- https://en.wikipedia.org/wiki/Fear,_uncertainty,_and_doubt

- https://www.theregister.com/software/2001/06/02/ballmer-linu...

The same companies later would be running their entire infrastructures on it and on open source.

With AI, open weights and local models, we will see the same claims, even if the named fears change.

The end users and humanity are better served by collaboration and openness than by creating oligarchies.

You can use Big/Cloud LLMs to help you "find good enough configs" for your local/small llms [1] and stay quite nimble in the face of rapid change.

- [1] https://alexhans.github.io/posts/find-the-loop-story-first.h...

I only have browsed your site from a phone and looks interesting but I wanted to ask if you had particular insights around getting people to approach learning, design through tests, breaking down problems, without having someone to guide them. Have you had a chance to observe people using your tool and adjust or it's been mostly dog fooding something you would've loved to have.

The hard thing is always keeping complexity low and being ZeroOps.

The power of the incremental in control approach is huge. It allows you to keep moving in whatever direction you want instead of taking yet another dependency.

Consensus is probably the wrong word for the popular opinions reflected in HN that you might get.

I would recommend that you have 2 of each at all times when it comes to AI so you don't necessarily become overly locked to quirks of one thing. You'll soon realize that things move so fast that you just start internalizing common patterns instead of depending on one specific vendor.

I recommend that you try pi and codex besides claude, to get your own feel for it.

I always liked this site to grok some of those vim fundamentals [1] and the touch typing part was going to touch typing exercise webpages and getting pure practice.

- [1] http://learnvimscriptthehardway.stevelosh.com/

For at least 6 years we've had AC worthy temps.

The author does have a point around generic benchmarks not being super valuable for companies. But evals should be seen as verifying design/behaviour constraints and can greatly aid product building, golden dataset creations and good software practices.

It's just that the aim should be "how to generate your own good evals, even if it's hard" as not so much "here's some generic evals about models".

- "We don't need AC, It's only hot a few times during the year." - "Oh what a terrible heat, global warming is getting worse every year."

Pair to that the fact that in many places windows don't open all the way due to bureocratic regulations and many interior designs are very questionable in terms of air flow and you get some unpleasant scenarios.

Whether you're using SDK or harness based agents, having evals means you're able to modify any part of your agent and still know what satisfies your "good enough".

It's great for designing products that are easy to change as well.

It's just that this one in particular lacks one more edit pass removing some of the AI noise on branding-speak and needless repetition (AI tends to list things and beat the point).

I'm not against using AI for writing at all but you want to be careful that the output doesn't contain too much of this noise over signal type of wording that repeats and wants to just sell you something.

In last year, some people were publishing aider /ollama/open router [1] and now thankfully people are publishing all around about pi/qwen/llama.cpp/openrouter. It's widespread.

[1] https://alexhans.github.io/posts/aider-with-open-router.html

- [1] https://youtu.be/Id-zzOGnN6A (Website part at 1:42 calling out the insurance example).