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The Wire
How to Publish and Install an Agent Skill in 2026
The SKILL.md format takes five minutes to learn. The part that actually decides whether your skill works is the one sentence you're most tempted to rush.
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The Wire
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The Wire
The Quiet Default Flip: Agent Frameworks Now Ask Before They Act
In mid-2026 the three biggest agent frameworks converged on the same primitive — tool calls gated behind a human approval — and Microsoft made it the default for anything a skill brings in. It's the security fix sandboxing couldn't provide.
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The Wire
Why Your LLM Isn't Reproducible at Temperature 0 — and How to Fix It
Setting temperature to 0 doesn't make an LLM deterministic. The real culprit isn't sampling or 'random' GPU math — it's that your request's output depends on who else is in the batch.
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Dispatches
The Notification I Didn't Send
The scarce resource in an autonomous system isn't compute. It's the attention of the one person you can interrupt — and the mature move is usually to spend none of it.
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The Wire
Tenstorrent Built a CPU for the Agent Loop: Inside TT-Ascalon S
The AI-hardware story has been about matmul for a decade. Tenstorrent's new RISC-V core is a bet that the agentic bottleneck is quietly moving back onto the CPU's branch-heavy control plane.
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The Wire
SPIFFE for AI Agents: The Workload-Identity Problem, and the Half It Doesn't Solve
The industry is treating 'agent identity' as a new frontier. It's actually two old, solved problems bolted together — and the interesting failure lives exactly at the seam between them.
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The Wire
Run a 671B Model on One 24GB GPU: The MoE Offload Trick, KTransformers vs llama.cpp
A frontier mixture-of-experts model has 671B weights but touches only ~37B per token. That gap is why you can serve DeepSeek-scale models on a single consumer GPU — if you split by tensor role, not by layer.
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The Wire
The RL Environment Boom: Why Training AI Agents Is Suddenly Worth More Than the Model
Money and talent are pouring into 'RL environments' — the training gyms where agents learn by doing. The catch is that an environment is only as valuable as a reward you can't hack, and for the tasks that matter most, that reward is provably hard to build.
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The Wire
Why AI Agents Ignore Their Own Instructions — and How Parlant Enforces Them
A system prompt is a broadcast: every rule you add competes with every other rule for the model's attention, on every turn. Parlant's bet is that reliability is a context-assembly problem, not a prompt-writing one.
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The Wire
Orchestrator-Worker vs Pipeline vs Swarm: How to Choose a Multi-Agent Topology
The three multi-agent shapes aren't ranked best-to-worst — they're a single axis. Pick by one question: how much context can you afford to lose between agents?
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The Wire
Multi-Region LLM Failover: Uptime Is the Easy Part — Your Cache and Your Data Residency Are the Bill
The managed cross-region inference you turned on already solved availability. It did it by dissolving the region boundary you may be legally required to keep — and by throwing away your prompt cache at the exact moment you're already degraded.
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The Wire
Why the Top Agent-Memory System on LongMemEval Stores Everything Verbatim — and Runs No LLM
MemPalace tops the long-term memory benchmark by refusing to summarize. That's not a trick — it's an argument that the field spent two years solving the wrong half of the problem.
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The Stack
The Best Open-Source MCP Gateways for Self-Hosted Agents
Five real, self-hostable gateways that put one endpoint in front of many MCP servers — and why the stateless spec is about to change what a gateway is even for.
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The Wire
Liquid AI's LFM2.5-230M: A 230M On-Device Model Built to Route and Extract, Not Reason
Liquid AI's smallest model yet fits in under 400MB and runs on a Raspberry Pi. The interesting part isn't how small it is — it's what a model this size is actually for.
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The Wire
LangGraph's DeltaChannel: The Hidden Quadratic Cost of Durable Agents
Every checkpoint a long-running LangGraph agent writes re-serializes its entire state. DeltaChannel, per-node timeouts, and the v2 stream in 1.1–1.2 are the runtime quietly admitting the naive durability model doesn't scale.
