News & guides
Map-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.
· 9 min read
max_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.
· 8 min read
MCP 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.
· 9 min read
MCP 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.
· 8 min readMCP 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.
· 8 min read
Measuring 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.
· 9 min read
MiniMax 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.
· 8 min readMiniMax 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 How to decide whether a superseded model is still the right one for your workload.
· 9 min read
MIT-Licensed Models: What You Can Actually Build On
GLM-5.2 and both DeepSeek V4 variants ship under MIT. Kimi does not. Why the licence column decides more architecture than any benchmark score.
· 9 min readModel Deprecation and Migration: Planning for the Sunset
Every model you depend on will be retired. How to track deprecation notices, size the migration and move without a quality cliff on the cutover day.
· 9 min readModel Drift in Production: Detecting Quality Change With No Error
Model output quality can degrade without a single failed request. The signals that move first, how to instrument them, and what to do when one shifts.
· 9 min read
Model Parallelism: How Huge Models Are Split to Run
Tensor, pipeline and expert parallelism split a model across devices in different ways. Each moves a different cost onto the network, and that decides latency.
· 9 min readReady to try it?
Every model behind one key. Start free and upgrade when it sticks.