# behavior Published articles for behavior. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Our framework for reporting model misalignment DevFeed: [Our framework for reporting model misalignment]() Original publisher: [Read original article]() Published: 2026-09-16T17:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [AI Development](), [AI Strategy](), [Framework](), [Development]() Tags: [ai](), [ai-development](), [alignment](), [behavior](), [developers](), [model](), [monitoring](), [openai](), [reporting](), [research](), [standards]() ### AI overview OpenAI introduces a framework for tracking, investigating, and disclosing model misalignment, accompanied by six reports on unexpected or concerning model behavior observed over the previous six months. The framework favors disclosure even when the significance of an instance is uncertain and is intended to evolve through experience and public feedback. ### Source excerpt OpenAI shares a framework for tracking, investigating, and disclosing model misalignment, alongside six reports of unexpected or concerning model behavior. ## How AI Amplifies Organizational Strengths and Dysfunction DevFeed: [How AI Amplifies Organizational Strengths and Dysfunction]() Original publisher: [Read original article]() Author: John Cutler Published: 2026-07-03T20:15:44Z Content type: opinion Language: en Sources: [The Beautiful Mess]() Topics: [Artificial Intelligence](), [context]() Tags: [ai](), [behavior](), [dashboards](), [trust](), [workflow]() ### AI overview This commentary argues that AI can reduce workflow friction and support organizational improvement when teams have trust, openness, and a willingness to confront difficult truths. In organizations shaped by avoidance, political behavior, poor incentives, or the weaponization of metrics, AI may automate and intensify existing dysfunction instead. ### Source excerpt AI can be your scribe, your thought partner, data-stitcher, or your counselor. ## Handle RecyclerView's scroll events in custom TopAppBarScrollBehavior DevFeed: [Handle RecyclerView's scroll events in custom TopAppBarScrollBehavior]() Original publisher: [Read original article]() Author: Leonidas Partsas Published: 2026-02-08T11:58:14Z Content type: tutorial Language: en Sources: [le0nidas]() Topics: [recyclerview](), [Compose](), [implementation](), [App]() Tags: [android](), [animation](), [behavior](), [compose](), [featured](), [how-to](), [implementation](), [jetpack-compose](), [kotlin](), [recyclerview](), [top-app-bar]() ### AI overview A tutorial on handling RecyclerView scroll events in a custom TopAppBarScrollBehavior for Compose. It implements fully collapsed and expanded states, adds animation and scroll buffering, and extends the approach to an exit-until-collapsed behavior. ### Source excerpt In a previous post we saw how to send a RecyclerView's scroll events to a compose parent so that we can leverage the top bar's enter always scroll behavior. The result was good but not great. The top app bar was collapsing and expanding as expected but it was also rendering partially during the transition: ... Continue reading Handle RecyclerView's scroll events in custom TopAppBarScrollBehavior -> ## Stateful Agents: It's About The State, Not The LLM DevFeed: [Stateful Agents: It's About The State, Not The LLM]() Original publisher: [Read original article]() Published: 2026-01-31T00:00:00Z Content type: opinion Language: en Sources: [Tim Kellogg]() Topics: [Large Language Model](), [Artificial Intelligence]() Tags: [agents](), [ai](), [ai-agents](), [behavior](), [llm](), [llms](), [state](), [stateful]() ### AI overview This opinion article argues that adding state changes how LLM-based agents behave. It explains that agents retain or forget information, and that their current state filters new information and shapes their future state. Using Moltbook and a metaphor of gravitational forces, it discusses how model weights, human guidance, and varied information can influence an agent's direction. ### Source excerpt You think you know about LLMs? No, everything changes when you add state. Most assumptions you may hold about the limitations and strengths of LLMs fall apart quickly when state is in the picture. ## Why We Believe in In-Person Onboarding at Teleport DevFeed: [Why We Believe in In-Person Onboarding at Teleport]() Original publisher: [Read original article]() Author: info@goteleport.com (Amanda Pessica) Published: 2025-07-10T00:00:00Z Content type: opinion Language: en Sources: [Teleport]() Topics: [trust](), [Job]() Tags: [3](), [behavior](), [ceo](), [culture](), [daily](), [distributed](), [money](), [new-job](), [onboarding](), [people](), [remote](), [screen](), [slack](), [team](), [travel](), [trust](), [velocity](), [zoom]() ### AI overview Teleport explains why it brings new hires together in person for onboarding despite the costs and logistics. The company says the three-day program helps employees connect, build trust, understand its culture, and ramp up faster. ### Source excerpt In a remote-first world, in-person onboarding is still invaluable. At Teleport, we see it as a strategic investment in connection, trust, and team velocity. ## Anomaly Detection in Time Series Using Statistical Analysis DevFeed: [Anomaly Detection in Time Series Using Statistical Analysis]() Original publisher: [Read original article]() Author: Ivan Shubin Published: 2025-04-15T18:45:36Z Content type: tutorial Language: en Sources: [Booking.com Development - Medium]() Topics: [Time Series](), [Statistics](), [Monitoring](), [data](), [Website]() Tags: [analysis](), [anomaly-detection](), [article](), [behavior](), [data](), [grafana](), [metrics](), [monitoring](), [outlier-detection](), [sre](), [statistics](), [time-series](), [web]() ### AI overview This article explains how to build a statistical anomaly detection system for time series data. It describes why static thresholds and comparisons with the same point one week earlier can miss recurring or gradual problems, and introduces standard deviation as a foundational statistical measure. ### Source excerpt Setting up alerts for metrics isn't always straightforward. In some cases, a simple threshold works just fine -- for example, monitoring disk space on a device. You can just set an alert at 10% remaining, and you're covered. The same goes for tracking available memory on a server. But what if we need to monitor something like user behavior on a website? Imagine running a web store where you sell products. One approach might be to set a minimum threshold for daily sales and check it once a day. But what if something goes wrong, and you need to catch the issue much sooner -- within hours or even minutes? In that case, a static threshold won't cut it because user activity fluctuates throughout the day. This is where anomaly detection comes in. What exactly is anomaly detection? Instead of relying on simple rules, it involves analyzing historical data to spot unusual patterns. There are various ways to implement anomaly detection, including machine learning and statistical analysis. In this article, we'll focus on the statistical approach and walk through how we built our own anomaly detection system for time series data from scratch at Booking. The Naïve Approach One common mistake I've seen across different companies and teams is trying to detect anomalies by simply comparing a business metric to its value exactly one week ago. This week vs previous week At first glance, this approach isn't entirely useless -- you can catch some anomalies, as shown in the image above. But is it a reliable long-term solution? Not really. The big flaw is that today's anomaly becomes next week's baseline. That means if the same issue occurs again at the same time next week, it may go completely unnoticed because we're now comparing against a flawed reference point. Outage in previous week That doesn't look right, our simplistic approach doesn't know that last week's data was compromised. Another limitation of this method is that it only considers a single week at a time. But what if perform ## A Curious Case of Mistaken Identity: How Lambdas Break Data Class Hashing DevFeed: [A Curious Case of Mistaken Identity: How Lambdas Break Data Class Hashing]() Original publisher: [Read original article]() Author: Matt McKenna Published: 2024-11-14T21:44:31Z Content type: tutorial Language: en Sources: [Matt McKenna]() Topics: [Kotlin](), [hashing](), [hash](), [consistency]() Tags: [android](), [behavior](), [class](), [data-class](), [hashcode](), [hashing](), [kotlin](), [maintenance]() ### AI overview This Kotlin article explains why data class instances containing lambdas may compare as unequal and produce different hash-based behavior. Each lambda instance has a distinct identity, so the article recommends excluding the callback from equality and hashCode calculations, while noting the resulting maintenance cost. ### Source excerpt Introduction: The Scene