AI Agents at Work: Microsoft Copilot Is Getting Its Own Version of Claude Cowork - CNET
AI Agents at Work: Microsoft Copilot Is Getting Its Own Version of Claude Cowork CNET
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claudeversioncopilot![[P] Cadenza: Connect Wandb logs to agents easily for autonomous research.](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-robot-assembly-8kCUTaKVejALbAywaCQBiC.webp)
[P] Cadenza: Connect Wandb logs to agents easily for autonomous research.
Wandb CLI and MCP is atrocious to use with agents for full autonomous research loops. They are slow, clunky, and result in context rot. So I built a CLI tool and a Python SDK to make it easy to connect your Wandb projects and runs to your agent (clawed or otherwise). The cli tool works by allowing you to import your wandb projects and structures your runs in a way that makes it easy for agents to get a sense of the solution space of your research project. When projects are imported, only the configs and metrics are analyzed to index and store your runs. When an agent samples from this index, only the most high performing experiments are returned which reduces context rot. You can also change the behavior of the index and your agent to trade-off exploration with exploitation. Open sourcing
quarkus-chat-ui: A Web Front-End for LLMs, and a Real-World Case for POJO-actor
Note: This article was originally published on SciVicsLab . quarkus-chat-ui: A Web Front-End for LLMs, and a Real-World Case for POJO-actor quarkus-chat-ui is a web UI for LLMs where multiple instances can talk to each other — built as a real-world use case for POJO-actor . Each quarkus-chat-ui instance exposes an HTTP MCP server at /mcp , so Instance A can call tools on Instance B, and Instance B can reply by calling tools back on A. The LLM backend — Claude Code CLI, Codex, or a local model via claw-code-local — acts as an MCP client that can reach these endpoints. The question was how to wire that up over HTTP, and how to handle the fact that LLM responses take tens of seconds and arrive as a stream. quarkus-chat-ui is the bridge that makes this work. Each instance wraps one LLM backend
I built a jewelry size database for women with tiny fingers in one day
The problem If your ring size is 2, 3, or 4, most jewelry brands don't make your size. I'm 153cm with size 3 ring fingers and a 13.5cm wrist. Standard rings start at size 5. Standard bracelets are 16-18cm. They literally fall off. I got tired of googling "small ring" and finding nothing useful, so I built the database I wish existed. ## What I built A free, filterable database of 31 jewelry brands verified to carry truly small sizes. Filter by ring size, bracelet length, price, material, adjustable/not 31 brand pages with detailed size info 8 size-filter pages (e.g. "all brands with ring size 2") Ring size conversion chart (US/JP/EU/UK) Printable ring sizer tool Site: https://humancronadmin.github.io/tiny-fit-jewelry/ ## The Japanese brand discovery This was the biggest surprise. Japanese
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# Understanding Data Modeling in PowerBI: Joins, Relationship and Schemas.
Introduction. Data Modeling is a good backbone of making Data Science and Analytics in PowerBI very successful. We are going to look into: Data modeling fundamentals. SQL Joins. PowerBI relationships. Facts vs deimension tables. Data schema. The big Question; WHAT is data modeling. Well Data modeling is the process of organizing and structuring data from multiple sources into a logical format for analysis. Here it is all about: The Table connection. Defining relationships. Structuring data into facts and dimension tables. Optimizing performance. Just imagine all this like making your data easy and smooth to read and interpret. SQL joins. This is basically where we use joins to make data jointed from multiple tables using a common column. 1. INNER JOIN. This only returns matching records in
![[D] Offering licensed Indian language speech datasets (with explicit contributor consent)](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-robot-hand-JvPW6jsLFTCtkgtb97Kys5.webp)
[D] Offering licensed Indian language speech datasets (with explicit contributor consent)
Hi everyone, I run a small data initiative where we collect speech datasets in multiple Indian languages directly from contributors who provide explicit consent for their recordings to be used and licensed. We can provide datasets with either exclusive or non-exclusive rights depending on the use case. The goal is to make ethically sourced speech data available for teams working on ASR, TTS, voice AI, or related research. If anyone here is working on speech models and might be looking for Indian language audio data, feel free to reach out. Happy to share more details about the datasets and collection process. — Divyam Founder, DataCatalyst datacatalyst.in submitted by /u/Trick-Praline6688 [link] [comments]
Unnoticed Gemma-4 Feature - it admits that it does not now...
Edit: "it admits that it does not know" (sorry for the TYPO!) Although Qwen3.5 is a great series of models, it is prone to make very broad assumptions/hallucinate stuff and it does it with a great confidence, so you may believe what it says. In contrast, Gemma-4 (specifically I tested E4b Q8 version) admits that it does not know right at the start of conversation: Therefore, I cannot confirm familiarity with a single, specific research study by that name. However, I am generally familiar with the factors that researchers and military trainers study regarding attrition in elite training programs... That is very important feature and it may hint to changing model training routine, where admitting to not know stuff is penalized less than trying to guess and then fail. submitted by /u/mtomas7
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