FundamentalsLoad Balancing in MoE: Stopping Experts Going Idle
An MoE model only delivers its promised capacity if traffic spreads across experts. Collapse during training and skew during serving are both real risks.
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.
Fundamentals
GuidesLogging LLM Requests Safely Without Building a Liability
The fields that make a model request debuggable are mostly not the fields that make it risky. How to log traffic that stays useful and minimal by default.
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FundamentalsLogits and Softmax: What a Model Really Outputs
A model does not emit words, it emits a score for every token in its vocabulary. What softmax does to those scores and why probabilities are not confidence.
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FundamentalsLong Context Degradation: How Quality Falls Before the Limit
Accuracy does not hold flat and then fall off a cliff at the context limit. It declines gradually, and the decline starts far earlier than most teams assume.
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AI AgentsMap-Reduce With Agents: Splitting Work That Actually Splits
Map-reduce over an LLM is not the same as map-reduce over data. Where the shape works, where the reduce step quietly loses information, and how to fix it.
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Fundamentalsmax_tokens and Truncation: Why Your Output Stops Mid-Sentence
A response that ends mid-word is almost never a model failure. It is a cap you set, or a default you never set. How truncation actually happens.
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AI AgentsMCP Server Design: Fewer Tools, Sharper Boundaries
Most MCP servers are a thin wrapper around an existing API, which is why agents use them badly. Design rules for servers models can actually operate.
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AI AgentsMCP Transport Options: stdio, HTTP and When Each Fits
MCP runs the same JSON-RPC messages over a local subprocess or an HTTP endpoint. The transport you pick decides your auth, scaling and debugging story.
ReadAI AgentsMCP vs Plain Tool Calling: When the Protocol Earns Its Keep
MCP and a plain function schema look identical to the model. The difference is who owns the integration, and that decides which one you should use.
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AI AgentsMeasuring Agent Success Rate Without Fooling Yourself
Success rate is the easiest agent metric to report and the easiest to game. Here is how to define it so the number predicts production behaviour.
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ModelsMiniMax M2.7 vs M3: How to Judge an In-Family Upgrade
Deciding whether to move from one generation of a model to the next, using what is actually verifiable about MiniMax M3 rather than a spec-sheet duel.
ReadModelsMiniMax M2.7: Should You Still Be Running It?
M2.7 predates M3 and its published specs are inconsistent across sources. How to decide whether a superseded model is still the right one for your workload.
ReadShowing 493–504 of 731 articles