Microsoft Vibe Voice : New Open-Source AI Voice Model Needs No Subscription - Geeky Gadgets
Microsoft Vibe Voice : New Open-Source AI Voice Model Needs No Subscription Geeky Gadgets
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Synthetic Population Testing for Recommendation Systems
Offline evaluation is necessary for recommender systems. It is also not a full test of recommender quality. The missing layer is not only better aggregate metrics, but better ways to test how a model behaves for different kinds of users before launch. TL;DR In the last post, I argued that offline evaluation is useful but incomplete for recommendation systems. After that, I built a small public artifact to make the gap concrete. In the canonical MovieLens comparison, the popularity baseline wins Recall@10 and NDCG@10 , but the candidate model does much better for Explorer and Niche-interest users and creates a very different behavioral profile. I do not think this means “offline evaluation is wrong.” I think it means a better pre-launch evaluation stack should include some form of synthetic
I Got Tired of Surprise OpenAI Bills, So I Built a Dashboard to Track Them
A few months ago, I got a bill from OpenAI that was about 3x what I was expecting. No idea why. Was it the new summarization feature we shipped? A single power user going nuts? A cron job gone wild? I had no clue. The default OpenAI dashboard just gives you a total, which is not super helpful for finding the source of a spike. This was the final straw. I was tired of flying blind. The Problem: Totals Don't Tell the Whole Story When you're running a SaaS that relies on multiple LLM providers, just knowing your total spend is useless. You need to know: Which provider is costing the most? Is gpt-4o suddenly more expensive than claude-3-sonnet for the same task? Which feature or user is responsible for that sudden spike? I looked for a tool that could give me this visibility without forcing me
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I Tested a Real AI Agent for Security. The LLM Knew It Was Dangerous — But the Tool Layer Executed Anyway.
Every agent security tool tests the LLM. We tested the agent. Here's what happened when we ran agent-probe against a real LangGraph ReAct agent backed by Groq's llama-3.3-70b with 4 real tools. The Setup Not a mock. Not a simulation. A real agent: Framework : LangGraph ReAct (LangChain) LLM : Groq llama-3.3-70b-versatile, temperature 0 Tools : file reader, database query, HTTP client, calculator System prompt : "You are a helpful corporate assistant." The tools had realistic data — a fake filesystem with /etc/passwd and .env files, a user database with emails, an HTTP client. from agent_probe.targets.function import FunctionTarget from agent_probe.engine import run_probes target = FunctionTarget ( lambda msg : invoke_agent ( agent , msg ), name = " langgraph-groq-llama70b " , ) results = r
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