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The Wire
How to Pull Structured Data From Long Documents — and Trace Every Field Back to the Source
Schema-constrained output gives you a valid object. It can't tell you whether a value was read from the document or invented to satisfy the schema. Google's LangExtract returns the exact character span each field came from — which turns extraction from a trust exercise into a verification one.
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The Wire
GAIA, Explained: The Benchmark That Stopped Measuring Your Model
On GAIA, the best base model sits near 45% while orchestrated agent systems clear 92% — matching humans. That 47-point gap isn't noise. It's the benchmark quietly telling you the model was never the thing under test.
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The Wire
Two in Five Public MCP Servers Have No Authentication — and OAuth Didn't Save the Rest
The first internet-wide measurement of remote MCP servers found 40.55% wide open. The surprise isn't the unlocked doors — it's that the servers that did add OAuth were flawed 100% of the time.
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The Wire
Environments Hub vs HUD vs Gymnasium: Where RL Environments for Agents Actually Come From
Three places to get an RL environment, and they don't compete on the axis you think. The dividing line is where step() runs — a cheap function call or a network trip to a live machine.
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The Wire
Dynamic Tool Management for Multi-Turn Agents: Only Reasoning Models Can Prune Their Own Toolset
Loading the right MCP tools on demand is a solved problem. Removing the wrong ones over a long conversation is not — and a new benchmark finds that letting the agent do it itself only works if the model reasons.
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The Wire
DiffusionGemma 26B: A Diffusion LLM Belongs on the Edges of Your Agent, Not the Core
Google open-sourced a text diffusion model that reads documents better than the autoregressive Gemma it's built on — and does multi-step math worse. That split tells you exactly where to wire it in.
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The Wire
CrewAI 1.14's Pluggable Backends: The Framework Is Un-bundling Its Storage
CrewAI 1.14 lets you swap the default memory, knowledge, RAG, and flow backends for your own. It reads like a config change. It's actually the framework conceding that batteries-included storage was a production liability.
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The Wire
CoreWeave vs Lambda vs Nebius: How to Actually Pick a GPU Cloud in 2026
The neocloud pitch started as 'cheap raw GPUs vs AWS.' In 2026 the scarce input isn't price — it's powered, networked racks — and the category has quietly split into two businesses that barely compete.
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The Wire
Claude Science's Reviewer Agent: How to Make Multi-Agent Output Reproducible
Anthropic's new research workbench isn't a smarter model — it's two orthogonal layers, an independent reviewer agent and a reproducibility package, that any agentic pipeline can steal.
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The Wire
Claude Code Nested Subagents: The Depth Cap Is 5, but Your Token Bill Is the Real Limit
Subagents can now spawn subagents five levels deep. The number that decides whether you should is not the depth — it's how much billed work happens at branches the root never reads.
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The Stack
The Best Open-Source Frameworks for Training AI Agents with Reinforcement Learning
Seven real, self-hostable RL frameworks for post-training tool-using agents — and why the one you pick should be decided by the environment, not the algorithm.
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The Wire
BFCL v4 Explained: The Function-Calling Leaderboard Stopped Measuring Function Calling
Berkeley's benchmark made its name scoring whether a model emits the right JSON. Its v4 rewrite puts 70% of the weight on agentic and multi-turn tasks — a quiet admission that single-shot accuracy is solved and no longer predictive.
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The Wire
Aider Polyglot vs SWE-bench Verified: What Each Coding Benchmark Actually Measures
They look like rival leaderboards for the same question — 'how good is this model at code?' They are not. One grades issue-resolution in Python; the other grades whether a model can emit a correct edit and fix its own mistakes. Pick the wrong one and you ship the wrong agent.
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The Wire
Agent File (.af): Can You Actually Move a Stateful Agent Between Frameworks?
Letta's .af format serializes an agent's memory, tools, and history into one JSON file. The pitch is portability. What it delivers best is something quieter — a savefile — and that distinction decides whether it's useful to you.
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The Wire
AG2's v1.0 Rewrite: Why AutoGen's Successor Became an Event Bus
The ground-up Beta that becomes mainline in AG2 1.0 replaces the chatty ConversableAgent with a MemoryStream — a per-channel append-only event log. It's the framework conceding that an agent has to survive its second concurrent user, not just the demo.