Notes, mostly on data & agents.

Notes on data engineering, distributed systems, Rust, and — lately — AI agents. I've been writing here since 2015; some posts are reference docs I keep coming back to, others are thinking out loud.

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I am building anyrouter.dev
AIJul 19, 2026 · 19 min

I am building anyrouter.dev

I am building AnyRouter to solve my problem of collecting free LLM tokens from everywhere and unifying the MCP Gateway. Yes, it's another router, but this is a router of routers.

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Cloudflare Agents Week so far

Cloudflare CI/CD — A TypeScript-native CI/CD system built on Workflows. Define pipelines in code instead of YAML: each CI step maps to a Workflow step with built-in retries, state persistence, and parallel execution via Promise.all(). Includes dependency caching through sandbox snapshots in R2, and self-healing builds powered by Think agents on Workers AI that detect failures and push fixes automatically. Trigger pipelines directly on artifact push events — no manual queue wiring required. Cloudflare Wallet — cloudflare.pay — Cloudflare's payments and billing surface. Keep an eye on the blog for more details as it lands. Agent Tracing — Out-of-the-box debugging for AI agent sessions. Session replay lets you inspect full conversation context: system instructions, model thinking, tool calls, and responses. Trace waterfall view shows time allocation across model calls, tool execution, and infrastructure calls. Supports Think, Flue, and AI SDK harnesses natively, with OpenTelemetry export to any OTLP-compatible backend. Enable in wrangler.jsonc with observability.traces.enabled: true. Pricing starts October 1, 2026: 200k events/day free, then 20M included/month on Paid at $0.60/M additional. Currently free while in beta. (blog.cloudflare.com/agents-on-cloudflare) @cloudflare/computer — Early-preview agent runtime that dynamically orchestrates between fast isolates and full Linux containers on a shared virtual filesystem. Each agent gets a dedicated filespace while the platform picks the right execution environment per task. FUSE mount + SQLite-backed storage + agent hibernation when idle. Built on just-bash for isolates, npm-capable for containers. Open-source with tutorials at github.com/cloudflare/computer. (blog.cloudflare.com/cloudflare-computer) Otel traces in wrangler dev — OpenTelemetry tracing now ships inside local development. Agents get the same observability they have in production, built directly into Wrangler and the Cloudflare Vite plugin — no external collector setup needed to iterate locally. Agent Development Lifecycle (ADLC) — Cloudflare is replacing the traditional SDLC with an Agent Development Lifecycle built for autonomous software factories where agents own the full dev loop. Cloudflare identifies seven requirements: programmatic, horizontally scalable, reproducible, real-time/push-based, atomic changes, permissioned access, and self-improving. Building a Software Factory — Cloudflare's own internal implementation runs on the same primitives it ships to customers: Artifacts for versioned code storage, Workflows for durable orchestration, Sandboxes for isolated execution, Workers AI for agent intelligence, and Dynamic Workflows for customer-defined jobs alongside platform-managed pipelines. Feature flagging via Flagship gives every change its own flag; gradual deployments roll out code to a percentage of traffic over time. Enforcing AI Standards — Cloudflare now enforces AI coding standards across all internal repositories automatically: lint, typecheck, build, and AI-specific checks gate every PR. The same system powers self-healing CI that detects failures, diagnoses root cause, and pushes a fix commit without human intervention. Cloudflare MCP Server — Powered by Code Mode and Dynamic Workers, giving agents programmatic access to the entire Cloudflare API: buy domains, create accounts, manage DNS, query D1, upload to R2 — anything the API can do, an agent can do. Local dev for everything — Remote bindings let you run agents locally against real production resources without deploying. The local dev environment now mirrors production runtime across DOs, Workflows, Queues, and Containers, so what works locally works in production.

AX

Dries Buytaert's regarding AX (Agent Experience): In the era of AI coding agents, software adoption depends on how easily these agents can "consume" your project. AI agents prioritize the cheapest path to completion; if your software is difficult to use, they will simply reject it without notice. To ensure your project is chosen, you must reduce three specific costs: Friction: The effort required to get the system running (installation, setup, environment configuration, and access/credentials). Abstraction: The effort required to understand what to do next (providing clear recipes, templates, scaffolds, and logical defaults). Verification: The effort required to confirm the work was successful (reliable tests, clear error messages, and inspectable system states). What helps AI agents (AX) also helps human developers (DX). By making your project easy for an agent to install, modify, and verify, you make it better for everyone.

