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Testing by Behavior, Not Implementation: Eliminating Spies Through Better Design

Testing software through its behavior, not its implementation, is crucial for better design.
Tests focusing on internal behavior rather than system achievements are brittle and untrustworthy.
Decomposing responsibilities and depending on interfaces can help create behaviorally focused services.
Testing externally visible outcomes instead of spying on internal behavior leads to more robust tests and better software design.

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Dev

301

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Legal Text AI Breakthrough: 98% Accuracy in Sentence Boundary Detection

NUPunkt and CharBoundary algorithms identify sentence boundaries in legal texts with high precision
Outperforms state-of-the-art solutions like spaCy and NLTK with 98% accuracy
Specifically designed for legal documents with complex sentence structures
Open-sourced as Python packages for wider adoption in legal text processing

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Dev

344

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M-Prometheus: Open LLM Judges Excel in 20+ Languages & Boost Text Quality

M-Prometheus is a suite of multilingual LLM judges designed for evaluating text in multiple languages.
Existing LLM judges work well for English but poorly for other languages, creating an unfair evaluation situation for AI systems in non-English languages.
M-Prometheus models range from 3B to 14B parameters and outperform existing open LLM judges.
Key factors for the success of M-Prometheus include proper backbone model selection and the utilization of native multilingual data.

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Dev

211

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Grasp As You Say: Robot Hand Learns Dexterous Grasping from Language. 87% Success!

DexTOG is a framework for generating task-oriented dexterous grasps based on natural language instructions
Uses a dual-branch architecture with language embeddings from CLIP and object point clouds
Achieves 87.4% success rate on the ContactDB dataset
Incorporates tactile feedback to improve grasping strategy

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Dev

112

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LLMs vs. Optimization: AI Struggles, Teams Excel - New CO-Bench Benchmark Reveals Gaps

CO-Bench is a new testing framework that measures how well AI language models can solve complex optimization problems.
Results from CO-Bench reveal that language models have difficulties with algorithm design.
However, the study also shows that collaboration among multiple AI agents can improve overall performance across various tasks.
The benchmark evaluated four language models: GPT-4, Claude 3, Gemini, and Llama 3.

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Dev

362

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Smarter AI: Agent Learns When to Use Knowledge, Cuts Waste

New approach called KnowSelf for AI agents to make smarter decisions
Introduces knowledgeable self-awareness to help AI agents know when to use external knowledge
Two-stage training process teaches AI to generate special tokens for different situations
Outperforms current methods while using less external knowledge

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Dev

130

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Zod vs Yup: Choosing the Right Validation Library for Your Frontend Project

Data validation is crucial in frontend development for ensuring data integrity, user experience, server efficiency, security, and user retention.
Zod and Yup are popular JavaScript libraries for schema validation, with Zod focusing on strong TypeScript integration and Yup providing flexibility.
Zod offers concise schema definitions with automatic type inference, while Yup offers a wide range of validation methods and customization options.
Both libraries integrate well with frontend frameworks like React Hook Form and Formik for efficient validation in applications.
Code examples demonstrate how Zod and Yup can validate email addresses, passwords, nested objects, and perform conditional validation.
Zod excels in TypeScript integration, lightweight structure, and developer-friendly syntax, while Yup boasts a larger community and extensive features.
Best practices for using Yup include centralizing validation schemas and optimizing performance for large forms.
Choosing between Zod and Yup depends on project needs, with Zod recommended for TypeScript projects and Yup for broader validation scenarios.
Experimenting with both libraries is advised to determine the best fit based on development style and project requirements.

