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Scenario-based tutorials with real screenshots, prompts, workflow templates and pitfalls.

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Qoder Free Credits Claim and Usage Management SOP

A hands-on SOP for claiming and managing Qoder's double promo: download and install (international qoder.com or China qoder.cn, across desktop, mobile, IDE, JetBrains plugin and CLI), sign up (the two editions keep separate accounts and quotas), confirm the free window works (selecting Qwen3.8-Flash in the model picker bills at a 0x coefficient, nothing to claim), then the daily 100 Credits rhythm (opens 10:00 daily, one claim per cycle, no carryover of missed days, each grant valid 30 days and stackable), usage management (check burn in the usage panel, let Qwen3.8-Flash carry routine work and save Credits for hard tasks), deduction rules (earliest-expiring credits are consumed first, in-plan before add-on packs on the same day), and a closing plan for when the window ends on September 30. UI details follow the actual client.

Octop Self-Hosted AI Assistant Deployment SOP

A hands-on SOP for deploying Octop: it starts with a decision framework on whether to self-host at all, then walks four install paths (one-line script, Windows PowerShell, Docker Compose, and the Tencent Cloud Lighthouse or CVM official image marketplace), runs octop init and octop run verbatim from the official README (default port 8088), changes default credentials on first login (the README hardcodes none, third-party reviews report admin/octop, and Docker init generates a random one), then configures models (OpenAI-compatible, Ollama, nearly 20 providers), experts with MBTI personas, connectors (Tencent Docs, OAuth, MCP) and IM channels, and closes with Docker Compose and PostgreSQL productionization plus a six-item pitfall table and a ten-item pre-launch checklist.

Intern-S2 in practice: from free API to scientific workflows

A hands-on SOP for accessing Intern-S2: for individuals and small teams the realistic path is the free API (chat.intern-ai.org.cn for online use, internlm.intern-ai.org.cn/api/strategy for quota), while institutions with compute can run the HuggingFace weights at internlm/Intern-S2-397B. It gives a three-way access comparison table, a minimal runnable Python call for the free API, an HF inference skeleton, two copy-paste prompt templates for scientific long-horizon tasks (molecule binder design, materials structure generation), plus Memory Decoder mounting notes and a ten-item pitfall list (free-tier rate limits, 397B out-of-memory, long-context truncation, the Preview model's 2026-10-31 shutdown and migration). Bottom line: start free on the API, do not jump straight to self-hosting a 397B model.

Wiring diagram-design into Claude Code: a hands-on SOP

A hands-on SOP for wiring the diagram-design diagram skill pack into a daily coding workflow, with commands taken verbatim from the project's official README. It runs in seven steps: first what the pack does and does not solve; then a per-host install and update command table (Claude Code's /plugin marketplace add and /plugin install, Codex's codex plugin marketplace add and plugin add, the copilot plugin family for GitHub Copilot, the droid plugin family with --scope user for Factory Droid, pi install plus /reload for Pi, a subdirectory URL import for Kiro, and a directory copy or symlink for OpenCode); then the first-run gate, which stops to ask when the default skin is untouched, and brand onboarding, which reads your site for palette and fonts, maps them to semantic tokens, checks WCAG AA contrast and emits a fidelity receipt; then drawing and self-check, with three copy-paste natural language prompts, the official six criteria for it working, and self_check.py printing OK as the pass condition; then export and import, covering the four dials, the diagram-only boundary, and what a fidelity ledger looks like; and multi-client brand isolation via named profiles plus a .diagram-design marker file. The seventh section is a ten-item pitfall table with symptom and cause for each: Claude Code disables auto-update by default for third-party marketplaces, Factory Droid tracks plugins by commit rather than manifest version, Pi has no auto refresh and needs pi update --extensions, Kiro copies rather than links, OpenCode copied installs never self-update, a legacy standalone npx skills add copy will not follow the Codex marketplace, a customized style-guide.md can be overwritten by package updates, the first PNG export fails without Playwright and Chromium, readers assume exports include the full layout, and motion HTML screenshots capture an intermediate frame. The core claim: the real barrier is not installation but update paths and output boundaries, and the fact that the official README spells out update commands per host is itself the signal that cross-host skill distribution and upgrades still have no unified answer.

LingBot-World 2.0 Local Small Model Deployment SOP

A hands-on SOP for running LingBot-World 2.0's 1.3B causal-fast locally: environment and dependencies (torch 2.4.0 or newer, flash-attn and the rest, commands taken verbatim from the official requirements.txt), then weights download (the 1.3B package ships DiT weights only, while T5, VAE and the tokenizer are shared with 14B, so you must point assets_dir at a 14B directory or it will not start), then a first successful clip (torchrun or the official run_fast.sh), then parameter tuning (frame_num must be 4n+1, local_attn_size 18, sink_size 6, chunk_size, base_seed, save_dir), and finally production and deployment paths (the official team releases no deployment code, so reference the SGLang cookbook or NVIDIA flashdreams), closing with eight pitfalls and a ten-item launch checklist. Key pitfalls: the hardware bar has three conflicting versions (README 1.3B example uses 4 GPUs, run_fast.sh reference says 2, media claim consumer single-card real time), so trust the repo, treat 2 GPUs as the reproducible floor, and mark single-card real time unconfirmed; ulysses_size must divide the attention head count (12 for 1.3B, 40 for 14B) and equal nproc_per_node; choose between causal_fast (4 steps per chunk, no CFG) and causal_pretrain (40 steps per chunk, with CFG); and the CC BY-NC-SA 4.0 license is non-commercial, so confirm authorization before any productization.

