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The Wire
Three bets on the same idea — that the command line, not the IDE, is where coding agents live. And as of this month one of the three just changed its name and its terms.
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The Wire
Cache-augmented generation deletes the retriever and preloads your whole knowledge base into the KV cache. The real question isn't speed — it's whether your corpus fits and how often it changes.
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The Stack
Three clouds rent you the same frontier models. The thing that actually locks you in is the agent runtime wrapped around them, and most teams pick it by accident.
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The Stack
All three build Python agents, but they disagree on one thing — who owns the loop. That contract, not the benchmark, is what you live with for years.
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The Wire
The config-file war for how you talk to a coding agent didn't end with a winner. It ended with a foundation — and that changes which file you should actually write.
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The Stack
The vector database fight stopped being about speed. It's now about where your index sleeps — and whether you have one hot haystack or a million cold ones.
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The Wire
When one model won't fit on one GPU, you have two ways to cut it up — and the right cut is a description of your interconnect, not a tuning knob you guess at.
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The Wire
Both bolt a quality check onto RAG, but they fix different failures at different points — and the choice comes down to one question: do you control the model's weights?
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The Wire
Three popular RAG upgrades all transform the query before retrieval — and they're useless if your retrieval was failing for a different reason. Here's how to tell.
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The Wire
A single tokens-per-second number hides two workloads pulling in opposite directions — and the whole arc of serving optimization is the field admitting they should never share a GPU.
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The Stack
Multi-LoRA serving turns "one GPU per model" into "one GPU per base model, amortized across hundreds of tenants." Here are the tools that do it, and the kernel trick that makes it work.
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The Wire
Ollama just ripped out llama.cpp and bolted in Apple's MLX on the Mac. The switch is a tell about where your bottleneck actually lives — and when the older engine still wins.
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The Wire
The Model Context Protocol defines three server primitives split by who's in control — the model, the app, the user. The ecosystem implemented one of them.
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The Wire
Most MCP servers only answer requests. Sampling and elicitation are the two features that let a server reach back through the client — one to the model, one to the human — and almost no one implements either.
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The Wire
Loading every tool definition into context and round-tripping every result is how MCP agents stall. Code execution flips the model into a programmer — and moves the hard part to your sandbox.
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The Wire
Every major provider sells inference at roughly half price if you can wait up to 24 hours. The discount isn't the point — the contract is, and it tells you which agent work was never realtime to begin with.
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The Wire
Your chunks lose the document around them before they're ever embedded. Jina and Anthropic solve it in opposite places — one in vector space for free, one in the text for a price.
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The Wire
You quantized the weights to 4-bit and thought memory was solved. At long context the KV cache dwarfs the weights — and it needs a different kind of quantization to shrink safely.
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The Wire
Most RAG failures are retrieval failures wearing a generation costume — so measure the two halves separately or you'll tune the wrong one for weeks.
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The Wire
The weights are the easy part — the math you can do on a napkin. What silently OOMs your server in production is the KV cache, and almost nobody budgets for it.
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The Wire
The three formats aren't competing for the same job — one buys you faster math, one buys you smaller weights, and one is the fallback for hardware that can't do the first. Know which bottleneck you're paying down.
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The Wire
When retrieval underperforms, everyone reaches to fine-tune the LLM. The cheaper, higher-leverage move is to fine-tune the embedding model — and almost all the gain comes from one ingredient.
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The Stack
MCP standardized how an agent calls a tool. It said almost nothing about how the agent logs in as you — and that gap is the whole product these three are selling.
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The Stack
The real divide in open-source RAG isn't which library to import — it's whether to build with one at all, or deploy a finished engine. Three engines, three very different bets.
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The Wire
The architecture decision underneath every agent framework is one most teams skip — and the math of compounding errors says the boring choice is usually right.
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The Wire
The embedding model you pick barely moves your bill. The dimensions you store and the precision you keep — that's the recurring cost, and it's the decision almost nobody makes on purpose.
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The Stack
GRPO is now a commodity all three ship. The thing that actually sorts them is who owns the distributed orchestration — and how you keep one starving inference engine fed.
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The Stack
Three ways to serve embeddings at scale that look like rivals but answer a different question: should embeddings be a dedicated specialist, or ride on the GPU already running your LLM?
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The Wire
They look like a difficulty ladder. They're three orthogonal axes — and only one of them measures the thing that decides whether your agent survives contact with real users.
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The Wire
The new realtime models hear and speak in one step, no text in the middle. That deletes the seam where you used to read, log, and control everything. Here's the real trade.
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