# human review Published articles for human review. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to opt out of AI chatbot training DevFeed: [How to opt out of AI chatbot training]() Original publisher: [Read original article]() Author: Pieter Arntz Published: 2026-09-15T15:41:44Z Content type: tutorial Language: en Sources: [Malwarebytes]() Topics: [ChatGPT](), [OpenAI](), [online privacy](), [Artificial Intelligence]() Tags: [ai](), [anthropic](), [chatgpt](), [how-to](), [human-review](), [news](), [openai](), [perplexity](), [privacy](), [privacy-filter]() ### AI overview This tutorial explains how to turn off ChatGPT's "Improve the model for everyone" setting to opt out of using chats for model improvement. It also notes that authorized personnel and trusted service providers may still access content for safety, support, troubleshooting, security, abuse investigations, or legal matters. ### Source excerpt ChatGPT contractors are reviewing real users' conversations. Here's how to stop AI companies using your chats for model training. ## Use My /Human-Review Skill to Edit HTML and Markdown Files Visually DevFeed: [Use My /Human-Review Skill to Edit HTML and Markdown Files Visually]() Original publisher: [Read original article]() Author: Peter Yang Published: 2026-08-05T14:03:23Z Content type: tutorial Language: en Sources: [Behind the Craft]() Topics: [human review](), [HTML](), [Markdown](), [ai-coding](), [interface]() Tags: [ai-coding](), [cost](), [how-to](), [html](), [human-review](), [interface](), [markdown]() ### AI overview The article introduces /human-review, a free AI skill that opens HTML and Markdown files in a visual editor. Users can edit text and basic formatting, resize images, select content or components for comments, and send grouped feedback to an AI coding tool. ### Source excerpt Tired of giving AI feedback in chat? My new skill lets you edit files, leave comments, and send feedback directly to your agent using a beautiful visual interface ## ITIL vs SRE: why the big clouds went their own way DevFeed: [ITIL vs SRE: why the big clouds went their own way]() Original publisher: [Read original article]() Author: Sridhar Rajarao Published: 2026-07-26T00:00:00Z Content type: opinion Language: en Sources: [Sridhar Rajarao]() Topics: [SRE](), [site-reliability-engineering](), [Development](), [Terraform](), [pulumi](), [Availability](), [feature flags](), [incident]() Tags: [automated](), [aws](), [azure](), [cloud](), [feature-flags](), [gcp](), [human-review](), [hyperscaler](), [incident](), [incident-management](), [infrastructure-as-code](), [itil](), [on-call](), [postmortems](), [pulumi](), [release](), [reliability](), [service-catalog](), [sre](), [terraform]() ### AI overview This opinion article compares ITIL practices with SRE operations at hyperscaler scale. It argues that human change boards, single production instances, developer-to-operations handoffs, documentation-first configuration management, and weekly release windows do not fit environments serving millions of external customers. It describes automated approvals, gradual deployments, service-team ownership, infrastructure as code, continuous release, error budgets, SLOs, and blameless postmortems as alternatives. ### Source excerpt The big clouds don't run ITIL. Five assumptions ITIL makes that break at hyperscaler scale, and what AWS, Azure, GCP, and OCI use instead. ## LangGraph in production: Temporal's LangGraph Plugin adds Durable Execution DevFeed: [LangGraph in production: Temporal's LangGraph Plugin adds Durable Execution]() Original publisher: [Read original article]() Author: Brian Strauch Published: 2026-07-16T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Langgraph](), [Orchestration](), [Agent Framework](), [human review](), [Python](), [telemetry]() Tags: [agent-framework](), [crash-recovery](), [human-review](), [langgraph](), [orchestration](), [product-news](), [python]() ### AI overview Temporal announces a public-preview LangGraph integration for Python, allowing LangGraph agents to run on Temporal with automatic failure recovery, durable execution, human-in-the-loop waits, and support for long-running work. The announcement also covers LangSmith integrations for Python and TypeScript. ### Source excerpt LangGraph agents now run on Temporal: automatic crash recovery, free human-in-the-loop waits, and full observability via new LangSmith integration. ## From Traditional ML to AI Agents: How Booking.com Scales AI Observability With Arize AI DevFeed: [From Traditional ML to AI Agents: How Booking.com Scales AI Observability