20 postsWhat I worked out while building: x402 and agent payments, the design discipline of unattended services, and the assumptions that real tests overturned.
AI video has just crossed the 30-second continuous-shot threshold, yet the cease-and-desist letters from Hollywood's Big Six remain. Now that image quality is no longer the issue, the three questions you should really ask before choosing a tool are ones no demo video can answer.
One report says the average designer’s AI tools went from 3 to 7 in a year, yet half are still looking for a main tool that feels right. The magic of these tools lives in those ten seconds of generation; what actually eats your time is the seams between tools.
Last week xAI pushed the price of AI video generation to the floor (officially about 86% lower than Sora 2 Pro, with voiceover built in). When making a video costs pennies, being able to make one is no longer an advantage — the expensive work has quietly shifted elsewhere.
On June 1, GitHub Copilot moved to usage-based billing. One user had AI run four tasks automatically and burned through half a month's credits in a day. Most people see a price hike, but the more important change is that AI tool pricing has shifted from fixed subscriptions to variable utility bills—and that affects anyone who works with these tools and has to manage costs.
A June report says 97% of teams are already using AI to write code, and 92% say they're faster. But only about three in ten actually pocket the time saved; the gap comes down to something that sounds boring.
PwC scanned a billion job ads: roles labeled “entry-level” are 7 times more likely than other entry-level positions to demand senior-level skills. AI has pulled the apprenticeship that used to let you build real ability out of entry-level jobs—the shell remains, but the ladder upward has broken a rung.
In the 2026 wave of layoffs, more and more companies are citing AI as the reason. But a survey of 350 large companies found that the ones cutting the deepest didn’t see better returns. What exactly are those words hiding, and for whom?
AI has gobbled up the most junior grunt work in the workplace, and in doing so it has also torn down the stepping stones where newcomers used to learn the ropes. If you can't start as a rookie, how do you become a veteran? That is the question most worth taking seriously this year.
The LiteLLM vulnerability that CISA added to its Known Exploited Vulnerabilities catalog this week isn’t a model jailbreak—it’s an endpoint that executes the command field in your HTTP request. The whole agent world is staring at the top of the stack comparing models, while the breach is in the least-watched layer of plumbing.
In a randomized controlled trial, experienced programmers using AI to write code felt 20% faster, but were actually 19% slower. The problem isn't AI—it's that most people aren't measuring it at all, and you can't manage what you don't measure.
The whole field keeps shouting, “Stop prompting and go write loops.” But what nobody says is this: a loop is an amplifier—it amplifies your acceptance criteria. If the acceptance check is unreliable, the loop just mass-produces garbage at high speed while you sleep.
Hacker News is arguing over whether local models can replace Claude for coding, and four hundred comments later no one is questioning the premise. What decides whether you can switch has never been how good the model is—it’s the harness wrapped around it.
Build 25–50 small AI apps in 12–18 months, each with a 30-second demo, a process write-up, and monetization entry points. Build trust through real work, and let the case library naturally surface the direction worth going all-in on.
An API your agent learned last week has to be relearned this week—every new service is a cold start. My answer isn't a vector database; it's a helpers directory plus git history.
The saying “Only by enduring the most hardship can you rise above others” contains a hidden trap: making money is hard and painful. But what if money is simply a reward for helping others solve problems? First there is thought, then speech, then action.
In a society famed for efficiency, patience has become a scarce resource. We are all slaves to instant gratification, yet we overlook the beautiful things that need time to mature.
As a child, closing my eyes was enough to enter another world. I want to build a virtual world: children can grow a rich inner life there, and adults can recover the original aspirations of their youth.
As children, our confidence was unshakable; we simply felt, “I can.” As we grew up, we learned to analyze things objectively and rationally, but lost that simplicity—the virus makes us deny ourselves.
Rushing for results means acting before conditions are ripe, and the outcome is predictable. But not everything is worth thinking through three times before acting—otherwise, life would be exhausting.
I studied 15 AI product research tools, and not one dares to tell sellers whether they can make money. The market is full of reporting tools, but decision-oriented tools are still nowhere. Independent developers don't carry big platforms' baggage and can fill that gap. Over the next two weeks I'm building a Chrome extension for intelligent Amazon review diagnostics.