Tootfinder

Back when I was diagnosed with #diabetes, I've found two apps to help me. Both were proprietary.
The first one came from the glucometer's manufacturer, and it featured the ability to copy its readings over Bluetooth. It also had a pretty useful bolus calculator. However, it was also annoying in a number of ways: it required an account with all the implied data sharing, was premiumware (though using their glucometer implied a free "pro" version), was quite childish in design, didn't respect disabled animations or dark theme.
The second one was an independent Polish app to compute carbs from meals. It wasn't perfect, but it had a rich database of products and quite a few convenient features (like copying meals or calculating carbs based on recipes). Unfortunately, the authors went full way into AI hype, and while I didn't use any of the "AI" features (they were premiumware anyway), the app itself was becoming increasingly crappy.
Eventually, this motivated me to look for a new app. I've settled for #Diaguard. It's just got the basics: meal composition and computing carbs, plus letting me log blood sugar and insulin doses manually. It used to have a bolus calculator before I used it, but it was removed over legal concerns. Still, it's open source, it's nice, it's got no crap and it respects the dark theme. And honestly, given that I've already reached the point of overriding bolus calculator, I've figured out it's enough for me.
In fact, it's so "enough" that I'm not even using it regularly. I mean, if my blood sugar is predictable and my meals are predictable, there's no reason to waste time logging them. The app is there to assist me when I need it, not force me to use it.
I still keep the two other apps installed. The first one in case I had doubts over insulin doses and wanted to use the bolus calculator. The second one over my database of meals; though I had already exported it, so I just need to figure out if the export is complete enough.
#Android

The "AI Industry" (to the degree that it exists) has burned billions of dollars with no real hope of recuperating that. Models offered by services, charging huge amounts for inference, don't really offer much, if anything, above open source models run locally. Many of the claims made by these companies, and their supporters, have turned out to be lies. It seems as though the whole thing is a huge grift.
Investments will likely never be recovered. We've already seen a bail out in the form of military contracts. We will see much more public money dumped in to these technologies. In fact, threatening workers and suppressing labor strength is so strategically valuable that I expect LLMs to just be publicly funded (by being folded into the military industrial complex).
On these grounds, it can be easy to reject the technology outright. After all, how can anything useful not be profitable?
Even for radicals, capitalism can cloud our lens. The fact is, the dominant technologies of our age tend to not be profitable. Or rather, they are "publicly funded, privately profitable" (/hums in propagandhi/). Just like oil.
Outside of Saudi Arabia, oil is mostly not profitable to get out of the ground. Even there, it had only historically been profitable because of massive global military investments. The oil-centric world we have today is largely built on, and kept in place by, massive government subsidies. Roads, street parking, and direct subsidies to oil companies are all massive investments that tie populations to the resource at the heart of the military industrial complex: oil.
Oil was strategically important, because you can't run a modern military without it. The fossil fuel economy doesn't exist because fossil fuels are so cheap, but because modern militaries are only really possible by militarizing the population.
Unprofitable things *are made profitable* to serve the strategic interests of authoritarian systems. Though we have better alternatives to most oil-based products, it would be hard to argue that oil is not a useful resource. We can acknowledge it's strategic importance while also recognizing that the elimination of oil is essential to the survival of humanity.
Returning to "AI," we're seeing the same type of thing but it's more obvious. After the crypto grift, this seems to just be another way to transfer money into the pockets of the rich.
But crypto wasn't exactly a grift. It had a strategic function to power. It wasn't what we were told it was. It didn't free us from central banks. It wasn't a new way to invest. It wasn't anonymous. But it did create a new way to bribe politicians. It did make it easier to funnel public money into the pockets of the far right.
"AI" can similarly be mostly a grift. It can fail to do most, or all, of the things it claims, and it can still fulfill strategic functions. If we dismiss it as "just another grift," I think we miss a lot. "Just a grift" isn't something sustainable. It is something that will die out. It's something we don't need to resist because it is self limiting. But a strategy is something different. A strategy is more complex. A strategy will be sustained, at any cost. A strategy must be actively resisted.

Circular Economy Synergies and Trade-offs in Data Centres
Vlad C. Coroam\u{a}, Oana Dumbrav\u{a}
https://arxiv.org/abs/2608.04571 https://arxiv.org/pdf/2608.04571 https://arxiv.org/html/2608.04571
arXiv:2608.04571v1 Announce Type: new
Abstract: This report analyses data centre (DC) sustainability and circularity, revealing existing synergies and trade-offs:
The PUE is too coarse, mixing cooling and power provisioning. It wrongly attributes server fan consumption and transformation losses to IT energy. It does not measure compute but infrastructure efficiency, which is already outstanding. Compute energy, however, is exploding. Better energy metrics for DCs would thus cover i) compute efficiency, ii) transformation efficiency, and iii) cooling overhead.
