标签：#AR

An octopus about the size of a golf ball was first spotted in 2015 near Darwin Island. A new study gives it both a formal description and a name.

Aaron Erickson discusses the evolution of AI workflows, shifting from "vibe checking" to building reliable, multi-agent frameworks. He explains how to combine deterministic software guardrails with agentic discovery, optimize agent hierarchies, leverage time-series foundation models, and implement rigorous evaluation pyramids to ensure architecture scales effectively in production. By Aaron Erickson

Deep Research Agentic Systems are AI Agents designed to conduct multi-step research for complex tasks using dynamic reasoning, multi-hop information retrieval, and generate structured analytical reports. Sarang Kulkarni from Thoughtworks spoke at Arc of AI Conference 2026 on how to deploy multi-agent research systems for deep reasoning, and the lessons learned from developing Deep Research Agents. By Srini Penchikala

submitted by /u/esporx [link] [留言]

https://preview.redd.it/ww14mzr2fm3h1.png?width=1890&format=png&auto=webp&s=79873d47ae79c7815ca3e7e91fd43141632174f5 https://www.youtube.com/watch?v=rr_uS4bf0B4&feature=youtu.be trained a 7MB open-source L4 self-driving AI that learns navigation, lane following, and drift recovery directly from visual and sensor input. designed for real-time autonomous driving on lightweight edge hardware like phones and embedded devices, without massive server-scale infrastructure. submitted by /u/moorish-prince [link] [留言]

I’ve spent the last few weeks obsessing over one goal: having a personal, self maintaining AI assistant that costs $0and can be controlled from my phone. It wasn't easy. I started with an AWS Ec2 with 50GB storage and t3.micro memory- minimal setup (using the free credits) and made Oracle Cloud instance ($300 free credits but just for a month so I used it for experimenting with local models) I was using Termius to SSH into everything from my phone At first I used OpenClaw. It was cool, but I spent more time fixing it than actually using it. I almost gave up until I saw a video about Hermes Agent. And i actually found Hermes while looking for how to fix an OpenClaw error on YouTube (thanks NetworkChuck 🙌🏽) He mentioned the exact same frustrations I was having, and that Hermes had been stable for a month. I didn't even finish the video before I pulled the repo. The best part? It had a "migrate from OpenClaw" feature. I was up and running in minutes. The hardest part is the rate limits. If you use cloud models especially for code, you hit a wall fast. My solution? The Fallback Chain. Initially I was using openrouter/owl-alpha (stealth models are usually flagships in testing, like big-pickle is deepseek v4) which has 1M context window and was on multiple rankings. Over time after I transitioned to Hermes, I wanted a bit more customization, while owl alpha was good at tasks, It’s nothing to talk about on roleplay, it just scrapes the surface of the character I set in SOUL md file. On my oracle instance I had been experimenting with local models (keep in mind, if you go local, you’ll be sacrificing speed but privacy. Ofc since the vms don’t have a gpu it would be slower, about 3-5 minutes for a simple response) The one I was most impressed with is Google’s Gemma-4-31b-it It played the role perfectly Buuut if you know Google, you’re familiar with their aggressive rate limiting. So I set up my agent to rotate through providers. I start with Gemma 4 for that perfect personal

Hi everyone, I'm finishing up my proposal for my undergraduate thesis for computer science on sign language recognition, specifically Filipino Sign Language and i want to ask what architecture to use for my methodology that is best, rn im considering Mediapipe Holistic + Transformers or Media Pipe Holistic + Mamba SSM. The only caveat is prev researches already done the first one and im not very familiar with the latter. Which do you think is the best method? Thank you submitted by /u/Unable_Let_6998 [link] [留言]

We're writing a research paper on explainable fraud detection GNN model and in the first step we're creating a basic Graph Neural Network for that. We're using the most famous dataset available on this topic i.e IEEE CIS Fraud Detection Dataset and implemented all necessary feature engineering on that data (although majority of feature engineering is already performed in the dataset). Then we constructed a heterogeneous graph on that dataset. Various transaction features like device, transaction id, amount are embedded as nodes and connected with transaction nodes. But the issue is after training the model isn't performing well. It is producing average AUC of 0.87, PR-AUC of 0.52, recall@5% around 0.57 and precision@5% around 0.37 (We tried GCN, GraphSAGE and GAT, all performs almost same for rest data) Whereas the SOTA models in this topic produce much better metrics. Can anyone tell where potentially we're doing things wrong? submitted by /u/LiveAccident5312 [link] [留言]

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