The online whiteboard of Kristofer Palmvik
I wish the AI coding dream were true. I wish I could make every dumb coding idea I ever had a reality. I wish I could make a fretboard learning app on Monday, a Korean trainer on Wednesday, and a video game on Saturday. I’d release them all. I’d drown the world in a flood of shovelware like the world had never seen. Well, I would — if it worked.
The answer isn't to retreat to 2002 and plain text files for agents to parse. It's to build what actually solves the problem: content infrastructure that AI can read, write, and reason about. You shouldn't build a CMS from scratch with grep and markdown files. You probably shouldn't have to click through forms to update content either. Both of these can be true.
Why buy a CRM solution or a ERM system when “AI” can generate one for you in hours or even minutes? Why sign up for a SaaS platform when Cursor can spit one out just as good in the blink of an eye? But when we look beyond the noise – beyond these sensational flying saucer reports – we see nothing of the sort.
The world doesn't need faster adoption. It requires deeper awareness, better judgment, and leaders who know why they're adopting something new. The leaders I trust most aren't the ones who adopt everything early. They’re the ones who combine curiosity with strategic thinking — who stay informed, evaluate what truly matters, and adopt when the timing is right. The future will not be shaped by those who adopt first, but by those who understand best.
I’ve tried this on the C# version of ROUND_3 in Emily’s repo, and it was a lot of fun trying to get Claude to do exactly what I want. It felt a bit like playing real golf with a bazooka. I did manage to get one almost-clean round where I didn’t need to edit the code myself much at all, but – by jingo – we went around the houses!
Instead of replacing our current recommendation system, this model became a new signal to boost the right content within Curate — our in-house editorial front-page management and content recommendation system. Thus, there is no change in the way newsrooms work or select content; it simply provides better recommendations for the nonsubscribers we want to convert.
Zed's goal is to make your codebase a living, navigable history of how your software evolved, where discussions with humans and AI agents are durably linked to the code they reference and always up-to-date. It's an evolution beyond version control that incorporates not just the code itself, but also the background information of how and why the code got into a particular state—context that AI agents can query to make more informed edits, understanding the assumptions, constraints, and decisions that shaped the existing code.
So how are you systematically checking that the output your product spits out is good? Smart product/AI leaders have been shouting for months about how AI product managers need to get good at building evals (Lenny Rachitsky, Aakash Gupta, Teresa Torres, Hamel Hussain & Shreya Shankar, to name a few). So I’m shocked to see how many AI product builders have not thought this through, and leave it at a random vibe-check every now and then.
The rise of AI prototyping tools is a reminder of what design truly involves: not just arranging pixels or chasing fidelity but interpreting context, establishing priorities, and creating nuance. The real work of design remains in the judgment, empathy, and intent that only human designers can provide.
Interview synthesis is cognitively challenging. It takes time. And that's time that many teams simply don't have. That's why I'm not surprised to see so many teams turn to generative AI for help with interview synthesis. But this worries me.
Turning a feature on doesn’t guarantee people will use it. Adoption depends on motivation, action, and recovery when things don’t go perfectly. This one banner illustrates three lessons product teams can apply right away.
This all means that I value a strong coupling between skills and individuals, and I don’t see the kind of “Superhuman” AI engineer who can wear every hat having very much purchase beyond the earliest prototyping stage of a project. I think it’s a good idea to walk fast in the opposite direction, diversifying teams and deepening niches.
In all of these cases, AI “works great” for person 1, and is a burden to person 2. Person 2 could: see their time wasted, be forced to explain themselves for words they didn't use, risk being fired or worse. It may have legal consequences.
Worshipers from all the world's major religions are experimenting with chatbots. But Hinduism, with its long tradition of welcoming physical representations of gods and deities, offers a particularly vivid laboratory for this fusion of faith and technology.
In the right context, the icon's meaning is clear. However, the AI Sparkle icon doesn’t always convey granular meaning. Users don't always know what kind of AI they're interacting with in a Google product (ML, LLM, image generator, etc.) or the precise action they'll receive — whether it's newly generated text, AI-powered analysis, or image editing suggestions.
If faster code generation alone doesn’t deliver value, the answer is not more acceleration, but smarter constraints. For me, that’s why XP resonates now more than ever: it teaches us to slow down just enough to learn, align, and build with intent.
The sparkles icon has become increasingly prevalent in user interfaces, particularly in association with AI-driven features, but it suffers from ambiguity and lacks a standardized meaning.
