ModelsKimi K3 vs Kimi K2.6: Is the Upgrade Worth 4x the Price?
K3 is the stronger model. K2.6 costs roughly a quarter as much per output token and activates a third of the parameters. When the older model is still the right call.
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.
Models
ModelsKimi K3 vs MiniMax M3: The Top and the Floor
One is the strongest open-weight model available. The other is among the cheapest capable ones and natively multimodal. The gap between them is a budget decision.
Read
ModelsKimi K3 vs Qwen 3.6: A Cluster or a Single GPU
K3 needs roughly 1.6TB of weights and multi-node serving. Qwen 3.6 27B is dense and fits on one accelerator. The comparison is about deployment, not benchmarks.
Read
FundamentalsKV Cache Explained: The Memory Behind Long Context
The KV cache is why generation is fast and why long context is expensive in memory rather than compute. What it stores and what it costs you.
Read
ModelsLatency-Adjusted Model Scoring: When Fast Beats Smart
Benchmarks measure what a model answers, never how long it took. How to weight latency into model selection for interactive and agentic workloads.
Read
FundamentalsLayer Normalisation: The Part That Keeps Training Stable
Why deep transformers need normalisation, what pre-norm changed, and why RMSNorm won. The connection to quantisation and low-precision serving.
Read
GuidesLlamaIndex Setup on a Custom OpenAI-Compatible Endpoint
Configure LlamaIndex against a non-OpenAI base URL, split the LLM from the embedding model, and avoid the defaults that silently call OpenAI anyway.
Read
GuidesLLM Classification: Fix the Taxonomy Before the Model
Most classification failures are label definition failures. How to design a taxonomy, handle abstention and imbalance, and check the model beats a cheap baseline.
ReadGuidesLLM Data Extraction: Design the Schema Before the Prompt
Extraction failures are usually schema failures. How to model absent, ambiguous and multi-valued fields, attach provenance, and validate what comes back.
Read
GuidesLLM Summarisation: Omission Hurts More Than Invention
Summaries fail by leaving things out, not by making things up. How to control what gets kept, pick a chunking strategy, and evaluate faithfulness cheaply.
Read
GuidesLLM Translation: Placeholders, Glossaries and Register
Machine translation is solved enough. What breaks in software localisation is interpolation syntax, terminology drift and missing context, not language quality.
Read
GuidesLLM-Powered Search: The Model Is Not the Retriever
Adding a model to search usually means query rewriting and reranking, not generation. Where each stage helps, what it costs in latency, and how it fails.
ReadShowing 481–492 of 731 articles