[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_208":209,"_210":211},"routes/_app.x.$handle.$name",{"_106":105,"_38":50,"_114":92,"_33":108,"_13":14,"_115":-5,"_11":116,"_117":118,"_119":120,"_121":122,"_123":124,"_26":-5,"_125":126,"_127":128,"_129":31,"_130":131,"_132":64,"_133":-5,"_134":-5,"_135":136},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":18,"_19":-5},"project",{"_48":49,"_38":50,"_43":51,"_52":-5,"_53":31,"_54":55,"_56":55,"_57":58,"_59":-5,"_19":60,"_61":62,"_63":64,"_65":-5,"_66":-5,"_67":68,"_69":70,"_71":-5,"_72":-5,"_73":-5,"_74":-5,"_75":76,"_77":76,"_78":79,"_80":79,"_81":79,"_82":76,"_83":76,"_84":85,"_86":87,"_88":89,"_90":31},"version",3,"changes",[25],"aiChatInfo",{"_20":21,"_22":-5,"_23":-5,"_24":-5},"userId","aiChatMessageId","cc4a9bc1-a94c-42c5-a582-945e16e2737c","aiChatThreadId","aiChatThreadUserId","aiChatThreadTitle",{"_26":27,"_28":29,"_30":31,"_32":31,"_33":34,"_35":34},"file",{"_36":37,"_38":34,"_39":26,"_40":42,"_43":44,"_45":14,"_46":47,"_30":31},"previous",{"_36":37,"_38":34,"_39":26,"_40":41},"deleted",false,"deletedHere","path","docs.html","latestPath","fileId","019e8f96-447e-713b-89f8-e2d864223014","name","fileType","content","\n\n\n\n\nAI Models API\n\n\n\n

AI Models API

Text and image generation over a single HTTP endpoint. No SDK, no keys to pass — just hit the URLs below.

\n\n

Base URL

All routes are relative to the deployment origin:

loading…

\n\n

Quick start

The fastest way to see it work — open this in your browser:

loading…

\n\n \n

Chat

Send a prompt, get text back. There are two ways to call it.

\n\n

\n GET\n /v1/chat?prompt=...\n

Good for quick tests and simple one-liners. Put your question in the prompt query parameter.

curl \"$BASE/v1/chat?prompt=What%20is%20the%20capital%20of%20France%3F\"

\n\n

\n POST\n /v1/chat\n

Use this when you want a system prompt, a full conversation, or generation settings. Send JSON.

\n\n

Body fields

\n \n \n \n \n \n \n \n \n

Field	Type	Notes
`prompt`	string	Shortcut for a single user message. Use this or `messages`.
`messages`	array	Full chat history. Each item is `{ \"role\": \"system\" \| \"user\" \| \"assistant\", \"content\": \"...\" }`.
`model`	string	optional · defaults to `openai/gpt-oss-120b:groq`
`temperature`	number	optional · 0–2, higher is more random
`max_tokens`	number	optional · cap on response length
`top_p`	number	optional · nucleus sampling

\n\n

Example — single prompt

curl -X POST \"$BASE/v1/chat\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"prompt\": \"Give me three names for a coffee shop.\"\n  }'

\n\n

Example — full conversation

curl -X POST \"$BASE/v1/chat\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"openai/gpt-oss-120b:groq\",\n    \"temperature\": 0.7,\n    \"messages\": [\n      { \"role\": \"system\", \"content\": \"You answer in one short sentence.\" },\n      { \"role\": \"user\", \"content\": \"Why is the sky blue?\" }\n    ]\n  }'

\n\n

Response

{\n  \"model\": \"openai/gpt-oss-120b\",\n  \"reply\": \"Sunlight scatters off air molecules, and blue light scatters most.\",\n  \"usage\": {\n    \"prompt_tokens\": 24,\n    \"completion_tokens\": 14,\n    \"total_tokens\": 38\n  }\n}

\n\n \n

Image

\n Describe an image, get back a hosted URL you can drop into an <img> tag.\n The server generates the image, uploads it for you, and returns the link.\n

