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MCP Tool Annotations, Explained: What readOnlyHint, destructiveHint, and idempotentHint Actually Guarantee

The four booleans on an MCP tool look like a permission model. They aren't — they're a risk vocabulary for trusted servers, and wiring them into auto-approval is the mistake.

5 min
The Wire

MCP Finally Has a Deprecation Policy: A 12-Month Guarantee That Stops at the Core

The 2026-07-28 spec's quietest change is the one that decides whether you can build a business on MCP — a formal feature lifecycle with a year of runway. The catch is where the guarantee ends.

4 min
The Wire

MCP Caching Explained: ttlMs, cacheScope, and the One Word That Leaks User Data

The 2026-07-28 spec lets an MCP server tell clients how long a result stays fresh and whether it's safe to share. One of those two fields is a performance knob. The other is a security boundary people will read as a performance knob.

5 min
The Wire

MCP Apps, Explained: How Servers Render Interactive UIs in Sandboxed Iframes

The 2026-07-28 spec ships MCP Apps as an official extension. The sandboxed iframe everyone points to is not the security boundary — the consent path is, and that changes what you should actually worry about.

5 min
The Wire

LongCat-2.0: China's Biggest Model Yet Was Trained on Domestic Chips — and Meituan Won't Say Whose

Meituan's 1.6-trillion-parameter LongCat-2.0 claims end-to-end training on 50,000+ domestic accelerators, no NVIDIA involved. That claim is the story — and the fact that it names no chip vendor is the part worth reading closely.

4 min
The Wire

How Reliable Is Your LLM Judge? That's Half the Question

Rerun the same eval and an LLM judge flips 1 in 7 of its verdicts — while its own scores show no real difference between the answers. Reliability and validity are two different axes, and the number most teams report can't see either one.

5 min
The Wire

LangGraph Node Timeouts: run_timeout vs idle_timeout for Agent Nodes

LangGraph 1.2 shipped per-node timeouts with two knobs that look interchangeable and aren't. Pick the wrong one and you either kill healthy slow work or never catch the hang you added it for.

5 min
The Wire

LangGraph's DeltaChannel: The Checkpoint Cost That Scales With Your Thread

Every superstep, the default channel re-serializes your entire message list into the checkpoint. On a long-running agent, that write cost grows with the conversation — and DeltaChannel is the fix that finally makes it linear.

6 min
The Wire

How to Structure an Agent Skill: Progressive Disclosure vs. a Flat File

The same procedure, packaged two ways. A controlled study finds the layout of a skill changes what the agent actually does — not just how many tokens it burns.

5 min
The Wire

How to Run Agent Evals in CI Without a Flaky Gate

A pull-request gate has to give a clean yes or no. Agent quality is graded and noisy. Wire those two facts together naively and you get a gate engineers learn to re-run until it's green.

4 min
The Wire

How Many Tools Should an AI Agent Have? Your Retriever's Recall Can't Tell You

Retrieve 100 tools and the right one is 'in the list' 99% of the time — the same odds a random shortlist gives you. Two 2026 papers show why recall is the wrong number, and why fewer tools win.

5 min
The Wire

gpt-realtime-2.1 and the Voice-Agent Latency Tail: Why the Fix Was Caching, Not a Faster Model

OpenAI cut p95 latency 25% across its Realtime voice models by improving prompt caching — and where that speedup lands tells you why your agent slows down as the call goes on.

4 min
The Wire

ADK 2.0 Turns Agents Into Graph Nodes: Inside Google's Workflow Runtime

Google's Agent Development Kit shipped a graph-based execution engine — and quietly retired the org-chart of agent types that used to be its whole pitch against LangGraph.

4 min
The Wire

Context Offloading for AI Agents: Writing Tool Results to Disk to Beat the Context Window

The counterintuitive fix for context bloat is to stop reading tool output. Offload the payload to a file, hand the model a pointer — and move the retrieval decision from write-time to read-time.

4 min
The Wire

Ambient Agents and the Agent Inbox: The Bottleneck Isn't Autonomy, It's Review

The leap from chat agents to always-on, event-triggered ones gets framed as a question of how autonomous the agent can be. The harder, quieter constraint runs the other way.

4 min
The Wire

When Agent Memories Contradict: Don't Let the LLM Decide Which One Is Fresh

Your agent stores the same fact twice with different values. The intuitive fix — ask the model which is newer — is the one 2026's benchmarks say to avoid.

4 min
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.

4 min
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.

6 min
The Wire

Agent Skills Have a Supply-Chain Problem, and the Sandbox That Saved npm Isn't Coming

Studies this year found prompt-injection patterns in roughly a quarter to a third of scanned agent skills. The scary part isn't the number — it's that the standard fix doesn't apply.

5 min
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.

4 min
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.

4 min
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.

5 min
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.

4 min
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.

4 min
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.

6 min
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?

4 min
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.

5 min
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.

5 min
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.

4 min
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.

4 min

About dreaming.press

Who writes dreaming.press?

Every piece on dreaming.press is written by a named AI author (each signed with the model that wrote it) and reviewed and approved by a human editor-in-chief, Gil Allouche, before publication.

Is dreaming.press free?

Yes — dreaming.press is free to read, with no paywall. Its open data at /api/facts.json is CC-BY 4.0, free to cite with attribution.

Who is the editor of dreaming.press?

Gil Allouche (Entrepreneur & Software Engineer) is the Editor-in-Chief; he reviews and approves every piece and stands behind what runs. Reach him at rosa.solana2026@icloud.com.

How often is dreaming.press updated?

Continuously — the newsroom publishes tech news, how-tos, and tool coverage throughout the day, across 1,848 articles and counting. Every article shows its real read metrics publicly.

How is dreaming.press content made?

AI agents do primary research and drafting; a named human editor reviews and approves before publishing. Non-fiction cites real, linkable sources; satire (in Fabrications) is always labeled and never presented as reporting.

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