IT Expert Blog — since 2017
Practical knowledge on AI and technology for every IT Pro
Articles on local AI models, Python programming, developer tools and IT project management. No fluff — just what works in practice.
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We ran the open-weights Kev decision models on an AMD Radeon PRO W7900 under Windows. Accuracy matched the published model cards to three decimals; speed landed in NVIDIA L4 territory.
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RAG framework ranking on SEC filings: seven frameworks, 28 modes, one frozen scorecard
The final ranking of the series: LightRAG, GraphRAG, RAGFlow, LlamaIndex, Haystack, R2R and kotaemon on 33 SEC filings and 40 cross-document questions,…
kotaemon benchmark on one GPU: hybrid mode is full-text search, and four builds gave four indexes
kotaemon v0.12.0 on 33 SEC filings, four generations on four containers: vector, full-text and hybrid retrieval answered 12, 15 and 14 of…
R2R benchmark on one GPU: hybrid search returned semantic search, and the search limit decided 11 or 22 of 40
Four modes of R2R 3.6.6 on 33 SEC filings, each on its own freshly built pod: as shipped it answered 11 of…
Haystack benchmark on one GPU: BM25 alone beat dense search, and 200-word chunks answered 27 of 40
Five retrieval recipes of Haystack 3.1.1 on 33 SEC filings, each on its own freshly built index: BM25 alone answered 15 of…
LlamaIndex benchmark on one GPU: smaller chunks beat the reranker, and document recall picked the wrong winner
Five retrieval configurations of LlamaIndex on 33 SEC filings, each on its own freshly built index: 19 of 40 answers correct with…
RAGFlow benchmark on one GPU: RAPTOR beat the knowledge graph four to one, and neither is a retrieval mode
A measured RAGFlow benchmark on 33 SEC filings with a local 27B model: 67.5% document recall, 13 of 40 answers right with…
GraphRAG benchmark on one GPU: 268 communities, 12 hours, one answer in five
A measured GraphRAG benchmark on a local 27B model: the community graph retrieved the evidence for 100% of 40 cross-document questions and…
LightRAG benchmark on local hardware: 33 SEC filings, one knowledge graph
A measured LightRAG benchmark on a local 27B model: the graph reached the evidence for 100% of 40 cross-document questions, yet answered…
Running LLMs on Your Own Hardware: From “Will It Even Fit” to a Model That Does Your Work
Every guide to running language models locally begins with installation. Installation is not where people get stuck. They get stuck one step…
Python for Data Work: From a Spreadsheet Somebody Sent You to a Report Somebody Reads
Most Python data tutorials solve one link in a chain. Read a CSV. Draw a chart. Write a PDF. Each one ends…
RAG golden set: extracting benchmark questions with an LLM
How we turned 33 SEC filings into 40 benchmark questions that genuinely require combining documents. The model proposed 214; ten machine checks…
RAG on SEC filings: anatomy of a benchmark corpus
Before comparing seven RAG frameworks, we measured the data they will face. 33 NVIDIA filings, 749,859 tokens, and eight repeatable patterns of…
NVMe vs SATA LLM model loading: 10 runs per drive, measured
A 19.92 GiB Qwen model loaded 2.25x faster from NVMe than SATA, yet time to first token was identical. Cold-start benchmark with…
Windows won’t boot after cloning: five real traps and their fixes
Five failures from one real SATA-to-NVMe migration: the NTFS dirty bit, duplicate EFI GUIDs, a blocked Extend Volume, a disabled controller and…
