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Digests from AI Engineer
Learn how to build a private, cost-effective AI workflow using an Obsidian vault and Superlinked's open-source inference engine. We cover self-hosting, model routing, and secure document processing in a controlled VPC.
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Reducto is an agentic document platform that turns messy, unstructured documents (scans, handwriting, redacted PDFs) into structured data for AI. It uses a rasterization-based approach and a multi-stage pipeline (Parse, Split, Extract, Edit, Classify).
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Learn how AI agents use real-time web data from sources like Amazon and Reddit to build product intelligence recommendations. We cover the shift from brittle scraping pipelines to scalable, resilient data collection using Bright Data's MCP.
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Jesse Hall explains that for voice agents, latency is a budget, but the goal is human-like conversation. He details why component leaderboards fail and how to build a robust, real-time pipeline.
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To escape AI lock-in, modern AI deployments must be modular. An AI gateway centralizes identity, manages budgets, and decouples the LLM, data connectors (MCP), and interfaces, ensuring maximum choice and ROI.
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MiniMax M3 combines agentic coding, 1M context, and native multimodality. The model uses MSA sparse attention, achieving 9x faster pre-fill and 15x faster decode vs. full attention.
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AI governance must move from prompt instructions to the infrastructure layer. Learn how a Gateway blocks data exfiltration, enforces rate limits, and controls costs in agentic workflows.
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Learn how a Context Engine moves beyond vector search to handle structural, relational, and temporal data queries, significantly improving AI agent reliability and cutting token burn.
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Discover how to build ultra-fast AI agents and MCP servers using WebAssembly, Spin, and Akamai Functions. Learn how to achieve global distribution with zero cold starts.
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Learn how to scale custom AI agents from local development to autonomous CI pipelines. Discover agentic workflows, marketplaces, and cross-repo automation using GitHub Copilot.
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AI agents pose a 'lethal trifecta' threat: private data access, untrusted content exposure, and external communication. Secure adoption requires three guardrails: CDEs, a Model Proxy, and an Agent Firewall.
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A 4B model trained with RL on financial data beat a 235B model on complex tasks. Specialization and simple rewards proved more effective than raw scale for enterprise AI.
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Building a production AI agent requires more than connecting APIs. Learn why a Context Graph—combining synced data, live lookups, skills, and memory—is essential for semantic querying and reliable business logic.
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Gabriel Martinez argues that AI accelerates 'slop'—work lacking judgment. He stresses that generation is cheap, but evaluation is expensive, advocating for diagrams and conventions over massive PRs.
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Anders Swanson details how to build a real memory layer for AI agents, moving beyond context windows and scratch files. He outlines a system using multi-model databases, hybrid search, and governance controls for persistent, scalable recall.
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Learn how Airbyte designed two interfaces—MCP and CLI—to make data actionable for AI agents. Discover best practices for building agent-friendly CLIs and understanding the trade-offs between MCP and CLI architectures.
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Learn how to build sophisticated AI agents using the Codex App Server. This open-source JSON-RPC protocol provides deep control over the Codex harness, enabling custom tools and multi-threaded operations.
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Stop re-prompting your coding agent. Instead, codify the rules you keep repeating into deterministic checks using linting. Learn how to keep APIs synced with the OpenAPI schema and manage qualitative code policies.
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AI is making PRs bigger and more numerous, turning CI/Code Review into a bottleneck. Discover agentic validation: an automated system that reviews, autofixes, and auto-merges PRs, keeping your development velocity high.
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Why are static benchmarks failing? Felipe Blanes details the 'benchmark illusion' in AI agent development and introduces the 'eval flywheel'—a customer-driven process for building truly reliable products.
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