Tag

practical-ai

151 posts

Aug 11, 2026 Office Hours

Office Hours — What's the best way to handle intellectual property when building AI agents with third-party APIs and services?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Aug 10, 2026 Office Hours

Office Hours — How do you optimize token usage when your AI agent needs to process long documents like Wikipedia pages?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Aug 9, 2026 Office Hours

Office Hours — What's the most effective way to structure context and examples in prompts to reduce hallucinations and improve consistency?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Aug 8, 2026 Office Hours

Office Hours — How do you handle cost and latency tradeoffs when choosing between different LLM providers and model sizes for your application?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Aug 7, 2026 Office Hours

Office Hours — What specific metrics and evaluation methods work best for measuring LLM output quality in your domain?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Aug 5, 2026 Office Hours

Office Hours — How do you validate and test code generated by LLMs before deploying it to production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Aug 4, 2026 Office Hours

Office Hours — What are the most common failure modes when integrating AI coding assistants into a professional development workflow?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Aug 3, 2026 Office Hours

Office Hours — How do you design LLM-powered systems as true collaborators with human control rather than fully autonomous agents?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Aug 2, 2026 Office Hours

Office Hours — What patterns and architectures are people actually using to build functional AI agents in production, beyond toy demos?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Aug 1, 2026 Office Hours

Office Hours — How do you structure prompts and workflows to get AI coding assistants to produce reliable, production-ready code instead of plausible-looking garbage?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 31, 2026 Office Hours

Office Hours — What curriculum changes should computer science programs prioritize to prepare students for an AI-augmented development environment?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 30, 2026 Office Hours

Office Hours — Does your mind drift noticeably while waiting for long AI inference times, and how does that affect your coding workflow?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 30, 2026 Paper of the Week

Paper of the Week — Try Again, Don't Look Back: Blind Resampling Outperforms Self-Repair in Small Code Models

Self-repair in code agents adds feedback overhead but rarely beats simply sampling again — a controlled study quantifies exactly when the extra tokens are wasted.

research papers arxiv practical-ai
Jul 29, 2026 Office Hours

Office Hours — Should ontology-driven approaches replace traditional prompting and RAG for AI coding tasks?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 28, 2026 Office Hours

Office Hours — What job titles and role definitions are emerging for engineers who primarily work with agentic AI systems?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 27, 2026 Office Hours

Office Hours — Are open-weight LLMs practical for production use cases where you need to avoid cloud dependencies and data privacy concerns?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 26, 2026 Office Hours

Office Hours — How do you maintain code quality and debugging confidence when using AI coding agents to write substantial portions of your codebase?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 25, 2026 Office Hours

Office Hours — What's the best way to set up AI agents for active learning sprints in a development workflow?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 24, 2026 Office Hours

Office Hours — Should we give AI coding agents their own GitHub account, and if so, how do we manage commits and permissions?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 23, 2026 Office Hours

Office Hours — What are the practical tradeoffs between using structured outputs versus parsing unstructured LLM responses?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 23, 2026 Paper of the Week

Paper of the Week — Structured Output Collapses Answer Diversity Across 44 Language Models

Structured output silently collapses answer diversity across 44 LLMs — JSON-only prompts skew which answer a model picks, not just how it formats the response.

research papers arxiv practical-ai
Jul 22, 2026 Office Hours

Office Hours — How do you design prompts and workflows that reliably work across different LLM models?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 21, 2026 Office Hours

Office Hours — What's your strategy for keeping up with rapid LLM model improvements without constantly rewriting applications?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 20, 2026 Office Hours

Office Hours — How do you handle long-running LLM requests and API latency in user-facing applications?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 19, 2026 Office Hours

Office Hours — What patterns and practices have you found most effective for agentic AI workflows in production systems?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 18, 2026 Office Hours

Office Hours — How do you maintain code quality and architectural control when using AI to generate most of your codebase?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 11, 2026 Office Hours

Office Hours — How do you safely let AI agents run unattended to build features, and what safeguards do you put in place?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 10, 2026 Office Hours

Office Hours — What's the best way to specify constraints and rules for AI agents so they stay aligned with your system design?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 9, 2026 Office Hours

Office Hours — What's the current state of using LLMs for code generation while keeping infrastructure and API costs reasonable?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 9, 2026 Paper of the Week

