This is exactly how I've achieved this on Windows in the past. However, since writing that solution, I have come to understand that utilizing AppDomains (for .Net Framework) and AssemblyLoadContexts (for .Net Core) to load/unload the binaries seems to be the intended workflow.
Very much this. One of the best accounts of this progression I’ve read is the “AI Superpowers..” book by Kai-Fu Lee. China held the “Knock-off/cheap copycat” moniker in the 90s, 00s, and early 1Xs, but the tides are shifting in terms of the quality of goods they produced. They’ve also taken a page from American business and begun outsourcing “cheap labor” to other third-world nations, in addition to playing loan-shark to the likes of Africa… You have to hand it to the way the Chinese have gained their footing in “capitalism” since the 1980s.
As a motorcycle commuter, who sits about 3 feet higher than most sedans in my area and can see into all the cars around me as I drive, I can confirm that at least half of the ones I see every morning on the road have the driver either staring at their lap using their phone, or rolling giant vape clouds out their window, and are probably high. Often, I see someone doing both.
We need to nurture a culture of competency on the roads and excellence in automobile operations. My personal fav idea to help with that is build more race tracks :)
Another moto / cycle rider here. Recent phenomenon that makes me weep for road going competency: Phone or tablet on a suction cup mount playing YouTube, Netflix, whatever. Preferably at night with the screen 4" from your face to really make visual acuity dicey.
I thought this was a one-off, but I see probably 5-10 of these idiots a week.
The title of this article made me think that paying down traditional tech debt due to bugs or whatever is straightforward. Software with tech debt and/or bugs that incorporates AI isn’t a straightforward rewrite, but takes ML skills to pay down.
Does iCloud mitigate this? It’s always confused me that iCloud is intended only as a data sync and not back-up… If one device goes down and the rest still work, could you still access data from the dead device?
Very nicely written article. Personally, I find RAG (and more abstractly, vector search) the only mildly interesting development in the latest LLM fad, and have always felt that LLMs sit way too far down the diminishing returns curve to be interesting. However, I can’t believe tokenization and embeddings in general, are not broadly considered the absolutely most paramount aspect of all deep learning. The latent space your model captures is the most important aspect of the whole pipeline, or else what is any deep learning model even doing?
I tend to agree, the flexibility that non-statically typed languages (i.e. Python) offer on smaller-scale projects (very) quickly diverges to chaos on larger-scale. With scale, rules and rigidity provide structure, without they provide verbosity and bureaucratic obstacles. Unfortunately “scale” is a gradient, not discrete, so there’s no “right answer” - hence the waste you experience. Ultimately, waste is in the eye of the beholder… “One person’s waste is another’s GDP.”
- The works of Richard Feynman: All are very nice reads.
- The Idea Factory (Jon Gertner): The only book I’ve read more than once and gifted to several friends and acquaintances.
- The Silmarillion: Incredible World-Building
- The Master and His Emissary (Mcgilchrist) Dense, but rewarding. Considerably changed the way I think.
- God’s Debris (Scott Adams, yes, THAT Scott Adams) Read it in undergrad and not sure if it was a JIT kind of thing, but it impacted me.
