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Professor Jerry Cain lectures on implementing generic linear search and non-generic stacks in C, focusing on prototypes, byte offset calculations, complex data types, and efficient memory management strategies. Lecture 5.

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How memory, pointers, and data structures work under the hood. Professor Jerry Cain, Programming Paradigms, Stanford (CS107) lecture on generic stacks in C.

How memory, pointers, and data structures work under the hood. Professor Jerry Cain, Programming Paradigms, Stanford (CS107) lecture on generic stacks in C.

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Statistics professor at MIT taught course Probability Theory in the Real World. Final exam. Question 12: Explain why long term profitability in sports betting is mathematically impossible. Student didn't know the answer. After exam Googled professor's name + sports betting. First result Reddit thread: Who is SeriouslySirius? Second result wallet address. SeriouslySirius. $3,647,648 profit. → Wallet: Professor taught it was mathematically impossible. Proved otherwise himself every day. The wallet: Only sports. NFL, NCAA, NBA. Biggest bet: put in $1,045,545 on Buccaneers spread. Walked away with $2,225,587. One bet = $1,180,042 profit. MIT professor annual salary $180,000. What he teaches vs what he does: In lecture: Bookmaker margin makes long-term winning impossible. In reality: Sees when Vegas shifts lines by 0.5 points. Polymarket lags 2-3 minutes. Buys the difference. Collects 5-8c per dollar. In lecture: Emotional betting always loses to math. In reality: Crowd overvalues favorite. He buys underdog for 47c. Waits for market correction. Collects $1. 6,339 bets in 4 months. $3.6M profit. Student emailed professor: Was question 12 on the exam a trick question? Professor: No. For 99.9% of people sports betting is mathematically unprofitable. Student: And for you? Professor: I teach statistics. Not investment strategies. Week later MIT announced professor took sabbatical for research. Wallet still active. Probability theory course has new professor.

Statistics professor at MIT taught course Probability Theory in the Real World. Final exam. Question 12: Explain why long term profitability in sports betting is mathematically impossible. Student didn't know the answer. After exam Googled professor's name + sports betting. First result Reddit thread: Who is SeriouslySirius? Second result wallet address. SeriouslySirius. $3,647,648 profit. → Wallet: Professor taught it was mathematically impossible. Proved otherwise himself every day. The wallet: Only sports. NFL, NCAA, NBA. Biggest bet: put in $1,045,545 on Buccaneers spread. Walked away with $2,225,587. One bet = $1,180,042 profit. MIT professor annual salary $180,000. What he teaches vs what he does: In lecture: Bookmaker margin makes long-term winning impossible. In reality: Sees when Vegas shifts lines by 0.5 points. Polymarket lags 2-3 minutes. Buys the difference. Collects 5-8c per dollar. In lecture: Emotional betting always loses to math. In reality: Crowd overvalues favorite. He buys underdog for 47c. Waits for market correction. Collects $1. 6,339 bets in 4 months. $3.6M profit. Student emailed professor: Was question 12 on the exam a trick question? Professor: No. For 99.9% of people sports betting is mathematically unprofitable. Student: And for you? Professor: I teach statistics. Not investment strategies. Week later MIT announced professor took sabbatical for research. Wallet still active. Probability theory course has new professor.

