I would definitely agree that data center investments would need to be coupled with energy investments. If this could act as a catalyst for more (sustainable) energy production that would be a net win for all IMO.
Do you feel poison fountain is actually effective? To me it seems like free chaos engineering for ingestion platforms. Wouldn't it paradoxically harden data ingestion?
Not sure about flawed code logic, but it is embarassingly easy to plant false information into models. Make a few static sites with random info, crosslink them, reference them on reddit a few times, then plant the payload there.
I've been trying to solve a lot of the issues brought up here, like personal automation, note taking, article summarizing/indexing, etc. What I've learned so far is that no level of AI-enabled automation will help me with my inherent ADD. All it means is that I have more started projects, but just as many finished ones. It has been a big enabler for things I'm already knowledgeable about, but the cognitive debt issue is real: A machine that thinks for me can't help me think better by itself.
To use your analogy, I would say the "blanket ban" attitude would be more like wishing all viruses would just go away, or have never existed in the first place, which:
1) is an impossible and unproductive attitude, and
2) fails to recognize the important contribution to evolution, genetic diversity, and our immune systems that viruses introduced, not to mention the possible beneficial applications that could exist by understanding it.
Rejecting something without nuance makes you more vulnerable down the road because it prevents you from building an effective immunity. Engaging with it is the only productive way to mitigate its downsides and promote its benefits.
Exactly, my vaccine against GenAI chatbots is to not use them, which is the equivalent of masking when covid vaccines were not available.
I am just waiting for the GenAI vaccines (ideally regulations, practically people rioting in municipal councils against gigantic data centers opening at their door).
My wife bought a Neo and has been very happy with it. I was wary of the 8gb memory limit but she is running claude code doing web development with a reasonable number of tabs open and no noticeable lag, so I'd say its definitely getting a lot of mileage out of it.
It honestly seems good enough that it might cannibalize Macbook Air sales.
After years of incremental upgrades to the Airs, a new entry level M5 Air gives you double the RAM, double the storage, and double the CPU and GPU performance of an M1 Air.
Hopefully used Airs will come up for sale more frequently, as they remain a step up from the Neo.
My M1 Air display just failed on me after 5 and half years of daily 10-16 hours of use. Considered how non repairable some parts in MacBooks are I'd rather recommend people buy more expensive new one. E.g you can find very good offers on new M2 M3 etc.
I wouldn't recommend any hardware, let alone a more expensive version of something, if my experience with it was a catastrophic failure like that in early age.
I am running Claude Code, Claude Desktop, Codex and Docker Desktop on a last generation Intel Air, that admittedly has 12 GB RAM. One has to be a bit careful with more apps. But I look forward to an upgrade. Maybe a Neo, but more likely a second hand M.
I can top that (he said bitterly). My wife is still gorgeous after 30+ years of marriage and is a 10x programmer. But she was happy when given the choice not to work when we married, and hasn’t touched a compiler in decades.
I did well in business, but the family joke is that I’d be a billionaire if I could have monetized her.
As others have said, the main benefit with Python over Rust is library support especially with ML features. The other gap as I see it with Rust is the lack of native flexible UI support. The nice thing about Rust though is it can serve as a very fast and stable core for an app and offload specifics to TS and Python as their strengths allow, so you get the best of all worlds.
My current goto for desktop apps is Tauri, which give us a rust backend and TS fronted (usually React). Local ML features can be easily loaded as a python sidecar. Production bundling can be a little challenging but it seems to work well so far.
Sidenote: Golang is also an amazing language for LLM use, I generally do most of my "infra" stuff in Golang over Rust, but either work fine most of the time.
I've been trying to toe the line here myself, here's how I've been doing it. For context, I pay for a Max 5x subscription.
My main goal is to maximize my subscription token usage while trying to comply with the rules, but its not clear where the line is for automation so I feel like I need to be clever.
- regular development (most token use): all interactive claude mode, standard use case
- automated background development: experimenting with claude routines (first-class feature, on subscription)
- personal non-nanoclaw claude automations (claude -p): uses subscription token, but only called as needed (generally just fix something if something in my homelab infra goes does down, its set up to not fire on an exact cron time)
- other LLM based automations: usually openrouter API key, cheap models as needed
- nanoclaw: all API key based, but since its expensive I keep usage mostly minimal and try to defer anything heavyweight to one of the other automation strategies (nanoclaw mainly just connects my homelab infra with telegram)
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