Named after the hundred-eyed watchman of Greek myth, Argus watches the education landscape: spotting new opportunities, pressure-testing the ventures we're building, and tracing every read back to the real-world signals behind it.
Signal 0 shows a 23-year teaching veteran unable to use Google Classroom and recommending paper as an alternative—this is not an edge case but a systemic onboarding failure documented at scale. Signal 17 shows a teacher who has settled for minimal feature use ('you get what you pay for') despite wanting more. Meanwhile, LLM-based conversational interfaces (signals 76, 77) are now capable enough to deliver low-latency, context-aware coaching within a browser session without requiring a human coach on the other end, making the unit economics of personalized teacher support viable for the first time.
Veteran teachers with deep pedagogical expertise but low digital fluency report that current LMS onboarding is so opaque they advocate abandoning the tools entirely, yet districts mandate their use—creating a silent compliance failure where teachers use only surface features or none at all.
Teachers with 10+ years of experience in districts that mandate Google Classroom or Canvas, particularly those in under-resourced schools without dedicated instructional technology coaches
A conversational, role-based onboarding and micro-coaching product that integrates directly into Google Classroom and Canvas via browser extension and mobile companion. Rather than static tutorials, it observes where a teacher is in the LMS workflow and delivers context-sensitive, jargon-free guidance—'You just uploaded a PDF; here's how to make it a student-editable assignment in two taps.' It tracks which features each teacher has successfully used and generates a personalized weekly suggestion to unlock the next highest-value capability, building competence incrementally without overwhelming. Districts license it as a perpetual professional-development tool with usage analytics replacing one-time workshop spend.
The real-world evidence the pipeline drew on to generate this idea.
Too hard to use. As a teacher of 23 years i cannot understand how to use this app. I recommend using paper only in classrooms.
As a teacher using GC, I find it helpful to upload and disburse information to students but it’s slow and clunky. It needs to be more streamlined and customizable but you get what you pay for. I’ll keep using it only because I can upload pdf documents that students can access when they miss class or lose the paper handout I gave them.
needs to update my classes faster
I give this app a 1 star review because of how poor the app is designed. This is nothing about the classroom work or anything, but at thr page where you can remove accounts listed in Google classroom, you can just remove it without needing verification which is terriblely designed. I lost my Google account because of it.
arXiv:2606.05121v2 Announce Type: replace-cross Abstract: Audio is continuous and interactive, yet most Large Audio Language Models (LALMs) remain offline and streaming systems usually specialize in ASR or spoken dialogue. We formalize the Audio Interaction Model, an always-on perceive--decide--respond paradigm that tracks context, decides whether intervention is warranted, and responds without stopping listening. We instantiate it with Audio-Interaction and introduce SoundFlow, coupling streaming-native data construction, comprehension-aware silence/response supervision, dual-loss training, and asynchronous FIFO inference. We also construct textsc{StreamAudio-2M, a 2.6M-item, 302k-hour corpus spanning 7 capability families and 28 sub-tasks, together with Proactive-Sound-Bench. Across 8 benchmarks, Audio-Interaction remains competitive on mainstream audio tasks while enabling spoken-instruction robustness, long-stream interaction, and proactive intervention.
arXiv:2606.02530v2 Announce Type: replace-cross Abstract: Aligning Large Language Models (LLMs) with human values often degrades their general capabilities, termed the alignment tax. Existing methods mitigate this by balancing dual objectives, which heavily rely on massive general-purpose data or auxiliary reward models. In this paper, we argue that, because safety features are inherently sparse within the output distribution, alignment requires localized modifications rather than global trade-offs. To this end, we propose SafeSteer, which performs on-policy distillation confined to safety tokens. First, we construct a safety teacher via activation steering. Based on this teacher, we develop a safety token selection algorithm. Consequently, SafeSteer restricts the reverse KL penalty to these tokens during training to preserve general capabilities. Experimental results across diverse models show that our SafeSteer achieves a superior trade-off between safety and general capability compa