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LiteParse is an open-source PDF parser that extracts text with spatial positioning and bounding boxes entirely on your machine, running 2-5ms per page without cloud dependencies or LLMs. It handles PDFs, Word docs, spreadsheets, and images, with optional OCR for scanned content.
- Benchmarks show LiteParse outperforms free competitors (pdf-inspector, opendataloader, markitdown) across three major document parsing tests, scoring 0.389 on ParseBench vs 0.283 for the next best model-free tool.
- Speed comes from avoiding ML models — uses PDFium for text extraction, optional Tesseract or PaddleOCR for scanned pages, and spatial layout reconstruction via grid projection.
- Available in Python, Node.js, Rust, and browser (WASM) with CLI tools; includes worker pool mode for parallel parsing with per-document timeouts to prevent pipeline stalls.
This article argues that Ollama’s custom backend runs 30–70% slower than llama.cpp, locked users’ models in a proprietary format, and dropped proper attribution before grudgingly reverting. It also criticizes Ollama Cloud’s reliability and warns that the platform has strayed from its local-first, open-source roots.
- Ollama's custom ggml fork runs 30-70% slower than llama.cpp (e.g., 30 vs 52 tokens/sec on RTX 5090 with Qwen3 Coder 32B), despite reverting to upstream llama.cpp in v0.30.0-rc15
- Ollama locked models into a proprietary hashed-filename registry format between 2024 and early 2026, preventing reuse with other tools
- Ollama Cloud Pro users report up to 95% failure rates, long timeouts, broken tool calling, and unanswered support tickets, undermining its local-first pitch
- Ollama mislabeled distilled DeepSeek-R1 variants as the full 671B model and launched its desktop app without source code despite open-source claims
Lakesail rewrote Apache Spark in Rust, removing the JVM layer. The new implementation runs eight times faster and cuts infrastructure costs by 94%.
- Rewrote Apache Spark's core in Rust (~200,000 lines), eliminating the JVM entirely while keeping familiar Spark APIs
- Claims ~8x throughput improvement on standard analytics workloads by avoiding GC pauses and Java bytecode overhead
- Reports ~94% cost reduction on AWS C5 instances running identical TPC-DS queries, due to lower CPU/memory use
- Core engine is open source (Apache 2.0) and called production-ready, though advanced Spark SQL window functions and some MLlib algorithms are still missing