Open-source AI model rivaling top proprietary models
DeepSeek is a Chinese AI lab that shook the industry with its open-source models achieving near-GPT-4 performance at a fraction of the cost. DeepSeek V3 and R1 are freely available and caused a major market reaction. Embraces open-source, allowing the community to build on its technology.
10 ready-to-use prompts — click any card to copy, or paste into the writer above.
Write a Python implementation of a Bloom filter from scratch. Include: the math behind the optimal number of hash functions for a given false positive rate, a full implementation, unit tests with edge cases, and a benchmark comparing memory usage vs a regular set at 1 million elements.
Design a URL shortener service handling 100 million URLs and 10 billion redirects per month. Cover: database schema choice and reasoning, cache strategy with eviction policy, short code generation algorithm avoiding collisions, horizontal scaling approach, and estimated infrastructure cost.
Solve this step by step, showing all work and reasoning at each step: [paste your math problem]. After the solution, explain which theorem or principle each step uses, and show an alternative approach if one exists.
This function works correctly in development but causes intermittent failures in production under high load. Analyze it for: race conditions, memory leaks, off-by-one errors in edge cases, and behavior with concurrent access. Show the root cause and fix: [paste your code]
Review this code as if teaching a junior engineer. For each issue: explain what is wrong, why it matters, the correct pattern with a code example, and a rule of thumb to remember. Rate the overall code quality 1-10 and give a priority fix order: [paste your code]
Translate this [source language] code to idiomatic [target language]. Do not just do a syntax translation, use the target language idioms, standard library, and best practices. Add comments explaining any significant pattern differences: [paste your code]
Solve this competitive programming problem. First analyze the time and space complexity of the naive approach. Then develop an optimized solution. Explain the key insight that makes it efficient, show the full solution with comments, and trace through the example input: [paste problem statement]
Build a mathematical model for [real-world scenario]. Define: the variables and their relationships, the governing equations, your assumptions and their justifications, edge cases where the model breaks down, and a Python implementation to simulate it with sample inputs.
Analyze this software architecture for: scalability bottlenecks at 10x current load, single points of failure, security vulnerabilities, performance hotspots, and technical debt that will become critical in 12 months. Prioritize issues by impact and suggest specific solutions: [paste architecture diagram or description]
Design a data pipeline to process [data source and volume]. Requirements: real-time or batch, transformation steps needed, error handling and dead letter queues, monitoring and alerting, and idempotency. Show the full implementation in Python with the orchestration code.