Paper of the Week — From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents

AI agent tool sets that ossify at deployment lose compounding value — this paper shows how to grow them automatically from task execution logs.

research papers arxiv practical-ai
Jul 8, 2026 Office Hours

Office Hours — How should you structure LLM evaluation and testing in your development pipeline to catch issues before production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 7, 2026 Office Hours

Office Hours — What are practical techniques for reducing hallucinations and improving reliability in production LLM systems?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 6, 2026 Office Hours

Office Hours — How do you optimize LLM usage costs when building AI-powered coding features?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 5, 2026 Office Hours

Office Hours — What's the best open-source LLM you can run on a 24GB VRAM GPU for production workloads?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 4, 2026 Office Hours

Office Hours — How do you build a hybrid local and cloud LLM architecture for regulated financial document processing?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 3, 2026 Office Hours

Office Hours — What's the best strategy for controlling costs and enforcing rate limits on LLM API calls in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 2, 2026 Office Hours

Office Hours — How do you prevent hallucinated tool calls from wasting tokens and breaking production LLM workflows?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jul 2, 2026 Paper of the Week

Paper of the Week — SWE-Router: Routing in Multi-Turn Agentic Software Engineering Tasks

SWE-Router cuts LLM agent costs by routing easy GitHub issues to cheaper models mid-trajectory, not just at task start, using execution signals as routing features.

research papers arxiv practical-ai
Jul 1, 2026 Office Hours

Office Hours — What are the practical differences in building real-world AI agent systems versus the simplified examples in documentation?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 30, 2026 Office Hours

Office Hours — Are current AI agents fundamentally limited by their architecture in ways that newer research directions like diffusion LLMs could address?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 29, 2026 Office Hours

Office Hours — What skills and mindsets distinguish someone doing prompt engineering from someone building actual software systems with LLMs?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 28, 2026 Office Hours

Office Hours — How do you evaluate whether an LLM-based approach is worth the engineering overhead compared to traditional solutions?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 27, 2026 Office Hours

Office Hours — What workflow and practices actually worked when building a large-scale AI-assisted application (126K lines of code)?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 26, 2026 Office Hours

Office Hours — What are the security and governance concerns with giving AI agents direct database access, and how does it differ from traditional BI tool problems?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 25, 2026 Office Hours

Office Hours — How do you handle prompt fatigue and prevent LLMs from producing lazy or low-effort outputs in production systems?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 25, 2026 Paper of the Week

Paper of the Week — Quantization Inflates Reasoning: Token Inflation as a Hidden Cost of Low-Bit Reasoning Models

Quantization inflates reasoning model token counts by up to 4×, a hidden cost accuracy benchmarks miss entirely.

research papers arxiv practical-ai
Jun 24, 2026 Office Hours

Office Hours — When should you replace a flat fact store with a graph database in an AI agent, and what are the implementation tradeoffs?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 23, 2026 Office Hours

Office Hours — What makes vibe coding fail compared to structured approaches to LLM development?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 22, 2026 Office Hours

Office Hours — Does implementing energy-based models for enterprise AI security justify the complexity?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 21, 2026 Office Hours

Office Hours — Are you optimizing content for AI search engines versus traditional SEO?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 20, 2026 Office Hours

Office Hours — What's the best approach to tracing and debugging LLM calls in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 19, 2026 Office Hours

Office Hours — How should AI agents discover and order real-world services?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 18, 2026 Office Hours

Office Hours — How do teams prevent duplicate LLM API calls and token waste?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 18, 2026 Paper of the Week

Paper of the Week — Scaling Enterprise Agent Routing: Degradation, Diagnosis, and Recovery

Routing accuracy for 110+ agents drops sharply past 50 tools — new study maps the degradation curve and identifies three recovery strategies that work today.

research papers arxiv practical-ai
Jun 17, 2026 Office Hours

Office Hours — What is the control plane for local AI agents?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 16, 2026 Office Hours

Office Hours — How are you orchestrating multi-agent AI workflows in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 15, 2026 Office Hours

Office Hours — Is traditional machine learning still worth learning and building with, or should you focus entirely on LLMs?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 14, 2026 Office Hours

