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Field SOP

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

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Tencent 770B Flagship Self-Hosting SOP: Eight H100s Can't Even Fit the FP8 Weights - Official Baseline Is 16x B200

A complete SOP for self-hosting Tencent's 770B flagship Hy4 preview. Cold water first, via the VRAM math: FP8 weights run about 770GB, and the official vLLM recipe states the baseline is 16xB200 or 8xB300 (weights + KV cache) - 8xH100 (640GB) cannot even fit FP8 weights, since 49B active params save compute but all 770B weights must sit in VRAM. Both deployment routes quote the official README verbatim: the vLLM prebuilt image (MTP speculative decoding num_speculative_tokens=3, FLASHMLA_SPARSE attention backend, hy_v4 tool/reasoning parsers) and the SGLang prebuilt image (NEXTN speculation, tp-size 8). Includes OpenAI-compatible invocation (temperature 0.9 / top_p 1.0; no_think to skip deep reasoning and save output tokens), AngelSlim self-quantization, the finetune pipeline, 7 pitfalls and a 10-item launch checklist; if you skip self-hosting, use Tencent Cloud TokenHub/OpenRouter or the two-week free tier on WorkBuddy/CodeBuddy.

GLM-5.3-Flash Integration SOP: Three Routes From ¥0.8/M Tokens to 100,000 Domestic GPUs - Visual Coding Running in a Day

A complete SOP for integrating GLM-5.3-Flash from zero and getting visual coding running in a day, across three routes: direct API (first result in 10 minutes via one curl call; official recommended params temperature 1, top_p 0.95, reasoning_effort max; thinking only supports enabled, and stream/tool_stream must be turned on as a pair); GLM Coding Plan subscription (wire 20+ coding tools to GLM in half an hour, three tiers at 118/538/1,078 RMB with 3x quota); and self-hosting the open weights (vLLM/SGLang, VRAM figures are engineering estimates). The core deliverable is the Visual Coding screenshot feedback loop: after each render, pass the UI screenshot back as image_url so the model iterates on its own output. Includes a parameter acceptance checklist, per-type token accounting, and 5 field-tested pitfalls.

GPT-Image 2 Commercial Image SOP: From Structured Prompts to Cost Control, 1 to 15 Cents per Image

"How much does one AI poster cost?" now has a precise answer in the GPT-Image 2 era: $0.006-0.211 per image on the official API, half price on Batch. This SOP walks the full pipeline from requirement to deliverable: translating needs into tiers (Instant/Thinking, low/medium/high, 3:1-1:3 ratios, and the 2025-12 knowledge-cutoff web-access pitfall); structuring prompts (the five-block atomic method - subject/layout/style/text-lock/constraints - with explicit text locking against garbling); calling the API (curl and Python examples against /v1/images/generations, with token-billing conversion); choosing channels (official, half-price Batch, Azure 4K, per-image gateways in a cost table); batching with a five-point QA checklist; and seven field-tested pitfalls (diminishing edit returns, billed input images, never shipping the low tier). Includes the $30-120 budget band for 1,000 posters.

Wan3.0 Hands-On SOP: The Full Pipeline from PPT to a 30-Second Finished Video

On 2026-08-24 Alibaba Cloud Bailian launched Wan3.0-Video: first-ever direct PPT/PDF/Word document input, 2-30 second single generations across 480P/720P/1080P, API billed per second at 0.3/0.6/1.2 CNY. This SOP walks the full pipeline from document to 30-second finished film: Bailian keys and region consistency, the zero-code entry points, the DashScope SDK (>=1.25.16) with async polling and the 24-hour video_url window, a timestamped storyboard prompt template, and the file-or-link reference-video workflow - plus a cost table (1080P/30s at 36 CNY list, 25.2 CNY on the 30% promo, 60-80 CNY with gacha budget) and 7 pitfalls.