\n\n

\n GET\n /v1/image?prompt=...\n

\n POST\n /v1/image\n

\n\n

Body / query fields

\n \n \n \n \n \n \n

Field	Type	Notes
`prompt`	string	What you want to see. Required.
`model`	string	optional · defaults to `Tongyi-MAI/Z-Image-Turbo`
`negative_prompt`	string	optional · things to avoid in the image
`raw`	boolean	optional · if true, returns the raw image bytes instead of a JSON URL

\n\n

Example

curl -X POST \"$BASE/v1/image\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"prompt\": \"An astronaut riding a horse, cinematic lighting\"\n  }'

\n\n

Response

{\n  \"prompt\": \"An astronaut riding a horse, cinematic lighting\",\n  \"model\": \"Tongyi-MAI/Z-Image-Turbo\",\n  \"url\": \"https://jerrycans-file.hf.space/files/gen-1717000000000.png\",\n  \"content_type\": \"image/png\"\n}

\n\n

Want the image bytes directly (e.g. to pipe into a file)? Add ?raw=true:

curl \"$BASE/v1/image?prompt=a%20red%20bicycle&raw=true\" --output bike.png

\n\n \n

Calling it from code

\n\n

JavaScript

const base = \"$BASE\";\n\n// chat\nconst chat = await fetch(`${base}/v1/chat`, {\n  method: \"POST\",\n  headers: { \"Content-Type\": \"application/json\" },\n  body: JSON.stringify({ prompt: \"Write a haiku about the sea.\" }),\n}).then(r => r.json());\nconsole.log(chat.reply);\n\n// image\nconst img = await fetch(`${base}/v1/image`, {\n  method: \"POST\",\n  headers: { \"Content-Type\": \"application/json\" },\n  body: JSON.stringify({ prompt: \"A lighthouse at dusk\" }),\n}).then(r => r.json());\nconsole.log(img.url);

\n\n

Python

import requests\n\nbase = \"$BASE\"\n\nchat = requests.post(f\"{base}/v1/chat\", json={\n    \"prompt\": \"Explain gravity to a five year old.\"\n}).json()\nprint(chat[\"reply\"])\n\nimg = requests.post(f\"{base}/v1/image\", json={\n    \"prompt\": \"A fox in a snowy forest\"\n}).json()\nprint(img[\"url\"])

\n\n \n

Errors

Failures come back as JSON with the matching HTTP status:

{\n  \"error\": \"Provide a 'prompt' describing the image\",\n  \"detail\": null\n}

\n \n \n \n \n \n \n \n

Status	Meaning
`400`	Bad input — missing prompt, malformed JSON, etc.
`404`	Route doesn't exist.
`405`	Wrong HTTP method for that route.
`429`	Rate limited upstream — slow down and retry.
`5xx`	Model or upload host had a problem. The `detail` field usually says what.

\n\n

Endpoints at a glance

\n \n \n \n \n \n \n

Route	Method	Does
`/`	GET	This page.
`/health`	GET	Liveness check.
`/v1/chat`	GET / POST	Text generation.
`/v1/image`	GET / POST	Image generation → hosted URL.

\n\n \n

\n\n\n\n\n\n","\n\n\n\n\nAI Models API\n\n\n\n

AI Models API

Text and image generation over a single HTTP endpoint. No SDK, no keys to pass — just hit the URLs below.

\n\n

Base URL

All routes are relative to the deployment origin:

loading…

\n\n

Quick start

The fastest way to see it work — open this in your browser:

loading…

\n\n \n

Chat

Send a prompt, get text back. There are two ways to call it.

\n\n

\n GET\n /v1/chat?prompt=...\n

Good for quick tests and simple one-liners. Put your question in the prompt query parameter.

curl \"$BASE/v1/chat?prompt=What%20is%20the%20capital%20of%20France%3F\"

\n\n

\n POST\n /v1/chat\n

Use this when you want a system prompt, a full conversation, or generation settings. Send JSON.

