Redoing work is now extremely cheap. Code in the small is less important than structural patterns and organisation of the code in the large. You can also build lots of prototypes to test an idea out. For this, vibe-coding is great, as long as the prototype is thrown away and rewritten properly later. This is fitting my better understanding of the shift in software development from vibe coding (hello Ruby on Rails would like a word) and using prompts to build design docs to THEN build software.

I think it’s actually the other way around. A truly great engineering organization is one where perfectly normal, workaday software engineers, with decent software engineering skills and an ordinary amount of expertise, can consistently move fast, ship code, respond to users, understand the systems they’ve built, and move the business forward a little bit more, day by day, week by week. Agree completely. And besides have you ever tried to manage that many egos on one team?

The IIHS came up with an easier way to repeatably measure and compare what a driver can see in a 180-degree forward-facing view out of a vehicle. The method involves a special portable camera rig that captures a driver’s view. That image is then processed to determine what percentage of the road in a specified radius is visible, and what’s blocked by the vehicle’s A-pillars, hood, and side-view mirrors. The result is an aerial view of where the driver’s vision is obstructed—the blind zone—as well as a percentage of the surrounding area that’s visible.

Speaking to the US Senate Banking Committee on Wednesday to give his semiannual monetary policy report, Powell told elected officials that AI’s effect on the economy to date is “probably not great” yet, but it has “enormous capabilities to make really significant changes in the economy and labor force.” No timeline given, but another signal that labor disruption is on the horizon. And fiddling with interest rates isn’t going to fix this one.

Since the start of 2023, more than half-a-million tech workers have been laid off, according to industry tallies. Headlines have blamed over-hiring during the pandemic and, more recently, AI. But beneath the surface was a hidden accelerant: a change to what’s known as Section 174 that helped gut in-house software and product development teams everywhere from tech giants such as Microsoft (MSFT) and Meta (META) to much smaller, private, direct-to-consumer and other internet-first companies.

Eoin Hinchy, cofounder and CEO of workflow automation company Tines, said his team had 70 failures with an AI initiative they were working on over the course of a year before finally landing on a successful iteration. As Jim Collins says, bullets then cannonballs. ‘AI’ covers so many types of solutions that to say you’re doing ‘AI’ is a lot like ‘we have a website’ in the late 90s. Congratulations on recognizing that the internet/ai is transformative.

Meta devised an ingenious system (“localhost tracking”) that bypassed Android’s sandbox protections to identify you while browsing on your mobile phone — even if you used a VPN, the browser’s incognito mode, and refused or deleted cookies in every session. Worth the entire read, but whats fascinating is that some engineer was given a problem of how to track users across apps and sessions and didn’t blink an eye at solving it.

With AI, code is becoming really cheap. This means that you can now build stuff that you only ever use once without feeling bad about it. Everything that you wish would make your current task easier can just be created out of thin air. Fits in with being more ambitious because the cost of writing code is zero. But knowing what code to write is priceless. Also some good ideas on gitworkrees and task delegation.

And that is where I’ve learned Postgres is probably one of the rare databases that supports Transactional DDL. MySQL, Maria, and Oracle don’t. I feel like Postgres has won the open source database world. The last time I reached for MySQL it was 2005 and ‘LAMP’ was the stack of choice. Quote Citation: TANIN, “One more reason to choose Postgres over MySQL”, JUN 14, 2025, https://tanin.nanakorn.com/one-more-reason-to-use-postgres-vs-mysql/

These statements betray a conceptual error: Large language models do not, cannot, and will not “understand” anything at all. They are not emotionally intelligent or smart in any meaningful or recognizably human sense of the word. LLMs are impressive probability gadgets that have been fed nearly the entire internet, and produce writing not by thinking but by making statistically informed guesses about which lexical item is likely to follow another.