GPT-Live-1 Realtime Voice API Integration SOP

A five-step SOP for taking OpenAI's GPT-Live-1 real-time voice API into production: (1) fit and non-fit - real-time phone voice agents and voice customer service versus local batch dubbing (see the same-batch VoiceStudio for the latter); (2) a pre-integration checklist - permissions and quota, inventory of text-pipeline changes, a regression baseline, and whether backend strong-model hand-off is needed; (3) the five integration steps - centralize auth and credentials (no hardcoded keys), a minimal runnable real-time voice script (WebSocket/HTTP skeleton with auth, session creation, audio-frame send/receive), integration with the business pipeline (feed recognition results to logic, re-inject backend output into synthesis), a backend strong-model hand-off design (when to call GPT-5.6 Sol / GPT-6 Astra and how to meter cost), then gradual rollout and monitoring (concurrency, duration distribution, retry, cost alerts); (4) voice-agent specifics - regression testing for interruption handling and noise robustness, and the state-management complexity of full duplex; (5) seven pitfalls and a ten-item launch checklist. Every price, rate limit and concurrency ceiling is marked "see official docs" rather than invented.

DeepSeek V4.1 Flash Integration SOP: Five-Step Migration

A five-step SOP for taking DeepSeek V4.1 Flash into production: (1) decide what should and should not migrate - leave production paths that depend on quirky legacy-model behavior alone for now; (2) a pre-migration checklist - inventory every config, env var and hardcoded string where the model name appears, and prepare a representative prompt set as a regression baseline; (3) the five migration steps - switch the model name to deepseek-flash (centrally managed, not scattered hardcoding), run a minimal verification script, diff outputs against the old model with attention to format stability and instruction following, roll out gradually behind a rollback switch, then watch failure rate, retry rate and output-length distribution; (4) tie it to agent workloads by comparing token consumption before and after the switch on the same batch of long-trajectory tasks, verifying the claimed KV Cache compression yourself rather than taking launch copy at face value; (5) six pitfalls and a ten-item launch checklist. Every price, rate limit and window figure is marked "refer to the official documentation" rather than invented.

LLaDA-Image Local Deploy SOP: Setup, Inference, Production

A five-step SOP for running Ant's open-source 6B image model LLaDA-Image: (1) environment setup with dependencies and mirror-accelerated downloads; (2) choosing among four weight variants (Base 50-step / Turbo 4-step, each in BF16 or FP8, with ModelScope for China); (3) generating the first image with minimal Base and Turbo commands; (4) advanced work - reference-image editing, text rendering, ComfyUI integration, and degradation strategies when VRAM runs short; (5) productionizing with batch queues, concurrency sizing, cost monitoring, result storage and graceful failure modes. Includes 6 pitfalls and a 10-item launch checklist, with every command copied verbatim from the official README; note the repo license is null, so confirm rights before commercial use.

Self-Hosting OpenMAIC: From Zero-Deploy to Agent Workbench

A complete SOP for getting OpenMAIC running from zero: (1) zero-deploy hosted mode with an access code from open.maic.chat; (2) standard local setup (pnpm >= 10: clone, pnpm install, .env, pnpm dev); (3) production (pnpm build && pnpm start, one-click Vercel, docker compose up --build); (4) advanced (Postgres persistence profile, ACCESS_CODE, MP4 export profile, Lemonade/FunASR local providers); (5) wiring it into agent workbenches (clawhub install openmaic or importing skills/openmaic/, generating classrooms from Feishu/Slack messages). Includes 6 pitfalls and a 10-item pre-launch checklist, with every command copied verbatim from the official README.

Kimi Dual Protocol: One Config for Codex and Claude Code

Moonshot announced on 2026-09-02 that the Kimi API natively supports dual protocols: OpenAI Responses (api.moonshot.cn/v1) plus Anthropic Messages (api.moonshot.cn/anthropic), with kimi-k3 as the flagship model. Hands-on SOP: point Claude Code's ~/.claude/settings.json ANTHROPIC_BASE_URL to /anthropic with model kimi-k3[1m]; set Codex's ~/.codex/config.toml wire_api="responses". This turns Kimi into a unified model-routing gateway — switch the backend without touching client code. Boundaries: Responses is text+image only, kimi-k2.7-code forces thinking, and the old ANTHROPIC_API_KEY must be removed.