A tiny inertial transformer for human activity recognition via multimodal knowledge distillation and explainable AI - Nature
<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFA1dEkyTGxMbktIWmhSYmJLUy1PVTVvN2FsSUI0Z2Q2Q1NtYWw1ZGhEc0wzeXBsakVPUnpqNnpuRnhhVFZYa1Vxb0FfY3pNaEo5VjNYRTV6VVp4T3B6RUM4?oc=5" target="_blank">A tiny inertial transformer for human activity recognition via multimodal knowledge distillation and explainable AI</a> <font color="#6f6f6f">Nature</font>
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Hate Speech Detection Still Cooks (Even in 2026)
The failure case you didn’t see coming In late 2025, a major social platform quietly rolled back parts of its LLM-based moderation pipeline after internal audits revealed a systematic pattern: posts in African American Vernacular English (AAVE) were flagged at nearly three times the rate of semantically equivalent Standard American English content. The LLM reasoner, a fine-tuned GPT-4-class model had learned to treat certain phonetic spellings and grammatical constructions as proxies for “informal aggression.” A linguist reviewing the flagged corpus found no aggression whatsoever. The failure wasn’t adversarial. It was architectural: the model had no representation of dialect as a legitimate register. Simultaneously, coordinated hate communities on adjacent platforms were having a producti
Transforming Raspberry Pi into an AI-Native Edge IDS for SMBs
<h2> The SMB Security Gap: Why the Edge Matters </h2> <p>Small and Medium Businesses (SMBs) are frequently described as the "soft underbelly" of the global supply chain. While large enterprises invest millions in centralized Security Operations Centers (SOCs) and high-end hardware, SMBs often operate with lean IT teams and limited budgets. However, the threats they face—ranging from sophisticated ransomware-as-a-service to targeted lateral movement—are just as potent. The traditional approach of backhauling all traffic to a central firewall is increasingly obsolete in a world of distributed work and IoT expansion. This is where <strong>how to set up IDS on raspberry pi</strong> becomes a critical question for cost-conscious security engineers.</p> <p>In the contemporary digital ecosystem,
DeepSource for Python: Static Analysis and Autofix
<p><strong>DeepSource provides one of the most thorough Python static analysis experiences available in 2026.</strong> Its Python analyzer covers over 150 rules across bug detection, security scanning, performance optimization, and code style enforcement - with a sub-5% false positive rate that keeps findings actionable rather than noisy. Combined with Autofix, which generates ready-to-apply code changes for detected issues, DeepSource turns Python static analysis from a reporting exercise into an automated remediation workflow.</p> <p>This guide covers everything you need to set up DeepSource for Python projects - from the initial <code>.deepsource.toml</code> configuration to advanced features like type checking integration, Django and Flask security rules, and coverage reporting with py
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Walmart expands AI-powered shopping — and checkout — with Google Gemini - axios.com
<a href="https://news.google.com/rss/articles/CBMidEFVX3lxTFBrSFVRNTBJMlZoWVRGNEdJNlpBbUl3RVpPRVlIVWQ0OFhtck5zSzdIRkt1bDQ5aDJTZ1g3SVVDZnNfSm1Nbk1DdGRyc2RqQWJnNkRZYjJlWEU5SGdKUjU3ZkdDekt6bHgxT3dwUEJFMU5URHFy?oc=5" target="_blank">Walmart expands AI-powered shopping — and checkout — with Google Gemini</a> <font color="#6f6f6f">axios.com</font>
Walmart teams up with Google’s Gemini for AI-assisted shopping - Retail Dive
<a href="https://news.google.com/rss/articles/CBMiigFBVV95cUxOX3g3TkoxOTZieXhpOWd2ZnBTNnM2Rl9rZTJ1WmlzMVZhUFlmVWlpWmVyOTZJUV9WcHIyR1VaeGxaQzZDYW1BeDRWbGVIWGx6UWpEdUJ4LXpoZk1YUDNHcnlJNTFKOWxCOXJDNm13V1NnNmFJRjFiM2FKUnp1VkdobmVTZ1NpN2ZEV2c?oc=5" target="_blank">Walmart teams up with Google’s Gemini for AI-assisted shopping</a> <font color="#6f6f6f">Retail Dive</font>
Google’s Gemini AI is getting a bigger role across Docs, Sheets, and Slides - The Verge
<a href="https://news.google.com/rss/articles/CBMiiAFBVV95cUxPMHdiN2dqSUwyNDlzaVRCU1RUSW1iYnZZdmgxVXJtUm9JR2pqbE5LQ3V3eWRZV3htREYwNDMwaThfYVd2RjhhQUZqZWRtVHd3aFhuOFRZMDNRbGQwUmFMTm0wckpLMThLTlZyU2RlX1ZfaGI2WThSMVEtLU9qZXlPSS11dzREUnBv?oc=5" target="_blank">Google’s Gemini AI is getting a bigger role across Docs, Sheets, and Slides</a> <font color="#6f6f6f">The Verge</font>
The Fallback That Never Fires
<p>Your agent hits a rate limit. The fallback logic kicks in, picks an alternative model. Everything should be fine.</p> <p>Except the request still goes to the original model. And gets rate-limited again. And again. Forever.</p> <h2> The Setup </h2> <p>When your primary model returns 429:</p> <ol> <li>Fallback logic detects rate_limit_error</li> <li>Selects next model in the fallback chain</li> <li>Retries with the fallback model</li> <li>User never notices</li> </ol> <p>OpenClaw has had model fallback chains for months, and they generally work well.</p> <h2> The Override </h2> <p><a href="https://github.com/openclaw/openclaw/issues/59213" rel="noopener noreferrer">Issue #59213</a> exposes a subtle timing problem. Between steps 2 and 3, there is another system: <strong>session model recon
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