matthorse

Amazing that after 70 years a lot of these practices are still in use and people fall for them. My favorite example are bar charts with truncated axis that are made to show extraordinary growth. But, as is well known, data doesn't matter as much as how you present it (or how much you hide)

Thanks for playing! Was it today's board? Friday's is on the harder side. If you want a simpler board you can try Monday's or Tuesday's (this one has quite an interesting shape in terms of your question about techniques).

Some interesting strategies come from the irregular region shapes. For example, you may notice that all 2s a region needs can only go in one row. If other regions have cells in that row, that often forces these cells to be a 1 or a 4 (or a 3 for 6x6 boards).

If I needed a new screenshot app and had to go through 1k+ apps released this year alone, I might as well just prompt claude to create my own tailor-made version. It's just like the JS frameworks vicious cycle.

How well does it handle larger files? And other book formats, like epub or mobi?

I'd love to see an actual result what it can do

I think that cloud made people forget how fast computers actually are. Hardware specs got reduced to "vCPUs" count, which isn't a useful abstraction if you are doing anything beyond basic stuff. Similarly, network attached storage became the default, and we started treating its latency as Postgres’s latency. These abstractions made choices seem simpler, and in an era of cheap hardware that was fine, since you could just throw more resources at the problem.

Writing code is a small part of everyday's job of a software engineer. The article's table reflects this fairly well.

AI compresses implementation time for an individual engineer, but architecture decisions, design reviews, integration, testing, deployment, and production validation remain largely serial activities. If code generation speeds up by 5x while those bottlenecks don't, you've mostly increased the team's work queue rather than its throughput.

With the current capabilities, models still need constant babysitting and course correction. An engineer who lacks the skills to guide them can end up creating more work for the rest of the team. AI makes it easy to generate code faster than you can understand it, and that cost is paid during code review, debugging, and maintenance by colleagues, whose confidence in that engineer's skills may be affected by his use of AI.

What looks like a productivity gain for one engineer can become a productivity loss for the team as a whole.

Such headlines sell well, but it's the same marketing mantra about ever more powerful models. And what does it say about the company engineering practices of performing tests?

How do you solve multi-device sync and conflict resolution? The model seems simple, but may leak Git semantics to the surface. How does it keep the high-frequency history useful: does it squash it periodically? Durable history and meaningful history are not quite the same thing.