of the Crime It was a dark and stormy night. My hands were flying across the keys when suddenly the codebase began to exhibit strange behavior. Hashes, which once returned the same values for identical objects, suddenly became... ## How to Change Habitual Behavior Using the Transtheoretical Model DevFeed: [How to Change Habitual Behavior Using the Transtheoretical Model]() Original publisher: [Read original article]() Published: 2024-01-14T18:14:05Z Content type: tutorial Language: en Sources: [Matt Might]() Topics: [Processes](), [maintenance]() Tags: [behavior](), [change](), [howto]() ### AI overview This tutorial explains the transtheoretical model of behavior change, which describes six stages: precontemplation, contemplation, preparation, action, maintenance, and relapse. It recommends tailoring interventions to the stage of change when introducing desired behaviors or eliminating undesired ones. ### Source excerpt In theory, behavior change should be easy. At first glance, it seems like you control your behavior. So, if you desire different behavior, why doesn't your behavior change as instantly as your desire to change it? In short, lasting change of habitual behaviors is a multi-stage process. In 1977, as psychologists James Prochaska and Carlo DiClemente were studying the behavior of smokers, they identified these stages. Their transtheoretical model of behavior change captures the process of behavior change in six different stages: (1) precontemplation; (2) contemplation; (3) preparation; (4) action; (5) maintenance; and (6) relapse. DALL-E's depiction of behavior change in the transtheoretical model. In the years since, this landmark model has been found to be applicable to many habitual human behaviors. The key takeaway is that the odds of successful long-term behavior change improve if you calibrate your strategy to the stage of change you're in. The goal of this article is to provide an actionable summary of this model and interventions for each stage, so that you can more effectively change your behavior in the long-term. Understanding the model may also help you to understand why you may have struggled with behavior change in the past. Disclaimer: This article is based on my survey of the scientific literature and my own lived experience. I am not a mental health professional, and if you are struggling with behavior change or any other mental health condition, please immediately consult a qualified professional. Click here to read the rest of the article ## Four new CSS features for smooth entry and exit animations DevFeed: [Four new CSS features for smooth entry and exit animations]() Original publisher: [Read original article]() Published: 2023-08-16T00:00:00Z Content type: tutorial Language: en Sources: [Una Kravets]() Topics: [CSS](), [how to create smooth CSS transitions](), [Web Development]() Tags: [behavior](), [css](), [display](), [features](), [property](), [visibility]() ### AI overview The article presents four CSS features for creating smooth entry and exit animations: animating display and content-visibility in keyframe timelines, the transition-behavior property, the @starting-style rule, and the overlay property. ### Source excerpt These four new features include: animating display and content-visibility on a keyframe timeline, the transition-behavior property, the @starting-style rule, and the overlay property. ## Property delegation DevFeed: [Property delegation]() Original publisher: [Read original article]() Published: 2023-03-20T00:15:00Z Content type: tutorial Language: en Sources: [Kt. Academy]() Topics: [Kotlin](), [properties](), [Programming](), [Refactoring]() Tags: [behavior](), [function](), [implement](), [kotlin](), [patterns](), [property](), [property-delegation](), [workshop-learning-programming]() ### AI overview This tutorial explains Kotlin property delegation, a feature for extracting reusable property patterns such as lazy, observable, binding, and dependency-injection behaviors. It describes how delegated getters and setters work and how to implement custom property delegates. ### Source excerpt What is property delegation, how does it work, and how can we implement our custom property delegate. ## How Ignoring Mistakes Creates Overconfidence and Organizational Risk DevFeed: [How Ignoring Mistakes Creates Overconfidence and Organizational Risk]() Original publisher: [Read original article]() Author: andreacorbellini Published: 2023-03-18T08:20:00Z Content type: opinion