Trade-offs exist between cooling energy and water as well as on-site and upstream water: Consuming water on-site lowers the cooling energy, which also lowers the water consumed upstream in power generation. For 'wet' electricity, there is little competition: It is worth spending more on-site energy to save both electricity and related upstream water. For 'dry' electricity, there is a trade-off.
Waste heat recovery brings energy circularity but has limited uses and is not the same energy quality, a fact not reflected by current metrics. A better metric would consider the avoided energy through heat recovery instead of the amount recovered.
Material circularity can be achieved by interpreting the 9R framework in the context of DCs. Circularity-enhancing measures can be categorised into product design, process design and business models, choice of materials, and operating conditions. Together, they have effects across all circularity levels.
The relation between DCs and the power grid is complex. Modern DCs present new challenges for the grid. Mitigation includes battery storage and onsite generation. These measures have, in turn, further consequences, both beneficial and detrimental. They can offer grid flexibility as well as innovations in the field of energy. But they also bring noise, pollution, and GHGs, and compete with the energy sector for resources.
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Automatic Field-of-View Adjustment for a View-Expansive Microscope via LSTM-Based Gaze and Pipette Motion Interpretation
Kenta Yokoe, Takuya Hara, Tadayoshi Aoyama
https://arxiv.org/abs/2608.10401 https://arxiv.org/pdf/2608.10401 https://arxiv.org/html/2608.10401
arXiv:2608.10401v1 Announce Type: new
Abstract: Intracytoplasmic sperm injection (ICSI) operators frequently adjust the field-of-view (FOV) during procedures, which interrupts workflow and increases procedure time. Conventional microscopes require manual objective lens switching and illumination adjustments to achieve different FOV sizes. We propose an AI-based automatic FOV adjustment method integrated with a view-expansive microscope. This microscope enables the simultaneous acquisition of a large FOV and high-resolution images using a single objective lens through multiview imaging with galvanometer mirrors and high-speed vision, thereby eliminating the need for physical lens exchanges. Our method utilizes a long short-term memory (LSTM) model to predict the appropriate FOV size based on real-time analysis of the pipette's position and velocity, combined with the operator's gaze position. The AI model is trained using ICSI procedure data from an expert with over five years of micromanipulation experience. Experimental evaluation with novice operators reveals that the proposed automatic FOV adjustment system significantly improves the ICSI procedure speed, reducing the average task completion time from 60.5 to 48.0 s (p < 0.001). The experiments also demonstrate that this improvement enables novice operators to achieve ICSI working speeds equivalent to those of expert operators.
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STCC: A Unified Source-Channel Semantic Token Coding Framework for Semantic Communications
Zhicheng Bao, Chen Dong, Sen Wang, Long Liu, Nan Ma, Hao Chen, Xiaodong Xu, Yinqiu Liu, Ping Zhang
https://arxiv.org/abs/2606.11819 https://arxiv.org/pdf/2606.11819 https://arxiv.org/html/2606.11819
arXiv:2606.11819v1 Announce Type: new
Abstract: Deep Joint Source-Channel Coding (JSCC) has emerged as a promising paradigm for overcoming the ``cliff effect" in wireless communications. However, existing Deep JSCC frameworks operate directly on raw analog data such as image pixels rather than the discrete semantic tokens that foundation models require. Moreover, traditional systems employ fixed, hand-designed constellations that treat all tokens equally, leading to catastrophic random errors under channel noise. In this paper, the Semantic Token Codebook Communication (STCC) is proposed as a unified source-channel semantic token coding framework designed to transmit the discrete semantic tokens of foundation models over noisy channels. The core of STCC is the Semantic Token Codec (STC). It accepts discrete tokens as input, which maintains compatibility with foundation models while employing a residual multiple layer perceptron, i.e., MLP-based encoder that learns geometrically structured constellations optimized with a triple-loss objective. This learned mapping forces the channel topology to align with the semantic embedding space, ensuring that channel noise results in topological errors rather than random corruption. This phenomenon is theoretically and empirically characterized, identifying ``Semantic Drift" in symbolic modalities and ``Structural Distortion" in perceptual modalities, where errors shift predictions to semantically or structurally similar tokens. Extensive experiments demonstrate that STCC significantly outperforms traditional systems in low-SNR regimes, effectively converting channel noise into semantic variations without requiring receiver-side modification.