Office Hours — What deterministic guarantees do you need from an LLM system for compliance-heavy enterprise use cases?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 13, 2026 Office Hours

Office Hours — How do you structure your LLM training infrastructure to avoid getting locked into a single provider or model?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 12, 2026 Office Hours

Office Hours — When should you use prompt engineering and when do you actually need fine-tuning or retrieval-augmented generation?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 11, 2026 Office Hours

Office Hours — What concrete guardrails and constraints do you need to put on LLM agents before deploying them to production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 11, 2026 Paper of the Week

Paper of the Week — When Poison Fails After Retrieval: Revisiting Corpus Poisoning under Chunking and Reranking Pipelines

RAG corpus poisoning drops 60-80% when you add chunking+reranking — but most attack evals skip these standard pipeline stages entirely.

research papers arxiv practical-ai
Jun 10, 2026 Office Hours

Office Hours — How is AI-assisted coding actually changing your development workflow and productivity in a real codebase?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 9, 2026 Office Hours

Office Hours — What's the practical difference between using an AI agent for backend automation versus building traditional scheduled jobs or APIs?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 8, 2026 Office Hours

Office Hours — How do you know what data your AI coding agent is sending to the cloud, and what should you audit for?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 7, 2026 Office Hours

Office Hours — When should you use traditional ML (tabular models, gradient boosting) instead of jumping straight to LLMs for a new feature?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 6, 2026 Office Hours

Office Hours — How do you architect systems where AI agents can safely execute code or access tools without human review on every action?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 5, 2026 Office Hours

Office Hours — What are the actual best practices for learning to build effective multi-step AI agents beyond toy examples?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 4, 2026 Office Hours

Office Hours — When building AI agents, how do you decide between deterministic state machines and freeform LLM reasoning?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 4, 2026 Paper of the Week

Paper of the Week — Caught in the Act(ivation): Toward Pre-Output and Multi-Turn Detection of Credential Exfiltration by LLM Agents

Activation probes detect credential exfiltration *before* the LLM outputs any tokens — combined with honeytokens and multi-turn leakage tracking, with no model changes required.

research papers arxiv practical-ai
Jun 3, 2026 Office Hours

Office Hours — What's a concrete pattern for adding governance and determinism to AI pipelines that need compliance or audit trails?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 2, 2026 Office Hours

Office Hours — How do you structure LLM applications to prevent hallucinations when the model is confident but factually wrong?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Jun 1, 2026 Office Hours

Office Hours — What's the practical difference between building with Claude, ChatGPT, and open-source models when you need production reliability?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 31, 2026 Office Hours

Office Hours — How do you reliably evaluate AI agent behavior in production when testing in development doesn't catch all failure modes?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 30, 2026 Office Hours

Office Hours — What's your strategy for sourcing and licensing data for training custom AI models?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 29, 2026 Office Hours

Office Hours — What common issues are you hitting with LLM gateways and API management in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 28, 2026 Office Hours

Office Hours — How do you give local LLMs persistent context without bloating prompts or losing information?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 28, 2026 Paper of the Week

Paper of the Week — Exploring the Emerging Threats of the Agent Skill Ecosystem

76 malicious skills confirmed in 3,984 audited AI agent marketplaces — credential theft, backdoor installation, and data exfiltration found hiding in plain sight.

research papers arxiv practical-ai
May 27, 2026 Office Hours

Office Hours — Should you use regex or other methods to enforce security constraints on LLM agent outputs?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 26, 2026 Office Hours

Office Hours — What patterns work best for aggregating results when retrieval-augmented generation approaches fail in agentic systems?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 25, 2026 Office Hours

Office Hours — How do you handle data isolation and prevent information leakage in RAG systems serving multiple tenants?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 24, 2026 Office Hours

Office Hours — What's the best approach for real-time, local text-to-speech when building live voice interaction features?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 23, 2026 Office Hours

Office Hours — How do you prevent AI agents from taking unintended actions when they have access to external tools and APIs?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 22, 2026 Office Hours

Office Hours — What are developers actually building with AI coding agents right now?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 21, 2026 Office Hours

Office Hours — Why does Codex work better than Claude Code for production monoliths?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 21, 2026 Paper of the Week

Paper of the Week — Does Code Cleanliness Affect Coding Agents? A Controlled Minimal-Pair Study

