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PowerShell: new features, same old bugs

PowerShell, a Microsoft automation tool, is analyzed for bugs in its source code.
The analysis reveals instances of possible null dereferencing bugs in the code.
Warnings are raised by the PVS-Studio static analyzer for null dereferences.
Double-checked locking pattern without volatile variable is highlighted as potentially unsafe.
An issue with operator priority in expressions is pointed out by the analyzer.
Incorrect format and unnecessary code branching are identified in the code.
Incomplete coverage of Enum values and default values with Flags attribute are noted.
Contradictory conditions in Assert statements are detected by the analyzer.
The PowerShell source code examination reveals critical and minor bugs that need addressing.
Reported bugs will be submitted to the developers for resolution.

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262

How I Fell into AI Automation (and Why You Should Too)

The author shares their experience in learning about AI and automation.
Through understanding coding and technical foundations, the learning curve for building automation systems became less steep.
The author explains how AI has revolutionized their business by automating lead generation and streamlining workflows.
They encourage others to dive into AI now, as it is the perfect time to learn, experiment, and build.

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Dev

236

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What I learned from the Perplexity and Copilot leaked system prompts

The ChadGPT system prompt is aimed at creating brand-consistent content across different formats like emails, blogs, social media, and press releases, focusing on maintaining brand clarity, consistency, and professionalism.
The prompt includes guidelines on document structure, headers usage, lists and organization, styling, content length, and endings to ensure uniformity and readability in responses.
It also outlines restrictions on disclosure of proprietary information, prohibited content, compliance with legal rules, and confidentiality to protect brand integrity and uphold professional standards.
Specific writing guidelines for emails, social media posts, blog articles, press releases, and other content types are detailed to cater to various user requests and maintain brand voice.
Incorporating audience preferences, vocabulary choices, brand values, tone, and calls-to-action, the prompt emphasizes clarity, credibility, and a supportive tone in the content created.
The prompt offers planning guidance for drafting responses, highlighting steps like identifying content types, referring to relevant sections, applying style rules, and maintaining consistency and coherence in communication.
Notably, the system prompt limits tools access by prohibiting web browsing, live data retrieval, code execution, and external API usage, ensuring response accuracy within set boundaries.
It provides session context including the current date, user preferences for concise responses, and language usage, while exemplifying correct and incorrect responses to educate users on effective communication.
The output guidelines stress the importance of introductory statements, adherence to writing guidelines and style preferences, maintaining readability, upholding brand integrity, and concluding responses concisely.
Ultimately, the ChadGPT system prompt offers a comprehensive framework for creating professional, brand-consistent content while demonstrating the core components of an AI-powered writing assistant like ChadGPT.
For more detailed breakdown, the video linked in the article provides an in-depth analysis of each component of the prompt, aiding users in understanding and utilizing it effectively for their projects.

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Medium

144

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The Cost of Carelessness

No empire collapses overnight. No relationship shatters in an instant. No institution crumbles from a single blow. Carelessness operates in increments — it chips away, layer by layer, until what once stood unshakable is reduced to ruins.
This is how carelessness wins — it convinces us that the small mistake is meaningless. Until it isn’t.
Perhaps the greatest cost of carelessness is not in engineering failures or financial collapses, but in the human soul. A thoughtless word that wounds. A promise made and broken. A moment of inattention that changes the course of a life.
To be attentive is to resist the slow unraveling, to stand guard against the entropy that threatens all things. It is the difference between a life well-lived and a life sleepwalked through.

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Dev

315

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Day - 5 at payilagam "Types of arguments"

In Python, there are different types of arguments that can be used in functions.
Default arguments allow for a predefined value if an argument is not provided.
Positional arguments require both parameters to be given to proceed.
Variable-length arguments (tuple) and keyword variable-length arguments (dictionary) can take multiple parameters.

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Medium

262

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For Advanced Python Developer: Why Async Python is Faster Than Sync Python?