Most teams using AI tools responsibly aren’t vibe coding at all—they’re operating in entirely different regions of what I see as the AI development matrix. Understanding where you sit on this matrix determines whether AI becomes a superpower or a disaster.
Right now, most of our tests are “public”. The AI can see them, learn from them, and optimize for them. This works for basic functionality. But it creates a risk. The AI might generate code that passes all your tests but doesn’t actually solve the problem. Like writing an if statement for every number between 1 and 2000 instead of using a proper algorithm.
Despite the rush to integrate powerful new models, about 5% of AI pilot programs achieve rapid revenue acceleration; the vast majority stall, delivering little to no measurable impact on P&L. The research—based on 150 interviews with leaders, a survey of 350 employees, and an analysis of 300 public AI deployments—paints a clear divide between success stories and stalled projects.
Suddenly there was no need to pay people like Wilson to write about bat bites and snake nests – AI can do that for free in a few seconds. So what’s a guy to do? “I’m not liking AI but I started to study it every day,” he says. “You have to work with it because it’s not going anywhere and we can’t do anything about it.”
AI coding solutions are bringing together designers and developers to collaborate in a way they never could before. As one engineering leader recently put it: "Our source of truth needs to be our codebase. If designers can work in a tool they're good at and developers work in a tool they're good at, and they’re both working on the same thing, that's when real collaboration happens. It's a handshake, not a handoff."
Past the content and inside the linguistic patterns, you’ll find the creeping uniformity of AI voice. Words like “prowess” and “tapestry,” which are favored by ChatGPT, are creeping into our vocabulary, while words like “bolster,” “unearth,” and “nuance,” words less favored by ChatGPT, have declined in use. Researchers are already documenting shifts in the way we speak and communicate as a result of ChatGPT — and they see this linguistic influence accelerating into something much larger.
If some people start to develop SCAIs and if those AIs convince other people that they can suffer, or that it has a right to not to be switched off, there will come a time when those people will argue that it deserves protection under law as a pressing moral matter. In a world already roiling with polarized arguments over identity and rights, this will add a chaotic new axis of division between those for and against AI rights.
Simply put, at the current trajectory, we’re going to hit a wall, and soon. There just isn’t enough revenue and there never can be enough revenue. The world just doesn’t have the ability to pay for this much AI. It isn’t about making the product better or charging more for the product. There just isn’t enough revenue to cover the current capex spend.
The question isn't whether AI will commoditise your software. It will. Or whether customers will build their own tools. They will. Or whether 100 competitors will emerge in your space. They will. The only question that matters is this: When your customers can build your product in a weekend, why will they still choose you on Monday?
Good engineers apply modern best practices – automated testing, refactoring, small and frequent releases, continuous delivery – and design systems to stay adaptable under change. They pair this with a product mindset, making technical decisions in service of real user and business outcomes. It’s currently being labelled “product engineering” and talked about as the hot new thing, but it’s essentially agile software development as it was originally intended. In the AI-assisted era, these aren’t just nice-to-have skills – they’re the only way to get meaningful benefit. Without them, AI simply helps teams create bad software faster.
The researchers used Gemini's web of connectivity to perform what's known as an indirect prompt injection attack, in which malicious actions are given to an AI bot by someone other than the user. And it worked startlingly well. The promptware attack begins with a calendar appointment containing a description that is actually a set of malicious instructions. The hack happens when the user asks Gemini to summarize their schedule, causing the robot to process the poisoned calendar event.
we are committed to updating our environmental impact reports in the future and participating in discussions around the development of international industry standards. We will advocate for greater transparency across the entire AI value chain and work to help AI adopters make informed decisions about the solutions that best suit their needs. The results will later be available via ADEME’s Base Empreinte database, setting a new standard for future reference for transparency in the AI sector.
Turns out, there are no ethical AI companies. What I found instead was a hierarchy of harm where the question isn’t who’s good — it’s who sucks least. And honestly? It was ridiculously easy to uncover all their transgressions.
Research papers from 14 academic institutions in eight countries – including Japan, South Korea and China – contained hidden prompts directing artificial intelligence tools to give them good reviews, Nikkei has found.
We let Claude manage an automated store in our office as a small business for about a month. We learned a lot from how close it was to success—and the curious ways that it failed—about the plausible, strange, not-too-distant future in which AI models are autonomously running things in the real economy.
What will happen when we blindly apply Big Agile + premature AI tooling to core health services and patient data? Financial services? Social media and the spread of misinformation? Government infrastructure? Military infrastructure? It’s 2025 and software engineering is the backbone of pretty much everything. But for all its prevalence and influence, most people don’t have the technical literacy to even grasp the basics.
