#scientific experiment documentation
41 related articles
·2 min
Why Sci-Fi Author Charlie Stross Refuses to Write with AI: A Professional Creator's Deliberate Stance
Hugo Award winner Charlie Stross refuses to use AI in his writing, citing copyright risks, creative value, and technical limitations—a professional author's deliberate stance on generative AI.
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·3 min
A.T.O.M Open-Source Cellular Network Simulation Tool: A Comprehensive Analysis of Features and Technical Architecture
Deep dive into A.T.O.M, an open-source cellular network simulation tool supporting 4G/5G/6G, coverage heatmaps, building obstruction detection, built with Go for network planning research.
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·3 min
How PhD Students Can Use AI for Programming Without Losing Their Coding Skills: A Layered Delegation Strategy
How can PhD students avoid coding skill atrophy when using AI programming assistants? This article proposes a layered delegation strategy with actionable advice for researchers.
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·3 min
Real-World GPS Accuracy Testing: How Sensor Fusion Improves Robot Localization
Real-world testing of u-blox NEO-M9N with IMU and wheel odometry fused via UKF achieves meter-level positioning. An honest look at low-cost GPS sensor fusion performance and limitations.
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·4 min
A Guide to Full-Stack Machine Learning Project Repository Structure: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and externalized configuration to help ML developers move from experimental code to production-grade engineering standards.
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·4 min
Full-Stack Machine Learning Project Repository Structure Guide: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and configuration externalization to help ML developers move from experimental code to production-grade engineering.
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·3 min
Using AI Agents to Write Research Experiment Logs: A Complete Methodology and Workflow
A detailed guide on using AI Agents to build Research Logs for scientific experiments, covering skeleton structure, daily workflows, pre-experiment thinking standards, code change tracking, and Agent-researcher division of labor.
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·5 min
A Beginner's Guide to Prompt Engineering: From One-Off Instructions to a Systematic Methodology
A beginner's guide to prompt engineering covering the four functions of prompts, the key differences from prompt engineering, a six-step systematic workflow, and critical technical and practical limitations.
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·3 min
Using AI Agents to Write Research Experiment Logs: A Complete Methodology and Workflow
Learn how to use AI Agents to build a Research Log for scientific experiments, covering structure, daily workflows, pre-experiment thinking, code change tracking, and human-AI division of labor.
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·3 min
Poolside Laguna Model Launch: Can Silicon Valley's Open-Source Push Catch Up to China's AI?
Poolside launches Laguna open-weight model after 18 months of silence, pitting 118B parameters against Kimi K3's 2.8 trillion. Can Silicon Valley's open-source push close the gap with Chinese AI?
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·3 min
Poolside Laguna Model Release: Can Silicon Valley Open Source Catch Up with Chinese AI?
Poolside releases its Laguna open-weight model after 18 months of silence, challenging Moonshot's Kimi K3 with 118B vs 2.8T parameters. Can Silicon Valley close the gap with Chinese AI?
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·3 min
Claude Opus's Autonomous Debugging Capabilities: What It Means When AI Proactively Writes Test Harnesses
New Claude Opus proactively writes test harnesses to observe runtime behavior. We analyze how this shift from passive code generation to autonomous debugging marks a key evolution in AI programming.
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·6 min
The Evolution of Claude Code: From Manual Review to 65% Fully-Automated PRs
Anthropic engineers reveal Claude Code's 18-month evolution: system prompt cut by 80%, 65% of PRs shipped automatically by AI, Claude Tag collaboration, and the safety logic behind auto mode.
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·2 min
Testing AI Scientific Engineering with the Three-Body Problem: From Numerical Integration to Chaos Visualization
A developer tasked GPT-5.6 Sol with building a three-body problem simulation site covering four integrators, chaos detection, and independent review. An in-depth look at AI's real scientific computing capabilities.
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Assess Your Engineering Team's AI Agen…
·3 min
Assess Your Engineering Team's AI Agent Maturity in 5 Minutes: A Tiered Framework and Key Dimensions
How to quickly gauge your engineering team's AI Agent adoption level? This article breaks down a four-tier AI maturity framework covering tool adoption, workflow integration, governance, and measurement.
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·3 min
A Practical Methodology for Non-Technical Professionals to Evaluate AI Translation Quality
No technical background? Learn how to rigorously evaluate AI translation accuracy and reproducibility. A practical hybrid scoring framework for NGOs and non-technical evaluators.
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The Complete AI Researcher Learning Ro…
·3 min
The Complete AI Researcher Learning Roadmap: A Beginner's Guide to Deep Learning
A structured AI/ML learning roadmap covering Python, math, machine learning, deep learning, and MLOps — with timelines, milestones, and free resource recommendations.
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·3 min
NASA Open-Sources the F´ Framework: A Space-Grade Toolkit for Flight Software and Embedded Systems Development
NASA's JPL open-sourced the F´ (F Prime) flight software framework: C++-based, component-driven, and validated in real space missions. Ideal for CubeSats, drones, and embedded systems, it has over 11,000 GitHub Stars.
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·6 min
An Efficient Learning Guide for Data Science: Study Partnerships and Hands-On ML in Practice
Struggling to learn data science alone? This article explores the value of study partnerships and pairs them with the classic Hands-On ML textbook to offer a phased learning plan from math foundations to deep learning.
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·4 min
From New Grad SWE to Platform Engineer: Career Paths and Tech Stack Choices in the AI Era
How can new graduates transition from software engineer to platform engineer? This article breaks down the path of joining as a Grad SWE first, then transferring internally, analyzes C# vs Python trade-offs, and offers a 14-month prep plan for AI/ML infrastructure.
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