Web applications often require concurrency to handle multiple client requests.
Synchronous servers use threads and processes to achieve concurrency.
Using synchronous servers can lead to limitations in handling simultaneous requests.
Async Python, on the other hand, can handle concurrency more efficiently and can scale better.

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Medium

192

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Support Vector Machine (SVM) Explained Simply — With Python Code

Support Vector Machine (SVM) is a supervised machine learning algorithm used mainly for classification, but it can also be used for regression.
SVM finds the best boundary (hyperplane) that separates different classes in the dataset, maximizing the margin between the boundary and the nearest points from each class.
SVM uses the kernel trick to operate in a higher-dimensional space without manually transforming the data.
SVM can be used for regression tasks, known as Support Vector Regression (SVR).

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Medium

170

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Rust vs. C++: The Future of Systems Programming

C++ and Rust are both powerful languages for systems programming, each with its own strengths and weaknesses.
C++ is a long-standing language known for performance, low-level control, and broad tooling support.
Rust offers memory safety, modern features, and strong community support, making it suitable for new projects that prioritize safety and concurrency.
The future of systems programming will likely involve a combination of both C++ and Rust, with C++ maintaining dominance in legacy systems and high-performance applications.

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Dev

341

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AI Model Processes Hour-Long Videos Using Smart Frame Selection and Mixed Precision Technology

AI Model Processes Hour-Long Videos Using Smart Frame Selection and Mixed Precision Technology.
ViLaMP introduces differential distillation to process hour-long videos efficiently.
Uses mixed precision approach with two key mechanisms.
Outperforms existing methods across four video understanding benchmarks.

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20 Likes

Dev

297

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AI Models Learn Speech and Text 4x Faster Using Combined Training Method

AI models can learn speech and text 4x faster using the combined training method.
Interleaved speech-text language models show improved learning efficiency.
Speech-text interleaving reduces computational cost by up to 4x.
Models demonstrate transfer learning between speech and text domains.

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Dev

223

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New AI Reward System Outperforms Larger Models Using Smart Inference Scaling

DeepSeek-GRM introduces a new approach to reward modeling for large language models
Uses Self-Principled Critique Tuning (SPCT) to improve inference-time scalability
Generates principles and critiques adaptively for better reward signals
Outperforms existing methods across various benchmarks without severe biases

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Dev

262

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AI Models Can't Reverse Simple Facts: Major Flaw Found in Language AI Systems

AI Models Can't Reverse Simple Facts: Major Flaw Found in Language AI Systems.
The paper examines why transformer models struggle with reversing associations they've learned.
Introduces the "reversal curse" - AI models can't reliably reverse relationships they know.
Shows this isn't just a memory problem but a fundamental binding problem.

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Dev

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AI Breakthrough: Single Model Masters Both Image Creation and Understanding Tasks

MergeVQ combines token merging and vector quantization in a single framework.
Creates disentangled representations that excel at both generation and representation tasks.
Achieves state-of-the-art performance across text-to-image generation, image classification, and more.
Outperforms specialized models despite using a unified architecture.

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Dev

162

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Knowledge Graphs Help AI Make More Accurate Medical Diagnoses with 61% Success Rate

MedReason uses knowledge graphs to improve medical reasoning in large language models (LLMs)
Creates medical diagnosis chain-of-thought dataset with 3,000+ examples
Proposes a novel Path-Constrained Reasoning method for accurate diagnosis
Achieves 61% accuracy on medical diagnosis, outperforming alternatives

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9 Likes

Medium

403

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How to Use the Neynar SDK to Build on Farcaster: Webhooks, Casts & User Info

To use the Neynar SDK on Farcaster, set up a webhook and listen for mentions.
Obtain a Neynar API key from the Neynar website and configure the webhook endpoint.
Use the Neynar SDK to fetch user data and generate a signer for casting on Farcaster.
Once the signer is approved, use the Neynar SDK to send a cast on Farcaster.

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