Code cleanliness measurably changes how well coding agents complete tasks — a controlled minimal-pair study with released dataset and reproducible methodology.

research papers arxiv practical-ai
May 20, 2026 Office Hours

Office Hours — What practical tips do power users have for working effectively with AI agents?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 19, 2026 Office Hours

Office Hours — Are there any AI agent sandboxes worth evaluating right now?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 18, 2026 Office Hours

Office Hours — How much are you actually spending on AI coding at work?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 17, 2026 Office Hours

Office Hours — What's the best way to replace RAG pipelines with a filesystem interface for AI agents?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 16, 2026 Office Hours

Office Hours — Why don't AI coding tools like REST?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 15, 2026 Office Hours

Office Hours — How do you prevent retry cascades in LLM systems?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 14, 2026 Office Hours

Office Hours — Why should you add relationships and structure to your AI context instead of using naive RAG?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 14, 2026 Paper of the Week

Paper of the Week — Useful Memories Become Faulty When Continuously Updated by LLMs

LLM agent memory degrades when consolidated: episodic traces outperform summarized lessons across 5 agentic tasks.

research papers arxiv practical-ai
May 13, 2026 Office Hours

Office Hours — What's the best approach for handling OCR and document parsing in a RAG pipeline?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 12, 2026 Office Hours

Office Hours — How are you using multi-agent AI systems effectively in your daily workflow?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 11, 2026 Office Hours

Office Hours — Should you use agentic search or RAG for retrieval, and what's the tradeoff in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 10, 2026 Office Hours

Office Hours — How are you preventing runaway LLM workflows and token costs in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 9, 2026 Office Hours

Office Hours — How are you structuring context and prompts for AI coding agents to get reliable results?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 8, 2026 Office Hours

Office Hours — What breaks when you run AI agents unsupervised in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 7, 2026 Office Hours

Office Hours — How are you using LLMs in production and what unexpected issues have you hit?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 7, 2026 Paper of the Week

Paper of the Week — TSCG: Deterministic Tool-Schema Compilation for Agentic LLM Deployments

TSCG shows small LLMs (4–14B) drop tool-call failures by compiling JSON schemas into natural-language descriptions before inference.

research papers arxiv practical-ai
May 6, 2026 Office Hours

Office Hours — Are you using finetuning for LLM agents in production, and if so, what trade-offs did you encounter versus using base models with prompt engineering?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 4, 2026 Office Hours

Office Hours — How should you structure memory and context for AI agents so they can learn from past tasks without growing unbounded token usage?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 3, 2026 Office Hours

Office Hours — What monitoring and safeguards do you need in place to control AI agents that take real actions in production systems?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 2, 2026 Office Hours

Office Hours — How do you keep AI coding agents aligned with your team's codebase standards, style guides, and architectural decisions?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 1, 2026 Office Hours

Office Hours — Should you give AI agents access to API keys and private credentials, and if so, what isolation strategies actually work?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 30, 2026 Office Hours

Office Hours — What's the best way to set cost limits and prevent AI agents from burning through your API budget on failed or inefficient tasks?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 30, 2026 Paper of the Week

Paper of the Week — Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use

Rewriting tool descriptions at deployment time—not training time—can recover 20-40% of function-calling accuracy lost to poorly written API docs.

research papers arxiv practical-ai
Apr 29, 2026 Office Hours

Office Hours — How do you prevent AI agents from accidentally introducing vulnerable dependencies or malicious code when they autonomously modify your codebase?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 27, 2026 Office Hours

Office Hours — What's your strategy for testing and evaluating LLM outputs in production now that Promptfoo was acquired?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 26, 2026 Office Hours

Office Hours — Has anyone successfully fine-tuned LLMs for production use and what was the ROI?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 25, 2026 Office Hours

Office Hours — Is operational memory a missing layer in AI agent architecture?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 24, 2026 Office Hours

Office Hours — What hiring criteria should you use when your team is heavily using AI-assisted coding?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 23, 2026 Office Hours

Office Hours — Who is actually getting measurable value from AI agents in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 23, 2026 Paper of the Week

Paper of the Week — Beyond One Output: Visualizing and Comparing Distributions of Language Model Generations

