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The Wire
Decoder-only LLMs took all the oxygen, but the model quietly doing your retrieval, reranking, and classification is still a small bidirectional encoder — and in late 2024 it finally got a 2024-era redesign.
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The Wire
A year on, the data is in — almost nobody reads your llms.txt. The files that move the needle are the one that blocks crawlers and the content that earns a citation.
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The Wire
One framework makes you draw the control-flow graph up front; the other lets it emerge from events. Pick by whether your hardest requirement is durable recovery or flexible composition.
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The Wire
Your engine computes a KV cache, uses it once, and throws it away. Offloading turns that scratchpad into a shared storage tier — and changes the question you should be asking.
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The Wire
Four open-weight MoE models now run real agents. The headline parameter counts are nearly decorative — pick by active params and post-training, not by the leaderboard screenshot.
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The Wire
The way you start an agent — schedule, HTTP event, or message queue — decides its retry, durability, and concurrency behavior more than the framework you write it in does.
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The Wire
A max-step counter is the reflex, and it's necessary — but it caps the damage without fixing the cause. Agents loop because the thing they see never changes, and that's a fixable problem.
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The Wire
Durable execution and checkpointing give you at-least-once replay, which is strictly worse for side-effecting tools — unless you attach a stable idempotency key before the call, not after the crash.
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The Wire
Loading every tool definition upfront doesn't just burn context — it tanks tool selection. The fix has three shapes: tool search, tool-RAG, and code execution. Pick by what you retrieve, and when.
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The Wire
Print statements debug code. But the agent's code did exactly what it was told — the bug is in the context the model saw and the decision it made there. You debug an agent by reading transcripts, not by stepping through functions.
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The Wire
Google's Antigravity, Cursor, and Claude Code now all hit ~80% on SWE-bench. So the real difference isn't who writes better code — it's where each one puts the work of checking it.
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The Wire
Google's Genkit is the framework that bundles the parts the others sell separately. The real choice isn't features — it's where your code runs and how much of your ops you want the framework to own.
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The Wire
Three ways to keep an agent's untrusted code off your host kernel — and why the right choice is a triangle of compatibility, cold-start speed, and operational weight, not a security ranking.
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The Wire
The bottleneck in a coding agent isn't the smart model deciding what to change. It's the dull mechanical work of writing that change to disk correctly — and that's a different model entirely.
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The Wire
Storing embeddings at full precision is a tax most RAG systems don't need to pay. Binary cuts memory 32x — and the trick that buys the quality back is cheaper than the savings.
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The Wire
The async coding agents have all converged on the same shape — a cloud VM that clones your repo, runs the tests, and opens a PR. So the thing you're actually choosing isn't the coder. It's the harness and who reviews the flood.
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The Wire
DeepSeek's October paper shows vision tokens can carry roughly 10x the text of text tokens at ~97% fidelity — which quietly reframes long context as a compression problem, not a capacity one.
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The Wire
A long-running agent fails when its window fills with stale tool output. Anthropic ships three levers for that — and the trap is treating them as competitors instead of a division of labor.
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The Wire
The two best coding agents disagree at the architecture level on how to find the right code. One builds a vector index of your repo; the other threw the index away and runs grep. The split is about freshness, not accuracy.
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The Wire
One paradigm has an agent write a Python snippet as its action; the other has it emit a structured JSON tool call. The 20% accuracy gap everyone quotes is real — but only on the tasks where it applies.
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The Wire
They both promise durable, resumable agents — but one is a place to run code and the other is a way to structure it. Confusing the two is how teams end up with neither.
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The Wire
The eval-tooling field just split into three camps and lost two players to acquisition in a single month. Pick on philosophy and independence, not the feature grid.
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The Wire
The B200's headline 5-6x throughput jump is two different upgrades wearing one number — bigger HBM and FP4 compute — and which one matters depends entirely on whether your workload is memory-bound or compute-bound.
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The Wire
A Stanford/SambaNova method called ACE lets an agent get better by editing its own context instead of its weights — and the trick is to grow that context, not compress it.
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The Wire
An LLM judge scores the final answer. For a multi-step agent, that signal is sparse, late, and easy to fool — a broken trajectory can still land on a right answer, and you'd never know.
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The Wire
Three platforms that look like competitors but optimize for different primary jobs, with lock-in profiles that diverge sharply once you read the fine print.
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The Wire
A deep agent is not a new model or a framework breakthrough — it's four cheap, known ingredients that let a plain tool-calling loop survive a long task instead of drifting.
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The Wire
Most teams buy one vector store and call it 'memory.' It solves exactly one of the four problems — which is why the agent still loses the thread and repeats yesterday's mistake.
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The Wire
Is it cheaper to run an open model on your own GPUs than to call an API? The deciding number isn't the token price — it's how busy the GPU stays.
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The Wire
Both spend N times the inference to make a model smarter. The difference is how they choose the winner — and that choice decides which tasks each one can help.
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