The online whiteboard of Kristofer Palmvik
There are really two questions that matter when it comes to the Wait Calculation for AI: How good? And how fast? In other words, how good can this particular run of AI technology (either LLMs or their immediate successors) get in terms of human achievement; and how fast will this happen?
Imagine a world without version control. A world where there is only one version of the software. This is the latest version, and the version that runs in production. Imagine not having to struggle with merge conflicts, getting rid of your git cheat sheet and always knowing exactly what code runs in production.
Create a shareable link for your prompt and open it instantly in ChatGPT, Claude, Gemini, Grok, or Perplexity — one click, and the prompt is ready to run.
We don't know where AI is taking us. Big US tech firms want to rush into that unknown future without guardrails; the Communist Party of China wants the state to oversee that research. One version promises a hyper version of consumer capitalism; the other, a world in which the state determines what you can or can't do with this technology.
We as an industry right now, have got an amazing tool that will forever change how we develop products (and do many other things too). But we are viewing it with the same focus at productivity, cost-reduction and efficiency gain that we did before this paradigm shift. This leads us wrong; instead lets think about how we can optimize the entire value chain and create real value. Who knows, maybe the bottleneck in our process cannot be solved with faster output?
The real multiplier today is the engineer who knows how to wield agents effectively and coordinate their output toward a result the company actually cares about. That means decomposing complex problems into tasks agents can execute reliably, reviewing and directing that output with enough judgment to catch what went wrong, and owning the integration into a real system that holds up in production. It also means communicating what was built, to whom, and why it matters. Working well with others, not just technically but in the full human sense: understanding what stakeholders need, translating across the technical and non-technical divide, making your work legible to the people who depend on it. These were always important skills. They are now non-negotiable ones.
LLM-API och AI-tjänster på egen GPU-hårdvara i Sverige. Bortom Cloud Act och Schrems II — all inferens stannar hos oss.
A semantic constraint engine for Claude Code & Codex. Forces agentic communication into minimal-token lithic structures. Retain 100% technical accuracy while destroying up to 87% of output latency.
We are discovering better ways of building software and operating organizations in the age of artificial intelligence by preserving, governing, and evolving the provenance of decisions that shape our systems.
My hope (once the dust settles) is that we come out the other side with more collaboration. Instead of competing for leverage, I'm hoping individual contributors find new ways to work together. For example, what if Product Managers and Engineers did more AI-driven pair programming? The PM could focus on customer behavior and product goals. The engineer could evaluate architecture, security, and maintainability. They would iterate together in real time, using LLMs.
This rule, in its most up-to-date form is as follows: “When a meeting, or part thereof, is held under the Chatham House Rule, participants are free to use the information received, but neither the identity nor the affiliation of the speaker(s), nor that of any other participant, may be revealed.” Essentially, the rule tells us that it’s ok to share information and stories from the session when we leave, but not to share who said what.
The more time I spend with coding agents, the more I become convinced that they are damn-near incompatible with working in teams. I've suggested this before, but I really think more people should be chewing on this. The bottleneck for software teams—the thing that's always made them less than the sum of their parts—is the handshake problem. It's the one thing from The Mythical Man-Month everyone remembers: "Adding manpower to a late software project makes it later."
Element utilises the decentralised Matrix open standard to create a resilient, zero trust, communications network. With no central point of failure, Element protects against global outages and ensures high availability through the use of redundant servers. Element also supports air-gapped and low bandwidth communications, including Mesh and satellite connectivity.
1,5 miljoner platser i världen har en Wikipedia-artikel men inget foto. Du kan ändra på det — genom att helt enkelt gå ut och utforska.
A civilization where no one starts anything spontaneously is a civilization where nothing starts. Not interesting conversations. Not interesting friendships. Not the unreasonable, unscheduled, unvalidated encounters that have historically been the substrate of basically every damn good thing that's ever happened to anyone. We optimised for efficiency and got loneliness. We built for scale and got isolation. We de-risked the transaction and killed the moment.
Apple and Google are helping users to find apps that create deepfake nude images of women, a new Tech Transparency Project investigation has found, showing how the platforms are key participants in the spread of AI tools that can turn real people into sexualized images.
