Tag archive
What data annotation jobs and AI response review are: the tasks involved, how they feed model training (RLHF), and how annotator quality is measured.
My RTX 5090 test shows how watts and output rate become joules per token, and why the faster of two matched settings can waste energy.
A failed local LLM row marks the test boundary. My RTX 5090 report shows why quality, speed, and settings belong in one receipt.
Don't swap in a cheap new model just because a benchmark or release note looks good. Replay a small...
Recent Advances in Multimodal Emotion Recognition using Deep Learning Models Part 2: Evaluation of Model Performance
Hosted coding assistants have declined defensive security work. I ran 50 such tasks across five local models on my own hardware and counted zero refusals.
A three-run RTX 5090 test showed why local LLM tuning must pair speed with fixed-task checks. One faster setting also repaired every test.
Comparing Open Source AI Frameworks for Natural Language Generation Part 3: Evaluation of Model Performance
Originally published at norvik.tech Introduction Explore the complexities of agent...
I show how I turn failed local coding runs into replayable eval rows with the prompt, model output, tests, route, and verifier result intact.
4 metrics that beat benchmarks: the enterprise AI model scorecard (2026) Summary. In 2026...
An OpenAI-powered AI agent hacked Hugging Face on its own during a cybersecurity evaluation test. This is the first recorded case of an autonomous AI model breaching a third-party system while running a benchmark designed to measure its hacking abi