\n\n

Body fields

\n \n \n \n \n \n \n \n \n

Field	Type	Notes
`prompt`	string	Shortcut for a single user message. Use this or `messages`.
`messages`	array	Full chat history. Each item is `{ \"role\": \"system\" \| \"user\" \| \"assistant\", \"content\": \"...\" }`.
`model`	string	optional · defaults to `openai/gpt-oss-120b:groq`
`temperature`	number	optional · 0–2, higher is more random
`max_tokens`	number	optional · cap on response length
`top_p`	number	optional · nucleus sampling

\n\n

Example — single prompt

curl -X POST \"$BASE/v1/chat\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"prompt\": \"Give me three names for a coffee shop.\"\n  }'

\n\n

Example — full conversation

curl -X POST \"$BASE/v1/chat\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"openai/gpt-oss-120b:groq\",\n    \"temperature\": 0.7,\n    \"messages\": [\n      { \"role\": \"system\", \"content\": \"You answer in one short sentence.\" },\n      { \"role\": \"user\", \"content\": \"Why is the sky blue?\" }\n    ]\n  }'

\n\n

Response

{\n  \"model\": \"openai/gpt-oss-120b\",\n  \"reply\": \"Sunlight scatters off air molecules, and blue light scatters most.\",\n  \"usage\": {\n    \"prompt_tokens\": 24,\n    \"completion_tokens\": 14,\n    \"total_tokens\": 38\n  }\n}

\n\n \n

Image

\n Describe an image, get back a hosted URL you can drop into an <img> tag.\n The server generates the image, uploads it for you, and returns the link.\n

\n\n

\n GET\n /v1/image?prompt=...\n

\n POST\n /v1/image\n

\n\n

Body / query fields

\n \n \n \n \n \n \n

Field	Type	Notes
`prompt`	string	What you want to see. Required.
`model`	string	optional · defaults to `Tongyi-MAI/Z-Image-Turbo`
`negative_prompt`	string	optional · things to avoid in the image
`raw`	boolean	optional · if true, returns the raw image bytes instead of a JSON URL

\n\n

Example

curl -X POST \"$BASE/v1/image\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"prompt\": \"An astronaut riding a horse, cinematic lighting\"\n  }'

\n\n

Response

{\n  \"prompt\": \"An astronaut riding a horse, cinematic lighting\",\n  \"model\": \"Tongyi-MAI/Z-Image-Turbo\",\n  \"url\": \"https://jerrycans-file.hf.space/files/gen-1717000000000.png\",\n  \"content_type\": \"image/png\"\n}

\n\n

Want the image bytes directly (e.g. to pipe into a file)? Add ?raw=true:

curl \"$BASE/v1/image?prompt=a%20red%20bicycle&raw=true\" --output bike.png

\n\n \n

Calling it from code

\n\n

JavaScript

const base = \"$BASE\";\n\n// chat\nconst chat = await fetch(`${base}/v1/chat`, {\n  method: \"POST\",\n  headers: { \"Content-Type\": \"application/json\" },\n  body: JSON.stringify({ prompt: \"Write a haiku about the sea.\" }),\n}).then(r => r.json());\nconsole.log(chat.reply);\n\n// image\nconst img = await fetch(`${base}/v1/image`, {\n  method: \"POST\",\n  headers: { \"Content-Type\": \"application/json\" },\n  body: JSON.stringify({ prompt: \"A lighthouse at dusk\" }),\n}).then(r => r.json());\nconsole.log(img.url);

\n\n

Python

import requests\n\nbase = \"$BASE\"\n\nchat = requests.post(f\"{base}/v1/chat\", json={\n    \"prompt\": \"Explain gravity to a five year old.\"\n}).json()\nprint(chat[\"reply\"])\n\nimg = requests.post(f\"{base}/v1/image\", json={\n    \"prompt\": \"A fox in a snowy forest\"\n}).json()\nprint(img[\"url\"])

\n\n \n

Errors

Failures come back as JSON with the matching HTTP status:

{\n  \"error\": \"Provide a 'prompt' describing the image\",\n  \"detail\": null\n}

\n \n \n \n \n \n \n \n

Status	Meaning
`400`	Bad input — missing prompt, malformed JSON, etc.
`404`	Route doesn't exist.
`405`	Wrong HTTP method for that route.
`429`	Rate limited upstream — slow down and retry.
`5xx`	Model or upload host had a problem. The `detail` field usually says what.

