The ultimate 1-click hunter for blocked macOS ports Discussion | Link

Hello Reddit I've been working on QSPR (Quantitative Structure-Property Relationship) analysis for chemical compounds mentioned in the Jean-Claude Bradley Open Melting Point Dataset . Basically the idea is to see how accurate a model can predict melting points of compounds using only topological indices. After some work on the topological indices (feature engineering), each compound was represented by 26 features. I trained a random forest model on the data and got a test r2 score of 0.66 (which is pretty respectable, given the constraints). However, the file size of the model was around 1.23GB. I didn't like it being that big, so I opened up PyTorch to build a custom deep learning architecture that could make predictions as accurately as the random forest but with much smaller file size. After around 2 weeks of research, I build a 270,000 learnable parameter model (1.3-1.4MB according to torchinfo) that got an r2 score 0f 0.6399. Given all this context, I wanted to ask the following question: Should I commit and work on publishing the results, or should I keep working on improving the model? Note: I'm obligated by my university to not give out intricate details of my research before publication, so please forgive me if such details are required for a high quality answer. However, I can give out the metrics achieved by my little deep learning model. Here it is: === Evaluation Metrics (Expected Value) === R² Score : 0.639910 MAE : 41.246754 MSE : 2989.062744 RMSE : 54.672322 NRMSE : 0.083469 MAPE : 11.69% The unit for MAE, MSE, RMSE and NRMSE is Kelvin (K). submitted by /u/AgiGamesYT [link] [留言]

I've written SQL for over a decade. Joins, subqueries, recursive CTEs – the whole deal. Then I tried a graph database (Neo4j). And my relational muscle memory kept getting in the way. I kept trying to "map foreign keys" instead of just… traversing edges. So I built myself a side‑by‑side cheat sheet: SQL → Cypher for everything from basic SELECTs to variable‑length paths that would require recursive CTEs in PG/SQL Server. Turns out, queries like "users who bought this also bought…" go from 30 lines of self‑joins to 6 lines of zig‑zag pattern matching. If you've ever felt frustrated with: multi‑hop join performance LIKE '%...%' on string scans or just the sheer noise of mapping join tables for many‑to‑many …give this 5‑min read a shot. The mental shift alone (relationships as physical edges, not ID matching) changed how I model data – even when I go back to SQL. submitted by /u/ostwal [link] [留言]

Google DeepMind and partners announce a $10M funding call for multi-agent safety research.

Referees for the 2026 World Cup will be wearing cameras positioned at their temples, allowing TV audiences to see a live view of the pitch from a vantage point they never have before.

According to new research, Trionda would show less unpredictable movements in actions such as corner kicks or free kicks. However, in powerful and long-distance clearances it would lose range.

The 48-team World Cup is not the only historic soccer event this year. Four titans are vying for control of video game soccer in the fiercest battle the industry has ever seen.

The organization claims that the FIFA tournament could have impacts on the rights of local people and visiting soccer fans in all three host countries.

The games start June 11 and end with a grand finale in New Jersey on July 19. There are 104 of them. Here’s how to watch ’em all.

Maybe it wasn't a great idea to let 14-year-olds post videos to Spotlight in the first place.

Findings from an exploratory user research study highlighting the unique insights and practical UX recommendations shared by participants with cognitive disabilities.

From anti-drone tech to face recognition, 2026 World Cup stadiums in the US, Canada, and Mexico are subjecting fans to an array of surveillance tech. Here’s what you need to know.

Most US World Cup stadiums are surrounded by surveillance cameras. Want to know if you’re being watched on your way to a match? These maps will help you.

The Argentine national team will be Google’s test bench and technological showcase during the World Cup.

Turn live websites into editable mockups Discussion | Link

Over the last few weeks, while learning LangGraph and agentic systems, I ended up building Co-Founder Memory . It's a stateful AI assistant with: • long-term memory • planning loops • self-correcting RAG • web search fallback • automated timeline summaries • project and preference tracking Nothing revolutionary — many ideas already exist. The goal wasn't to reinvent memory, but to understand how these systems work by actually building one. A lot of concepts only started making sense once I had to connect them together: graph-based workflows with LangGraph memory extraction and storage retrieval and validation loops routing and planning nodes maintaining context across sessions Building it taught me far more than watching tutorials ever did. Repo: https://github.com/Somay-kousis/Co-Founder-Memory I'm currently entering my 3rd year at IIITM Gwalior and looking for ML / GenAI internships . If you're building interesting things around LLMs, agents, RAG, or AI products, I'd love to connect. Always happy to chat with fellow builders as well 🚀 AI #GenerativeAI #LangGraph #RAG #LLM #MachineLearning #Internship

Azure API Management shipped a Unified Model API that lets clients speak one format while APIM transforms requests to Anthropic, Vertex AI, and other backends. Content safety policies now cover MCP tool calls and Agent-to-Agent payloads alongside LLM traffic. Token metrics expanded to track reasoning, cached, and audio tokens across providers. By Steef-Jan Wiggers