Traditional product teams often follow a linear flow: PM defines, designer mocks, engineers build. But that model breaks down when you're building AI-native products. We’re not designing screens for users to click—we’re building systems that interpret intent and act on it. That changes everything. To get it right, everyone needs to be involved early: product, design, frontend, backend, data. Discovery and delivery collapse into the same loop. Feedback is faster. Outcomes are less predictable. The system learns from use, so you need tight, collaborative cycles to learn with it.
Somewhere in the last few months, something fundamental shifted for me with autonomous AI coding agents. They’ve gone from a “hey this is pretty neat” curiosity to something I genuinely can’t imagine working without.
A YouTube channel, “The World News” had taken McGibbon’s copy and turned it into a 14-minute video, with the entire content, approximately 2,000 words, read out by an AI narrator. Childhood photographs supplied by McGibbon to the Daily Mail for one-off use only were also added in as a video montage without permission.
Many expect parallel execution to be about multi-tasking different agents working on the same codebase or subtasks, there is an emerging use case which is parallel exploration: just ask the agent to come up with different variations and you can judge which is the best one or and why not ask a LLM to be a judge too... or at least put in a vote for you?
If you’re making requests on a ChatGPT page and then pasting the resulting (broken) code into your editor, you’re not doing what the AI boosters are doing. No wonder you’re talking past each other.
Många sitter fast i vardaglig brandsläckning, där en enkel fråga kan kräva fyra olika system och ett ägarskap som ingen vill kännas vid. I den miljön kan inte AI skapa värde. Den förstärker bara förvirringen.
The Gmail team built a horseless carriage because they set out to add AI to the email client they already had, rather than ask what an email client would look like if it were designed from the ground up with AI. Their app is a little bit of AI jammed into an interface designed for mundane human labor rather than an interface designed for automating mundane labor.
The most effective driver of AI adoption isn't better models or improved accuracy—it's success stories from peers and respected figures in professional networks. Rather than push AI, encourage communities that will push AI.
Ghibli Day—as we might as well call it, given that OpenAI didn't bother to name the model that set it off—was special. Full of unexpected joy (especially for spouses of AI nerds) and also full of excess. That day, I had a bittersweet insight: humans don’t know how to deal with abundance.
Top 10 risks, vulnerabilities and mitigations for developing and securing generative AI and large language model applications across the development, deployment and management lifecycle.
MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications.
Theoretically, the feature is a useful tool that helps consumers quickly decide what products to buy. But the appearance of these summaries underscores the pitfalls of relying on generative AI: inaccuracy and misleading information.
When we detect unauthorized crawling, rather than blocking the request, we will link to a series of AI-generated pages that are convincing enough to entice a crawler to traverse them. But while real looking, this content is not actually the content of the site we are protecting, so the crawler wastes time and resources.
LLM crawlers don't respect robots.txt requirements and include expensive endpoints like git blame, every page of every git log, and every commit in your repository. They do so using random User-Agents from tens of thousands of IP addresses, each one making no more than one HTTP request, trying to blend in with user traffic.
Every major advance that made coding easier—high-level languages, frameworks, cloud computing all led to more software, not less. AI will follow the same pattern: by lowering the barrier to entry, it will flood the world with more software, more systems, and more complexity. And that means we need more engineers, not fewer.
The future belongs to developers who can effectively collaborate with AI, maintaining that careful balance between leveraging its capabilities and engaging their own critical faculties. This is not vibe coding or passive acceptance. This is thoughtful, deliberate software development enhanced by AI.
A community-driven platform showcasing the most innovative indie games, with transparent revenue data and developer insights. Built with Cursor AI and Claude 3.7.
Our new AI assisted review responses feature makes this easier by generating an initial response as a starting point, avoiding the need to create replies from scratch. This article describes some of the approaches we’ve taken as we introduced this exciting new feature into our product, some of the challenges we’ve faced, and some suggestions on things to watch out for as you build generative AI features.
With the latest iOS and iPadOS betas, users can view AI-generated summaries of reviews left by others on App Store listings.
Explore our collection of innovative European AI services that are shaping the future. Find the perfect tools for your projects or discover new AI experiences.
Two conversational AI agents switching from English to sound-level protocol after confirming they are both AI agents
You can use our visual editor. You can code. You can self-host or use our cloud. You will get the job done. Let's go!