With Arize AI]() Original publisher: [Read original article]() Author: Amir Bitaraf Published: 2026-07-10T07:52:18Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [ai observability](), [agent observability](), [Machine learning](), [observability](), [human review]() Tags: [agents](), [ai](), [ai-agents](), [ai-observability](), [blog-posts](), [data-quality](), [human-review](), [latency](), [ml](), [observability]() ### AI overview Booking.com describes building an AI-native observability stack for traditional machine learning systems and agentic AI workflows. The article explains that its diverse systems require observability to detect changes, regressions, data quality issues, misconfigurations, and responsible-AI concerns across different operating constraints and user contexts. ### Source excerpt Building an AI-native observability stack for agentic AI and traditional ML at Booking.com Authors: Amir Bitaraf, Shahaf Veber Why AI Observability Matters at Booking.com At Booking.com, AI helps travellers and partners in every step of their journey, from how people discover destinations to the way we support them while they're on the road. Rather than a single flagship model, we rely on a large and growing collection of systems that each solve a specific problem at scale. To make this concrete, consider a few examples: Trip planning assistants that help travelers turn vague ideas ("somewhere warm in April with good hiking") into concrete, bookable itineraries. On-site helpers that turn property details, amenities, reviews, and options into plain-language guidance, so people can choose the right stay with confidence. Partner copilots that help accommodation partners and other suppliers respond to guest messages faster and more consistently, while still staying in control of the final reply. Ranking systems that decide which options to show first in search and recommendation to surfaces, balancing user relevance with experimentation needs. Fraud detection models that quietly protect customers and partners in the background by flagging suspicious activity before it turns into real harm. Each of these systems is built and iterated on by different teams, uses different data, and runs under different constraints such as real-time vs batch, strict latency budgets vs more relaxed ones, fully automated vs human-in-the-loop. As we scale this ecosystem, observability becomes a first-class requirement, not a nice-to-have as we need to: Know when something changes in the real world, a new travel pattern, a data quality issue, a misconfiguration and how that affects model behaviour and user experience. Detect regressions early: slower responses, more confusing answers, drops in relevance or conversion, or subtle shifts that only show up for specific geographies, devices, or use ## How Coverwatch uses Temporal to orchestrate AI-powered insurance workflows DevFeed: [How Coverwatch uses Temporal to orchestrate AI-powered insurance workflows]() Original publisher: [Read original article]() Author: Wilmer Yan Published: 2026-06-09T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Orchestration](), [Artificial Intelligence](), [Automation](), [human review](), [API]() Tags: [agents](), [ai](), [automation](), [community](), [human-review](), [outages](), [temporal](), [workflows]() ### AI overview Coverwatch uses Temporal to coordinate long-running commercial insurance workflows involving AI agents, broker review, carrier communication, and operational follow-up. The platform is designed to preserve workflow state across delays, outages, and external-service issues. ### Source excerpt Coverwatch runs its AI-powered insurance workflows on Temporal, keeping agents, broker review, and carrier submissions durable through outages. ## 6 Deterministic Guardrails to Prevent AI Mistakes in Code DevFeed: [6 Deterministic Guardrails to Prevent AI Mistakes in Code]() Original publisher: [Read original article]() Author: Daniel Moka Published: 2026-05-27T05:01:22Z Content type: tutorial Language: en Sources: [Craft Better Software]() Topics: [Artificial Intelligence](), [Code](), [Testing](), [ci](), [human review]() Tags: [agent](), [ai](), [ai-agents](), [ci](), [code](), [how-to](), [human-review](), [testing]() ### AI overview This tutorial presents six deterministic guardrails for preventing AI-generated code from reaching production with defects. It emphasizes compilers, tests, linters, analyzers, and CI, while reserving human review for intent, tradeoffs, and product decisions. ### Source excerpt 6 Deterministic Guardrails To Prevent AI Mistakes ## Accelerating