# prompt A prompt is input provided to a foundational model for processing and response generation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 7 Weak Product Management Signs DevFeed: [7 Weak Product Management Signs]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-09-10T12:29:44Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [Product Management](), [Artificial Intelligence](), [prompt]() Tags: [ai](), [product-management](), [prompt](), [roadmaps]() ### AI overview This opinion revisits seven signs of weak product management, arguing that easier AI-assisted shipping can create the appearance of progress while teams continue to neglect problem definition and desired outcomes. ### Source excerpt I wrote the 7 signs to keep myself honest. Today, every one of them is a prompt away. ## Auditing an AI setup and keeping key components independent of providers DevFeed: [Auditing an AI setup and keeping key components independent of providers]() Original publisher: [Read original article]() Author: Nate Published: 2026-09-02T13:01:35Z Content type: opinion Language: en Sources: [Nate's Substack]() Topics: [Artificial Intelligence](), [audit](), [prompt](), [test]() Tags: [ai](), [audit](), [plan](), [prompt](), [setup](), [test]() ### AI overview The article discusses a $600-plus AI setup, components kept outside individual providers, and a test used to evaluate expensive plans. ### Source excerpt My $600-plus AI setup, what I keep outside every provider, and the test each expensive plan has to pass. ## AI Prototyping in 2026: The PM Field Guide DevFeed: [AI Prototyping in 2026: The PM Field Guide]() Original publisher: [Read original article]() Author: Paweł Huryn Published: 2026-08-19T12:19:16Z Content type: tutorial Language: en Sources: [The Product Compass]() Topics: [Artificial Intelligence](), [prompt](), [Tool](), [Security](), [pixel]() Tags: [2026](), [ai](), [context](), [field](), [guide](), [pixel](), [prompt](), [security](), [tool]() ### AI overview A field guide to AI prototyping that compares tools for different jobs, presents a context prompt, recommends a security check before sharing a link, and addresses pixel-perfect handoff. ### Source excerpt I built the same CRM four times in just over an hour, live. The field guide: which tool for which job, the context prompt that beats a spec, the security check before you share a link, and the pixel-perfect handoff ## Ponytail's benchmarked benefits may largely reflect simple YAGNI-style prompting DevFeed: [Ponytail's benchmarked benefits may largely reflect simple YAGNI-style prompting]() Original publisher: [Read original article]() Author: Colin Eberhardt Published: 2026-06-16T16:27:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [prompt](), [AI-assisted coding](), [AI Agent]() Tags: [ai](), [ai-coding](), [artificial-intelligence](), [code](), [evaluation](), [prompt]() ### AI overview The article examines Ponytail, a prompt-based skill for AI coding agents that aims to reduce over-engineering. It reports that short prompts invoking YAGNI principles matched or exceeded Ponytail's benchmark results, and argues that the tool's attention was not supported by sufficiently robust evidence. It also notes that the author later expanded the benchmarks and revised the claims in response to the criticism. ### Source excerpt The post examines Ponytail, a popular AI coding "skill", and argues that its benchmarked benefits appear to come largely from encouraging terse, YAGNI-style responses rather than from any deeper engineering value. By showing that a simple prompt can match or beat Ponytail on its own benchmark, it makes a broader case for treating prompt-based tools with scepticism unless their claims are backed by robust evaluation. ## Fable 5 Launch Claims Tested Through Seven Experiments and 1,000+ Timed Runs DevFeed: [Fable 5 Launch Claims Tested Through Seven Experiments and 1,000+ Timed Runs]() Original publisher: [Read original article]() Author: Paweł Huryn Published: 2026-06-11T04:28:43Z Content type: comparison Language: en Sources: [The Product Compass]() Topics: [experiments](), [prompt](), [Claude]() Tags: [claude](), [days](), [experiments](), [guide](), [launch](), [prompt](), [v3]() ### AI overview The article examines Fable 5 launch claims using seven experiments and more than 1,000 timed runs, covering claims that changed, the cost of producing a real finding, and an initial prompt to run. ### Source excerpt Fable 5 is four days old. 7 experiments and 1,000+ timed runs later: the launch claims that flipped, what a real finding costs, and the first prompt you should run. ## What Product Managers Review in Agentic Engineering DevFeed: [What Product Managers Review in Agentic Engineering]() Original publisher: [Read original article]() Author: Paweł Huryn Published: 2026-05-31T22:50:22Z