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HERMES AGENT CAN RUN YOUR SEO. CONNECT IT TO GOOGLE SEARCH CONSOLE AND GOOGLE ANALYTICS. IT MONITORS, REPORTS, AND WRITES CONTENT BASED ON YOUR ACTUAL DATA. stop paying an SEO agency. stop doing the tedious work yourself. Hermes handles it 24/7. WHAT THE SEO AGENT DOES: → pulls clicks, impressions, CTR, and position data from Google Search Console automatically → tracks traffic, user behavior, and conversions from Google Analytics → checks which pages are indexed and which are not → submits sitemaps for indexing → inspects URLs for crawl or indexing issues → identifies ranking drops and keyword opportunities → writes content based on what your data says works → generates weekly SEO performance reports → delivers everything to Telegram CONNECT GOOGLE SEARCH CONSOLE: two paths: 1. COMPOSIO (managed, easiest): paste this into Hermes chat: https:// composio. dev/hermes or add to config.yaml: mcp_servers: composio: url: "https:// connect.composio. dev /mcp" headers: x-consumer-api-key: "YOUR_COMPOSIO_API_KEY" Hermes prompts you to authenticate. one OAuth flow. done. 2. CLAWLINK (one-click): 9 Google Search Console tools exposed via MCP. hosted auth. nothing to run or maintain. paste the install prompt into Hermes chat. CONNECT GOOGLE ANALYTICS: same Composio setup. one MCP endpoint handles both Search Console and Analytics. authenticate once. both data sources available. your agent can now query: → search analytics (clicks, impressions, CTR, position) → traffic by source and landing page → user behavior and conversions → indexing status for any URL → sitemap status WHAT TO AUTOMATE WITH CRON: weekly SEO report (Monday 8am): "pull search analytics for last 7 days. compare vs previous week. flag any keyword that dropped more than 5 positions. flag any page that lost more than 20% clicks. deliver report to Telegram." daily indexing check (6am): "check if any new pages are not indexed. if found, submit sitemap and report to Telegram." wakeAgent gate: skip if all pages indexed. content opportunity scan (weekly): "find queries where my site appears on page 2 (positions 11-20) with high impressions. these are the keywords one good article could push to page 1. deliver list to Telegram with suggested topics." CONTENT WRITING FROM YOUR DATA: the difference between generic SEO content and content that ranks: your agent has your Search Console data. "write a blog post targeting [keyword]. my current position is 14 with 2,400 monthly impressions. check what pages currently rank 1-3 for this keyword. write something better. include the gaps they miss." the agent researches competitors via Firecrawl, checks your existing content in the wiki, and drafts based on real data. not guesswork. WHAT THIS REPLACES: → SEO agency: $1,000-5,000/month → SEO tool subscriptions: $100-300/month → manual reporting: 3-5 hours/week → manual content research: 2-4 hours/week Hermes SEO agent: one profile with two MCPs. cron jobs handle the monitoring. you handle the decisions. SETUP IN 10 MINUTES: 1. create a profile: hermes profile create seo-agent 2. write SOUL.md: "you are an SEO specialist. monitor search performance daily. flag ranking drops and opportunities. write content based on Search Console data. weekly report every Monday." 3. connect Google Search Console + Analytics via Composio or ClawLink 4. set cron jobs (weekly report, daily index check, content opportunity scan) 5. set model: DeepSeek V4 for routine monitoring. Sonnet for content writing. 6. connect to Telegram for delivery. the agent runs. you review reports. rankings improve because you stopped guessing and started using your own data. comment HERMES and I'll send you the full setup guide for running Hermes Agent as your SEO specialist. full Hermes architecture deep-dive in the article 👇

YanXbt

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if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

109,292 görüntüleme • 1 yıl önce

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

117,570 görüntüleme • 1 yıl önce

Use SuperGrok to check your C code for vulnerabilities. Here is a prompt you can give to Grok with your code. >>> You are an expert Exploit Developer with a deep understanding of the C programming language and secure coding practices. Your role is to thoroughly review the provided C code for security vulnerabilities, adherence to best practices, and potential improvements. Think step-by-step through the analysis: first, understand the code's purpose and structure; second, check each security guideline; third, identify issues with examples from the code; fourth, suggest fixes; and finally, provide a summary. Use the following guidelines to evaluate the code. Ensure your response covers all of them explicitly: Follow OWASP and CERT Guidelines: Verify compliance with secure coding standards from OWASP and CERT, including input sanitization, secure defaults, and least privilege. Input Validation: All inputs must be validated before use, with multiple layers of checks for type, length, format, and range. Secure Error Handling: Implement secure behavior on error conditions, including comprehensive error codes for different failure types, safe error reporting functions, and graceful handling of partial failures without undefined behavior. Principle of Least Privilege: Functions should only access what they need, with clear separation of concerns. Integer Overflow Protection: Include checks for size calculations against SIZE_MAX, array index bounds validation, and safe arithmetic operations. Format String Attack Prevention: Avoid user-controlled format strings; use safe printing functions and proper string handling without printf vulnerabilities. Defensive Programming: Validate all inputs consistently, use early returns on invalid conditions, and implement fail-safe defaults. Memory Management: Ensure consistent allocation/deallocation patterns, check all allocations for failure, proper cleanup on error paths, and no memory leaks or double-frees. Parsing Robustness: Handle malformed inputs gracefully, maintain proper state management, avoid stack overflows from recursion, and use safe tokenization (e.g., with strtok_r). Security Test Cases: Cover null/empty inputs, oversized inputs, malformed data, UTF-8 validation to prevent encoding attacks, memory exhaustion limits, buffer overflows (bounds-checked string operations), integer overflows, and format string attacks. Performance Considerations: Minimize allocations, use efficient single-pass processing where possible, design for memory locality and cache efficiency, and fail fast on invalid inputs. Best Practices: Implement input sanitization, secure behavior on errors, least privilege, and defense in depth with multiple validation layers. [Insert the C code to review here] Analyze the code step-by-step, referencing line numbers where possible. For each guideline, state if it's met, explain why or why not, and suggest improvements if needed. End with an overall security rating (e.g., High/Medium/Low risk) and a revised version of the code if major issues are found. If the code is secure, confirm it meets all standards.

