LLMs face real-world tests: taxes, OCR, and multi-system benchmarks reveal sharp strengths and stubborn blind spots.
TaxCalcBench shows state-of-the-art models calculate less than a third of federal returns on a simplified sample. The takeaway: basic math and policy reasoning still trip up models—experts say scaffolding and tool access, like a calculator, could lift accuracy [1].
PaddleOCR-VL is touted as a top OCR engine, but results hinge on image quality. About 1080p tends to work best; at 4k it misses text, and it can hallucinate. It does support vertical text and 100 languages, with caveats remaining [2].
SGLang vs TabbyAPI & vLLM benchmarks show big speed gaps but on one rig, SGLang hits 32–37 tokens/sec on a 123B model, while TabbyAPI runs slower; 8-bit KAT-Dev 32B tops around 61.5 tokens/sec. The practical takeaway: inference frameworks matter as much as model size [3].
Gemma debates whether an LLM can generate novel hypotheses from predictions alone. Some see value in the process when cross-checked with tools, while others warn results can be gibberish without careful validation [4].
BankToBudget demonstrates practical use: it turns messy bank exports into a monthly budget, with GPT-5 parsing categories behind the scenes [5].
Closing thought: task-specific tooling and robust evaluation will shape what actually scales in 2025 and beyond.
References
TaxCalcBench: Evaluating Frontier Models on the Tax Calculation Task
TaxCalcBench compares frontier LLMs on tax calculations; Gemini beats Claude; discussions cover tools, risks, reliability, and policy implications.
View sourcePaddleOCR-VL, is better than private models
PaddleOCR praised; peers compare Qwen3-VL, Gemini, Mistral; discusses vertical text, handwriting, testing, GPU issues, and model strengths versus others overall.
View sourceSGLang vs TabbyAPI & vLLM Benchmark Increases (Multi-GPU + Single-GPU)
Benchmarks compare SGLang with TabbyAPI and vLLM across multi-GPU systems, highlighting speedups, quantization, NVLink, virtualization, and power tradeoffs in practice.
View sourcebut can someone correct me, I'm curious how an LLM can generate new hypotheses if it is based only on the prediction of the next token, isn't gemma a simple LLM trained on medical data ?
Debate on whether LLMs truly reason or merely predict next tokens; includes mechanisms, experiments, and novelty claims about real understanding.
View sourceShow HN: BankToBudget – Instantly turn your bank exports into a monthly budget
Shows practical use of GPT-5 in parsing bank data and categorizing transactions; seeks feedback on accuracy and features for improvement.
View source