Giving away an extra jetson orin nano. Comment what you need it for and especially if you’re mid project and need it I’ll DM you if I like what you’re building and it’s yours.
MACHINE LEARNING
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AI Error Monitoring Agent: Code and Functionality Detailed
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You can find the code for the error monitoring Agent here: https://
github.com/airweave-ai/er
ror-monitoring-agent
… Here's how it works: – Pulls raw errors from Sentry or Azure Log Analytics
– Clusters them semantically by root cause (20 errors become ~4 actual issues)
– Searches GitHub for the exact code -
Questioning context window utility in AI models
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what makes this paper good is the question it asks, not just the answer we spent years building longer context windows. 128k. 1M tokens. the race was always "fit more in" nobody stopped to ask: is most of what we're fitting in actually helping? turns out the model's own words
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Video Models Over Language Models for Robotics Manipulation Tasks
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Taking inspiration from VideoJAM, would the physical consistency of the generated videos also improve? Accurate action prediction requires physically plausible imagination, conversely physically plausible imagination is best supported when it is consistent with feasible actions. Seonghyeon Ye (@SeonghyeonYe) VLAs (from VLMs) ❌ => WAMs (from Models) ✅ Why WAMs? 1️⃣ World Physics: VLMs know the internet, but Models implicitly model the physical laws essential for manipulation. 2️⃣ The "GPT Direction": VLAs are like BERT (rely heavily on task-specific post-training). WAMs are like GPT (pre-train & prompt), unlocking incredible zero-shot transfer! What I want to see in 2026: 📈 Scaling Laws: We will see much clearer scaling laws for robotics compared to VLAs. 🤝 Human-to-Robot Transfer: Unlocking massive transfer capabilities using video as a shared representation space. 🤖 Zero-Shot Mastery: Moving from short-horizon tasks to long-horizon, dexterous manipulation without task-specific demonstrations. We recently open-sourced the checkpoints, training and inference code. Dive into the research! 👇 📄 Paper: arxiv.org/abs/2602.15922 💻 Code: github.com/dreamzero0/dreamz… 🤗 HF: huggingface.co/GEAR-Dreams/D… — https://nitter.net/SeonghyeonYe/status/2024501978106061056#m
→ View original post on X — @shiqi_yang_147, 2026-03-03 10:53 UTC
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Artificial Intelligence Saturation and the Future of Work
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(Artificial) Intelligence saturation and the future of work #AI #AIio #AIInnovation #ML #DataScience #Futureofwork @HaroldSinnott @fogoros @iainljbrown @NandoDF @katecrawford @drhassanrashidi @YuHelenYu ow.ly/ywzW30sTyEJ
→ View original post on X — @terence_mills, 2026-03-02 22:00 UTC
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Computer Use moving from research to production with impressive demos
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We're soon going to see Computer Use move from research to production use cases.
— m_ric (@AymericRoucher) 2 mars 2026
Standard Intelligence's demos are stunning, the model seems to have a very natural way of interacting with the computer. It can even drive a car through a webapp!
And Anthropic is working on it as… pic.twitter.com/FeGvZVFNeNWe're soon going to see Computer Use move from research to production use cases. Standard Intelligence's demos are stunning, the model seems to have a very natural way of interacting with the computer. It can even drive a car through a webapp! And Anthropic is working on it as
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Software Architecture Proposal for AIGNE Framework
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to be clear about what this is and isn't. this is a software architecture proposal, not a benchmark-beating system. the implementation is in the AIGNE framework with two exemplars: a memory-enabled chatbot and an MCP-based GitHub assistant. proof of concept, not production at
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Three-Stage Pipeline Between Filesystem and Token Window
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between the filesystem and the token window sits a three-stage pipeline. the context constructor selects and compresses context from the filesystem into a token-budget-aware input. it queries recency and relevance metadata, applies summarization, and produces a manifest
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Three Tiers of Persistent Context Explained
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the paper defines three tiers of persistent context, each with different lifecycles. scratchpads (/context/pad/) are temporary working notes scoped to a task. think of them as the agent's rough draft space. episodic memory (/context/memory/episodic/) holds session-bounded
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Reasoning requires basic knowledge for effective search guidance
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You still need a bit of knowledge in any reasoning, just to guide you in where to look. For instance, you'll do better search on geopolitics if you know a bit about the general trends. Qwen just demonstrated that this base level of knowledge can be attained in a very compact