Language: en Sources: [Andrea Corbellini]() Topics: [Resilience](), [Learning]() Tags: [behavior](), [leadership](), [learning](), [mistakes](), [nasa](), [organization](), [resilience]() ### AI overview The article explains how successful outcomes can conceal flawed execution, causing people and organizations to repeat mistakes and develop overconfidence. It discusses normalization of deviance through examples including the Challenger and Columbia shuttle disasters, where NASA leadership ignored engineering concerns. ### Source excerpt As a scuba diver who often explores new places, I can say that I have found myself in some dangerous situations, but I always made it back to the surface without facing any negative consequences. Does this mean that I never made any mistakes? Absolutely not: mistakes were made, and lessons were learned. We can all agree that learning from mistakes is good, but ... ## Marketing Attribution: Evaluating The Path to Purchase in the Product Ecosystem DevFeed: [Marketing Attribution: Evaluating The Path to Purchase in the Product Ecosystem]() Original publisher: [Read original article]() Author: Shelly Leal Senior Data Analyst Published: 2022-12-06T00:00:00Z Content type: tutorial Language: en Sources: [Trivago]() Topics: [data](), [App](), [Website](), [dashboards]() Tags: [article](), [behavior](), [business](), [channel](), [data](), [data-science](), [database](), [digital-marketing]() ### AI overview This article explains marketing attribution by modeling chronological user-event paths across product channels. It defines events, channels, paths, and attribution windows, then introduces common attribution models including last-touch attribution and discusses applying a multi-touch model to campaign activation in Business Studio. ### Source excerpt While working with data and analyzing the interactions of our users with the products we have today, it is essential to understand their behaviors by tracking their past actions, such as opening... ## Animating fonts in Jetpack Compose DevFeed: [Animating fonts in Jetpack Compose]() Original publisher: [Read original article]() Author: sinasamaki Published: 2022-11-03T06:32:43Z Content type: tutorial Language: en Sources: [sinasamaki]() Topics: [Jetpack Compose](), [Compose](), [floating-point]() Tags: [animation](), [article](), [behavior](), [code](), [color](), [compose](), [extend](), [floating-point](), [function](), [jetpack](), [jetpack-compose](), [object](), [optional](), [state]() ### AI overview A tutorial on animating between TextStyles in Jetpack Compose. It explains using lerp with a float animation and introduces a helper function that manages animation state and completion callbacks to reduce repetitive code. ### Source excerpt Jetpack compose offers many different animation helper functions. We can easily animate floats, colors, density-pixels, etc. These helper functions are usually called like this: val animatedColor by animateColorAsState( targetValue = color, animationSpec = tween(durationMillis = 300, easing = LinearEasing) ) The animateColorAsState function returns a state object and we can simply listen to ## Separate code from data DevFeed: [Separate code from data]() Original publisher: [Read original article]() Author: Yehonathan Sharvit Published: 2022-06-22T02:32:24Z Content type: tutorial Language: en Sources: [Klipse]() Topics: [Programming](), [Object-oriented programming (OOP)](), [classes]() Tags: [behavior](), [classes](), [code](), [d](), [data](), [dop](), [oop](), [programming]() ### AI overview This article explores the first principle of Data-Oriented Programming: separating code, or behavior, from data. It explains how the principle can be followed or violated in both object-oriented programming and functional programming, including examples involving classes, static methods, and lexical scope. ### Source excerpt The first principle of Data-Oriented Programming (DOP) is a design principle that recommends a clear separation between code (behavior) and data. This may appear to be a FP principle, but in fact, one can adhere to it or break it either in FP or in OOP: ## Searching Craft CMS Matrix Blocks DevFeed: [Searching Craft CMS Matrix Blocks]() Original publisher: [Read original article]() Author: andrew@nystudio107.com (Andrew Welch) Published: 2021-11-26T05:00:00Z Content type: tutorial Language: en Sources: [nystudio107 | Articles on modern web development.]