Your users do not know your name. They do not know which taxes you are paying or what is consuming your energy behind the scenes. They just know whether the product solves their problem or not. Whether it is getting better or standing still. Whether it feels like someone who understands them built it — or whether it feels like it was built by a committee responding to whoever shouted loudest.
The phenomenon that a functionality, effect or issue suddenly fails to appear or work as planned when one tries to demonstrate it to others.
The work is yours if you brought the judgment, the context, the decisions about what to build and what to leave out. The work is yours if you can defend every choice in it. The keystrokes are a delivery mechanism. They always were.
ASP.NET Core has traditionally offered two caching options: in-memory caching and distributed caching. Each has its trade-offs. In-memory caching using IMemoryCache is fast but limited to a single server. Distributed caching with IDistributedCache works across multiple servers using a backplane. .NET 9 introduces HybridCache, a new library that combines the best of both approaches. It prevents common caching problems like cache stampede. It also adds useful features like tag-based invalidation and better performance monitoring.
The plan was to survey all existing caching methods, understand each team’s needs, and then build a single internal product: an adaptive, hybrid cache that would work both in local memory and on Redis, with proper observability, monitoring, and administration tools, and with a set of advanced features that none of the existing approaches could offer. After a few months of work, Alan Cache was born.
I want to pull on a thread that we talked about in the beginning - the three emerging camps of peoples relationships to AI. This sits right at the heart of my own tension right now - I’m trying to stay on the frontier, discovering the patterns that work and those that don’t. At the same time, I’m thinking about my peers, and the impact of these changes on them and on our profession. I feel like I’m losing my ability to even talk to some folks, and it stresses me out.
The coordination problem does not change. The need for someone to own the outcome does not change. The fragility of interfaces does not change. The cost of getting decisions wrong does not change. Organisations that understand this will use AI to make their teams more effective without assuming they can make them smaller in proportion. They will recognise that a 10-person team producing the output of 30 needs better coordination structures, not fewer coordinators.
The goal of writing is not to have written. It is to have increased your understanding, and then the understanding of those around you. When you are tasked to write something, your job is to go into the murkiness and come out of it with structure and understanding. To conquer the unknown.
Nothing you own is finished. Everything exists in a state of permanent incompletion, permanently needing. Your phone needs updates, needs charging, needs storage cleared, needs passwords rotated. Your apps need permissions reviewed, terms accepted, preferences re-configured after every update. Your subscriptions need evaluating, need renewing, need canceling, need justifying to yourself every month when the charge appears. The purchase isn't the end of anything. It's the first day of a relationship you didn't agree to, with no clean way out.
it appears that the model uses functional emotions—patterns of expression and behavior modeled after human emotions, which are driven by underlying abstract representations of emotion concepts. This is not to say that the model has or experiences emotions in the way that a human does. Rather, these representations can play a causal role in shaping model behavior—analogous in some ways to the role emotions play in human behavior—with impacts on task performance and decision-making.
Most people wing it. They sit down with AI and improvise. That's like walking into a kitchen and tossing random ingredients in a pan. Sometimes it works. Usually it doesn't. Good prep changes the result. The best AI users don't know magic words. They've prepped their ingredients: who they're cooking for, what they're making, how it should taste.
I believe every person has a perspective worth communicating, and not because the algorithm demands it, but because honest expression contributes to something larger than any one post. It creates resonance. One person’s willingness to say something real gives permission to the next person, and then the next. That’s how movements get built and how communities form. That’s how ideas actually travel.
Software development is one of the most capital-intensive activities a modern company undertakes, and it is also one of the least understood from a financial perspective. The people making daily decisions about what to build, what to delay, and what to abandon are rarely given the financial context to understand what those decisions actually cost. This is not a coincidence. It is a structural condition that most organizations have maintained, quietly and consistently, for roughly two decades.
Developers who try to micromanage agent output will struggle. Developers who learn to specify, verify, and iterate will thrive. The 10% that went up 1000x is judgment, specification, and verification. The 90% that went to zero is the typing.
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.
5246 links collected between and