The online whiteboard of Kristofer Palmvik
If you’re building a service that will eventually run multiple instances, this article is for you. I think you should run at least two instances from day one. Doing so helps you uncover hidden bugs early—like port conflicts, stale caches, and locking issues—so you can avoid expensive rewrites later.
I have no idea what I actually believe about how AI will transform the industry. What I know is that if I get to work building it, I will learn what it is that I believe. They will reinforce each other. I will find my footing through walking the road and doing the work.
We could ask… "If there are no blockers, why isn’t the work done yet?" "If there are no blockers, why are there two tickets with your name on them?" "If there are no blockers, why wasn’t there an update to this ticket today?" Almost certainly you’ll get a list of reasons that is mostly made up of real blockers that the team doesn’t think of as being blockers.
The people who aren’t seeing the problems in the code, have never trained their RAS to look for problems in the code, and so they honestly don’t see them. “The code all looks great to me”. This is why we continue to propagate poor code - because we can’t fix what we can’t see.
if something went wrong with our AI systems tomorrow, an unexplainable output, a biased decision, a data breach, a regulatory inquiry, who in this organisation would I call first? If the answer is a committee, a shared inbox, or a long pause followed by uncertainty, you already know what you need to build. One person. Clear mandate. Real authority. Full accountability.
Garage is a lightweight geo-distributed data store that implements the Amazon S3 object storage protocol. It enables applications to store large blobs such as pictures, video, images, documents, etc., in a redundant multi-node setting. S3 is versatile enough to also be used to publish a static website.
Turso is the lightweight database that scales to millions of instances. Build agents, AI assistants, and intelligent apps by deploying databases everywhere: on servers, browsers, and devices, just like files. Turso is a complete SQLite drop-in replacement, built for the agentic future.
The Claude Code source had been exposed, and the entire dev community was in a frenzy. My girlfriend in Korea was genuinely worried I might face legal action from Anthropic just for having the code on my machine — so I did what any engineer would do under pressure: I sat down, ported the core features to Python from scratch, and pushed it before the sun came up. The whole thing was orchestrated end-to-end using oh-my-codex (OmX) by @bellman_ych — a workflow layer built on top of OpenAI's Codex (@OpenAIDevs).
This is, without exaggeration, one of the most comprehensive looks we’ve ever gotten at how the production AI coding assistant works under the hood. Through the actual source code. A few things stand out: The engineering is genuinely impressive.
With the launch of models like Claude Opus 4.5, it suddenly became possible to ask AI to build something for you, and it’d do it in a nearly fully functional way. That level of accuracy led to people taking a hands off approach to app building, and even enabled people who’ve never coded before to make apps. Whether or not you like this trend is another discussion. Either way, there’s one thing that holds true: App Store review isn’t cut out for it.
Paperclip is a Node.js server and React UI that orchestrates a team of AI agents to run a business. Bring your own agents, assign goals, and track your agents' work and costs from one dashboard. It looks like a task manager — but under the hood it has org charts, budgets, governance, goal alignment, and agent coordination.
To design the most effective combinations, the engineers used AI to evolve novel body configurations. Instead of sticking with standard dog- or human-like designs, the AI churned out strange new “species” of machines that no human engineer would have conceived. When connected to other modules, the metamachines undulate like seals, bound like lizards, or spring like kangaroos.
Operators of AI models in Europe should pay "a revenue-based levy... reflecting their use of content publicly available online," Arthur Mensch wrote in an op-ed for the Financial Times. "Proceeds would flow into a central European fund dedicated to investing in new content creation and supporting Europe's cultural sectors," he added.
The act of programming has lived in extract for 45 years and we’re used to that,” he said. Then the genie of generative AI coding assistants escaped from the bottle, “and all of those certainties have been thrown out of the window,” he said. Exploration doesn’t look very much like engineering from the books. “It’s about cutting corners to get answers, throwing away what you’ve done, starting over, being creative, sniffing out opportunities,” Beck said.
Self-hosted GitHub Actions runners can be weaponized into persistent backdoors that communicate entirely over trusted channels. Because all traffic flows to github.com, traditional network defenses are largely blind to the threat.
Everyone is talking about how quickly they’re building things, how many agents they’re using at the same time, and how much time they’re saving. I feel like if I’m working at a regular speed, I’m not doing enough.
A garden is a collection of evolving ideas that aren’t strictly organised by their publication date. They’re inherently exploratory – notes are linked through contextual associations. They aren’t refined or complete - notes are published as half-finished thoughts that will grow and evolve over time. They’re less rigid, less performative, and less perfect than the personal websites we’re used to seeing.
In my opinion and in short : I think digital gardens as digital spaces where people grows their ideas from seed to tree : 🌱 -> ☘️ -> 🍀 -> 🌿 -> 🌲 -> 🎄
Semantic anchors are well-defined terms, methodologies, and frameworks that serve as reference points when communicating with Large Language Models (LLMs). They act as shared vocabulary that triggers specific, contextually rich knowledge domains within an LLM’s training data. Think of them as shortcuts to rich context - instead of explaining a complex methodology from scratch, you can invoke a semantic anchor and the LLM will activate its entire knowledge base about that concept.
The developers who will be best positioned in three years are the ones who started genuinely engaging with agentic coding today. Not using it as a fancy autocomplete. Actually engaging with it, letting go of implementation as the primary measure of their contribution, developing fluency in a new kind of collaboration between human judgment and machine capability.
Our migration to Hive Gateway is a story of pragmatic engineering execution. A small team replaced the core routing layer of Sweden’s largest property platform in under two months, with zero user-visible downtime and no latency regression.
Throughout the ages, humans have come up with ideas. Some ideas never become more than a thought. Others spread globally, affecting people for a long time. The life of ideas seems to be an eternal cycle of birth, life, death, and rebirth.
They may look at a very high-fidelity, live-data prototype and think that it can’t be all that hard to make the leap to something we can actually sell and service and that our customers can run their business on. But I’ve now witnessed more than a few product managers embarrass themselves in front of their engineers. So this article is intended for these people.
the whenwords library contains no code. Instead, whenwords contains specs and tests. The installation instructions are comically simple, just a prompt to paste into Claude, Codex, Cursor, whatever.
Temporal is not just a better API. It's proof that the JavaScript community can solve long-standing problems together. After nearly 30 years, JavaScript finally has a modern datetime API. And this time, we got it right.
As AI generates more of the code, the nature of how teams collaborate around changes is shifting. Review is one of the few systematic places where humans on a team exercise judgment together about the system they share. What they’re judging is changing – less mechanical correctness, more intent and direction – but the collaborative act is worth protecting.
AI in the workplace is transforming the technical systems. Much less attention is being paid to the cultural systems that surround it. New tools can be exciting, especially to management and motivated individual contributors. For the rest of your teams they can clearly be seen as threats to the “way we do things around here.” If we don’t address those systemic cultural issues we’ll never be able to take full advantage of these new tools in a way that truly maximizes their benefit.
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