mark_l_watson

I agree that pi is very good, don’t agree that it is the last harness (I have written 4 harnesses myself this year, fantastic experience.)

I mostly use local models. I use pi with the strongest models my 64g mac can handle, and for fast light work I use ‘pool’ with Laguna 2.1 XS 6 bit quant. I am happy :-)

Nice story! As an aside: my wife and I only buy pasta imported from Italy because it is simply so much better tasting than pasta made with commercial US-grown wheat. I don’t know why.

The food industry in the US is pretty much messed up. We try to only buy fresh produce and fresh fish, staying away from processed food - but it is difficult. I read something about the composition of the modern Butter Finger candy bar (fake chocolate, lots is chemically processed seed oils) compared to the almost wholesome ingredients used 30 or 40 years ago.

Indeed, recommendation algorithms have some positive benefits and a lot of downsides also. As a deep learning practitioner I worked on recommendation systems for two companies, but as a human being I have my own way of dealing with them: I like to once or twice a month dedicate some time to finding good content to consume: my wife likes streaming media so we have all the subscriptions, so I look around for about three movies that look good and download them so when I want to watch something I choose from my downloads. I spend time on Kobo (eBooks) and Libre.fm (audio books) to make sure I have purchased good reads that are all queued up for my enjoyment.

Finally, Apple has a great tool called ScreenTime that I can set limits on HN, YouTube, etc. I set a 15 minutes a day limit for each, but I can always add 15 minute increments if I find a good rabbit hole to explore.

Nice! I have been using herdr for a few months, love it and for me it is a step up from years using tmux. The new native Herdr app is yet another step up. I use multiple Macs with Tailscale connections and the Herdr app makes jumping around to different projects and different machines easier.

I think I bought the Emacs Manual from Richard Stallman in 1979, give or take a year. I still live in Emacs, even writing my own AI coding harness inside Emacs.

I suspect that I am slightly more productive in VSCode for large code bases, but it just makes me feel happier and content to use Emacs.

This is just a hunch since I have not used Argon very much, and I have not used it for coding (deepseek 4.1 flash 1017 with dsh, and qwen 3.8 max are so good for coding tasks that local models have difficulty with).

My hunch is that Google will end up being best for non coding tasks. Google can concede coding to other vendors who pay Google for infra.

While I agree with you in general, I think the real value of AI (for me, I wouldn’t want to try to change anyone else’s mind on this) lays in three areas: 1. all the little wins saving time at little tasks like analyzing a Haskell, Java, etc. error stacks; and all sorts of little time wasting problems. All those saved minutes add up. 2. Brainstorming before coding, and even before design. I like to write up ideas in a Markdown file and then ask one or two strong AIs to push back, to try to fill in what I might have missed. 3. Non-tech thinking: without any formal training, I enjoy reading philosophy. I frequently ask for second-takes from Gemini on what I am reading; once again, I am looking to just improve my own understanding.

AI should make us better, not just churn out trillions of lines of code.

The very idea of massive vibe coding efforts, where devs go to sleep with a hoard of agents churning away, makes me uncomfortable.

Even the Alibaba $6/month plan supplies a reasonable amount of API use, and Qwen 3.8 Max is a killer model for long time frame agentic use.

Literally everything I do is Open Source software and Open Content writing so I am fine with the lack of privacy using direct Chinese APIs. And, for a little more spend, just use US companies like Fireworks.ai or Ollama Cloud for more privacy, or run on-premise locally and get the best grade of privacy.

As of this morning, that does not work for me.

Another new annoyance: the Settings app has 2 red notification icons because Apple is trying to sell me extended AppleCare. Apparently no way to turn off these notifications.

Have you used Nemotron-3.5-lightening? I don’t use it as much as Poolside’s (excellent!!) Laguna XS 2.1 6bit, but the new Nemotron model is good.

I think NVIDIA does want small open models running on-prem to explode as a market! Lots of smaller GPU installations for companies who wisely want on-prem inference.

Of course NVIDIA will also keep making a ton of money selling to hyper scalers, but not forever: Chinese chips are getting better, Google, Microsoft, Amazon, etc. designing their own inference chips.

NVIDIA is handling this brilliantly.

Maybe? I am using a 3 bit quant for Qwen Next Flash in a 64G Mac using Sushi and the results for coding, design, refactoring, etc. are very good.

That said, after being satisfied with Poolside.ai’s Laguna XS 2.1 4 bit quant, when I upgraded to a 64G Mac, I now only run the 6 bit quant and the results are better.

Unfortunately, I think that benchmarks are not useful for me, and I need to take time seeing what works for me: this is one reason that when I find a combination of model, harness, etc. that works for me, then I tend to use it for a month or two before looking around for new stuff.

Great product idea. I have not paid OpenAI for a subscription for a couple of years, I’m tempted now. I do about 75% of my work using local models and most of the rest using the deepseek 4.1 flash API. That said $20 to experiment with this new product for a month sounds like a pretty fun idea.