# pitfalls Published articles for pitfalls. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## \[Aug 2026\] AI Community -- Activity Highlights and Achievements DevFeed: [\[Aug 2026\] AI Community -- Activity Highlights and Achievements]() Original publisher: [Read original article]() Author: Nari Yoon Published: 2026-09-17T05:12:15Z Content type: article Language: en Sources: [Google Developer Experts - Medium]() Topics: [Google AI](), [Google](), [google-antigravity](), [Artificial Intelligence](), [MCP Server](), [computer-use](), [Fine-tuning]() Tags: [2026](), [agentic-ai](), [ai](), [ai-coding-agents](), [ai-studio](), [antigravity](), [api](), [automation](), [community](), [computer-use](), [gemini](), [google](), [google-ai](), [google-cloud](), [mcp](), [mcp-server](), [multi-agent](), [ocr](), [paper](), [pitfalls]() ### AI overview A monthly roundup of Google AI community activities and achievements, covering Antigravity prototyping and engineering, AI coding agents, MCP-based remote control, computer-use agent orchestration, earthquake research, and TPU fine-tuning and migration guidance. ### Source excerpt We love sharing the accomplishments of the Google AI communities over the month. We appreciate all the hard work and dedication of our community members. Without further ado, here are the key highlights by products! Agentic DevelopmentAntigravityPrototype App: OCR and Text Extraction by the author Prototyping and Bringing Ideas to Application Using Google AI Studio and Antigravity 2.0 by AI GDE Joan Santoso (Indonesia) shares a rapid prototyping workflow building an AI-powered Form Extractor using the Gemini API, featuring a lightweight OCR and text extraction workflow. Antigravity Engineering Series by GDE Amulya Bhatia (Germany) focuses on key features of Antigravity 2.0 across 10 articles covering topics such as multi-agent orchestration, safety architecture, and workflow automation, accompanied by source code examples. (image soruce) Remote Control for Google Antigravity: Drive Your AI Coding Agent From Telegram 🛰 by GDE Nicola Guglielmi (Italy) introduces an open-source MCP server that turns Telegram into a remote control surface for AI coding agents. Before the Quake: How Antigravity CLI's AI Agents & IoT Data Predict Earthquakes by GDE Kanshi Tanaike (Japan) introduces the paper establishing Unified LAIC-AGW Theory by integrating ultra-dense IoT weather data with seismic moment tensors. It demonstrates a pre-seismic early warning capability by capturing enthalpy anomalies and acoustic-gravity waves. ADKAI GDE Henry Ruiz (US) and AI GDE Margaret Maynard-Reid (US) AI GDE Henry Ruiz (US) and AI GDE Margaret Maynard-Reid (US) introduced UISurf: An Operator-Centric Multi-Agent Platform for Observable and Cross-Environment UI Automation at the Agentic AI Summit 2026. They highlighted how the model-agnostic framework leverages the Google Cloud and Gemini ecosystems, such as GEAP and ADK, to orchestrate and evaluate computer-use agents across web, desktop, and mobile environments. Frameworks and ResearchTPU Introduction to SFT on TPU with Tunix -- 10 pitfalls until 2 ## ln Command in Linux: Hard Links vs. Symlinks DevFeed: [ln Command in Linux: Hard Links vs. Symlinks]() Original publisher: [Read original article]() Author: Hayden James Published: 2026-07-17T14:57:25Z Content type: tutorial Language: en Sources: [Hayden James]() Topics: [Linux](), [Filesystems]() Tags: [blog](), [filesystem](), [guide](), [how-to](), [linux](), [pitfalls](), [sysadmins]() ### AI overview This practical Linux guide explains how hard links and symbolic links use the ln command but behave differently. It describes inodes, hard-link persistence after deleting an original name, and restrictions such as same-filesystem requirements and limits on linking directories. ### Source excerpt Hard links and soft links in Linux both use the ln command but behave very differently. This practical guide explains exactly how each works, when to use which, and how to avoid the common pitfalls that catch even experienced sysadmins. Continue reading... ## AI Coding Tip 025 - Pair Every Skill With a Pitfalls File DevFeed: [AI Coding Tip 025 - Pair Every Skill With a Pitfalls File]() Original publisher: [Read original article]() Author: Maxi Contieri Published: 2026-06-20T00:51:51Z Content type: tutorial Language: en Sources: [Maximiliano Contieri - Software Design]() Topics: [ai-coding](), [coding](), [Artificial