AI Engineer
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Prototype Big, Deploy Small: A Framework for On-Device AI
Stop defaulting to expensive frontier models. By using a 'prototype big, deploy small' framework and rigorous local evals, you can replace costly cloud inference with smaller, faster, and more private on-device models.
AI EngineerWhy Product Strategy Beats Prompting in the AI Era
As AI makes coding cheap, the bottleneck for software development has shifted upstream. Success now depends on human-centric skills: eliciting requirements, mapping processes, and validating business value before writing a single line of code.
Building Deterministic Infrastructure for Non-Deterministic AI Agents
To move AI agents from demos to production, engineers must shift focus from prompt engineering to building a robust 'agent control plane' that enforces determinism, safety, and resource governance over stochastic model outputs.
The Agentic AI Engineer: Eval-Driven Development Loops
The Agentic AI Engineer automates the agent development lifecycle—spec, build, evaluate, diagnose, and optimize—using a multi-agent system to remove the human bottleneck from production-ready AI agent maintenance.
The Prompt is the Platform: Agentic Engineering for Distributed Systems
By moving agents upstream into the design phase using deterministic simulation, developers can synthesize bespoke, production-ready implementations from abstract specifications rather than relying on general-purpose libraries.
Automating ETL Pipeline Recovery with RL Agents
A reliable, safety-first architecture for ETL pipeline remediation that uses deterministic anomaly detection, Q-learning for action selection, and an external safety layer to reduce MTTR by 99.85%.
Debugging AI Agents: Why Replayability Beats Determinism
Stop chasing bitwise determinism in LLMs. Instead, implement a 'record and replay' architecture to capture agent state transitions, enabling you to debug production failures by re-running traces with mocked nodes.
Optimizing Voice-In, Visuals-Out AI Experiences
To build delightful AI agents, prioritize 'voice-in, visuals-out' interactions. By using fast models, eager inference, and aggressive prefix caching, you can meet the 1-second latency threshold required for seamless user interaction.
AI EngineerAI-Driven Multi-Document Correlation for Financial Compliance
Moving from isolated document validation to cross-document intelligence using graph-based entity correlation and probabilistic risk modeling significantly improves fraud detection and reduces false positives in enterprise compliance.
Stop Writing Tone Instructions: Use a 4-Layer AI Architecture
Stop relying on a single system prompt for brand voice. Instead, use a four-layer architecture—Immutable Identity, Situational Mode, Example-Anchored Voice, and a Deterministic Veto—to separate instructions from verification.
AI EngineerBuilding a Personal AI Research OS
Transform a fragmented 'Second Brain' into a living research system by using a file-based index and a three-layer architecture (Raw, Index, Wiki) instead of complex vector databases.
Building and Scaling Production AI Agents at OpenGov
OpenGov scales its 'OG Assist' agent platform by moving away from pre-built frameworks to a custom, Effect-TS native agent loop, prioritizing observability, human-in-the-loop safety, and modular tool-based architecture.
Solving the 'Amnesia' Problem in AI Coding Agents
Current AI coding agents are limited by 'repo-bound' vision and lack of episodic memory. Polygraph solves this by creating a meta-harness that provides agents with a unified dependency graph and shared session state across repositories.
The Log Is The Agent: Rethinking AI Agent Architecture
Treating the session log as the primary, durable primitive for AI agents—rather than the model or runtime—enables reliability, portability, and true ownership of agent state.
AI EngineerRecursive Coding Agents: Managing AI Geniuses
Recursive Language Models (RLMs) improve agent reliability by treating context as an object of computation, allowing agents to decompose complex tasks into recursive sub-agent calls that verify and execute work symbolically.
Engineering Principles for Agentic Systems
Building AI agents is not about writing prompts, but architecting systems. By applying traditional software engineering principles—decomposition, state management, and separation of concerns—you can build reliable, maintainable agentic systems that move beyond simple, brittle LLM interactions.
The Miranda Hypothesis: Why Persona Evals Fail
Current persona-based AI benchmarks measure 'convincingness' rather than historical fidelity, leading to 'Miranda distortion' where models prioritize culturally dominant narratives (like the Hamilton musical) over primary documentary records.
The Production AI Playbook: Deploying Agents at Enterprise Scale
Moving AI from demo to production requires shifting focus from model selection to five pillars: evaluation, observability, data foundation, orchestration, and governance.
AI EngineerOptimizing Video Diffusion for Real-Time Generation
Achieve real-time video generation by stacking quantization, caching, and step distillation to reduce the standard 50-step denoising process to as few as 1-8 steps.
AI EngineerWhy MCP and ChatGPT Apps Use Double Iframes
To securely render third-party UI, ChatGPT uses a double-iframe pattern: an outer iframe provides a sandboxed environment on a unique subdomain, while an inner iframe uses 'srcdoc' to render the app, preventing cross-origin storage access and CSP violations.
AI EngineerBuilding Internal AI Data Workspaces with Studio
WorkOS built 'Studio,' an internal tool that allows non-technical staff to query business data and generate deterministic, reusable JavaScript widgets, bypassing the traditional bottleneck of filing engineering tickets for SQL queries.
AI EngineerBuilding Agent-Ready Websites with WebMCP
WebMCP is a proposed web standard that allows developers to expose site functionality as structured tools for AI agents, replacing brittle screen-scraping with direct, reliable API-like interactions.
Sustainable AI Development: Balancing Infinite Scaling with Human Limits
To avoid burnout in the era of AI-driven coding, developers must shift from manual execution to an 'agent-orchestrator' model that uses verification gates, voice-first workflows, and remote control to maintain productivity while reclaiming personal time.
Optimizing AI for Tool Use via RL and Data Quality
Improving model performance for complex tasks often requires teaching tool discipline through RL and high-quality data rather than scaling model size. A 4B parameter model outperformed a 235B model by learning to inspect schemas and self-correct errors.
AI EngineerSovereign AI: Efficiency and Ownership with Gemma 4
Gemma 4 models offer high intelligence-to-size ratios, enabling local execution on consumer hardware and sovereign control over data, now supported by an Apache 2.0 license to simplify enterprise procurement.
Building Self-Driving Products: From Signals to PRs
PostHog is building an automated pipeline that ingests product observability data, groups related signals, and uses AI agents to research and submit pull requests, allowing developers to wake up to green PRs instead of dashboards.
Deploying GPU Workloads Directly from Your IDE with RunPod Flash
RunPod's Flash SDK allows developers to deploy and iterate on GPU-accelerated Python functions directly from their IDE using a simple decorator, eliminating the need for manual Docker builds and container registry management.
AI EngineerRAG is Not Dead: The Shift to Iterative Agentic Retrieval
RAG isn't dying; it's evolving from simple vector search into iterative, agentic retrieval. The key is treating semantic search as 'cached compute' that allows agents to narrow down massive datasets to the 'right million' tokens efficiently.
Building Multimodal Audio Applications with Gemini 3
Google DeepMind's Gemini 3 models enable unified audio understanding, steerable speech generation, and real-time multimodal interaction, allowing developers to build complex audio-to-audio applications with structured outputs.
Scaling Transformer Training to 5 Million Tokens
To train models with multi-million token contexts, you must stack memory-optimization techniques—including context parallelism, activation checkpointing, and a novel method called 'Untied Ulysses'—to bypass GPU memory bottlenecks.
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