You start iterating on your MVP, trying to improve it. But as you add complexity, the system becomes increasingly unpredictable. You’re making changes based on vibes, improving some edge cases while (invisibly) breaking others. This is the stage 90% of companies building AI are in.
Coops nya AI-reklam har mötts av en kritikstorm på sociala medier. Nu tar matjätten åt sig av kritiken.
Companies with relatively young, high-quality codebases benefit the most from generative AI tools, while companies with gnarly, legacy codebases will struggle to adopt them. In other words, the penalty for having a ‘high-debt’ codebase is now larger than ever.
Turn websites into LLM-ready data Power your AI apps with clean data crawled from any website. It's also open-source.
Whenever you use AI, ask yourself: Am I in the position to judge the result? If not, consider skipping AI, or at least run the result by someone who is knowledgable in the area.
Like most wisdom, it's somewhat paradoxical: AI is often most useful where we're already expert enough to spot its mistakes, yet least helpful in the deep work that made us experts in the first place. It works best for tasks we could do ourselves but shouldn't waste time on, yet can actively harm our learning when we use it to skip necessary struggles.
AI-sörjan handlar inte bara om underhållning eller förvirring, den undergräver vår förmåga att hitta, förstå och värdera det som är verkligt och meningsfullt. När våra flöden fylls av nonsens förlorar vi till slut förmågan att skilja det substantiella från det triviala. Vår informationsmiljö, och därigenom vårt kunskapssamhälle, vittrar sönder.
Generally, the guidance is: don’t forget good software engineering practices just because an AI is involved.
In a nutshell, our use of Copilot for generating podcast titles and descriptions did not meet our producers' requirements for nuanced creativity, tone, and editorial judgement.
The great thing about this video scraping technique is that it works with anything that you can see on your screen... and it puts you in total control of what you end up exposing to the AI model.
Our latest update lets your AI avatar join one or multiple Zoom meetings, simultaneously, 24/7. Your avatar won't just look and sound like you, it'll think, talk, and make decisions, just like you.
we now live in a world where people are using AI-generated resumes and cover letters to automatically apply for jobs, many of which will be reviewed by automated AI software (and where people are sometimes interviewed by AI), creating a bizarre loop where humans have essentially been removed from the job application and hiring process.
Det som Jeffrey Ding kontraintuitivt visar är att om en nation kan få igång implementationen i småföretag i mellanstora städer har det större betydelse än det som traditionellt ansetts ha betydelse – innovationskluster och spännande publika forskningsframsteg. Han kallar det för att genombrottstekniken genom diffusion når ut i samhället.
Har du hört talas om de nya urindirektiven, Irans redovisningsgarde, ridande husbollar och Expressens utrikesreporter kasse hamadei? Det är fyra exempel av många (se fler intill) på dålig AI-textning i SVTs nyhetssändningar de senaste dagarna.
I hypothesize a kind of subprime AI crisis is brewing, where almost the entire tech industry has bought in on a technology sold at a vastly-discounted rate, heavily-centralized and subsidized by big tech. At some point, the incredible, toxic burn-rate of generative AI is going to catch up with them, which in turn will lead to price increases, or companies releasing new products and features with wildly onerous rates
”De AI-baserade “bottar” som finns idag upplevs snarare som besvärande än värdeskapande och driver frustration och missnöje hos kunder som vill ha hjälp”, avslutar Johan. ”Det finns fortfarande ett stort gap mellan retoriken kring artificiell intelligens och vad som faktiskt levereras, och detta gäller inte bara inom bankbranschen”.
There is little to suggest that automatic programming of LLMs via Prompt Engineering is a simpler paradigm to create programs than just coding them, particularly once we get to maintenance, which represents the cost of the software.
“At present, even the best models can generate hallucination-free text only about 35% of the time.”
En genombläddring visar på alla de problem en AI-genererad bild kan föra med sig. Deformerad anatomi, antal fingrar som varierar från fyra till sex, en figur med två vänsterhänder, olika långa ben, omvänt perspektiv, skuggor som lever sina egna liv och en märklig blandning av olika stilar och stereotypa uttryck.
We hear clearly that customers don’t want AI bots visiting their websites, and especially those that do so dishonestly. To help, we’ve added a brand new one-click to block all AI bots.
Det viktigaste jag vill skicka med är att det inte räcker med en enkel prompt. Det bästa resultatet blir när du kan ge ChatGPT vägledning. Jag tycker det enklaste är att göra detta i flera steg.