\n\n

Endpoints at a glance

\n \n \n \n \n \n \n

Route	Method	Does
`/`	GET	This page.
`/health`	GET	Liveness check.
`/v1/chat`	GET / POST	Text generation.
`/v1/image`	GET / POST	Image generation → hosted URL.

\n\n \n

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\"\";\n if (url && !url.startsWith(\"http\")) {\n url = PUBLIC_BASE + url;\n }\n if (!url) throw new Error(\"upload host returned no url\");\n return url;\n}\n","label",[150],"3f09e559-5d47-4a4a-b199-1f0d20527d38","main.ts",["D",1780525450283],"http","import { chat, HfError, textToImage } from \"./hf.ts\";\nimport { uploadImage } from \"./upload.ts\";\nimport { parseVal, readFile } from \"https://esm.town/v/std/utils/index.ts\";\n\nconst JSON_HEADERS = { \"Content-Type\": \"application/json\" };\n\nfunction json(data: unknown, status = 200) {\n return new Response(JSON.stringify(data, null, 2), {\n status,\n headers: JSON_HEADERS,\n });\n}\n\nfunction errorJson(message: string, status = 400, detail?: unknown) {\n return json({ error: message, detail: detail ?? null }, status);\n}\n\n// Reads a JSON body, tolerating empty bodies.\nasync function readBody(req: Request): Promise> {\n const text = await req.text();\n if (!text.trim()) return {};\n try {\n return JSON.parse(text);\n } catch {\n throw new Error(\"Request body ifs not valid JSON\");\n }\n}\n\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n const path = url.pathname.replace(/\\/+$/, \"\") || \"/\";\n\n // ---- Docs page ----\n if (path === \"/\" && req.method === \"GET\") {\n const html = await readFile(\"/docs.html\", import.meta.url);\n return new Response(html, {\n headers: { \"Content-Type\": \"text/html; charset=utf-8\" },\n });\n }\n\n // ---- View source ----\n if (path === \"/source\") {\n return new Response(null, {\n status: 302,\n headers: { Location: parseVal().links.self.val },\n });\n }\n\n // ---- Health check ----\n if (path === \"/health\") {\n return json({ ok: true, time: new Date().toISOString() });\n }\n\n // ---- Chat (text) ----\n if (path === \"/v1/chat\") {\n if (req.method === \"GET\") {\n const prompt = url.searchParams.get(\"prompt\") ||\n url.searchParams.get(\"q\");\n if (!prompt) {\n return errorJson(\"Pass a prompt: /v1/chat?prompt=hello\", 400);\n }\n return await handleChat({\n messages: [{ role: \"user\", content: prompt }],\n model: url.searchParams.get(\"model\") || undefined,\n });\n }\n\n if (req.method === \"POST\") {\n let body: Record;\n try {\n body = await readBody(req);\n } catch (e) {\n return errorJson((e as Error).message, 400);\n }\n\n // Accept either { prompt } or full { messages: [...] }.\n let messages = body.messages as any;\n if (!messages && typeof body.prompt === \"string\") {\n messages = [{ role: \"user\", content: body.prompt }];\n }\n if (!Array.isArray(messages) || messages.length === 0) {\n return errorJson(\n \"Provide 'prompt' (string) or 'messages' (array)\",\n 400,\n );\n }\n\n return await handleChat({\n messages,\n model: typeof body.model === \"string\" ? body.model : undefined,\n temperature: typeof body.temperature === \"number\"\n ? body.temperature\n : undefined,\n max_tokens: typeof body.max_tokens === \"number\"\n ? body.max_tokens\n : undefined,\n top_p: typeof body.top_p === \"number\" ? body.top_p : undefined,\n });\n }\n\n return errorJson(\"Use GET or POST on /v1/chat\", 405);\n }\n\n // ---- Image (text-to-image, uploaded to host) ----\n if (path === \"/v1/image\") {\n let prompt: string | null = null;\n let model: string | undefined;\n let provider: string | undefined;\n