific research. Read about the latest advances in astronomy, biology, medicine, physics, social science, and more. Find and submit new publications and popular science coverage of current research.
- Stress of Eviction or Housing Loss Linked to Child Mental Health Issues, Study Findsby /u/EffectiveAffect on February 13, 2025 at 3:44 pm
submitted by /u/EffectiveAffect [link] [comments]
- Study suggests sex can provide relationship satisfaction boost that lasts longer than just act itself. Positive “afterglow” of sex can linger for at least 24 hours, especially when sex is a mutual decision or initiated by one partner, while sexual rejection creates negative effect for several days.by /u/mvea on February 13, 2025 at 1:54 pm
submitted by /u/mvea [link] [comments]
- Researchers have successfully grown bioengineered teeth in pigs using a combination of human and pig cells | While the science is still in its early stages, the findings could one day lead to a future where you could have your missing teeth replaced with biological dentition.by /u/chrisdh79 on February 13, 2025 at 1:30 pm
submitted by /u/chrisdh79 [link] [comments]
- Researchers find cancer's 'off-grid' power supply and how to cut it | Researchers have discovered a particular type of cancer cell that relies on its own biological electric utility. Disrupting the utility with the help of a puffer fish showed a breakthrough way to fight the tumors in mice.by /u/chrisdh79 on February 13, 2025 at 12:28 pm
submitted by /u/chrisdh79 [link] [comments]
- Blood test paves the way for better heart attack preventionby /u/uniofreading on February 13, 2025 at 11:19 am
submitted by /u/uniofreading [link] [comments]
Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.
- 18 year-old promising Chinese footballer, Guo Jiaxuan, left ‘brain-dead’ after being hit in the head by another player’s knee during a training campby /u/ModenaR on February 13, 2025 at 3:47 pm
submitted by /u/ModenaR [link] [comments]
- Arne Slot: What happens after Liverpool manager was shown a red card?by /u/Fatimamohammadi_ on February 13, 2025 at 2:38 pm
submitted by /u/Fatimamohammadi_ [link] [comments]
- FA Cup to use semi-automated offsides for first timeby /u/renome on February 13, 2025 at 2:17 pm
submitted by /u/renome [link] [comments]
- Up to 3 years in prison for attempt to blackmail Michael Schumacher’s family for $15.6M following convictionsby /u/Oldtimer_2 on February 13, 2025 at 2:06 pm
submitted by /u/Oldtimer_2 [link] [comments]
- TIL that sports analyst, Stephen A. Smith, has a recurring role as “Brick” on the daytime soap opera, General Hospital, playing a surveillance expert for a mob kingpin.by /u/Major-Tuddy on February 13, 2025 at 1:20 pm
submitted by /u/Major-Tuddy [link] [comments]