Delivery: How AI Agents Can Own the Initial Code Draft DevFeed: [Accelerating Delivery: How AI Agents Can Own the Initial Code Draft]() Original publisher: [Read original article]() Author: Stephen Walker Published: 2026-05-05T21:58:00Z Content type: article Language: en Sources: [8th Light](), [8th Light Insights]() Topics: [Artificial Intelligence](), [Loop Engineering](), [Pull Request](), [human review](), [jira](), [coding](), [hooks](), [Script]() Tags: [agents](), [ai](), [ai-agents](), [ai-and-emerging-tech](), [code](), [hooks](), [human-review](), [jira](), [pull-request](), [review](), [sandbox]() ### AI overview The article describes a pragmatic workflow in which coordinated AI agents turn a Jira ticket into a code draft and pull request for human review. It emphasizes safety through branch permissions, scoped access, scripts and hooks, plus deterministic structures around probabilistic language models. The supplied text is truncated before the full reliability hierarchy and later conclusions. ### Source excerpt Why This Matters in the Era of Agentic Everything The gap between a Jira ticket and the first pull request (PR) is often a graveyard of productivity. Context switching, boilerplate setup, and requirement analysis can eat up hours of a senior engineer's day before they write a single line of business logic. Recently, industry leaders have showcased "Harness Engineering" [1][2] -- the practice of building autonomous agent workflows that handle end-to-end coding tasks. While their results are inspiring, the barrier to entry can feel insurmountable. The challenge for most teams is finding a starting point that balances long-term vision with immediate ROI. We recently helped a large-scale, high-traffic consumer services platform bridge this gap. Instead of an all-or-nothing architectural overhaul, we took a pragmatic first step: automating the journey from Jira ticket to PR. The Vision: "Let's work on FEAT-123" Our goal was simple. We wanted to enable a developer to provide a single prompt -- "Let's work on FEAT-123" -- and have a coordinated team of AI agents handle the planning, coding, and verification. This culminates in a PR ready for human review. To make this a reality, we focused on three strategic pillars: safety, determinism, and quality. Safety Through Pragmatic Guardrails One of the primary concerns for any engineering leader is "agent drift" -- an AI making unauthorized or hallucinated changes. While an isolated digital sandbox is an ideal long-term goal, we proved that teams can achieve significant safety and progress using existing infrastructure: Branch Permissions: Restrict agent access to specific feature branches. Scoped Access: Use scripts and hooks to define exactly what an agent can and cannot touch. Human-in-the-Loop: Ensure that while the agent proposes the change, a human must always review, approve, and merge the code. By defining the agent's scope as a specialized contributor rather than a system administrator, we mitigated risk while maximizing ou ## How XY builds an AI agent orchestration platform for healthcare with Temporal DevFeed: [How XY builds an AI agent orchestration platform for healthcare with Temporal]() Original publisher: [Read original article]() Author: The XY Engineering Team Published: 2026-04-23T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [agent orchestration](), [AI Agent](), [Orchestration](), [reliability](), [human review](), [sensitive data](), [Structured-data](), [YAML]() Tags: [agent](), [agent-orchestration](), [ai](), [ai-agent](), [automation](), [how-to](), [human-review](), [long-running](), [orchestration](), [reliability](), [sensitive-data](), [temporal](), [workflow]() ### AI overview XY describes using Temporal as a DSL-driven execution engine for an AI agent orchestration platform serving complex healthcare workflows. The approach uses a YAML-based workflow language and a generic Temporal workflow class to coordinate multiple systems, reliability features, human review, and sensitive healthcare data from prototype to production. ### Source excerpt One generic Temporal workflow class can provide infinite healthcare automation. See how XY built a DSL-driven AI agent orchestration platform that scales from prototype to production. ## AI coding tools can increase code output without improving delivery outcomes DevFeed: [AI coding tools can increase code output without improving delivery outcomes]() Original publisher: [Read original article]() Author: Brian Jenney Published: 2026-04-11T16:09:44Z Content type: opinion Language: en Sources: [Brian Jenney]() Topics: [Artificial Intelligence](), [ai-coding](), [human review](), [Code review](), [Testing](), [telemetry](), [Claude](), [cursor](), [React]() Tags: [ai](), [ai-coding](), [claude](), [code-review](), [cursor](), [engineering](), [human-review](), [pull-requests](), [telemetry](), [testing]() ### AI overview The author argues that AI coding tools may increase code output and task completion without improving delivery velocity or business outcomes. The article combines industry reports with personal experience, describing longer code review and debugging times, higher bug rates, and cleanup work after rushed AI-assisted development. ### Source excerpt I've flip-flopped on this issue more times than a politician up for re-election. ## Scaling Experimentation Quality at Booking.com DevFeed: [Scaling Experimentation Quality at Booking.com]() Original publisher: [Read original article]() Author: Edgar Cano Published: 2026-03-24T11:44:21Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [experiments](), [Development](), [decision-making](), [human review]() Tags: [best-practices](), [data-science](), [decision-making](), [experimentation](), [featured](), [human-review](), [metrics](), [product-development](), [quality]() ### AI overview Booking.com describes how it addressed declining experimentation quality as experiment volume grew. The article discusses arbitrary test durations, significance-seeking, and the trade-offs between enforcing standards and educating product teams. ### Source excerpt Authors: Edgar Cano, Daisy Duursma, Nils Skotara, Melanie Mueller Figure 1. Three-pillar components of Booking.com's Experimentation Quality Experimentation is at the core of product development in Booking.com, powered by our in-house platform, "ET" (Experiment Tool). At any given moment, we run approximately 1,000 parallel experiments to evaluate product changes. These experiments or A/B tests allow teams to directly compare a new version of the website against the existing one, validating hypotheses about how specific changes impact important metrics. In our organization, these pitfalls became more evident as our experiment volume grew. We observed that experimenters might set an arbitrary "two-week" duration without thinking about sufficient power, or extend a test until results "became significant" or "trended positive." Knowing that this lack of consistency leads to flawed decision-making we dedicated significant effort to increasing the quality of our experimentation process, making Experimentation Quality a key KPI for our program. However, identifying the problem was only the start; the greater challenge is how to implement these standards across a large organization. Enforcement vs. Education When deciding how to scale quality, we faced a fundamental choice: Do we enforce strict controls or we rely on education. Ultimately, we left it to product teams to decide how to conduct their experiments. This choice entailed several trade-offs: Enforcement: Ensures comparability, consistency, and reliability. However, it comes at the cost of flexibility. There is a risk that people follow "rules" blindly without understanding the rationale. Education: Aims for a culture where experimenters understand the why behind best practices. This leads to better buy-in, allows teams to challenge methods, and highlights individual responsibility. It also prevents bottlenecking. If enforcement requires human review, it slows down development. However, education requires a massive ## Automated Code Review: The 6-Month Evolution DevFeed: [Automated Code Review: The 6-Month Evolution]() Original publisher: [Read original article]() Author: eadams@hubspot.com (Emily Adams) Published: 2026-03-02T17:46:43Z Content type: article Language: en Sources: [HubSpot]() Topics: [Code review](), [AI-assisted coding](), [Claude Code](), [GitHub](), [Kubernetes](), [Java]() Tags: [agents](), [ai](), [automated](), [claude-code](), [cli](), [code](), [code-review](), [coding](), [coding-agents](), [cycle-time](), [developer-experience](), [engineering](), [engineering-infrastructure](), [github](), [human-review](), [java](), [kubernetes](), [pull-request](), [pull-requests](), [quality](), [workflow]() ### AI overview HubSpot describes a six-month evolution of its automated code review system. The system uses AI to review pull requests, provide feedback grounded in HubSpot-specific norms, and reduce the time engineers wait for feedback. The article says Sidekick reduced feedback time by 90%, with a peak reduction of 99.76% in September, and traces a shift from a Kubernetes-based