News & guides
LLM Latency: Why Time to First Token Is the Number to Watch
LLM latency is two different problems wearing one name. Understanding prefill and decode tells you which knob to turn when a request feels slow.
· 9 min read
LLMs for Log Analysis Without Sending Them Your Logs
You cannot fit a day of logs in a context window and should not try. Template mining, diffing and sampling first — then a model on the reduced set where it earns its keep.
· 9 min readLocal Models vs API: Running the Numbers
Local inference is free at the margin and expensive everywhere else. Work through the memory arithmetic, the real break-even, and where each option genuinely wins.
· 9 min read
Long-Running Agents: Durability, Compaction and Restarts
An agent running for hours hits limits a chat never does: context exhaustion, process restarts, stale state. The patterns that keep long jobs alive.
· 9 min read
MCP Explained: One Protocol Instead of N×M Integrations
The Model Context Protocol standardises how agents connect to tools and data. What it solves, how it works, and when a plain function call is enough.
· 8 min read
Measuring AI ROI for Developers Without Fooling Yourself
Token counts are not returns. Here is how to convert engineer hours into money, pick a metric that survives scrutiny, and avoid the usual measurement traps.
· 8 min read
Migrating Off the OpenAI API Without Breaking Production
Swapping providers is two lines of config. Keeping quality, cost accounting and error handling intact is the actual work. A migration plan that survives contact with users.
· 9 min read
MiniMax M3 vs DeepSeek V4 Pro: Same Score, Different Models
Both land in the mid-forties on the Artificial Analysis index. They are not interchangeable, and the tie is a good lesson in why aggregate scores mislead.
· 9 min read
MiniMax M3: Multimodal Agent Work From a 428B Open Model
M3 is the smallest of the frontier-class open models and the only one that takes video natively. A look at sparse attention, the licence, and where it fits.
· 9 min read
Mixture of Experts: Why Trillion-Parameter Models Are Fast
MoE models decouple parameter count from compute per token, which is why a trillion-parameter model can be cheap to serve. Here is the mechanism and what it costs you.
· 8 min readModel Distillation Explained
How a small model inherits the behaviour of a much larger one, what gets lost along the way, and why distillation is the reason cheap models got good so fast.
· 8 min read
Model Routing: Send Cheap Work to Cheap Models
Most requests do not need your most capable model. Routing by task cuts cost sharply and adds resilience — here is how to build it without a mess.
· 8 min readReady to try it?
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