EN · International edition · 18/10/2026
Inferama daily · 18/10/2026
The most important AI developments since the previous newsletter, selected for editorial relevance.
News coverage period: 17/10/2026 08:30 → 18/10/2026 08:30 (Europe/Madrid).
RLHF: What It Is, How It Works, and Where Its Limits Lie
Reinforcement learning from human feedback uses human judgments or preferences to guide model tuning. A common workflow generates responses, collects comparisons, trains a learned reward model, and optimizes a policy; each stage involves choices and limitations that matter.
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Reranking: What It Is, How It Works, and Its Limits in Retrieval Systems
Reranking is a second scoring stage in retrieval systems that evaluates query–document pairs to refine an initial set of candidates. This glossary entry explains how it works, distinguishes two-stage retrieval from vector search alone, and examines operational limits in RAG and semantic search.
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How to instrument an AI application to investigate failures, latency, and usage
A practical guide to logging each request and its stages without indiscriminately storing sensitive data. It covers an event schema, an instrumentation example, privacy criteria, diagnostic dashboards, and a process for investigating failures and distinguishing API-reported usage from final charges.
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Designing Safe Degradation: A Guide to AI Automations
Learn how to define failure states, recovery policies, and acceptance tests that prevent duplicate effects in AI-powered automated workflows.
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AesCode 32B: Verification Protocol for Local and Self-Hosted Deployment Costs
An operational guide to auditing the real costs of AesCode 32B, distinguishing between technical specifications confirmed by Microsoft and economic variables that require internal estimation, given that no public API pricing exists for this model.
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DeepSeek updates V4.1-Flash and opens Search and Fetch APIs
V4.1-Flash-1017 improves on DeepSeek’s reported results for agentic tasks and lowers the observed cost per task in its DeepSWE evaluation. The company has also made Search and Fetch available through APIs; performance comparisons for the new checkpoint are, for now, internal evaluations.
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GGUF, Quantization, and VRAM: Estimate the Memory Needed to Run Local Models
Q4_K_M, Q5_K_M, and Q8_0 alone do not tell you how much hardware you need. Learn to estimate weight memory, calculate the KV cache, and validate a setup with task-specific speed and quality tests.
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Analyze customer feedback with AI: choose between search, classification, and generation
Learn how to distinguish finding comments, discovering themes, applying known categories, and drafting summaries—then choose a suitable technical approach based on your data, constraints, and tolerance for errors.
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How to Audit AI-Generated SQL Before Using It
A query can run without errors and still calculate a metric incorrectly. This protocol explains how to inspect joins, filters and aggregations, test in a controlled environment, and compare results with known references.
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Article 10 of the AI Act: Training Data Requirements and Practical Application
A legal-operational analysis of the quality, governance, and documentation criteria required by Regulation (EU) 2024/1689 for datasets used in high-risk AI systems.
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