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Dev

366

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Supercharge Claude AI with Solana: Build Your Agentipy MCP Server (MEDUSA Client)

The Agentipy MCP Server bridges AI models like Claude with the Solana ecosystem, enabling interactions with tools such as checking balances, transferring tokens, deploying SPL tokens, and more.
Prerequisites include Python v3.8+, Claude Desktop, a dedicated burner wallet private key for Solana, Solana RPC URL, and optional API keys.
Installation involves cloning the repository, setting up a virtual environment, installing dependencies, and configuring the server.
Configuration entails setting up the server environment with credentials and informing Claude Desktop how to communicate with the server.
Server setup involves creating a .env file and running the server script, ensuring the MCP server is active for tool usage.
Tools available include actions for Solana, Allora Network, Jupiter Aggregator, deBridge, Pyth Network, and CoinGecko for various blockchain operations.
Safety precautions include using a burner wallet, protecting the .env file, monitoring activities, selective auto-approvals, testing on devnet/testnet, and keeping Agentipy updated.
Troubleshooting tips cover common issues like server connections, tool errors, installation problems, and suggestions for resolving them.
Internals of the server include server.py which initializes SolanaAgentKit, run scripts for server activation, requirements.txt for dependencies, and tool definitions within Agentipy library.
Contributions to the project are encouraged, with the repo link provided and the project licensed under MIT.

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Dev

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🐞 Conversations Between a Developer and Their Bugs

The 'It Works on My Machine' Bug: Your machine is my vacation home. I only act up when there are real consequences.
The Heisenbug: I’m not a bug. I’m a feature… that only appears when no one’s watching.
The Typo That Broke Everything: One small typo for you. One giant embarrassment for your code review.
The One That Works Only on Fridays: The code runs fine on Fridays because it can feel the weekend.

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3 Likes

Medium

366

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The Mathematical Visionary: Ada Lovelace’s Algorithmic Imagination

Ada Lovelace, the woman who wrote a future she'd never live to see.
Daughter of Lord Byron, she was raised with a diet of numbers, angles, and logic.
Her visionary mind saw more in Charles Babbage's Analytical Engine - poetry, music, and art.
Despite her illness, Ada Lovelace left behind blueprints for a century she'd never see, becoming the daughter of poetry and the mother of computing.

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Dev

219

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Forgot the CAPTCHA Again? This Simple C#/.NET Open-Source Package Can Be Your Bot-Stopping Hero!

The open-source package nscaptcha can help prevent bots and spam from accessing websites.
The package acts as a CAPTCHA, a bouncer that ensures only legitimate users can sign up or access the website.
The nscaptcha package is easy to install and provides a simple solution to prevent bot-related issues.
Documentation and code for the package can be found on GitHub and NuGet respectively.

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Medium

Industries already experiencing AI transformations

Industries like healthcare, finance, and marketing are already experiencing AI transformations.
AI-powered diagnostic tools are assisting doctors in detecting diseases in the healthcare sector.
AI is automating risk analysis and fraud detection in the finance industry.
AI algorithms are being used to personalize advertising and customer experiences in marketing.

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Medium

129

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From 10 Users to 1 Million: How We Scaled Using DevOps Principles

Embracing DevOps principles helped in scaling the company from 10 users to 1 million.
To improve deployment process, continuous integration and deployment (CI/CD) pipelines were introduced.
By implementing centralized logging and monitoring tools, the company was able to identify and address performance issues.
The changes resulted in significant time savings and a reduction in production bugs.

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7 Likes

Medium

409

Marker Interfaces: The Empty Interfaces That Speak Volumes

Marker Interfaces serve as tags or labels for classes.
They indicate a certain trait, capability, or eligibility for specific processing.
Other parts of the system check for the presence of the marker interface.
Marker interfaces can be used to define special logic based on their presence.

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24 Likes

Medium

349

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90% of Developers Miss This Silent Performance Killer — Is Your Code Safe from Memory Leaks?

Memory leaks can be a silent performance killer for developers.
Memory leaks occur when an application retains memory it no longer needs, leading to sluggish performance or system crashes.
Common causes of memory leaks include uncleared timers or intervals, global variables without proper declaration, closures holding unused references, and detached DOM elements.
Developers should be aware of these potential memory leak scenarios and take appropriate measures to ensure their code is safe from memory leaks.

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