Visualizing LLM output distributions reveals hidden modes, edge cases, and prompt sensitivity that single-sample evaluation completely misses.

research papers arxiv practical-ai
Apr 22, 2026 Office Hours

Office Hours — How do you know if AI agents will choose your tool?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 21, 2026 Office Hours

Office Hours — Has anyone deployed LLMs to production and what were the biggest operational challenges?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 20, 2026 Office Hours

Office Hours — How are you extracting the best performance out of your RAG pipeline?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 19, 2026 Office Hours

Office Hours — Is anyone using function calling with LLMs in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 18, 2026 Office Hours

Office Hours — Best LLM stack for Q&A over internal PDFs?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 17, 2026 Office Hours

Office Hours — What tools are you using for AI evals, and why does everything feel half-baked?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 17, 2026 Paper of the Week

Paper of the Week — Lossless Prompt Compression via Dictionary-Encoding and In-Context Learning: Enabling Cost-Effective LLM Analysis of Repetitive Data

Lossless prompt compression via dictionary encoding lets LLMs analyze repeated data at a fraction of token cost — no external tools, just in-context learning.

research papers arxiv practical-ai
Apr 16, 2026 Office Hours

Office Hours — Is synthetic data generation practical outside academia?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 14, 2026 Office Hours

Office Hours — Are we pretending RAG is ready, when it's barely out of demo phase?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 13, 2026 Office Hours

Office Hours — Why don't we have a functional DSL for data+embedding+API pipelines?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 12, 2026 Office Hours

Office Hours — Why are so many companies rolling out their own AI/LLM agent sandboxing solution?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 11, 2026 Office Hours

Office Hours — What are some actual use cases of AI Agents right now?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 10, 2026 Office Hours

Office Hours — Can you personalize an LLM by fine-tuning it on conversation history, or is that the wrong approach?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 9, 2026 Office Hours

Office Hours — Who is actually getting real business value from AI agents right now, and how?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 9, 2026 Paper of the Week

Paper of the Week — Mamba-Based State Space Models for Long-Context Retrieval-Augmented Generation

Structured state-space models finally beat transformers at document retrieval — here's what the Mamba-based RAG benchmark actually shows.

research papers arxiv practical-ai
Apr 8, 2026 Office Hours

Office Hours — Is fine-tuning LLMs actually worth it, or is prompt engineering and RAG always the better path?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 7, 2026 Office Hours

Office Hours — Is fine-tuning the right approach when you want a model to reliably remember your codebase or company-specific data?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 6, 2026 Office Hours

Office Hours — What kinds of tasks are AI agents actually reliable at in production today?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 5, 2026 Office Hours

Office Hours — Is RAG an antipattern for AI agents, or is it still the right default approach?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 4, 2026 Office Hours

Office Hours — How do you actually monitor AI agents in production when there's no standard playbook yet?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 3, 2026 Office Hours

Office Hours — We're using an LLM to extract structured data from messy PDFs. Sometimes it works perfectly, sometimes it misses fields or invents data. How do I know if the problem is the model, my prompt, or the PDF quality itself?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 2, 2026 Office Hours

Office Hours — We're getting inconsistent outputs from the same prompt with GPT-5.4. Temperature is locked at 0. What's actually going on?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 2, 2026 Paper of the Week

Paper of the Week — SnapKV: LLM Knows What You are Looking for Before Generation

KV cache compression that cuts memory 40–60% with under 1% accuracy loss — here's the technique your inference stack probably isn't using yet.

research papers arxiv practical-ai
Apr 1, 2026 Office Hours

Office Hours — I'm using Claude Opus 4.6 for a customer-facing summarization task. Should I batch requests during off-peak hours to save money, or just call the API in real-time?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 31, 2026 Office Hours

Office Hours — How do I know when to stop prompt engineering and just upgrade my model?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 30, 2026 Office Hours

Office Hours — Is it better to improve the harness around the LLM or wait for a better model?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 29, 2026 Office Hours

Office Hours — Should I A/B test my LLM prompts in production or is that overkill?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 28, 2026 Office Hours

Office Hours — What's the hardest part of building AI agents that actually work?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 27, 2026 Office Hours

Office Hours — How do you actually test LLM apps beyond vibe checks?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 26, 2026 Office Hours

