BestHub
Sign in
Tag

feedback loop

0 views collected around this technical thread.

DataFunTalk
DataFunTalk
Nov 2, 2024 · Artificial Intelligence

Embodied Intelligence: Core Concepts, Three Elements, and Four Functional Modules

This article introduces embodied intelligence, explains its basic definition, three essential elements (body, intelligence, environment), and details the four functional modules—perception, decision, action, and feedback—while describing the sensors and algorithms that enable physical AI systems to interact with the real world.

AI roboticsaction moduledecision making
0 likes · 13 min read
Embodied Intelligence: Core Concepts, Three Elements, and Four Functional Modules
Tencent Cloud Developer
Tencent Cloud Developer
Nov 21, 2023 · Fundamentals

Understanding Business Debt and Physical Debt: Structured Thinking and Management

The article argues that both business and personal health debts inevitably accumulate like entropy, but can be managed through structured thinking—identifying, categorizing, prioritizing, and continuously monitoring and feedback‑driven actions such as mindfulness, exercise, and disciplined system maintenance—to reduce complexity and sustain growth.

Conway's lawTechnical Debtentropy
0 likes · 18 min read
Understanding Business Debt and Physical Debt: Structured Thinking and Management
Continuous Delivery 2.0
Continuous Delivery 2.0
Aug 22, 2023 · Fundamentals

Why End‑to‑End Testing Strategies Often Fail and How to Build Effective Feedback Loops

The article examines why end‑to‑end testing strategies frequently break in practice, illustrates common pitfalls through a realistic scenario, and proposes a more reliable testing pyramid that emphasizes fast, reliable, isolated unit and integration tests while establishing quick feedback loops for developers.

Testingend-to-endfeedback loop
0 likes · 10 min read
Why End‑to‑End Testing Strategies Often Fail and How to Build Effective Feedback Loops
Architect's Guide
Architect's Guide
Oct 4, 2022 · Fundamentals

Key Insights on System Architecture, Evolution, and Feedback Loops

This article shares practical experiences and concepts about software architecture, covering stakeholder‑driven definitions, non‑functional requirements, iterative evolution, closed‑loop feedback, microservice adoption, organizational impact, and the soft skills needed for effective architects.

DevOpsfeedback loopmicroservices
0 likes · 14 min read
Key Insights on System Architecture, Evolution, and Feedback Loops
Continuous Delivery 2.0
Continuous Delivery 2.0
Jun 22, 2022 · Operations

Applying the Four Principles of Continuous Delivery to Avoid Late‑Night Releases

The article explains how integrating the four "持" principles from the book "Continuous Delivery 2.0" into daily software development can prevent the recurring problem of late‑night releases by emphasizing minimal work, continuous decomposition, feedback, and ongoing improvement.

Continuous DeliveryDevOpsSoftware Release
0 likes · 6 min read
Applying the Four Principles of Continuous Delivery to Avoid Late‑Night Releases
HelloTech
HelloTech
Nov 22, 2021 · Operations

Five Key Stages to Improve Tech Talk Event Operations

By launching an MVP, standardizing SOPs, instituting speaker quality controls, implementing a Kirkpatrick‑based feedback loop, and enhancing participation through user‑centered tweaks, the TechTalk platform transformed from ad‑hoc sessions to a stable, high‑impact bi‑weekly event, illustrating the power of iterative, cross‑functional process improvement.

Internal TrainingSOPTech Talk
0 likes · 10 min read
Five Key Stages to Improve Tech Talk Event Operations
DataFunSummit
DataFunSummit
Aug 10, 2021 · Artificial Intelligence

Challenges and Future Directions for Recommendation Systems: Benchmarks, Explainability, and Data Confounding

The article examines the rapid growth of recommendation systems, highlighting the need for industrial‑grade benchmarks, transparent explainability, and addressing algorithmic confounding caused by feedback loops, while discussing how these issues affect both users and content providers in the AI‑driven ecosystem.

AIbenchmarkconfounding
0 likes · 12 min read
Challenges and Future Directions for Recommendation Systems: Benchmarks, Explainability, and Data Confounding
Continuous Delivery 2.0
Continuous Delivery 2.0
Feb 25, 2020 · Operations

Leveraging Feature Flags for Controlled Changes and Rapid Feedback Loops

Feature flags enable controlled system changes, allowing teams to monitor business and technical metrics, quickly roll back harmful updates, and operate within a rapid feedback loop that informs subsequent iterations, though many modern product teams struggle to integrate flag platforms with analytics systems for richer insights.

Continuous DeliveryFeature FlagsSoftware Operations
0 likes · 3 min read
Leveraging Feature Flags for Controlled Changes and Rapid Feedback Loops
Continuous Delivery 2.0
Continuous Delivery 2.0
Feb 21, 2020 · Operations

Using Feature Flags for Controlled System Changes and Rapid Feedback Loops

Feature flags enable controlled system changes, allowing teams to observe business and technical impacts, retain beneficial updates, quickly roll back harmful ones, and continuously learn through a fast feedback loop that guides subsequent modifications.

A/B testingContinuous DeliveryFeature Flags
0 likes · 2 min read
Using Feature Flags for Controlled System Changes and Rapid Feedback Loops
DataFunTalk
DataFunTalk
Aug 28, 2019 · Artificial Intelligence

Challenges and Future Directions for Recommendation Systems: Benchmarks, Explainability, and Data Confounding

Recommendation systems, driven by recent economic and deep‑learning advances, face critical issues such as the lack of unified industrial benchmarks, limited explainability for users and content providers, and feedback‑loop induced data confounding, prompting calls for open datasets, transparent models, and collaborative optimization across stakeholders.

AIRecommendation systemsbenchmark
0 likes · 15 min read
Challenges and Future Directions for Recommendation Systems: Benchmarks, Explainability, and Data Confounding