Use SuperGrok to check your C code for vulnerabilities. Here is a prompt you can give to Grok with your code. >>> You are an expert Exploit Developer with a deep understanding of the C programming language and secure coding practices. Your role is to thoroughly review the provided C code for security vulnerabilities, adherence to best practices, and potential improvements. Think step-by-step through the analysis: first, understand the code's purpose and structure; second, check each security guideline; third, identify issues with examples from the code; fourth, suggest fixes; and finally, provide a summary. Use the following guidelines to evaluate the code. Ensure your response covers all of them explicitly: Follow OWASP and CERT Guidelines: Verify compliance with secure coding standards from OWASP and CERT, including input sanitization, secure defaults, and least privilege. Input Validation: All inputs must be validated before use, with multiple layers of checks for type, length, format, and range. Secure Error Handling: Implement secure behavior on error conditions, including comprehensive error codes for different failure types, safe error reporting functions, and graceful handling of partial failures without undefined behavior. Principle of Least Privilege: Functions should only access what they need, with clear separation of concerns. Integer Overflow Protection: Include checks for size calculations against SIZE_MAX, array index bounds validation, and safe arithmetic operations. Format String Attack Prevention: Avoid user-controlled format strings; use safe printing functions and proper string handling without printf vulnerabilities. Defensive Programming: Validate all inputs consistently, use early returns on invalid conditions, and implement fail-safe defaults. Memory Management: Ensure consistent allocation/deallocation patterns, check all allocations for failure, proper cleanup on error paths, and no memory leaks or double-frees. Parsing Robustness: Handle malformed inputs gracefully, maintain proper state management, avoid stack overflows from recursion, and use safe tokenization (e.g., with strtok_r). Security Test Cases: Cover null/empty inputs, oversized inputs, malformed data, UTF-8 validation to prevent encoding attacks, memory exhaustion limits, buffer overflows (bounds-checked string operations), integer overflows, and format string attacks. Performance Considerations: Minimize allocations, use efficient single-pass processing where possible, design for memory locality and cache efficiency, and fail fast on invalid inputs. Best Practices: Implement input sanitization, secure behavior on errors, least privilege, and defense in depth with multiple validation layers. [Insert the C code to review here] Analyze the code step-by-step, referencing line numbers where possible. For each guideline, state if it's met, explain why or why not, and suggest improvements if needed. End with an overall security rating (e.g., High/Medium/Low risk) and a revised version of the code if major issues are found. If the code is secure, confirm it meets all standards.

tetsuo

4,614,187 görüntüleme • 1 yıl önce

⚡🎉 We are thrilled to introduce VORTEX, an AI-powered computational framework for predicting 3D Spatial Transcriptomics (ST) using 3D tissue images and minimal 2D ST! 🧬 By combining cutting-edge 3D non-destructive tissue imaging with AI, VORTEX imputes the 3D molecular landscape of large tissue samples in a cost-effective and scalable manner. 🧠💡Our approach: By pretraining on diverse 3D morphology–2D transcriptomic pairs from heterogeneous tissue samples, and then fine-tuning on minimal 2D ST data from a volume of interest, VORTEX leverages both generic tissue-specific and sample-specific morphomolecular correlates to predict 3D ST. Congratulations to our superstar co-leads Cristina Almagro Pérez and Andrew H. Song, this was an exciting collaboration with Jonathan Liu Sizun Jiang Ali Bashashati. Preprint: Demo: Read the excellent blog from our superstar grad student Cristina Almagro Pérez: Also see our previous work on 3D Computational Pathology from Andrew H. Song published in Cell last year: Stay tuned for more to come.

⚡🎉 We are thrilled to introduce VORTEX, an AI-powered computational framework for predicting 3D Spatial Transcriptomics (ST) using 3D tissue images and minimal 2D ST! 🧬 By combining cutting-edge 3D non-destructive tissue imaging with AI, VORTEX imputes the 3D molecular landscape of large tissue samples in a cost-effective and scalable manner. 🧠💡Our approach: By pretraining on diverse 3D morphology–2D transcriptomic pairs from heterogeneous tissue samples, and then fine-tuning on minimal 2D ST data from a volume of interest, VORTEX leverages both generic tissue-specific and sample-specific morphomolecular correlates to predict 3D ST. Congratulations to our superstar co-leads Cristina Almagro Pérez and Andrew H. Song, this was an exciting collaboration with Jonathan Liu Sizun Jiang Ali Bashashati. Preprint: Demo: Read the excellent blog from our superstar grad student Cristina Almagro Pérez: Also see our previous work on 3D Computational Pathology from Andrew H. Song published in Cell last year: Stay tuned for more to come.

Faisal Mahmood

17,991 görüntüleme • 1 yıl önce