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from my link log —
A design space exploration of async/await.
https://cel.cs.brown.edu/blog/design-space-async-await/
saved 2026-09-15 https:/…

A Design Space Exploration of Async/Await
Every language pitches async/await as synchronous-looking code, and the same two keywords appear everywhere. But behind the shared syntax hide nine independent design decisions, and no two of seven modern runtimes agree on what even the simplest async programs should do! In this paper we map the design space of async/await so you can find out where your language stands.

This is the Wikipedia definition for human intelligence. Artificial intelligence (in the form of LLMs) does literally none of these things.
It doesn't act by itself, it isn't self-aware[1], it doesn't learn[2], it doesn't understand or form concepts[3], it doesn't logic or reason[4], it doesn't recognize patterns[5], plan, innovate, solve problems, make decisions[6], retain information or even use language to communicate[7].
LLMs are a simulated model (aka it's not real) of human intelligence. Models, in scientific parlance, are just that: they don't try to explain how a process works, they try to achieve the same outcome as a natural process (usually in an effort to perhaps learn something about that process).[8]
There simply is no "AI". Anyone telling you about how LLMs are sentient or sapient or will murder us is 100% bullshitting.
[1] There is nothing to be aware about, they don't have physicality. They're literally long lists of numbers.
[2] Models themselves are static, they're the result of machine learning, but can't grow beyond the initial state.
[3] LLMs don't "know" anything. It's matrix multiplications without permanence or embodiment. https://en.wikipedia.org/wiki/Understanding
[4] Because they can't understand or form concepts, they can't reason about them.
[5] This requires memory, which LLMs don't have. https://en.wikipedia.org/wiki/Pattern_recognition_(psychology)
[6] Memory and understanding are required. https://en.wikipedia.org/wiki/Decision-making
[7] All it does it getting an input in the form of a lot of numbers and returns statistically likely follow-up numbers.
[8] "A model is an informative representation of an object, person, or system." https://en.wikipedia.org/wiki/Model

Since it was relevant to a discussion I just had on here and is something most people probably haven't thought about much (unless you've taken one of a handful of philosophy classes), I thought I'd try to lay out a key piece of Descartes' Meditations (#philosophy

Two Brunettes & A Gay
Indulge in an hour of witty banter and lively discussions with these cheeky 30-somethings, along with special guests from the entertainment industry and beyond...
Great Australian Pods Podcast Directory: https://www.greataustralianpods.com/two-br…

Build-Authorized Evidence for Opaque Calls: A Fail-Closed Rewrite-Authority Boundary
Zhonghua Yi (Toka Language Research Group)
https://arxiv.org/abs/2607.18949 https://arxiv.org/pdf/2607.18949 https://arxiv.org/html/2607.18949
arXiv:2607.18949v1 Announce Type: new
Abstract: Detached semantic facts about opaque native providers do not by themselves justify compiler rewrites: rewrite authority must be confined to the accepted fact, selected provider and build, caller, callback environment, observation, and runtime target. We present a build-authorized path-effect interface that enforces this boundary through fail-closed authorization and link receipts. The design separates receipt closure, callback-environment closure, and projection identity, and passes accepted facts to LLVM through a narrow internal API. We use one-hop topology-load reuse as a minimal observable witness of authority, not as the optimization target.
A conservative LLVM consumer reuses a pointer observation only from a noalias root or one constant nonzero projection. Rocq models prove conditional refinement and authority non-amplification under explicit effect, alias, compiler/ABI, and target-resolution premises. We instantiate checked production with Toka: a source-summary gate emits exact LLVM IR, a separate IR checker accepts only a bounded topology-preserving subset, and only accepted IR is compiled into the receipt-bound provider object. A bounded static Darwin/arm64 profile also checks the final direct branch target.
Across issuer-declared readv, recvmsg, and Cairo boundaries, authorized IR retains each opaque call, reduces the relevant loads from two to one, and preserves observed results; mismatched providers, builds, callbacks, projections, and unsupported IR remain neutral. A libjpeg case is rejected because its callback environment is open, while a bound callback singleton demonstrates the supported closure rule. The contribution is a checked deployment-compiler boundary with an explicit trust and applicability frontier, not a uniquely expressive effect encoding or a new load-elimination algorithm.
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@… You may already know about this. Just in case you don't, here!
Point-&-Click Showcase & Steam Event
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Point-&-Click Showcase & Steam Event - Application Form ➚
Point-&-Click Showcase is an upcoming [Trailer Showcase] and [Steam Event] organized by Rusty Lake and Amanita Design. We have one mission: Uplift and celebrate point-and-click games of all genres and the amazing teams behind them! As OG point-and-click Flash devs, we want to highlight the classics, the favorites, as well as promising upcoming titles (and help boost your sales and wishlists!). What is the Important Information? ➚ Form Submission Deadline: September 27, 2026 ➚ Event Dates:…

Träd, Gräs, och Stenar - "Rock för Kropp och Själ" (1972)
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Generalization Error Estimation for Primal--Dual Algorithms in Non-Smooth Regression
Kai Tan, Pierre C Bellec
https://arxiv.org/abs/2608.13870 https://arxiv.org/pdf/2608.13870 https://arxiv.org/html/2608.13870
arXiv:2608.13870v1 Announce Type: new
Abstract: This paper studies trajectory-wise estimation of generalization error for primal--dual algorithms in non-smooth regression. Motivating examples include \(\ell_1\)-penalized least absolute deviations regression and square-root Lasso regression, where the data-fitting loss is non-differentiable and existing risk estimators for gradient-type optimization paths do not apply directly. We develop a general recursive framework that includes the Chambolle--Pock algorithm and related primal--dual splitting methods. We estimate risk by correcting each in-sample fitted value with a weighted combination of past dual iterates. The ideal weights are Stein derivative contractions and depend on the design covariance. We construct replacement weights from observable derivative contractions of the fitted-signal trajectory, yielding a covariance-free, data-driven correction. For high-dimensional Gaussian designs and fixed finite iteration horizon, we prove finite-sample guarantees for both estimators. For square-root ridge, we further establish a matched-Gaussian universality result beyond Gaussian designs. Numerical experiments show that the proposed estimators accurately track the out-of-sample risk along finite optimization paths.
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