let negative_prompt: string | undefined;\n let raw = url.searchParams.get(\"raw\") === \"true\" ||\n url.searchParams.get(\"raw\") === \"1\";\n\n if (req.method === \"GET\") {\n prompt = url.searchParams.get(\"prompt\") || url.searchParams.get(\"q\");\n model = url.searchParams.get(\"model\") || undefined;\n provider = url.searchParams.get(\"provider\") || undefined;\n negative_prompt = url.searchParams.get(\"negative_prompt\") || undefined;\n } else if (req.method === \"POST\") {\n let body: Record;\n try {\n body = await readBody(req);\n } catch (e) {\n return errorJson((e as Error).message, 400);\n }\n prompt = typeof body.prompt === \"string\" ? body.prompt : null;\n model = typeof body.model === \"string\" ? body.model : undefined;\n provider = typeof body.provider === \"string\" ? body.provider : undefined;\n negative_prompt = typeof body.negative_prompt === \"string\"\n ? body.negative_prompt\n : undefined;\n if (body.raw === true) raw = true;\n } else {\n return errorJson(\"Use GET or POST on /v1/image\", 405);\n }\n\n if (!prompt) {\n return errorJson(\"Provide a 'prompt' describing the image\", 400);\n }\n\n try {\n const img = await textToImage({\n prompt,\n model,\n provider,\n negative_prompt,\n });\n\n // raw=true streams the bytes straight back instead of uploading.\n if (raw) {\n return new Response(img.bytes, {\n headers: { \"Content-Type\": img.contentType },\n });\n }\n\n const uploadedUrl = await uploadImage(img.bytes, img.contentType);\n return json({\n prompt,\n model: img.model,\n url: uploadedUrl,\n content_type: img.contentType,\n });\n } catch (e) {\n return handleError(e);\n }\n }\n\n return errorJson(`Unknown route: ${path}`, 404);\n}\n\nasync function handleChat(opts: Parameters[0]): Promise {\n try {\n const result = await chat(opts);\n return json({\n model: result.model,\n reply: result.reply,\n usage: result.usage,\n });\n } catch (e) {\n return handleError(e);\n }\n}\n\nfunction handleError(e: unknown): Response {\n if (e instanceof HfError) {\n return errorJson(\n e.message,\n e.status >= 400 && e.status < 600 ? e.status : 502,\n e.detail,\n );\n }\n return errorJson((e as Error).message || \"Something went wrong\", 500);\n}","aimodel3",[161],"cb266856-380f-49f0-b9fc-a6ccc849d26d","hf.ts","019e8f96-447e-713b-89f8-e668085501e2",["D",1780525450283],"// HuggingFace router calls. Every request bills to the org in ORG via X-HF-Bill-To.\n\nconst HF_TOKEN = Deno.env.get(\"HF_TOKEN\");\nconst BILL_TO = Deno.env.get(\"ORG\") || \"hugging-science\";\nconst ROUTER = \"https://router.huggingface.co/v1\";\n\nfunction billingHeaders(extra: Record = {}) {\n return {\n \"Authorization\": `Bearer ${HF_TOKEN}`,\n \"X-HF-Bill-To\": BILL_TO,\n ...extra,\n };\n}\n\nexport type ChatMessage = { role: \"system\" | \"user\" | \"assistant\"; content: string };\n\nexport interface ChatOptions {\n messages: ChatMessage[];\n model?: string;\n temperature?: number;\n max_tokens?: number;\n top_p?: number;\n}\n\n// Single-turn helper: pass a string prompt, get a string back.\nexport async function chat(opts: ChatOptions) {\n if (!HF_TOKEN) throw new Error(\"HF_TOKEN env var is not set\");\n\n const model = opts.model || \"openai/gpt-oss-120b:groq\";\n const body: Record = {\n model,\n messages: opts.messages,\n stream: false,\n };\n if (opts.temperature != null) body.temperature = opts.temperature;\n if (opts.max_tokens != null) body.max_tokens = opts.max_tokens;\n if (opts.top_p != null) body.top_p = opts.top_p;\n\n const res = await fetch(`${ROUTER}/chat/completions`, {\n method: \"POST\",\n headers: billingHeaders({ \"Content-Type\": \"application/json\" }),\n body: JSON.stringify(body),\n });\n\n const text = await res.text();\n if (!res.ok) {\n throw new HfError(`chat request failed (${res.status})`, res.status, text);\n }\n\n const data = JSON.parse(text);\n const message = data.choices?.