Freeload OpenAI's Top Agent Foundation: The Codex Harness Integration SOP - a Three-Stage Rocket from One Command to a Production Runtime

The integration SOP for freeloading OpenAI's top agent foundation: a three-stage rocket. Stage 0 authentication (login_chatgpt / login_api_key; keys in env vars, never prompts) -> Stage 1 codex exec, one command for CI/scripts -> Stage 2 the Codex SDK as a callable function (startThread/run, outputSchema structured output, thread persistence in ~/.codex/sessions with resumeThread breakpoint resume, runStreamed event streams, sandbox and filesystem-granularity permission config examples) -> Stage 3 codex app-server as a production runtime (JSON-RPC 2.0, generate-json-schema, first-class Approvals semantics, -32001 overload retry, /readyz /healthz probes). Includes a 10-item pre-launch checklist and five classic pitfalls (keys in prompts, trusting sandbox defaults, non-Git directories, re-running instead of resuming, jumping to app-server on day one). All examples from the official repo docs; not legal or security-compliance advice.

Agent Guardrails SOP: If OpenAI Got Burned, Where Is Your Permission Checklist?

A deployment SOP for putting reins on AI agents: even OpenAI just crashed (its Aug 18 slowdown announcement), so ordinary teams need engineered guardrails all the more. Five steps: a three-question risk tier (L1 read-only / L2 sandboxed writes / L3 production, money, or external systems) -> least-privilege credentials (keys in env vars never in prompts, scoped tokens with expiry, physical separation of production and agent credentials) -> the sandbox execution layer (selection conclusions from this batch's comparison: E2B for fastest launch, CubeSandbox for self-hosted out-of-the-box, agent-sandbox on K8s - with a minimal E2B example) -> monitoring and approval gates (full behavioral logs, human approval for high-risk actions, and the cheap-model-watches-expensive-model AI-monitor pattern) -> circuit breakers and incident response (dual token-and-action caps, auto-pause on anomalies, a four-column post-mortem template). Includes a 10-item pre-launch checklist and five classic pitfalls. Not legal advice.

Before You Hand Your AI Agent a Wallet: A Grounded SOP from Sandboxed Budgets to Real Payments, with Three Circuit Breakers

An SOP for wiring payments into AI agents: five steps - a three-question scope check (90% of needs stop at quotas) -> the sandbox layer (prepaid isolation, the QPS/daily/per-request cap trio, read-only payment tools, full logging; 7-day graceful-degradation gate) -> choose a rail (the stripe/ai official repo MCP path vs the x402-fetch npm package) -> hands-on integration (read-only-first MCP JSON config plus wrapFetchWithPayment code) -> three circuit breakers (limits / allowlist / human approval) and a launch checklist. Five pitfalls: credentials in prompts, capless launches, limits without allowlists, skipping the sandbox, and forgetting refunds and reconciliation. Not legal or investment advice.

Give Your AI Content a Legal ID Card: A Labeling Compliance SOP (Explicit + Implicit Labels, Hands-On)

An AI content labeling compliance SOP: four hands-on steps under the labeling Measures + GB 45438 - a three-question scope check -> explicit labels (per-content-type table + platform declaration toggles) -> implicit labels (full c2patool install / manifest.json trainedAlgorithmicMedia / embed-and-verify commands) -> publish and keep records. Five pitfalls: platform re-compression strips metadata / label-stripping tools are unlawful and ineffective / do not hide explicit labels / watermarks are just one implicit form / write labeling duties into outsourcing contracts. Not legal advice.

Hands-on SOP: One npx Line to Run DeepSeek Harness - Install, Four Modes, Trajectory Replay, and Your First Plugin

A DeepSeek Harness quickstart SOP: environment prep -> one-line launch via npx @deepseek-ai/dsh web (127.0.0.1:3080) -> mode-selection mnemonic (work in Standard / orchestrate in Code / test models in Minimal / write plugins in Creator) -> trajectory replay & fork for debugging -> your first dsh-plugin. 5 pitfalls: no production on v0.1 / audit plugins first / budget tokens (peak-hour price hike) / two network chokepoints / spartan UI is by design. Per the official repo.