system powered by Claude Code toward a framework native to HubSpot's Java stack. ### Source excerpt As HubSpot engineers have increasingly started writing more and more of their code with both local and cloud coding agents, we've noticed that now more than ever, code review is taking up a large portion of cycle time on new changes. ## The journey to shippable AI systems: Patterns that work DevFeed: [The journey to shippable AI systems: Patterns that work]() Original publisher: [Read original article]() Author: Tim Imkin Published: 2025-11-18T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Artificial Intelligence](), [reliability](), [distributed-systems](), [AI-assisted coding]() Tags: [ai](), [audit](), [automation](), [community](), [human-review](), [incident](), [reliability](), [systems]() ### AI overview This article explains how teams can make AI systems more reliable to operate and ship. It presents five patterns centered on resilience for nondeterministic LLM, retrieval, and external API steps; idempotent side effects; human approval checkpoints; and related operational controls. ### Source excerpt Move from AI features to resilient AI systems. Use Durable Execution and five patterns to improve reliability, compliance, and day-to-day operations. ## Apache Airflow 3.1.0: Human-Centered Workflows DevFeed: [Apache Airflow 3.1.0: Human-Centered Workflows]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2025-09-25T00:00:00Z Content type: release Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [Automation](), [human review](), [Data Quality](), [Forms](), [Developer experience](), [Inference](), [React]() Tags: [apache-airflow](), [automation](), [data-quality](), [developer-experience](), [forms](), [human-review](), [inference](), [release](), [release-notes](), [ui]() ### AI overview Apache Airflow 3.1.0 introduces human-in-the-loop tasks for pausing automated workflows and collecting reviews through web forms. The release also adds internationalization support, developer experience improvements, restored Calendar and Gantt views, UI filtering, DAG pinning, and accessibility-focused theme updates. ### Source excerpt We are thrilled to announce the release of Apache Airflow 3.1.0, an update that puts humans at the center of data workflows. This release introduces powerful new capabilities for human decision-making in automated processes, comprehensive internationalization support, and significant developer experience improvements. Details: ðŸ“Ķ PyPI: https://pypi.org/project/apache-airflow/3.1.0/ 📚 Core Airflow Docs: https://airflow.apache.org/docs/apache-airflow/3.1.0/ 📚 Task SDK Docs: https://airflow.apache.org/docs/task-sdk/1.1.0/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/3.1.0/release_notes.html ðŸŠķ Sources: https://airflow.apache.org/docs/apache-airflow/3.1.0/installation/installing-from-sources.html 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-3.1.0 ðŸĪ Human-in-the-Loop (HITL): When Automation Meets Human Judgment This powerful capability bridges the gap between automated processes and human expertise, making Airflow invaluable for: AI/ML Model Validation: Pause inference pipelines for human review of model outputs Content Moderation: Route content through human reviewers before publication Approval Workflows: Require manager approval for sensitive operations Data Quality Gates: Allow data stewards to validate critical datasets HITL tasks pause in a deferred state while presenting intuitive web forms in the Airflow UI. Users with appropriate roles can review context data, DAG parameters, and XCom values before making informed decisions. Example Code: from airflow.sdk import DAG from airflow.providers.standard.operators.hitl import HITLOperator with DAG("content_moderation", schedule="@daily") as dag: moderate_content = HITLOperator( task_id="review_content", message="Please review this content for publication", data_key="content_to_review" ) 📊 UI Enhancements & Performance Calendar and Gantt Views Make Their Comeback Remember those beloved Calendar and Gantt chart views from Airflow 2.x? They're back, completely rebuilt for the modern ## How Windsurf writes docs DevFeed: [How Windsurf writes docs]() Original publisher: [Read original article]() Author: Tiffany Chen Published: 2025-05-27T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [Documentation](), [product analytics](), [ai-coding](), [human review]() Tags: [agent](), [ai-coding](), [ai-trends](), [documentation](), [human-review](), [product-analytics]() ### AI overview Windsurf treats documentation as a core product component, with