Intelligence](), [Documentation]() Tags: [ai](), [ai-coding](), [coding](), [memory](), [mistakes](), [modular](), [pitfalls](), [skills]() ### AI overview This tutorial recommends placing an append-only PITFALLS.md file beside each SKILL.md to record an AI's recurring mistakes, their triggers, and the correct behavior. The goal is to preserve corrections across sessions while keeping skill documentation focused on the normal workflow. ### Source excerpt TL;DR: Add a PITFALLS.md next to every SKILL.md so your AI never repeats the same mistake twice. Common Mistake ❌ You write a great SKILL.md. The AI follows it well most of the time. Then it does som ## double, BigDecimal, or Fixed-Point? DevFeed: [double, BigDecimal, or Fixed-Point?]() Original publisher: [Read original article]() Author: Stefano Fago Published: 2026-06-14T00:00:00Z Content type: article Language: en Sources: [Nicolas Fränkel]() Topics: [Java]() Tags: [bigdecimal](), [engineering](), [floating-point](), [java](), [numbers](), [performance](), [pitfalls](), [precision]() ### AI overview A Java-focused article explains how to choose among double, BigDecimal, and fixed-point arithmetic based on required precision, rounding rules, and performance constraints. It covers IEEE 754 binary representation, floating-point equality pitfalls, tolerance-based comparisons, and production concerns such as serialization, testing, and concurrency. ### Source excerpt There is an evergreen debate in the Java world: should you always use BigDecimal for money? The short answer is no. The real answer is: it depends on your computational context: the precision you need, the rounding rules you must follow, and the performance budget you have. The problem is that this conversation is often driven by dogma rather than engineering. ## TCV vs ACV: SaaS Sales Math (with Examples and Pitfalls) DevFeed: [TCV vs ACV: SaaS Sales Math (with Examples and Pitfalls)]() Original publisher: [Read original article]() Author: Aarthi Poonia Published: 2026-06-08T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [Finance]() Tags: [examples](), [guide](), [metric](), [metrics](), [pitfalls](), [saas](), [saas-finance](), [sales]() ### AI overview This guide explains Total Contract Value (TCV) and Annual Contract Value (ACV), including their formulas, worked examples, and differences across single-year, multi-year, ramped-pricing, and auto-renewing contracts. It highlights how confusing the metrics can distort pipeline reporting, commissions, and ARR reporting. ### Source excerpt TCV vs ACV explained for SaaS founders. Definitions, when to use each metric, common pitfalls, and how multi-year contracts complicate the picture. ## Prorated Billing Explained: How Proration Works for SaaS Subscriptions DevFeed: [Prorated Billing Explained: How Proration Works for SaaS Subscriptions]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-06-01T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [stripe]() Tags: [billing](), [examples](), [implementation](), [pitfalls](), [saas](), [subscription-billing](), [subscriptions](), [upgrades]() ### AI overview A guide to prorated billing for SaaS subscriptions, explaining how partial-period charges and credits are calculated for upgrades, downgrades, seat changes, trial conversions, and cancellations. It includes calculation examples and implementation pitfalls. ### Source excerpt Prorated billing for SaaS - how proration is calculated on upgrades, downgrades, mid-cycle changes, and add-on seats, with examples and common implementation pitfalls. ## Real-Time Streaming Usage Metering for SaaS and AI Products DevFeed: [Real-Time Streaming Usage Metering for SaaS and AI Products]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-19T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [real-time](), [Streaming](), [Latency](), [Software as a service](), [Artificial Intelligence](), [dashboards]() Tags: [ai](), [architecture](), [dashboards](), [how-to](), [infrastructure](), [latency](), [pitfalls](), [real-time](), [saas](), [streaming](), [usage-based-billing]() ### AI overview This article explains real-time streaming usage metering for SaaS and AI products. It defines real-time behavior as updating usage aggregates, enforcing caps, and sending threshold notifications within seconds, and discusses the infrastructure tradeoffs and implementation pitfalls. ### Source excerpt Build a real-time streaming usage metering pipeline that supports live