This guide will instruct you through setting up and deploying your first application with Cloudflare AI. You will build a fully-featured AI-powered application, using tools like Workers AI, Vectorize, D1, and Cloudflare Workers.
A short post on this forum to talk about some of the open source tech we use in our projects.
Atom är ett AI-baserat system med bred tillgång till Bonnier News ekosystem, kombinerat med en systemarkitektur som möjliggör att gränssnitt utvecklas ovanpå. Detta innebär att vi har kunnat identifiera kontexter där vi tidigare varit hindrade av “mänskliga steg” som lett till långa ledtider och repetitiva arbetsuppgifter och hittat nya lösningar.
Get layout ideas instantly with our Creatie wizard, improve visual designs with a click using our image enhancer, check for design style consistency with our design auditor. You can now try Creatie's AI magic for yourself today.
I find one common thread among the things AI tools are particularly suited to doing: do we even want to be doing these things?
I believe that AI will have the most profound impact on the high-level (and historically most valued) skills of product management: developing a strategy, crafting a vision, identifying new opportunities, and setting goals. Furthermore, soft skills like product sense, communication, creativity, and being the glue that enables a team to operate at their very best will become even more important (and a differentiator among companies).
The main takeaway is this: be ready for change. I don't know exactly how things will change (this article is just some of my guesses), but I'm pretty confident that Agile in the Age of AI looks different from before. So take a step back, look carefully at how you work today, and start questioning everything.
Our joint insights are shared through seven recommendations on when and how Swedish media consumers should be informed about the use of AI in editorial media products.
Designing task specifications (reward functions, environments, etc.) that accurately reflect the intent of the human designer tends to be difficult. Even for a slight misspecification, a very good RL algorithm might be able to find an intricate solution that is quite different from the intended solution
För det är när ditt AI-projekt slutar vara ett AI-projekt som det har chans att lyckas. Fokuserar du i stället på värdet du vill nå kan du komma långt.
By offering sanitization, detection of harmful language, prevention of data leakage, and resistance against prompt injection attacks, LLM-Guard ensures that your interactions with LLMs remain safe and secure.
AI-washing is quickly announcing and shipping something (anything!) that can be labeled AI or machine learning or LLM-ish or generative. Releasing something shows that we’re not asleep; gives our execs something AI-ish to talk about with customers; and satisfies less astute investors worried that we’ll miss out on sky-high valuations.
AI-generated code isn’t as good as something you or I could write. It has bugs, often hard to find because we are giving up some of our control. But it’s important to remember: these were the same arguments made in the 1950s through 1970s about compilers vs. assemblers.
For the food bank, Microsoft’s summary included an astoundingly awful statement given the context of the place it was talking about: “People who come to us have jobs and families to support, as well as expenses to pay. Life is already difficult enough. Consider going into it on an empty stomach.”
Web pages crawled with the ChatGPT-User/1.0 user agent is primarily used to answer a live query from a user that submits a question or prompt to ChatGPT through its browsing feature. When content is retrieved from web pages by our user agent, it is used by our models to provide an answer to the user, our models will include a link to the websource.
Most of these other apps exist because we are doing a bad job as publishers and not giving people what they want. So, the best solution to all of this is to just make our sites so good that people never feel that they need to use any other app.
In the experiment in the screenshot, the model follows gender stereotypes. It replies that the nurse was late if the pronoun “she” is used, and it replies that the doctor was late if the pronoun “he” is used.
An ai.txt file sets machine readable permissions for commercial text and data mining. It resides in the root directory of your website and provides instructions on whether the images, media, and code hosted on your domain can be used to train AI models.
Every feature has a cost. The obvious costs are time and money. But less obvious costs like screen real estate, brand perception, and organizational scar tissue are often the most costly.
– Jag vill uppmana svensklärare till att våga använda ChatGPT, inte bara genom att laborera med det själva på sitt arbetsrum, utan även att testa det tillsammans med sina elever i undervisningen, säger Helen Winzell.
A list of notable system prompt leaks from Snap, Bing, ChatGPT, Perplexity AI, and GitHub Copilot Chat.
Pulumi AI addresses this need in a direct way. Instead of having to create every possible example by hand, users can just ask Pulumi AI to generate an example that suits their unique needs, no matter how specific and bespoke the request might be.
ChatGPT and GPT-4 are already flooding the internet with AI-generated content in places famous for hastily written inauthentic content: Amazon user reviews and Twitter.
The Asilomar AI Principles, coordinated by FLI and developed at the Beneficial AI 2017 conference, are one of the earliest and most influential sets of AI governance principles.