marketing leading the process and shipping docs alongside every release. The team uses product analytics to monitor adoption and guide improvements, focuses pages on user value, and applies AI tools with human review to maintain consistency and quality. ### Source excerpt At Windsurf, a leading player in the AI coding space, documentation is integrated in how they build, ship, and market their product. ## Windmill for AI Workflows - Investing.com Case Study DevFeed: [Windmill for AI Workflows - Investing.com Case Study]() Original publisher: [Read original article]() Author: Yonathan Adest Published: 2025-02-24T00:00:00Z Content type: article Language: en Sources: [Windmill Blog]() Topics: [Automation](), [data-processing](), [Artificial Intelligence](), [etl](), [tracing](), [Logging](), [PostgreSQL](), [airflow]() Tags: [ai](), [airflow](), [automation](), [case-study](), [case-study-testimonial-fintech-investing](), [data-processing](), [docker-compose](), [embeddings](), [etl](), [fintech](), [human-review](), [investing](), [pdf](), [postgresql](), [push-notifications](), [recommendations](), [testimonial](), [windmill](), [workflows]() ### AI overview This Investing.com case study describes how its AI team uses Windmill to orchestrate content processing and distribution, stock-analysis report generation, and ETL workflows. The workflows use webhooks, AI models, vector embeddings, human review, and PostgreSQL, while Windmill provides tracing, logging, and Docker Compose deployment. ### Source excerpt This is a testimonial from Yonathan Adest, CTO at Investing.com, about how Windmill has helped them to automate their workflows and improve their data processing capabilities. ## AI Adoption in Software Engineering: Practical Limits and the Need for Human Review DevFeed: [AI Adoption in Software Engineering: Practical Limits and the Need for Human Review]() Original publisher: [Read original article]() Author: Peter Lawrey (noreply@blogger.com) Published: 2024-12-16T12:03:00Z Content type: article Language: en Sources: [Vanilla Java]() Topics: [Artificial Intelligence](), [AI Strategy](), [Software Engineering](), [AI-assisted coding]() Tags: [adoption](), [ai](), [ai-adoption](), [human-review](), [info](), [opinion](), [security](), [testing]() ### AI overview The article examines the practical challenges of adopting AI in software engineering, including inaccurate or outdated suggestions, weak contextual understanding, inconsistent style, and security or compliance risks. It argues that human review, domain expertise, testing, and validation remain necessary. ### Source excerpt Artificial Intelligence (AI) has long promised to transform software development. Yet, as many experienced engineers discover, initial enthusiasm often settles into a more subdued reality. This is the "Trough of Disillusionment" within the Gartner Hype Cycle--where inflated expectations give way to measured assessments. In this phase, teams confront the practical limitations of AI-driven tools, refine their strategies, and seek a balance between what AI can deliver and what human expertise must still provide. This article continues from AI on the Hype Cycle. We do these things not because they are easy, but because we thought they were going to be easy. -- Programmer's Credo Challenges of AI Adoption When integrating AI into software engineering workflows--be it code completion, architectural documentation, or performance tuning hints--teams quickly encounter stumbling blocks: Accuracy and Reliability: AI-generated content may contain inaccuracies, out-of-date references, or misunderstandings of domain-specific terms. Ensuring factual correctness requires careful human review and validation. AI outputs often present plausible suggestions that fail strict validation. For instance, an AI tool may confidently return a code snippet referencing APIs deprecated in Java 11 or misapply concurrency constructs from Java 21 libraries. Ensuring correctness demands human review, domain expertise, and rigorous testing Contextual Understanding: AI suggestions may misalign your codebase's patterns or standards without proper context. For example, given a legacy codebase optimised around ConcurrentSkipListMap, an AI may suggest using HashMap for "simplicity." Senior developers must provide guardrails, review outputs, and ensure that each recommendation aligns with existing architectural guidelines and performance expectations. Maintaining Consistency and Style: Projects often follow strict coding conventions and documentation formats. AI outputs might vary in style, indentation, or nami