dashboards, hard caps, and instant overage. Architecture and pitfalls. ## Billing API Gateway Calls: Architecture for API as a Service DevFeed: [Billing API Gateway Calls: Architecture for API as a Service]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-18T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [API](), [Amazon API Gateway](), [Software as a service](), [telemetry](), [data]() Tags: [api](), [api-gateway](), [architecture](), [batching](), [build](), [guide](), [infrastructure](), [instrumentation](), [pipeline](), [pitfalls](), [saas](), [usage-based-billing]() ### AI overview A guide to designing an API call metering pipeline for API-as-a-service products. It covers gateway instrumentation, asynchronous batching, aggregation, customer attribution, failure handling, and reconciliation against gateway logs. ### Source excerpt Build a metering pipeline that bills API calls accurately at high volume. Architecture, batching, and pitfalls for API as a service products. ## Metering LLM Token Usage: An Architecture Guide for AI SaaS DevFeed: [Metering LLM Token Usage: An Architecture Guide for AI SaaS]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-17T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Large Language Model](), [Software as a service](), [Instrumentation](), [telemetry]() Tags: [ai](), [architecture](), [guide](), [instrumentation](), [llm](), [pitfalls](), [queue](), [reconciliation](), [saas](), [token](), [usage-based-billing]() ### AI overview This architecture guide explains how to build a production LLM token-metering pipeline for AI SaaS. It covers instrumentation at the provider-call boundary, queued billing events, hosted aggregation, customer attribution, retry deduplication, partial-failure handling, and reconciliation. ### Source excerpt Build a metering pipeline for LLM token usage that survives retries, supports multiple providers, and feeds clean billing events. Architecture and pitfalls. ## Dynamic Pricing at Checkout: Pay What You Want and Beyond for SaaS DevFeed: [Dynamic Pricing at Checkout: Pay What You Want and Beyond for SaaS]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-16T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [configuration]() Tags: [article](), [checkout](), [implementation](), [pitfalls](), [pricing](), [saas]() ### AI overview This article explains dynamic pricing at checkout for SaaS. It covers pay-what-you-want pricing, programmatic amounts, quote-based deals, variable seat counts, and optional minimum and maximum bounds using a single product. ### Source excerpt Use dynamic pricing checkout to support pay what you want, programmatic amounts, and quote based deals on a single product. Implementation patterns and pitfalls. ## SaaS Product Bundling: Collections, Cross-Sells, and AOV Lift DevFeed: [SaaS Product Bundling: Collections, Cross-Sells, and AOV Lift]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-09T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [Software]() Tags: [billing](), [checkout](), [collections](), [conversion](), [customer](), [guide](), [pitfalls](), [pricing](), [saas]() ### AI overview This guide explains how SaaS product collections, cross-sells, and tiered plan selection can increase average order value. It covers collection structure, bundling math, checkout patterns, cannibalization risks, and conversion pitfalls. ### Source excerpt How SaaS product collections drive AOV lift through unified plan selection, cross-sells, and clean upgrade paths. The bundling math, checkout patterns, and pitfalls. ## Developing a Rust-based IoT device with AI DevFeed: [Developing a Rust-based IoT device with AI]() Original publisher: [Read original article]() Author: John Lee Published: 2026-04-20T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [Rust](), [AI-assisted coding](), [Internet of things](), [ESP32]() Tags: [ai](), [ai-assistants](), [ai-coding](), [blog](), [coding-agents](), [esp32](), [how-to](), [iot](), [llm](), [pitfalls](), [rust](), [verify]() ### AI overview This article describes a rough, hands-on workflow for building a WiFi, BLE, and provisioning device with Rust and AI on the ESP DualKey, an ESP32-S3-based kit from M5Stack. It emphasizes clear specifications, pinned crates, reference implementations, and a tight verification loop. ### Source excerpt AI assistants are most effective on ESP32 Rust firmware when you supply clear specs, pinned crates, reference