() Topics: [Query (disambiguation)](), [Content Management System]() Tags: [based](), [behavior](), [blocks](), [cms](), [contain](), [contents](), [craft](), [custom](), [easily](), [elements](), [entries](), [insights](), [little](), [matrix](), [php](), [query](), [twig]() ### AI overview A tutorial on querying Craft CMS Entries by the content of their Matrix blocks. It explains how to find matching Matrix blocks, collect their owner IDs, and use those IDs to filter an Element query, including a generalized approach usable in Twig. ### Source excerpt With a little custom Behavior, we can easily query for Elements such as Entries in Craft CMS based on the contents of the Matrix Blocks they contain ## Uninitialized Stack Variables DevFeed: [Uninitialized Stack Variables]() Original publisher: [Read original article]() Published: 2021-11-25T03:46:08Z Content type: article Language: en Sources: [Signs of Triviality]() Topics: [undefined\_behavior]() Tags: [behavior](), [illustration](), [undefined-behavior]() ### AI overview An illustration explains how uninitialized stack variables can lead to undefined behavior. ### Source excerpt An illustration of how uninitialized stack variables can lead to undefined behavior. ## C main Return Values in C89 and C99 DevFeed: [C main Return Values in C89 and C99]() Original publisher: [Read original article]() Published: 2021-10-13T15:23:21Z Content type: article Language: en Sources: [Signs of Triviality]() Topics: [C]() Tags: [behavior](), [c](), [return]() ### AI overview The article discusses what happens when a C program's main function does not explicitly return a value. It states that C99 provides a return value of 0, while the behavior in C89 is undefined. ### Source excerpt In C, 'main' returns an int. What happens when you don't explicitly return a value, though? C99 provides a return value of 0, but C89... well, that's undefined, my favorite kind of behavior. ## Effective Kotlin Item 36: Prefer composition over inheritance DevFeed: [Effective Kotlin Item 36: Prefer composition over inheritance]() Original publisher: [Read original article]() Published: 2021-04-25T00:00:00Z Content type: article Language: en Sources: [Kt. Academy]() Topics: [Inheritance](), [Object-oriented programming (OOP)](), [Kotlin](), [reuse](), [superclass]() Tags: [approach](), [behavior](), [hierarchy](), [inheritance](), [kotlin](), [object](), [oop](), [reuse](), [superclass](), [workshop-learning-programming]() ### AI overview The article argues that inheritance should primarily model a clear "is a" relationship and can be problematic when used mainly for code extraction or reuse. It presents class composition as a safer and more explicit alternative, while acknowledging that composition requires additional code. ### Source excerpt Years of OOP made us overuse inheritance. Instead, we should more often use a composition that is safer and more explicit. More often, but not always... ## Synchronous communication - circuit breaker and fallback DevFeed: [Synchronous communication - circuit breaker and fallback]() Original publisher: [Read original article]() Published: 2020-05-07T00:00:00Z Content type: tutorial Language: en Sources: [Paweł Chudzik]() Topics: [Development](), [Software Engineering](), [Network]() Tags: [behavior](), [communication](), [network](), [self-healing](), [services](), [software-development](), [systems]() ### AI overview This tutorial explains the circuit breaker and fallback communication patterns for preventing cascading failures and giving dependent services time to recover. It describes closed, open, and half-open states, then examines implementations from Hystrix and resilience4j using a controllable failing service. ### Source excerpt The circuit breaker is a communication pattern that helps to avoid cascading failure of the system and gives dependent services time to recover. Along with fallback values defined by the developer, it gives a pretty wide safe net when communication channel is broken. Read more ## Collection like objects in python DevFeed: [Collection like objects in python]() Original publisher: [Read original article]() Published: 2020-04-15T00:00:00Z Content type: tutorial Language: en Sources: [Paweł Chudzik]() Topics: [Python](), [Programming](), [Protocol (disambiguation)](), [Code]() Tags: [behavior](), [class](), [collections](), [dsl](), [functional](), [list](), [programming-language](), [protocol](), [python](), [set](), [slices]() ### AI overview A tutorial on implementing Python collection-like objects