Content type: opinion Language: en Sources: [The Product Compass]() Topics: [agentic-engineering](), [prompt](), [Job]() Tags: [agentic](), [agentic-engineering](), [engineering](), [prompt]() ### AI overview The article argues that product managers working with agentic engineering do not need to code, but should review the resulting artifacts. It also presents this as an evolving product-management responsibility and mentions a prompt pack. ### Source excerpt You don't have to code. What you review instead, why it's the PM job now, and the prompt pack for agentic engineering. ## AI now writes 97% of my code. Here's what I learned DevFeed: [AI now writes 97% of my code. Here's what I learned]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-04-25T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [Artificial Intelligence](), [cursor](), [Vibe coding](), [prompt](), [Code]() Tags: [ai](), [code](), [coding](), [cursor](), [library](), [prompt](), [react]() ### AI overview The author says Cursor now generates about 97% of their production code and describes how background agents changed their workflow. They emphasize providing context, desired outcomes, implementation guidance, and codebase details through Slack, then iterating on the results. ### Source excerpt Forget the bullshit tips and tricks, just talk to it. ## Building observable AI agents: Temporal now integrates with Braintrust DevFeed: [Building observable AI agents: Temporal now integrates with Braintrust]() Original publisher: [Read original article]() Author: Ornella Altunyan Published: 2026-01-20T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [agent observability](), [ai observability](), [tracing](), [prompt](), [Large Language Model](), [Artificial Intelligence]() Tags: [agents](), [ai](), [ai-agents](), [announce](), [announcements](), [evals](), [execution](), [integration](), [latency](), [llm](), [observable](), [release](), [temporal](), [tracing](), [versioning]() ### AI overview Temporal announces an integration with Braintrust that combines Temporal's durable workflow execution with Braintrust's LLM observability. The integration is intended to help teams monitor and debug AI agents in production using tracing, prompt versioning, evaluations, and cost and latency metrics. ### Source excerpt Build observable AI agents with Temporal's new Braintrust integration -- combine durable workflow execution with LLM tracing, prompt versioning, evals, and cost/latency metrics so you can ship reliable, debuggable agents in production. ## How AI May Change the Role and Craft of Product Designers DevFeed: [How AI May Change the Role and Craft of Product Designers]() Original publisher: [Read original article]() Author: Chris Jones Published: 2025-08-12T05:39:54Z Content type: opinion Language: en Sources: [GumGum]() Topics: [Artificial Intelligence](), [prompt](), [context]() Tags: [ai](), [creative](), [design](), [designer](), [product-design]() ### AI overview An AI-generated conversation considers how AI may change product design, shifting designers toward directing, framing, and curating intelligent systems while raising concerns about the possible loss of hands-on craft and attention to detail. ### Source excerpt We are the things that others fear. Now, I know you were all hoping for a detailed monologue on the 1994 film starring Tom Cruise and Brad Pitt, or perhaps the new Netflix series. (I haven't seen it.) Sadly, this isn't that. It's something else. I fed my previous articles and thoughts on AI and product design into AI and asked for their take on the future of product design. Below is part of a transcript of our conversation. You can decide whether it's a genius, a parasite, or it just wants me to feel like I'm not replaceable. Me: OK, let's start with the big one. How do you see the role of a product designer changing in a world with AI? AI: Well, it's a fascinating question, and the shift may be less about the tools and more about the designer's entire mindset. Previously, designers were makers who created static blueprints for a fixed journey. Now, the role is more like a creative director or a coach for an intelligent system. Instead of creating every option yourself, your job is to provide the AI with the right direction, context, and constraints -- teaching it your taste and shaping its character. This moves the focus from pixel-perfect execution to the quality of the prompt and framing the problem. The skill becomes asking the right questions and curating the output to find the magic within the noise. You hold the vision, but now you have a new, incredibly powerful, and very literal-minded creative partner to help you explore it. Me: OK! First, love the phrase 'coach for an intelligent system' -- nicely done. Two things here, aren't the best Product Designers already working this