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AI tools
From AI/ML system building to data pipeline engineering
Read articleTechValid JSON can still be wrong: checking evidence in LLM outputs
Separate schema compliance from factual validation, then use evidence IDs, units, source comparisons, and explicit failure states to build research automation.
Read articleTechAI Research Stack 2026: Designing a Quant LLM System Layer by Layer
Treat cloud and local LLMs, RAG, agents, and GPUs as one system, and route each job by data sensitivity, task uncertainty, and how its output will be verified.
Read articleTechRunPod vs Vast.ai: GPU rental cost structure and how to choose by workload
Compares RunPod and Vast.ai rental types, interruption rules, billing units, and storage and data-transfer charges from their official docs, then sets out selection criteria for local LLM experiments, training, and batch backtests.
Read articleTechAutomating Quant Research with the Claude API: Decide the Criteria Before the Model
Instead of comparing model names, design a Claude API integration around task shape, context length, batch and cache cost structure, structured output, and data retention.
Read articleTechChoosing quant hardware: comparing GPUs, Macs, and DGX Spark by workload
Splits local LLM, GPU backtest, and data-processing needs into model memory, memory bandwidth, and CUDA or Metal support, then sets out where NVIDIA GPUs, Apple Silicon, GB10 systems, and cloud rental fit, using official specifications.
Read articleTechCollecting crypto market data from exchange APIs: Binance and Bybit requests and parsing checks
Endpoints, parameters, and rate limits to match against the docs when pulling candles, funding rates, and order books from Binance and Bybit public market-data APIs, plus parsing code you can verify offline.
Read articleTechLocal LLMs vs Cloud APIs: How to Split Quant Research Work
Use data boundaries, utilization-based cost, context settings, output validation, and explicit routing rules to decide which work runs on a local model and which goes to a cloud API.