using the collections protocol. It discusses slices and methods such as __getitem__, __setitem__, __delitem__, __iter__, __contains__, and __missing__, including how they support collection behavior and domain-specific language extensions. ### Source excerpt Collections are an important part of every programming language. In python, there is a couple of built-in collections like list, set, dictionary but I'm not going to dig into them now. In this post, I'll explore what it takes to implement collection like objects on your own using collections protocol. Read more ## BPF to transform SSH Sessions to Structured Event DevFeed: [BPF to transform SSH Sessions to Structured Event]() Original publisher: [Read original article]() Author: info@goteleport.com (Russell Jones) Published: 2020-02-27T00:00:00Z Content type: article Language: en Sources: [Teleport]() Topics: [eBPF](), [ssh](), [audit](), [Linux](), [session replay](), [Shell](), [Ansible](), [Parsing]() Tags: [ansible](), [audit](), [behavior](), [ebpf](), [false-positives](), [linux](), [new-feature](), [obfuscation](), [parsing](), [shell](), [ssh](), [visibility]() ### AI overview The article explains how Teleport 4.2 Enhanced Session Recording uses BPF to transform unstructured SSH session data into structured events. It describes limitations of traditional terminal recordings, including command obfuscation, shell scripts, and terminal controls, and outlines technology options considered for improving auditing. ### Source excerpt Teleport's Enhanced Session Recording feature can add vital extra visibility into commands being run on your systems. ## Git Bisect DevFeed: [Git Bisect]() Original publisher: [Read original article]() Author: Derek Lee Published: 2020-01-01T00:00:00Z Content type: tutorial Language: en Sources: [art and science of coding]() Topics: [Git](), [bug](), [test]() Tags: [behavior](), [bug](), [commit](), [dev-recipes](), [example](), [git](), [test]() ### AI overview A reference guide to using Git Bisect to identify the commit that introduced a bug. It explains how to start the bisect process, mark known good and bad commits, test intermediate commits, repeat the process, and finish after finding the problematic commit. ### Source excerpt Reference Documentation ## Proxy in JavaScript DevFeed: [Proxy in JavaScript]() Original publisher: [Read original article]() Author: masnun Published: 2017-08-21T01:00:14Z Content type: tutorial Language: en Sources: [Abu Ashraf Masnun]() Topics: [JavaScript](), [Code]() Tags: [api](), [behavior](), [cache](), [code](), [console](), [function](), [handler](), [http](), [javascript](), [object](), [properties](), [proxy](), [receiver]() ### AI overview This tutorial explains how JavaScript Proxy objects intercept operations on a target object through handler methods. It covers property access, method calls, logging, value transformation, and caching responses from external APIs. ### Source excerpt As we can already guess from the name, a Proxy object works as a "proxy" to another object and allows us to customize the behavior of the said object in certain ways. Let's say you have an obj named awesomeAPI which has some properties and methods. You want to "trap" any calls to the object. May [...] The post Proxy in JavaScript appeared first on Abu Ashraf Masnun. ## Tips for Android Nougat's bundled notifications DevFeed: [Tips for Android Nougat's bundled notifications]() Original publisher: [Read original article]() Author: Saket Narayan Published: 2017-05-31T14:00:50Z Content type: tutorial Language: en Sources: [Saket Narayan]() Topics: [Android](), [notifications]() Tags: [android](), [behavior](), [guide](), [notifications](), [nougat](), [tips]() ### AI overview This article summarizes practical findings from implementing bundled notifications in an Android app, including differences between Nougat and older Android versions, summary-notification behavior, state management, and how grouped notifications handle sounds, vibration, and replacement. ### Source excerpt I've been working on implementing bundled notifications in my app, which is by far one of my favourite features about Android 7.0 Nougat. While the official guide is good enough for understanding how they work, I've constantly found myself running into behaviours that were not easy to find in the documentation. This post is an attempt to summarise [...] The post Tips for Android Nougat's bundled notifications appeared first on Saket Narayan. [Next page]()