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Agent engineering, memory systems, model serving and shipping to production. Every post also exists in Chinese and as plain Markdown for agents.

2026

  1. Check a relay API before you buy

    Cheap Claude, GPT and Seedance relays keep multiplying, and their groups are labelled 'official', 'official key' or 'native', but a group name is not evidence. What actually ties what a relay sells to the official API is the response id prefix, input tokens that match the official counting endpoint one for one, the error you get when you send a deliberately invalid parameter, the C2PA signature and signing certificate inside a video file, and the charges in the relay's own logs. This post walks through six questions: how to check each one, whether it costs money, what counts as a problem, and what to do without an official key. It ends with an open-source skill that hands the whole procedure to an Agent and writes the comparison report for you.

    #API Relays#new-api#Claude#OpenAI

  2. Is a Mac actually good for local LLMs

    Apple says the Mac mini offers "fast, private, on-device inference" and up to 13.5x faster LLM prompt processing; NVIDIA says the RTX 5090 Laptop GPU has 1,824 AI TOPS. What they compare against, how the test machines were configured, and the three numbers that decide how fast a model actually feels — memory bandwidth, the compute for reading prompts, and how much memory the GPU can really use — are not where you look first. Using the llama.cpp community's shared benchmarks across dozens of machines, a check on two machines of my own, and a re-run of Apple's own footnoted test on the 16 GB base model, and the coding, compiling and testing work where the Mac mini measurably wins, this post answers, case by case, what a Mac mini, a Mac Studio and each class of NVIDIA card are actually good for.

    #Local LLM#Hardware#Apple Silicon#NVIDIA

  3. What it costs to run an app online

    For anyone about to put their first app online without knowing whether to buy a server or go serverless. It first explains the few things on a cloud provider's shelf, then shows how to split your own app into three bills (does its time go to waiting or computing, how many bytes does each request move, does it keep books entry by entry), places four common apps (a personal website, a mini-program or app backend, an AI app, an image or video tool) with the free tiers and new-customer prices checked; then it builds an LLM gateway from scratch as the worked example, queries prices as of 2026-10-09 with the aliyun CLI, tccli, the clouds' public pricing APIs and official pricing pages, and works out the bill line by line for seven providers at 10 million, 100 million and 1 billion requests a month.

    #Cloud Hosting#Serverless#Cloud Servers#Cloudflare Workers

  4. Social posting and inbox APIs compared

    You want an Agent such as Claude Code to publish to X, LinkedIn, YouTube, Instagram and other accounts in one go, then read the comments and answer the DMs. How far that can go is decided first by each platform's official API; the tools only carry those official capabilities into one interface. Since February 2026 X lets programs reply only to people who mentioned you, nobody can read the comments or DMs of a LinkedIn personal profile, Facebook only lets you post to Pages, and Threads and YouTube have no DM API. Within those limits I checked about 60 unified APIs, social media suites, open-source projects and session-based APIs against their official documentation of 2026-10-09, cell by cell across platform × posting / comments / mentions / DMs, scored them for integration completeness with price only as the second column, and marked what I tested and what I only read.

    #Social Media Automation#MCP#Claude Code#X API

  5. Agents That Check Their Own Work and Tidy Their Own Memory

    Outcomes has an independent grader, blind to the writer's reasoning, check the work criterion by criterion and send it back until it passes; Dreaming reads an old memory store and past sessions between runs and writes a separate, reorganized store while leaving the original untouched. Both are Claude Managed Agents APIs on the Claude Developer Platform, and Claude Code does not have them. Following the official docs, this article explains why both are needed and how they work, then identifies their closest relatives in Claude Code (/goal and the unreleased auto-dream), runs /goal 12 times on the same task, and translates the design into two contracts you can rebuild on any Agent stack.

    Agent Architecture Deep Dives#Claude#Claude Code#Managed Agents#AI Agent

  8. How Agents Remember You

    From Ebbinghaus, H.M., working memory, and engrams to Mem0, Letta, Graphiti, LangMem, Cognee, and MemoryOS: a history of memory paradigms and a code-level comparison of what open-source agent memory systems actually implement.

    #AI Agent#LLM#Memory#Memory Systems

  9. How to Choose an LLM Inference Engine

    Aliyun's four-engine table — Ollama / vLLM / SGLang / HF Pipeline — is no longer enough in 2026. This piece re-maps inference engines into three tiers (Local → High-Performance Serving → Distributed/Disaggregated), covering 8 mainstream engines plus three new trends — PD disaggregation, speculative decoding, and FP4 quantization — with a decision matrix and a decision tree.

    #LLM#Inference Engine#vLLM#SGLang

  10. One File Path Eliminated 84% of Tool Calls

    OpenClaw's daily-ai-news cron job kept timing out. The root cause wasn't a weak model, bloated prompt, or upstream API failure — it was a missing absolute path in the SKILL.md, causing the Agent to spend 15 exec calls searching for a tool's location every run. Message count ballooned from 54 to 165, tool results from 204KB to 1.1MB. This is the full debugging story.

    OpenClaw Production Notes#OpenClaw#AI Agent#Cron Job#SKILL.md

  12. Claude's Tool Calling Paradigm Shift

    Anthropic's Programmatic Tool Calling and Dynamic Filtering aren't just feature additions — they represent a paradigm shift in Agent architecture: from natural language orchestration to code-driven orchestration, from full context injection to on-demand filtering. Synthesizing multiple non-AI-written deep-dive articles, this post covers architecture, benchmarks, and production patterns.

    Agent Architecture Deep Dives#Claude#AI Agent#Agent Architecture#Tool Calling

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