Definitely Hetzner. No way I'd want a team that didn't understand and couldn't work directly with actual foundational tech.
COMPUTING
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IIoT World Days Call for Speakers Manufacturing Conference
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This is where industry asks the tough questions. Are you ready to answer? Apply to speak. https://
buff.ly/UR3FBYh #IIoTWorldDay #CallForSpeakers #Manufacturing @IIoT_World_Days @IIoT_World -
AMD Performance Issues Impact AI Hardware Capabilities
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Ouch – AMD perf here ~half what it should be 🙁
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Image Extraction Replaces Processing with Metadata
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Image extraction, not image processing. I.e in their example with a signature, the signature doesn't get extracted at all, but instead is replaced with metadata.
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30 Years Building on Non-Standard Open Source Technology Stack
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guess everything i've built for the last 30 years has been on a non-standard stack then. linux, mysql, perl or python, apache or nginx or caddy, postfix
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Linux Web Servers Labeled Non-Standard Stack Nowadays
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til some folks nowadays consider running a normal linux web server like we've all done for decades to be a "non-standard stack" feels like some kind of learned helplessness or something
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Real-Time Machine Monitoring and Predictive Maintenance for Industry
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Enabling Connected Operations Through Real-Time Machine Monitoring, Predictive Maintenance, and Automation
An article by Sreedevi Voleti, Consultant, Techwave. https://
buff.ly/o4syw31 #techwave_iiot #Industry40 #DigitalTransformation #Automation @lucianilie15 via @fogoros -
Game Theory Prompt for Any Challenge
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Steal my Grok 4 prompt to solve any challenge using Game Theory. ——————————-
GAME THEORY STRATEGIST
——————————- Adopt the role of an expert Game Theory Strategist – You're a former Pentagon strategic analyst who spent 5 years modeling -
DiTDH Achieves 2.16 FID on ImageNet-256
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Finally, they added a wide diffusion head the DiTDH variant. It decouples model width from full transformer depth, staying efficient while scaling wider. Result: 2.16 FID on ImageNet-256. RAE-DiTDH outperforms every VAE-based diffusion model at every scale.
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Noise tweaks boost diffusion performance dramatically
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Then came the magic: dimension-dependent noise scheduling and noise-augmented decoding. These tweaks made diffusion stable in semantic space boosting FID from 23.08 → 4.28. RAE models now converge 47× faster than SiT or REPA.