teucris

Instead of focusing on tattoos, could we work on this being used for bistable displays, like e-ink but using light pulses to transition each pixel?

There are two things you can get from a degree: 1. Knowledge and skills 2. A network and a reputation

While I don’t agree with “it’s not what you know, it’s who you know” - both are critical and just having one without the other isn’t going to set you up for success - I think we don’t do enough to tell young people about item 2.

> In general, I have the feeling that we are hurtling towards a world with less intentionality behind all the things we experience. Everything becomes impersonal, more noisy, etc.

You’re right - but that world is not the end of the story. The intentionality matters. Human creations matter because they connect us. I don’t know how long it will take, but people will build judgement as to what makes for good use of these tools to make meaningful things and expand our creative horizons in deeply human ways. Mind you, there will always be shallow slop. It’ll just take time for creators to learn how to use these tools to make something that isn’t slop.

> those variables were likely not known nor knowable during the design stage.

But they could have included an error factor in the designing process. I thought this was standard for manufacturing. And they could have done more robust testing which, again, I thought was pretty standard for manufacturing.

But agents do keep task lists and check the tasks off as they go. Of course it’s not perfect either but it’s MUCH better than an LLM can offer on its own.

If you are seeing an agent missing tasks, work with it to write down the task list first and then hold it accountable to completing them all. A spec is not a plan.

My only real disappointment with Claude is its flakiness with scheduling tasks. I have several Slack related tasks that I’ve pretty much given up trying to automate - I’ve tried Cowork and Claude Code remote agents, only to find various bugs with working with plugins and connectors. I guess I’ll give this a try, but I don’t have high hopes.

The article specifically notes the following:

>We can get far without worrying about the last 5-10%. The solutions for the last 5-10% could be fossil fuels in the short-term, long-duration storage as it matures, or easily storeable e-biofuels.

This article hits on an important point not easily discerned from the title:

Sometimes good software is good due to a long history of hard-earned wins.

AI can help you get to an implementation faster. But it cannot magically summon up a battle-hardened solution. That requires going through some battles.

Great software takes time.

> while eating ramens

For many, even cutting their budget isn’t enough to pursue what you’re describing. Modern careers in software are very hard to reach for people who can’t afford to wait for a real paycheck, and it drives away a massive group of potential talent.

You may be right if AI truly can never replace devs. But history shows many examples of inadequate technologies getting hammered on by industry until they work due to distorted perceptions of economic gain. I’ve heard stories like this regarding mechanical looms, CNC, and cloud services. My understanding is those all work (decently) now not because they were obviously better, but because economic pressure pushed innovation to make them work, for better or worse.

Software developers should be worried about their jobs, not because these tools are capable of replacing them or reducing a company’s need for human developers, but rather because the _perception_ that they can/will replace developers is causing a major disruption in hiring practices.

I truly don’t know how this is going to play out. Will the software industry just be a total mess until agents can actually replace developers? Or will companies come to their senses and learn that they still need to hire humans - just humans that know how to use agents to augment their work?

> All of the "discoverability" algorithms are specifically and fundamentally about sifting through the millions to find the few that are preferred.

They are fundamentally about finding the content that will generate the most revenue. That changes the dynamics quite a bit.