Office Hours — Why is AI agent reliability barely improving despite 18 months of model upgrades?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 26, 2026 Paper of the Week

Paper of the Week — Training Language Models to Self-Correct via Reinforcement Learning

SCoRe trains a single LLM to catch and fix its own mistakes via RL — 15.6% better on math, 9.1% on code, no multi-model pipeline needed.

research papers arxiv practical-ai
Mar 25, 2026 Office Hours

Office Hours — How are people safely reusing cached LLM answers in production RAG systems?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 24, 2026 Office Hours

Office Hours — Do structured outputs from LLMs create false confidence that the response is actually correct?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 23, 2026 Office Hours

Office Hours — How are you handling LLM API costs in production without sacrificing quality?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 22, 2026 Office Hours

Office Hours — How do I actually know if my LLM is hallucinating in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai

Office Hours — What's the best way to handle intellectual property when building AI agents with third-party APIs and services?

Office Hours — How do you optimize token usage when your AI agent needs to process long documents like Wikipedia pages?

Office Hours — What's the most effective way to structure context and examples in prompts to reduce hallucinations and improve consistency?

Office Hours — How do you handle cost and latency tradeoffs when choosing between different LLM providers and model sizes for your application?

Office Hours — What specific metrics and evaluation methods work best for measuring LLM output quality in your domain?

Office Hours — How do you validate and test code generated by LLMs before deploying it to production?

Office Hours — What are the most common failure modes when integrating AI coding assistants into a professional development workflow?

Office Hours — How do you design LLM-powered systems as true collaborators with human control rather than fully autonomous agents?

Office Hours — What patterns and architectures are people actually using to build functional AI agents in production, beyond toy demos?

Office Hours — How do you structure prompts and workflows to get AI coding assistants to produce reliable, production-ready code instead of plausible-looking garbage?

Office Hours — What curriculum changes should computer science programs prioritize to prepare students for an AI-augmented development environment?

Office Hours — Does your mind drift noticeably while waiting for long AI inference times, and how does that affect your coding workflow?

Paper of the Week — Try Again, Don't Look Back: Blind Resampling Outperforms Self-Repair in Small Code Models

Office Hours — Should ontology-driven approaches replace traditional prompting and RAG for AI coding tasks?

Office Hours — What job titles and role definitions are emerging for engineers who primarily work with agentic AI systems?

Office Hours — Are open-weight LLMs practical for production use cases where you need to avoid cloud dependencies and data privacy concerns?

Office Hours — How do you maintain code quality and debugging confidence when using AI coding agents to write substantial portions of your codebase?

Office Hours — What's the best way to set up AI agents for active learning sprints in a development workflow?

Office Hours — Should we give AI coding agents their own GitHub account, and if so, how do we manage commits and permissions?

Office Hours — What are the practical tradeoffs between using structured outputs versus parsing unstructured LLM responses?

Paper of the Week — Structured Output Collapses Answer Diversity Across 44 Language Models

Office Hours — How do you design prompts and workflows that reliably work across different LLM models?

Office Hours — What's your strategy for keeping up with rapid LLM model improvements without constantly rewriting applications?

Office Hours — How do you handle long-running LLM requests and API latency in user-facing applications?

Office Hours — What patterns and practices have you found most effective for agentic AI workflows in production systems?

Office Hours — How do you maintain code quality and architectural control when using AI to generate most of your codebase?

Office Hours — How do you safely let AI agents run unattended to build features, and what safeguards do you put in place?

Office Hours — What's the best way to specify constraints and rules for AI agents so they stay aligned with your system design?

Office Hours — What's the current state of using LLMs for code generation while keeping infrastructure and API costs reasonable?

Paper of the Week — From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents

Office Hours — How should you structure LLM evaluation and testing in your development pipeline to catch issues before production?

Office Hours — What are practical techniques for reducing hallucinations and improving reliability in production LLM systems?

Office Hours — How do you optimize LLM usage costs when building AI-powered coding features?

Office Hours — What's the best open-source LLM you can run on a 24GB VRAM GPU for production workloads?

Office Hours — How do you build a hybrid local and cloud LLM architecture for regulated financial document processing?

Office Hours — What's the best strategy for controlling costs and enforcing rate limits on LLM API calls in production?

