How to Detect and Block Malicious Bots in APIs
Layer IP reputation, behavioral analysis, rate limits, TLS fingerprints, and CAPTCHAs to detect and block malicious bots targeting your APIs.
NanoGPT Blog
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357 posts found for 'api'
AI Model Testing Protocols: Best Practices
Practical guidelines for testing AI models: define objectives, build golden datasets, run edge-case and adversarial tests, version control, and monitor drift.
Ultimate Guide to AI Model Storage Needs
Practical guide to AI storage: VRAM/RAM sizing, NVMe vs HDD, checkpoints, object storage, and caching strategies to prevent GPU stalls and cut costs.
AI Infrastructure: Redundancy vs. High Availability
Compare redundancy and high availability for AI infrastructure — tradeoffs in cost, recovery time, and how combining them improves resilience.
Latency Profiling for Large Language Models
Guide to profiling LLM latency: measure TTFT, TPOT, and ITL; use PyTorch, Nsight, and tracing; optimize batching, quantization, and memory bandwidth.
How to Monitor AI Models for Errors
Monitor AI models to catch silent failures—track hallucinations, data drift, latency, token costs, set alerts, and automate retraining.
RNN Variants: Performance Comparison
Compare Vanilla RNNs, LSTMs, and GRUs—memory, speed, parameter trade-offs and best use cases for short, medium, and long sequence tasks.
Scaling Depth vs. Width: Cost Trade-Offs
Wider models win for throughput; deeper models win for reasoning — the right mix, not raw size, controls AI cost, latency, and performance.
Multimodal Data Pipelines: Scalability Best Practices
Treat multimodal pipelines as first-class systems: modularize by modality, partition and shard data, autoscale components, and reduce wasted compute.
Top 5 Tools for Scalable Churn Prediction
Compare five scalable churn prediction tools — features, AI models, integrations, and pricing to match small teams through large enterprises.