implementations (often ESP-IDF C), and a tight verify loop. The article discusses good practices, pitfalls, discipline, and entropy. A brief explanation about the device is also included, along with the repository and all artifacts. ## Keeping Meeting Apps Alive in the Background on iOS and watchOS DevFeed: [Keeping Meeting Apps Alive in the Background on iOS and watchOS]() Original publisher: [Read original article]() Author: Roland Published: 2026-03-26T14:55:52Z Content type: tutorial Language: en Sources: [RisingStack]() Topics: [iOS](), [watchOS](), [App](), [Artificial Intelligence]() Tags: [ai](), [apps](), [audio](), [case-study](), [code](), [examples](), [ios](), [meetings](), [pipeline](), [pitfalls](), [production](), [watchos]() ### AI overview This tutorial explains how a real-world meeting app handled reliable background recording and post-recording processing on iOS and watchOS. It covers audio interruptions, resuming recordings, AVAudioSession configuration with mixWithOthers, and background transcription and AI processing, including BGContinuedProcessingTask on iOS 26+. ### Source excerpt Building an app that records meetings, generates transcriptions, and produces AI analysation sounds straightforward - until you try to make it reliable while the user goes in and out of your app, takes calls, plays music, or checks notifications. This post walks through how we handled background recording and post-recording processing on iOS and watchOS [...] The post Keeping Meeting Apps Alive in the Background on iOS and watchOS appeared first on RisingStack Engineering. ## Santa's Python Pitfalls: A Java Developer's Guide to Staying Safe This Christmas DevFeed: [Santa's Python Pitfalls: A Java Developer's Guide to Staying Safe This Christmas]() Original publisher: [Read original article]() Author: Steve Poole Published: 2025-12-24T03:03:09Z Content type: tutorial Language: en Sources: [Java Advent Calendar]() Topics: [Python](), [pip](), [PyPI](), [Maven Central](), [Java](), [Maven]() Tags: [2025](), [attacks](), [dependency](), [developer](), [guide](), [install](), [internet](), [java](), [maven-central](), [mirror](), [model](), [packaging](), [pitfalls](), [pypi](), [python](), [registry](), [trust]() ### AI overview A Java-focused guide to Python package-management risks. It contrasts Maven Central with PyPI, highlighting flat package naming, trusting dependency resolution, typosquatting, dependency confusion, and unsafe installation commands. It recommends version pins, known sources, private registries, and lock files. ### Source excerpt Just like that it happened. You, a disciplined Java developer, are now installing Python. Like everything in 2025, it just arrived. One day, you were running a tidy mvn install, the next, you're learning about virtual environments and fighting an unfriendly pip install that won't explain what it just pulled from the internet. Good news: [...] The post Santa's Python Pitfalls: A Java Developer's Guide to Staying Safe This Christmas appeared first on JVM Advent. ## Fitting parts DevFeed: [Fitting parts]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2025-11-12T14:37:00Z Content type: opinion Language: en Sources: [Alex Ewerlof Notes]() Topics: [distributed-systems](), [systems]() Tags: [distributed-systems](), [pitfalls](), [systems]() ### AI overview The article argues that system performance depends on how parts fit and interact, rather than on optimizing each part separately. It applies this idea to hiring for culture add and to technology choices in complex distributed systems, cautioning against enforcing a single technology stack or relying on a golden-hammer solution. The supplied text ends mid-discussion, so the article's complete recommendations are not available. ### Source excerpt What does it mean? How is it abused? And how to work around the pitfalls? ## Escaping the Build Trap DevFeed: [Escaping the Build Trap]() Original publisher: [Read original article]() Author: Tom Published: 2025-09-20T00:00:00Z Content type: opinion Language: en Sources: [Tomasz Gągor]() Topics: [Product Management](), [Tech Lead]() Tags: [book](), [business](), [collaboration](), [effective](), [examples](), [experimentation](), [leadership](), [management](), [metrics](), [pitfalls](), [product-management](), [team](), [tech-lead]() ### AI overview A review of Melissa Perri's book Escaping the Build Trap, focusing on its explanation of Product