What a great essay. Extremely long and it did lose my attention a bit but it conveys something important. The impact is not measured in leaps and bounds but in what you lose along the way. Working on the code for me meant I had an intuition of why things were done a certain way, track down those odd behaviours by knowing where to look. Analysing bugs and thinking of features doesn't take into consideration the strength of the architecture anymore, unless you keep that effort of understanding up.
He has a Lightphone which he hates. That's how I know it is working. He built a gaming PC last year, so I am aware that Pandora's box is open there. But look, it has been thirteen years of playing whackamole with YouTube. I can't keep him off it. They watch it at school. They give him chromebooks that I have no control over. And the stuff I do have control over the control is tenuous. I have largely given up. He is pretty much grown now. But man, has it felt like gaslighting when people are like, "Do parenting. Use the settings. Get a dumb phone. Problem solved."
Deep Computing have started taking orders for the final product and the Preorders are shipping within the next 6 weeks. They will be shipping from China I expect, but it's a proper shop front.
And then all the heuristics you've learnt change under you and you're stuck doing 100-1000 more hours of learning with a drop in quality during that time.
KiCad sounds to me like a great target for a project based Nix Shell install.
Always have the right version for it "locked". It works well with most tools except those that save stuff in the .config folder as it messes up isolation.
If you find the nix language daunting, for basic stuff like nix shell setup its easy but also LLMs are good for it.
The pin mapping barrier was quite off-putting to me. However I've been tracking progress in the Zephyr RTOS project and the whole line is getting better support by the day
We've deployed mattermost at my company because it meets most requirements that slack did minus the SSO. Surprisingly used by some big government agencies (NASA/USAF)
Review: An anonymous "distinguished CEO and engineer" suggests if you can't complete a feature in a day, delete your progress (except for tests) and start again the next day.
The author then recounts advice he gives to juniors, which is to stash the work and rewrite it, claiming that the next day the work will be rewritten in 25% of the time and 2x quality. This is unsubstantiated though. For juniors this suggests it will help them develop their capabilities to reason about implementations of problems without needing to face a a large amount of them.
The author then gives another advice which is to ask for a solution to a problem then after the initial proposal, ask for a 24h solution. This is meant to generate "the real solution". He likens it the a path algorithm heuristic to reach your goal quicker.
Overall the methods are not well discussed in terms of pros and cons, nor substantiated with experiments.
Opinion: I think they may help some juniors who need to build up experience and may become stuck in development patterns. But they would rarely be useful to develop someone to be a senior, if all they do is chase fast implementations. In a way the post gives conflicting advice: write twice and write better, and think twice and think about the fastest way to achieve the goal, instead of engineering a problem.
The author hasn't really convinced me of these approaches, and especially the last one smells of eXtreme Go Horse.
Review: Chonkie is an MIT license project to help with chunking your sentences. It boasts fixed length, word length, sentence and semantic methods. The instructions for installing and usage are simple.
The Benchmark numbers are massaged to look really impressive but upon scrutiny the improvements are at most <1.86x compared to the leading product LangChain in a further page describing the measurements. It claims to beat it on all aspects but where it gets close, the author's library uses a warmed up version so the numbers are not comparable. The author acknowledged this but didn't change the methodology to provide a direct comparison.
The author is Bhavnick S. Minhas, an early career ML engineer with both research and industry experience and very prolific with his GitHub contributions.
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