Tootfinder

That peace of mind that comes when you finally manage to "bootstrap" all your infrastructure via #IaaC on your own infrastructure 🧘
#devops

Re-reading Virgil Dupras' two intro essays for his Collapse OS project hits quite a bit harder in 2026 than it already did in back in 2021 when I first encountered the project, also because so many developments and political vision failures over these last 5 years have only been accelerating us towards the precipice:
https://collapseos.org/why.h…

Grand coverage of on @… by Jake Edge of Timothy Samplet's talk at FOSSY about bootstrappable builds, The Full Source Bootstrap, Trusting Trust, GNU Mes, GNU Guix and Germ.
https://lwn.net/Articles/1088279/

{ggdist}: Visualizations of distributions and uncertainty #rstats #ggplot

SynAct: A Reasoning-Acting Large Language Model Agent for Adaptive Synthesis Optimization
Fangzhou Liu, Peiyi Han, Jiawei Liu, Yuan Pu, Zhuolun He, Rongliang Fu, Tsung-Yi Ho, Bei Yu
https://arxiv.org/abs/2608.12751 https://arxiv.org/pdf/2608.12751 https://arxiv.org/html/2608.12751
arXiv:2608.12751v1 Announce Type: new
Abstract: Logic synthesis transforms RTL designs into gate-level netlists, where PPA results are highly sensitive to the choice of optimization commands, making synthesis tuning both high-dimensional and expensive. Previous approaches fall into two categories: automated methods, which perform black-box search over fixed action spaces with limited decision-level interpretability, and LLM-based methods, which typically generate static scripts upfront and cannot adapt to evolving circuit states. We present SynAct, an adaptive closed-loop LLM reasoning--acting agent that iteratively diagnoses live synthesis reports and reasons over the current circuit state, retrieved tool knowledge, and historical optimization experience to issue targeted commands. SynAct focuses on improving timing, particularly worst negative slack (WNS), while maintaining balanced area and power trade-offs. Experiments on a commercial synthesis tool across 14 designs show that SynAct reduces average WNS to 27% of that from bootstrap synthesis.
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Beyond headcount and human capital: The Effective Cognitive Population as a decomposable capacity unit for AI-era planning
Kwan Soo Shin
https://arxiv.org/abs/2608.09642 https://arxiv.org/pdf/2608.09642 https://arxiv.org/html/2608.09642
arXiv:2608.09642v1 Announce Type: new
Abstract: National planning counts population, human capital, and artificial-intelligence preparedness in separate ledgers. Demographic accounting has advanced from headcount to skills-adjusted stocks and still debates how much age structure retains once skills are modeled, yet no existing unit carries the conditions under which preparedness becomes productive capacity. This study introduces the Effective Cognitive Population (ECP), a decomposable unit that weights population by capability and by the conditions under which capability is deployed, anchored to the World Bank Human Capital Index Plus (HCI ) and the non-overlapping dimensions of the IMF AI Preparedness Index. The architecture is portable in principle; the case tested here is artificial intelligence, which has a published preparedness index. For 144 countries, HCI becomes a productivity level, AI opportunity uses digital infrastructure and innovation integration, conversion governance uses regulation and ethics, and the benchmark is ECP = N H(1 AC). Against 2024 total output on identical population bases, ECP raises criterion R-squared from 0.849 for the HCI -adjusted stock to 0.882 and lowers leave-one-country-out RMSE from 0.723 to 0.641, with the working-age comparison identical and bootstrap intervals excluding zero. Eighty-nine of 144 countries move at least ten rank positions from headcount, mostly through the human-capital adjustment itself. Results are stable across denominators, vintages, aggregation forms, and a 27-rule multiverse. The direct A by C interaction is not statistically supported, so the conjunction is a planning rule rather than causal complementarity. ECP is a diagnostic ledger whose scope excludes forecasts of population decline and estimates of AI's causal productivity effect.
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