Workstation 部落格
LLM
標記為 LLM 的文章。
Private AI Hosting in the UK: AWS Bedrock vs Microsoft Azure vs Your Own GPUs
Where is customer financial data processed, and what does each AI call cost? A comparison of AWS Bedrock, Microsoft Azure, UK and EU AI providers and your own GPUs for a typical UK finance workload, with costs at three volumes, a data-residency workflow and the break-even point for a dedicated GPU.
Workstation閱讀 
NVIDIA RTX PRO 6000 Blackwell Server Edition: One GPU for AI, Graphics and Simulation in a UK Server Room
96 GB of GDDR7, FP4 Tensor Cores and RT Cores in a standard PCIe card: what NVIDIA's RTX PRO 6000 Blackwell Server Edition does, why it suits companies running private AI in rooms they already have, the three system sizes from a four-GPU 2U server to a 32-GPU rack like AMAX's RackScale 32, and what to check before ordering.
Balinder Walia閱讀 
Build AI Agents with NebulaDB: Semantic Search, RAG and a Visa AI Agent
How we built semantic search, RAG and a Thai Visa AI Agent on NebulaDB, with real queries, query plans and retrieval scores measured on our 8.3M-document production server, and the guards that keep private data and invented numbers out of the answers.
Balinder Walia閱讀 
Private AI for Law Firms: Choosing an Offline-First Workstation Your Clients Can Trust
How a law firm chooses a private-AI workstation that runs with no internet connection: who uses it, which model, how much GPU memory, how isolated it must be and who sets it up. With real prices from a real quote, where it was over-specified, and the Starter, Workforce and Enterprise builds we would actually ship.
Balinder Walia閱讀 
TuneBench: How Well Can AI Agents Calibrate Power System Models?
TuneBench tests LLM agents on calibrating generator and battery models within 15 simulations. Only Opus 5.5 and GPT-6 hit median R² above 0.995 in every test. What that figure means, its limits, and how to build the same kind of benchmark.
Agentic AI in banking: efficiency you can defend to a risk committee
Banking AI agents rarely fail on one bad reply; they fail across a session. How to scope tools, test whole conversations against mock systems and measure reliability with pass^k, so the time saved survives audit and production.
Wikipedia banned AI-written articles: what teams that reuse it should change
English Wikipedia now prohibits LLM-written article text, yet volunteers still flag a steady share of new drafts as AI-generated. If your search, chatbot or knowledge base reuses Wikipedia, here is how to pin revisions, watch edits, review changes and attribute correctly.
DynaCI and runtime-grounded CI: better GitHub Actions workflows
DynaCI, an October 2026 research preprint, drafts GitHub Actions workflows from traces of real local runs. What its results show, what they do not, and a playbook to lint, test, harden and review every workflow you ship.
Observability for Agentic AI: Tracing, Evaluating and Auditing Agents
AI agents fail quietly: a confident answer from the wrong document, a tool called with the wrong argument. Learn how to trace every model, retrieval and tool step with OpenTelemetry, attach quality scores, and keep the records auditors ask for.