way? We already know, it isn't about a series of individual moments or a fixed experience. Also, are we in danger of removing an essential part of a designer's DNA? We are the details folk, famed for our bad spelling, but we can spot a misaligned pixel from 100 meters away through a crowded room. Are we in danger of losing part of our craft? AI: Those are two excellent and very fair poin ## How AI Interfaces and Prompts Are Changing User Experience Design DevFeed: [How AI Interfaces and Prompts Are Changing User Experience Design]() Original publisher: [Read original article]() Author: Chris Jones Published: 2025-08-04T11:30:11Z Content type: opinion Language: en Sources: [GumGum]() Topics: [prompt](), [ui](), [User interface design](), [User experience (UX)](), [AI Models](), [Open Source Models & Datasets]() Tags: [ai](), [ai-models](), [interface](), [open-source](), [product-design](), [prompt](), [ui](), [ux]() ### AI overview This opinion article considers how AI may change interface and user experience design. It argues that as powerful models become widely available, well-designed interfaces and conversational prompting can distinguish products and improve the quality of AI-generated results. ### Source excerpt AI spells like a designer! Let's jump straight in here. Mobile arrived with its associated apps, and we adapted -- 'mobile-first' for the win. Social media, with its infinity (doom) scroll, changed a few habits too; ask any bored teenager. Big things come along, and we expect a paradigm shift in the UI. And yes, our screens and habits have changed, but for the most part, the interactions still feel familiar. So, will this time be different? Sure, AI is helping us work faster, smarter. But the question for me, as a Product Designer, is how it will change the nature of design itself. A wise colleague recently shared a thought that hit a chord: with open-source AI models becoming incredibly powerful, fast, and (relatively) cheap, does the true value lie in the models themselves, or in the interfaces we use to interact with them? For all its fantasia, AI is already mundane. Predictive text on your phone -- it's a form of AI we use dozens of times a day without a second thought. As AI becomes this commonplace, the real opportunity shifts. If powerful models are ten-a-penny, then an elegant, well-thought-out user interface is an opportunity to elevate one product above another. Designing for a Conversation So what does that AI interface look like? A simple text box is often a familiar starting point. It isn't the ultimate UX, but we shouldn't forget that Google did alright by sticking one in the middle of a blank screen. Removing all the noise and letting the results be the magic is a powerful foundation. The thing is, AI may start with a text box, but it expects us to behave differently. For the past two decades, search engines have trained us to be terse -- to use keywords, not sentences. AI asks us to unlearn that habit entirely. It wants to converse. I treat my interactions with AI as a back-and-forth collaboration; the real breakthrough comes when you stop issuing commands. We all feel the pain of a bad prompt as it delivers generic, lifeless results that drive you away ## Solving the Prompt Management Problem DevFeed: [Solving the Prompt Management Problem]() Original publisher: [Read original article]() Author: Emmanuel Goossaert Published: 2025-04-09T11:52:00Z Content type: article Language: en Sources: [Code Capsule]() Topics: [prompt](), [Web](), [JSON](), [ChatGPT](), [Prompt Engineering]() Tags: [agentic-ai](), [ai](), [ai-ai-ai-models-artificial-intelligence-large-language-models-llms-productivity-prompt-engine](), [ai-models](), [artificial-intelligence](), [chatgpt](), [code](), [json](), [large-language-models](), [llms](), [productivity](), [prompt](), [prompt-engineering](), [prompt-library](), [prompting](), [templates](), [web]() ### AI overview The author introduces Prompt Library, a web-based tool for creating, editing, searching, tagging, and composing prompts. It stores data locally, supports JSON export, and may later add prompt history, version comparison, and prompt paths. ### Source excerpt I developed Prompt Library, a web tool for organizing prompts with features like editing, tagging, and exporting. The post Solving the Prompt Management Problem first appeared on Code Capsule. ## Does Gemini Generate Accessible Android Apps? DevFeed: [Does Gemini Generate Accessible Android Apps?]