Dynamic Workflows with GLM 5.1

I am using more a lot of differrent models with Claude Code, this here is reazied We can also asking Claude to customize model and budget for each step in workflows.

MAI-Thinking-1, MAI-Code-1-Flash

| Benchmark | MAI-Thinking-1 | MAI-Code-1-Flash | Claude Haiku 4.5 | Sonnet 4.6 | Opus 4.6 | Kimi K2.6 | GLM-5.1 | | ---------------------- | -----------------: | -------------------: | --------------------------: | -------------------------: | -------------------------: | -----------------------------: | ---------------------------------------: | | SWE-Bench Pro | 52.8 | 51.2 | 35.2 | - | 53.4 | 58.6 | 58.4 | | SWE-Bench Verified | 73.5 | 71.6 | 66.6 | 79.6 | 80.8 | 80.2 | - | | SWE-Bench Multilingual | - | 65.5 | 62.7 | 75.9 | 77.8 | 76.7 | - | | AIME 2025 | 97.0 | - | 80.7 no tools / 96.3 Python | 95.6 | 99.8 | - | - | | AIME 2026 | 94.5 | 92.5 | 83.3 | - | - | 96.4 | 95.3 | | HMMT Feb 2026 | 84.9 | - | - | - | - | 92.7 | 82.6 | | GPQA Diamond | 84.2 | 84.6 | 73.2 | 89.9 | 91.3 | 90.5 | 86.2 | | LCB v6 | 87.7 | - | - | - | - | 89.6 | - | | AMO Bench | - | 40.0 | 16.0 | - | - | - | - | | Frontier Math | - | 6.3 | 2.8 | - | - | - | - | | HLE | - | 18.0 | 9.5 | 33.2 no tools / 49.0 tools | 40.0 no tools / 53.0 tools | 34.7 no tools / 54.0 tools | 31.0 no tools / 52.3 tools | | Frontier Science | - | 58.2 | 42.3 | - | - | - | - | | Artifacts Bench | - | 36.4 | 36.6 | - | - | - | - | | Terminal-Bench 2.0 | 46.0 | 54.8 | 41.6 | 59.1 | 65.4 | 66.7 | 63.5 Terminus-2 / 69.0 self-reported | | IF Bench | - | 75.0 | 46.1 | - | - | - | - | | Advanced IF | - | 71.4 | 56.9 | - | - | - | - | | Robust IF Bench | - | 61.2 | 45.0 | - | - | - | - | | τ²-Bench | - | 71.7 | 54.7 | - | - | - | - | Sources Microsoft AI, MAI-Thinking-1 technical report, for MAI-Thinking-1 scores and several comparison values. (Microsoft AI) Microsoft AI, Introducing MAI-Thinking-1, for headline AIME 2025 and AIME 2026 values. (Microsoft AI Microsoft AI, Introducing MAI-Code-1-Flash, for MAI-Code-1-Flash vs Claude Haiku 4.5 benchmark values. (Microsoft AI) Anthropic, Claude Haiku 4.5 announcement, for official Haiku context and Anthropic-reported SWE-Bench Verified score caveat. (Anthropic) Anthropic, Claude Sonnet 4.6 System Card, for Sonnet 4.6 SWE-Bench Verified and SWE-Bench Multilingual values. (Anthropic) Anthropic, Claude Opus 4.6 System Card, for Opus 4.6 SWE-Bench Verified and SWE-Bench Multilingual values. (Anthropic) Moonshot AI, Kimi K2.6 Tech Blog / Model Card, for Kimi K2.6 benchmark values. (Kimi) Z.ai, GLM-5.1: Towards Long-Horizon Tasks, and ModelScope GLM-5.1 card, for GLM-5.1 values. (Z.ai) Terminal-Bench official leaderboard, for Terminal-Bench 2.0 reference leaderboard context. (tbench.ai)

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