Why Production AI Systems Need Deterministic Control Layers, Not Just LLMs

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AI automation tutorials often demonstrate connecting a large language model to a database, but this approach is insufficient for production environments. In finance and enterprise workflows, relying solely on probabilistic AI behavior poses significant compliance and reliability risks. LLMs predict tokens and do not inherently understand business logic or regulatory requirements. Experts recommend wrapping AI models within deterministic control systems that include validation gateways, automated circuit breakers, and immutable audit trails. The core argument is that guardrails should be engineered into the infrastructure, allowing AI to operate safely within defined boundaries rather than self-policing.

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Baidu's Unlimited-OCR Parses 40-Page PDFs in One Pass With Flat Memory Use

Baidu has released Unlimited-OCR, an open-source model capable of processing multi-page PDFs in a single forward pass without the memory growth that typically plagues OCR pipelines. The model replaces standard attention layers with a mechanism called Reference Sliding Window Attention (R-SWA), which maintains a fixed-size token cache instead of an ever-growing one, keeping memory and latency constant across long documents. With 3 billion total parameters but only 500 million activated via a Mixture-of-Experts architecture, the model runs closer to the cost of a 500M model at inference time. It scored 93.23 on OmniDocBench v1.5, roughly 6.2 points above the DeepSeek-OCR baseline it was trained from, suggesting the efficiency gains did not come at the cost of accuracy. The model is released under an MIT license and requires an NVIDIA GPU with at least 12 GB of VRAM for practical use.

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ProgrammingDEV Community ·

Engineer Streams USB Mouse Over WiFi Using Netevent and Systemd

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ProgrammingDEV Community ·

JavaScript Function Types: Key Differences in Hoisting, This, and Usage

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ProgrammingDEV Community ·

PostgreSQL Cursors Slash Export Times by Replacing Random Reads with Sequential Scans

A data pipeline exporting hundreds of gigabytes from PostgreSQL in 2,000-row batches was running far slower than expected, with the bottleneck traced to how the database read data from disk. Using a WHERE id > X LIMIT 2000 pattern caused Postgres to perform up to 2,000 random page lookups per batch via a B-tree index, forcing the disk head to constantly seek across different locations. Switching to a server-side cursor allowed Postgres to scan the heap sequentially, reading each 8KB page exactly once and enabling the OS to prefetch ahead predictably. This change required no schema modifications — only a different query pattern — yet dramatically reduced I/O overhead at scale. However, cursors hold an open transaction for their entire duration, which can block vacuum and inflate write-ahead logs on very large or long-running exports.

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