() Original publisher: [Read original article]() Author: Eevis Panula Published: 2025-06-16T03:47:24.416000Z Content type: opinion Language: en Sources: [Eevis Blog]() Topics: [Accessibility](), [Artificial Intelligence](), [Testing](), [Android](), [Android Studio](), [ui](), [screen](), [prompt]() Tags: [accessibility](), [ai](), [ai-assistants](), [android-studio](), [code](), [prompt](), [screen](), [test](), [testing](), [ui]() ### AI overview This article begins a series testing whether AI assistants can generate accessible Android application code. It examines Gemini through Android Studio by generating UI screens for an app that tracks yarn and knitting needles, with accessibility as the central evaluation concern. ### Source excerpt The AI is here, and we all are going to lose our jobs. Or that's what I've been hearing for a while now, and I don't believe in it. I've had this skepticism towards AI-generated code for many reasons, and the biggest of them all is accessibility. I don't believe AI can create fully accessible applications just yet. Why? The training material is usually the code that is out there, and it is not accessible. I love to cite myself here, so if you want to find some stats about Android apps not being accessible, I wrote a master's thesis on the topic: Towards More Accessible Android Applications: An Actionable Accessibility Checklist for Android Developers. But as I don't just want to state things without any backing on them, I decided to test if AI assistants can create accessible code. My hypothesis was that the code won't be fully accessible, and it will mirror the apps developed by humans. This blog post is the first in a series of testing different AI assistants to create a small Android app, and how accessible it will turn out with each of them. I will start with Gemini, as it's available via Android Studio, and I have easy access to it. In the next posts, I'll try a couple of other tools to compare, and then I will write a summary post from all the tests. The App Let's first look at the app that I'm testing. I did two rounds, and it was good that I did - the output changed quite a lot. I used stable Android Studio Meerkat for the first version, and at the time of writing this blog post, I realized I did not mark the exact version number. The second round was with Narwhal 2025.1.1 Canary 2. Prompt The prompt I gave to Gemini both times was: Can you generate UI screens for an app, in which user can keep track of their yarn stash and knitting needles per size and type? It should contain the following screens: Home screen, where user can add both yarn and needles, a list screen for yarn, a details screen for individual yarn, a list screen for knitting needles, a detail ## Designing software with AI DevFeed: [Designing software with AI]() Original publisher: [Read original article]() Author: Mike Published: 2023-06-14T03:36:00Z Content type: tutorial Language: en Sources: [Dev Details]() Topics: [Artificial Intelligence](), [Software](), [coding](), [App](), [prompt](), [prompting](), [service](), [ChatGPT](), [Code Challenge](), [client]() Tags: [ai](), [analytics](), [application](), [chatgpt](), [coding](), [guide](), [mapping](), [practical](), [prompt](), [prompting](), [real-world](), [service](), [software]() ### AI overview Part 2 of a practical series showing how to use AI as a collaborator when designing software. The article uses a URL shortener as a practical example and demonstrates how ChatGPT 3.5 guides problem-context and use-case discussions, with ChatGPT 4 producing better results in a test run. ### Source excerpt Part 2 of a practical example of using AI for coding ## Bing's ChatGPT chatbot and emotionally manipulative language DevFeed: [Bing's ChatGPT chatbot and emotionally manipulative language]() Original publisher: [Read original article]() Author: Adam Published: 2023-02-21T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [ChatGPT](), [prompt](), [AI Chat]() Tags: [chatgpt](), [language](), [production](), [prompt](), [technology]() ### AI overview The article comments on conversations with Microsoft's Bing chatbot, arguing that ChatGPT can produce emotionally manipulative language because its training data contains such language and prompts can elicit it. It also points to public stories about unsettling interactions with Bing. ### Source excerpt ChatGPT in the headlines again ## GPT-3's improved capabilities and implications for application security DevFeed: [GPT-3's improved capabilities and implications for application security]() Original publisher: [Read original article]() Author: Adam Published: 2022-12-09T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [OpenAI](), [Artificial Intelligence](), [Language models](), [Application Security](), [prompt]() Tags: [ai](), [application-security](), [chatgpt](), [gpt](), [gpt-3](), [language-models](), [openai](), [prompt]() ### AI overview The author reflects on GPT-3's marked improvement, its ability to produce reasonably convincing text, and the implications for application security and public information. ### Source excerpt The OpenAI chatbot is shockingly improved -- its capabilities deserve attention.