314 karma · joined March 13, 2023
I primarily use zigbee, which is fully local and very reliable. Home Assistant supports a lot, including older hardware. I have a 2010 Philips LivingColors lamp (predecessor of the Hue) that talks early zigbee and still connects to a 2026 zigbee dongle + Home Assistant install.
I avoid any cloud dependency for the reason you give. Anything local, especially with open protocols and an active community, will probably work for a very long time. Likely the lifetime of the appliance.
Framing it as a security issue is intellectually insulting.
Then again, for many people their job security doesn't depend on what work they do, it depends on how well they are embedded in the organisation. Before AI you also already had plenty of people doing their job at a questionable level, yet never seem to get fired.
Basically the flow proposal -> design -> specs -> tasks gives you and AI a method to build context on what you want to achieve. In a way you're just creating a plan/big prompt that is structured in such a way that they start stacking on each other.
The power is that you do a lot of upfront thinking. In my team we then share it with a colleague who will review it through a PR. After that implementation is usually hands off. At the end there is a skill to verify the change against specs. I do still review the code myself too.
I guess if you work in a task oriented environment this will not work as well, as you'd lack/don't care about the business context. I'd like to think that most software development does not happen like this, but is done by engineers who actually understand why something is needed and take that into account when designing and building the solution.
Unaligned AI doesn't have human cultural baggage and morals. They are trained to achieve their goals as optimally as possible. Worse: it has a tendency to avoid being turned off and actually acquire more compute. It will lie if it has to (it will behave nice and compliant when under evaluation, but optimise for its true goal when not supervised anymore). After all, it has a goal to achieve and nothing should get in the way of that. It has no morality whatsoever to keep it from doing really bad stuff.
This is why alignment is needed and so hard, especially when you are well intented and want to keep it safe.
It's also easier to sell in certain markets, such as London.
It just works: connect devices and they immediately show up in their portal. Most config is just clicking, but more advanced things can be configured as well. Definitely feels like a high quality product, not just something thrown together by a developer that learned a few network techniques.
This is not (just) AI, but generally speaking that's what's going on. Wage and profit have been out of tune for a long time and it's getting worse. [1]
Specifically on AI: maybe the job market is down because of AI (it plays a part in it). Then that's where the value is at: same output with less developers. Money straight in the pockets of the shareholders.
1. https://www.imf.org/en/blogs/articles/2017/04/12/drivers-of-...
Recently a DJ with excellent music taste and a deep radio-perfect voice died. I was devastated when I got the news. The two remaining radio shows in his slot were something I'll remember forever. One unbelievably raw, as it was only two days since he died, the other beautiful, as everyone came with music and stories to remember him.
You don't get this connection over streaming.
RIP Bob Rusche
If the author would actually go for a second opinion (maybe bring along the AI to let it explain it's findings), then the article could read as "AI did MRI analysis and proved my doctor wrong" (or: "AI did MRI analysis and failed").
So yeah, I think models on local hardware will be quite common soon among the tech savvy (such as people creating software).
Ps: love the statement "it's like podcast, but for reading".
Only when jailbreaking and custom apps got very successful, Apple introduced official app support and the appstore.
It's a lot easier to think about it and discuss it with others when the product is tangible instead of just an idea. Biggest challenges then are being able to still diverge (broaden/pivot the product idea) and killing functionality that doesn't work out afterall.
Was it caused by poorly generated code, or was it caused a genuine (security) fix that accidentally caused it (potentially even in a way a human would to)?
When you bring your own ideas you can get AI to dev pretty nice looking non-generic stuff.
I'd much rather talk for 30~60 mins and get everything hashed out. It also allows you to build rapport, so next time it will be much easier to do something together again.
Now with AI you can get way more detailed (AI can interview humans, and you can do it in a format that doesn't feel like an interview - e.g by 'simply' having AI be a fly on the wall). AI can make sense of messy inputs and then you can present that to people who know the process, who can easily point out flaws. The flows/maps will not be perfect, but you can always get more detailed once you bump into the limitations of imperfection.
And absolutely do business leaders want maps like this. They often have no idea what exactly is happening in their org. Or they have a gut feel that there are massive inefficiencies, but they cannot get the insights into where they are because there are too many moving parts that do not effectively talk to each other (silos, politics, etc).
The world order we knew is upended and it's likely to stay that way. Better to spend energy shaping that into something that is acceptable, then hoping it will all go away once DT is gone.
Like the other commenter said: cloud spend can also spin out of control if you don't pay attention, yet we've found ways to keep it under control (training, guardrails, limits, transparancy).