Management, experimentation, metrics, organizational perspective, and creating real value. ### Source excerpt Escaping the Build Trap How Effective Product Management Creates Real Value Author: Melissa Perri This was the first book on Product Management I've ever read, and I'm glad I started with such a solid one. In my daily work, I collaborate closely with product owners, and I wanted to better understand their role: How do they work? What are their priorities? And how can our collaboration be more effective? What I appreciated most about this book is how clearly it structures the profession of Product Management and breaks down its many facets. ## runBlocking in practice: Where it should be used and where not DevFeed: [runBlocking in practice: Where it should be used and where not]() Original publisher: [Read original article]() Published: 2025-09-01T00:00:00Z Content type: tutorial Language: en Sources: [Kt. Academy]() Topics: [runBlocking](), [Coroutines](), [Kotlin](), [kotlin-coroutines]() Tags: [android](), [async](), [await](), [best-practices](), [blocking](), [coroutines](), [dispatcher](), [guide](), [kotlin](), [pitfalls](), [retrofit](), [runblocking](), [workshop-learning-programming]() ### AI overview A practical guide to runBlocking in Kotlin Coroutines. It explains how runBlocking converts suspending calls into blocking calls, blocks the calling thread until completion, and creates a new coroutine hierarchy. The article discusses appropriate uses, code smells, and alternatives, including an Android Retrofit interceptor example. ### Source excerpt A comprehensive guide to using runBlocking in Kotlin Coroutines, including best practices and common pitfalls. ## Best Practices for Running Neon in Production DevFeed: [Best Practices for Running Neon in Production]() Original publisher: [Read original article]() Author: Russ Dias Published: 2025-08-18T17:26:31Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [autoscaling](), [configuration](), [Database](), [Latency](), [dashboards](), [Development]() Tags: [autoscaling](), [best-practices](), [capacity](), [compute](), [configuration](), [dashboards](), [database](), [jobs](), [latency](), [performance](), [pitfalls](), [postgres](), [product](), [reports](), [work]() ### AI overview This article presents production guidance for Neon, focusing on configuration choices such as autoscaling, branching, and scale to zero. It recommends higher compute capacity and disabling scale to zero on production branches to avoid cold-start latency and maintain performance during traffic spikes, while retaining scale to zero for cost savings in suitable development, staging, testing, and internal-tool environments. It also introduces lightweight read replicas for read-heavy workloads such as analytics dashboards, background jobs, and reports. ### Source excerpt In Neon, we're always trying to strike a balance between out-of-the-box configurations that work for most people and the flexibility to accommodate a wide range of use cases. By tweaking features like autoscaling, branching, and scale to zero, you can optimize Neon for many diffe... ## Speed with Rigor: testing smarter with group sequential design DevFeed: [Speed with Rigor: testing smarter with group sequential design]() Original publisher: [Read original article]() Author: Gabriela Lewenfus Published: 2025-08-05T17:25:29Z Content type: article Language: en Sources: [OLX]() Topics: [Testing](), [A/B Testing](), [experiments](), [data](), [implementation]() Tags: [a-b-testing](), [ab-testing](), [data](), [efficiency](), [experiment](), [experimentation](), [false-positive](), [implementation](), [pitfalls](), [sequential-testing](), [statistics](), [testing]() ### AI overview The article explains how group sequential testing can replace fixed-horizon A/B tests with monitoring at predefined intervals while controlling error rates. It discusses how this approach addresses peeking, may allow ineffective tests to stop early, and involves practical trade-offs and implementation challenges. ### Source excerpt generated by chatgptIntroduction In the world of A/B testing and experimentation, it's tempting to check results frequently and stop tests earlier if they seem significant (a practice known as peeking). However, this practice dramatically inflates the false positive rate, leading to misleading conclusions. Imagine you're running an A/B test, and after just 10 days, the results look like a clear win. You might be