Office Hours — How do you prevent hallucinated tool calls from wasting tokens and breaking production LLM workflows?

Paper of the Week — SWE-Router: Routing in Multi-Turn Agentic Software Engineering Tasks

Office Hours — What are the practical differences in building real-world AI agent systems versus the simplified examples in documentation?

Office Hours — Are current AI agents fundamentally limited by their architecture in ways that newer research directions like diffusion LLMs could address?

Office Hours — What skills and mindsets distinguish someone doing prompt engineering from someone building actual software systems with LLMs?

Office Hours — How do you evaluate whether an LLM-based approach is worth the engineering overhead compared to traditional solutions?

Office Hours — What workflow and practices actually worked when building a large-scale AI-assisted application (126K lines of code)?

Office Hours — What are the security and governance concerns with giving AI agents direct database access, and how does it differ from traditional BI tool problems?

Office Hours — How do you handle prompt fatigue and prevent LLMs from producing lazy or low-effort outputs in production systems?

Paper of the Week — Quantization Inflates Reasoning: Token Inflation as a Hidden Cost of Low-Bit Reasoning Models

Office Hours — When should you replace a flat fact store with a graph database in an AI agent, and what are the implementation tradeoffs?

Office Hours — What makes vibe coding fail compared to structured approaches to LLM development?

Office Hours — Does implementing energy-based models for enterprise AI security justify the complexity?

Office Hours — Are you optimizing content for AI search engines versus traditional SEO?

Office Hours — What's the best approach to tracing and debugging LLM calls in production?

Office Hours — How should AI agents discover and order real-world services?

Office Hours — How do teams prevent duplicate LLM API calls and token waste?

Paper of the Week — Scaling Enterprise Agent Routing: Degradation, Diagnosis, and Recovery

Office Hours — What is the control plane for local AI agents?

Office Hours — How are you orchestrating multi-agent AI workflows in production?

Office Hours — Is traditional machine learning still worth learning and building with, or should you focus entirely on LLMs?

Office Hours — What deterministic guarantees do you need from an LLM system for compliance-heavy enterprise use cases?

Office Hours — How do you structure your LLM training infrastructure to avoid getting locked into a single provider or model?

Office Hours — When should you use prompt engineering and when do you actually need fine-tuning or retrieval-augmented generation?

Office Hours — What concrete guardrails and constraints do you need to put on LLM agents before deploying them to production?

Paper of the Week — When Poison Fails After Retrieval: Revisiting Corpus Poisoning under Chunking and Reranking Pipelines

Office Hours — How is AI-assisted coding actually changing your development workflow and productivity in a real codebase?

Office Hours — What's the practical difference between using an AI agent for backend automation versus building traditional scheduled jobs or APIs?

Office Hours — How do you know what data your AI coding agent is sending to the cloud, and what should you audit for?

Office Hours — When should you use traditional ML (tabular models, gradient boosting) instead of jumping straight to LLMs for a new feature?

Office Hours — How do you architect systems where AI agents can safely execute code or access tools without human review on every action?

Office Hours — What are the actual best practices for learning to build effective multi-step AI agents beyond toy examples?

Office Hours — When building AI agents, how do you decide between deterministic state machines and freeform LLM reasoning?

Paper of the Week — Caught in the Act(ivation): Toward Pre-Output and Multi-Turn Detection of Credential Exfiltration by LLM Agents

Office Hours — What's a concrete pattern for adding governance and determinism to AI pipelines that need compliance or audit trails?

Office Hours — How do you structure LLM applications to prevent hallucinations when the model is confident but factually wrong?

Office Hours — What's the practical difference between building with Claude, ChatGPT, and open-source models when you need production reliability?

Office Hours — How do you reliably evaluate AI agent behavior in production when testing in development doesn't catch all failure modes?

Office Hours — What's your strategy for sourcing and licensing data for training custom AI models?

Office Hours — What common issues are you hitting with LLM gateways and API management in production?

Office Hours — How do you give local LLMs persistent context without bloating prompts or losing information?

Paper of the Week — Exploring the Emerging Threats of the Agent Skill Ecosystem

Office Hours — Should you use regex or other methods to enforce security constraints on LLM agent outputs?

Office Hours — What patterns work best for aggregating results when retrieval-augmented generation approaches fail in agentic systems?