[0]?.message?.content ?? \"\";\n return {\n model: data.model ?? model,\n reply: message,\n usage: data.usage ?? null,\n raw: data,\n };\n}\n\n// Text-to-image. Uses the official HF inference client so provider routing\n// (replicate/fal/etc.) is handled for us. Billing goes to the org via billTo.\n// Returns raw image bytes + the content type the provider gave us.\nexport async function textToImage(opts: {\n prompt: string;\n model?: string;\n provider?: string;\n negative_prompt?: string;\n}) {\n if (!HF_TOKEN) throw new Error(\"HF_TOKEN env var is not set\");\n\n const model = opts.model || \"Tongyi-MAI/Z-Image-Turbo\";\n const provider = (opts.provider || \"replicate\") as any;\n\n const { InferenceClient } = await import(\"npm:@huggingface/inference\");\n const client = new InferenceClient(HF_TOKEN, { billTo: BILL_TO });\n\n try {\n const result = await client.textToImage({\n provider,\n model,\n inputs: opts.prompt,\n parameters: opts.negative_prompt ? { negative_prompt: opts.negative_prompt } : undefined,\n });\n\n // The client returns a Blob.\n const blob = result as Blob;\n const contentType = blob.type || \"image/png\";\n const bytes = new Uint8Array(await blob.arrayBuffer());\n return { bytes, contentType, model, provider };\n } catch (e) {\n const msg = (e as Error)?.message || String(e);\n throw new HfError(`image request failed: ${msg}`, 502, msg);\n }\n}\n\nexport class HfError extends Error {\n status: number;\n detail: string;\n constructor(message: string, status: number, detail: string) {\n super(message);\n this.name = \"HfError\";\n this.status = status;\n this.detail = detail;\n }\n}\n",[168],"2cf7339e-a884-4bb9-9361-887cb7e7424d",["D",1780525458020],2,[34],"pulls","branches","forks","envVars",{"_184":175,"_186":68},"connections",{"_184":68,"_185":68},"count","unfilledCount","emptyCount",{"_48":173,"_38":34,"_36":37,"_152":-5,"_46":47,"_45":14,"_154":31,"_43":201,"_13":175,"_39":26,"_40":42,"_30":31,"_158":-5,"_192":202,"_33":203},{"_48":167,"_38":168,"_36":169,"_152":-5,"_46":47,"_45":153,"_154":64,"_43":198,"_13":68,"_39":156,"_40":171,"_30":31,"_158":-5,"_192":199,"_33":200},{"_48":160,"_38":161,"_36":62,"_152":-5,"_46":47,"_45":153,"_154":64,"_43":195,"_13":68,"_39":163,"_40":164,"_30":31,"_158":165,"_192":196,"_33":197},{"_48":149,"_38":150,"_36":151,"_152":-5,"_46":47,"_45":153,"_154":64,"_43":191,"_13":68,"_39":156,"_40":157,"_30":31,"_158":-5,"_192":193,"_33":194},["D",1780525450283],"children",[],[150],["D",1780525450283],[],[161],["D",1780525450283],[],[168],["D",1780525458020],[],[34],["D",1780525450283],{"_106":105,"_113":108},{"_106":105,"_107":108,"_109":108,"_19":60,"_110":111,"_112":31},{"_13":91,"_38":92,"_48":47,"_93":49,"_94":95,"_96":97,"_98":55,"_99":100,"_101":102,"_103":50,"_104":105},"ctx",{"_84":-5,"_134":241,"_242":243,"_244":245,"_246":31,"_247":31,"_248":31,"_249":64,"_250":-5,"_251":-7,"_252":-7,"_253":68,"_254":31,"_255":31,"_256":-5,"_257":31,"_258":31,"_259":31,"_260":-5},"ENV",{"_212":213,"_214":215,"_216":92,"_217":218,"_219":220,"_221":222,"_223":224,"_225":226,"_227":228,"_229":230,"_231":232,"_233":234,"_235":236,"_237":238,"_239":240},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

Diff · v3