tempted to end the experiment early and move forward with the release of the new feature. After all, why wait another two weeks as originally planned? But here's the catch: the more often you check the results, the higher the chance you'll spot a "win" that isn't real. Frequent peeking increases the risk of false positives -- in other words, drawing the wrong conclusion just by chance. To prevent peeking while ensuring statistical power, a common practice is to set a minimum sample size that must be reached before ending the experiment. However, this fixed horizon approach can extend the experiment's duration and delay decision-making. Group sequential testing (GST) provides a statistical framework to monitor results at predefined intervals while maintaining control over error rates. At OLX, we migrated from fixed horizon experiments to GST to achieve faster insights, lower costs, and greater efficiency, enabling our teams to make smarter, data-driven decisions with speed and confidence. In this post, I'll explain how GST works, how it can accelerate experiments while still preventing the pitfalls of peeking, and how we can use it even to stop ineffective tests early. Finally, I'll discuss real-world trade-offs and implementation challenges. Peeking is a trap Before we dive into GST, let's first understand what "peeking" is and why it's so concerning. Peeking occurs when the experimenter repeatedly checks an experiment's results and finishes it once a statistically significant effect appears. This inflates the false positive rate, leading to unreliable conclu ## Common Foreign Key Pitfalls and Optimizations in Django DevFeed: [Common Foreign Key Pitfalls and Optimizations in Django]() Original publisher: [Read original article]() Author: Haki Benita Published: 2025-07-14T21:00:00Z Content type: article Language: en Sources: [Haki Benita]() Topics: [Django](), [Code](), [implementation]() Tags: [article](), [articles](), [deprecated](), [django](), [documentation](), [how-to](), [orm](), [pitfalls](), [review]() ### AI overview An article about foreign keys in Django that demonstrates common pitfalls, potential optimizations, and implicit behavior. It uses a product catalog example and discusses constraints, auditing fields, ordering, and replacing deprecated unique_together usage with UniqueConstraint. ### Source excerpt Constraints keep the integrity of your system and prevent you from shooting yourself in the foot. Foreign keys are a special type of constraint because, unlike unique, check, and primary keys, they span more than one relation. This makes foreign keys harder to enforce and harder to get right. In this article, I demonstrate common pitfalls, potential optimizations, and implicit behavior related to foreign keys. ## Context Package Semantics In Go DevFeed: [Context Package Semantics In Go]() Original publisher: [Read original article]() Published: 2025-02-10T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Go Language](), [context](), [Programming](), [Software]() Tags: [api](), [ardan-labs](), [blog](), [cancellation](), [go](), [go-programming](), [golang](), [goroutines](), [latency](), [pitfalls](), [programming]() ### AI overview This tutorial explains the semantics of Go's Context package, including request-scoped data, deadlines, cancellation signals, and how to introduce contexts early when designing APIs. It also discusses managing goroutine lifetime and latency in services. ### Source excerpt Although first introduced in 2014, the Context package remains a crucial component of Go programming, enabling efficient management of request-scoped data, deadlines, and cancellation signals. As the Go ecosystem continues to evolve, understanding the Context package's semantics is vital for developing reliable and maintainable software. This blogpost provides an in-depth exploration of the Context package's semantics, highlighting best practices and common pitfalls to help developers effectively leverage this powerful tool. ## Breaking down common documentation mistakes DevFeed: [Breaking down common documentation mistakes]() Original publisher: [Read original article]() Author: Tiffany Chen Published: 2025-01-23T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [Documentation](), [Tutorial](), [Framework](), [User experience (UX)]() Tags: [best-practices](), [documentation](), [how-to](), [pitfalls](), [technical](), [technical-writing](), [ux]() ### AI overview The article examines