AI AgentsDetecting Agent Loops Before They Burn Your Budget
A stuck agent repeats the same failing action until something stops it. How to detect repetition cheaply and what to do once you have.
ReadNotes on models, agents, and what inference costs
Practical writing for developers building with large language models — how they work, how to pick one, and how to keep the bill predictable.
AI Agents
Cost & PricingDoes Streaming Cost More? What You Actually Pay For
Streaming changes when tokens arrive, not how many are billed. Where it does affect spend, and the abandonment case that genuinely wastes money.
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FundamentalsDPO vs RLHF: Two Routes From Preferences to Model Behaviour
Direct preference optimisation drops the reward model and the RL loop. What that simplification buys, what it costs, and why open-weight work leans on it.
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GuidesEmacs LLM Setup: A Model Endpoint Without the Mouse
Wire an OpenAI-compatible endpoint into Emacs — package choices, auth-source instead of hardcoded keys, buffer versus region workflows, streaming and keybindings.
ReadCost & PricingEscalation Rate: The Metric That Decides Your Default Model
The share of tasks your cheap model cannot finish decides whether a two-tier setup saves money or quietly doubles it. How to measure and act on it.
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ModelsEvaluating Models on Your Own Codebase
Public benchmarks rank a sample that is not your repository. How to choose tasks, source ground truth and size a set that actually predicts your work.
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GuidesEvaluating Prompt Changes Without Fooling Yourself
You edited a prompt and the output looks better. Here is how to find out whether it actually is, with paired runs, enough samples and judges you can trust.
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ModelsEvery 1M-Context Model Compared: Which Window Is Real
Five open-weight models now advertise 1M tokens. Advertised context and usable context are different things. How to tell which window actually holds up.
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FundamentalsExpert Routing in MoE: How a Token Picks Its Path
The router decides which experts see each token, and that one small network shapes quality, throughput and why identical prompts can behave differently.
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GuidesExponential Backoff That Works on LLM APIs
The retry algorithm in detail: full versus decorrelated jitter, deadline propagation, idempotency, what to classify as retryable, and how to test it before production does.
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ModelsFallback Model Selection: Choosing Your Second Model Well
A fallback that behaves nothing like your primary turns an outage into a quality incident. How to pick a second model and prove it works.
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GuidesFew-Shot Example Management: Curation Beats Quantity
Few-shot examples decay, contradict each other and leak into output. How to choose them, order them, keep them in sync with your schema and know when to drop them.
ReadShowing 109–120 of 404 articles