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ProtoGIB-Workload: Learning Workload-Specific Neural Topology Prototypes across Subjects
Yuzhe Zhang, Yixi Zhang, Shengdian Jiang, Chengxi Xie, Jihong Wang, Huan Liu, Man Yao, Minnan Luo, Chao Shen
https://arxiv.org/abs/2608.10647 https://arxiv.org/pdf/2608.10647 https://arxiv.org/html/2608.10647
arXiv:2608.10647v1 Announce Type: new
Abstract: Reliable electroencephalography (EEG)-based mental workload recognition is crucial for adaptive human-centered systems, yet practical deployment requires models to generalize to users unseen during training. Although functional connectivity graphs are widely adopted to capture workload-related neural interactions, they inherently entangle task-relevant structures with subject-specific physiological traits and sample-level noise. This entanglement often leads models to learn structural shortcuts, severely degrading cross-subject generalization. To address this, we propose ProtoGIB-Workload, a novel framework that explicitly regularizes and aligns graph structures for subject-independent workload recognition. Our approach introduces a Stochastic Graph Information Bottleneck (SGIB) to compress dense correlation priors into compact, task-relevant subgraphs, filtering out input-related redundancy. Crucially, to prevent the retention of subject-specific spurious edges, we propose a Class-Conditional Topology Stabilizer (CTS). Leveraging the fixed electrode coordinates of EEG data, CTS operates directly on graph-generation probabilities to encourage consistent edge-generation statistics across different subjects sharing the same workload class. Extensive experiments on two public EEG workload datasets and one in-house EEG cognitive load dataset of air traffic controllers under strict leave-one-subject-out (LOSO) protocols demonstrate that ProtoGIB-Workload significantly outperforms state-of-the-art temporal and graph-based baselines, improving the cross-subject Macro-F1 score by an average of 5.15% (up to 6.34%). Further analyses confirm that our method successfully extracts stable, cross-subject consistent neural connectivity patterns.
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Collaborative System Failure Prognostics via Federated Longitudinal-Survival Modeling
Fan Yang, Madelyn Weller, Dimuthu Fernando, Hila Livneh, Yuxin Wen
https://arxiv.org/abs/2607.26038 https://arxiv.org/pdf/2607.26038 https://arxiv.org/html/2607.26038
arXiv:2607.26038v1 Announce Type: new
Abstract: Time-to-event modeling provides a systematic framework for estimating time-dependent failure risk, reliability, and remaining useful life (RUL) from longitudinal condition monitoring data. However, applying these models to distributed prognostics remains challenging because sensor trajectories and failure-time records are often stored across organizations or operational sites and cannot be centrally pooled due to privacy or proprietary constraints. Moreover, the classical Cox proportional hazards model relies on a nonseparable partial likelihood involving global risk sets, making direct optimization difficult under standard federated learning protocols. This paper presents a federated longitudinal-survival modeling framework for collaborative system failure prognostics. The proposed framework combines longitudinal sensor representation learning with a client-separable discrete-time hazard objective, enabling multiple clients to collaboratively train a prognostic model without sharing raw sensor measurements or individual failure records. Time-dependent representations extracted from multivariate sensor histories are used to estimate interval-specific failure hazards, reliability curves, and system RUL. Experiments on the four C-MAPSS turbofan engine degradation subsets under simulated decentralized settings demonstrate that the proposed framework consistently improves prognostic performance over isolated local training while maintaining performance comparable to centralized training across heterogeneous operating conditions and failure modes. These results demonstrate the potential of federated longitudinal-survival modeling for collaborative, data-aware condition monitoring and system failure prognostics.
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ROLoad-PMP: Securing Sensitive Operations for Kernels and Bare-Metal Firmware
Wende Tan, Chenyang Li, Yangyu Chen, Yuan Li, Chao Zhang, Jianping Wu
https://arxiv.org/abs/2608.13287 https://arxiv.org/pdf/2608.13287 https://arxiv.org/html/2608.13287
arXiv:2608.13287v1 Announce Type: new
Abstract: A common way for attackers to compromise victim systems is hijacking sensitive operations (e.g., control-flow transfers) with attacker-controlled inputs. Existing solutions in general only protect parts of these targets and have high performance overheads, which are impractical and hard to deploy on systems with limited resources (e.g., IoT devices) or for low-level software like kernels and bare-metal firmware. In this paper, we present a lightweight hardware-software co-design solution ROLoad-PMP to protect sensitive operations from being hijacked for low-level software. First, we propose new instructions, which only load data from read-only memory regions with specific keys, to guarantee the integrity of pointees pointed by (potentially corrupted) data pointers. Then, we provide a program hardening mechanism to protect sensitive operations, by classifying and placing their operands into read-only memory with different keys at compile-time and loading them with ROLoad-PMP-family instructions at runtime. We have implemented an FPGA-based prototype of ROLoad-PMP based on RISC-V, and demonstrated an important defense application, i.e., forward-edge control-flow integrity. Results showed that ROLoad-PMP only costs few extra hardware resources (< 1.40%). Moreover, it enables many lightweight (e.g., with negligible overheads < 0.853%) defenses, and provides broader and stronger security guarantees than existing hardware solutions, e.g., ARM BTI and Intel CET.
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