common documentation mistakes, including mixing content types, unclear audience targeting, basic quality problems, and using documentation to compensate for product or UX issues. It recommends organizing content with the Diátaxis framework, defining audiences, and maintaining quality standards. ### Source excerpt Good documentation just feels right, while bad docs leave you frustrated--but pinpointing what went wrong can be tough. ## Common pitfalls when building generative AI applications DevFeed: [Common pitfalls when building generative AI applications]() Original publisher: [Read original article]() Author: Chip Huyen Published: 2025-01-16T00:00:00Z Content type: opinion Language: en Sources: [Chip Huyen]() Topics: [Generative AI](), [foundation-models](), [AI Development](), [Large Language Model](), [Optimization]() Tags: [foundation-models](), [generative-ai](), [llm](), [mistakes](), [optimization](), [pitfalls]() ### AI overview This article discusses common pitfalls in building applications with foundation models. It cautions against using generative AI where simpler optimization methods may be cheaper and more reliable, and distinguishes solving a problem from merely testing whether generative AI can be applied. ### Source excerpt As we're still in the early days of building applications with foundation models, it's normal to make mistakes. This is a quick note with examples of some of the most common pitfalls that I've seen, both from public case studies and from my personal experience. Because these pitfalls are common, if you've worked on any AI product, you've probably seen them before. 1. Use generative AI when you don't need generative AI Every time there's a new technology, I can hear the collective sigh of senior engineers everywhere: "Not everything is a nail." Generative AI isn't an exception -- its seemingly limitless capabilities only exacerbate the tendency to use generative AI for everything. A team pitched me the idea of using generative AI to optimize energy consumption. They fed a household's list of energy-intensive activities and hourly electricity prices into an LLM, then asked it to create a schedule to minimize energy costs. Their experiments showed that this could help reduce a household's electricity bill by 30%. Free money. Why wouldn't anyone want to use their app? I asked: "How does it compare to simply scheduling the most energy-intensive activities when electricity is cheapest? Say, doing your laundry and charging your car after 10pm?" They said they would try it later and let me know. They never followed up, but they abandoned this app soon after. I suspect that this greedy scheduling can be quite effective. Even if it's not, there are other much cheaper and more reliable optimization solutions than generative AI, like linear programming. I've seen this scenario over and over again. A big company wants to use generative AI to detect anomalies in network traffic. Another wants to predict upcoming customer call volume. A hospital wants to detect whether a patient is malnourished (really not recommended). It can often be beneficial to explore a new approach to get a sense of what's possible, as long as you're aware that your goal isn't to solve a problem but to test ## Power struggles and long-term strategy in startups DevFeed: [Power struggles and long-term strategy in startups]() Original publisher: [Read original article]() Author: Denis Čahuk Published: 2024-12-30T14:44:46Z Content type: opinion Language: en Sources: [Crafting Tech Teams]() Topics: [Tech Debt](), [Software Engineering](), [Agile](), [Self-organizing Team]() Tags: [agile](), [case-studies](), [collaboration](), [communication](), [complexity](), [cto](), [engineering](), [leadership](), [management](), [pitfalls](), [project-management](), [recommendations](), [roadmaps](), [safety](), [software-development](), [startups](), [team-dynamics](), [tech-debt]() ### AI overview This podcast discussion examines power struggles, short-term thinking, technical debt, and operational inefficiencies in startups and scale-ups. It covers team dynamics, project management, unplanned work, communication, psychological safety, and aligning engineering efforts with business goals. ### Source excerpt Mario Tomić, fractional CTO joined Adrian Stanek and Denis Čahuk for an exchange of situational studies and examples of power struggles and lack of long term strategy in startups. Highlights below 👇 [Next page]()