{"id":2408,"date":"2026-08-26T20:03:40","date_gmt":"2026-08-26T20:03:40","guid":{"rendered":"https:\/\/convly.ai\/?p=2408"},"modified":"2026-08-26T20:03:40","modified_gmt":"2026-08-26T20:03:40","slug":"lm-studio-chicago","status":"publish","type":"post","link":"https:\/\/convly.ai\/ar\/lm-studio-chicago\/","title":{"rendered":"LM Studio \u0641\u064a \u0634\u064a\u0643\u0627\u063a\u0648: \u0645\u0627 \u0627\u0644\u0630\u064a \u062a\u0639\u0646\u064a\u0647 \u0647\u0630\u0647 \u0627\u0644\u0639\u0645\u0644\u064a\u0629 \u0627\u0644\u0628\u062d\u062b\u064a\u0629 \u0648\u0643\u064a\u0641\u064a\u0629 \u062a\u0634\u063a\u064a\u0644 \u0627\u0644\u0646\u0645\u0627\u0630\u062c \u0645\u062d\u0644\u064a\u064b\u0651\u0627"},"content":{"rendered":"<div class=\"convly-tldr\">\n<ul>\n<li><strong>There is no &#8220;LM Studio Chicago&#8221; server, region, or edition.<\/strong> LM Studio is a desktop app for Windows, macOS and Linux; inference runs on your own CPU\/GPU, so location is wherever your machine is.<\/li>\n<li><strong>To run it in Chicago, just run it:<\/strong> install the app, download a GGUF or MLX model, then start the OpenAI-compatible server at <code>http:\/\/localhost:1234\/v1<\/code>.<\/li>\n<li><strong>If &#8220;Chicago&#8221; means data residency<\/strong> (BIPA, HIPAA, client contracts), local inference is the strongest possible answer \u2014 prompts never reach a vendor.<\/li>\n<li><strong>If you wanted a Chicago design or photo studio named &#8220;LM Studio,&#8221;<\/strong> that is an unrelated local business, not this software.<\/li>\n<\/ul>\n<\/div>\n<p>LM Studio is a free desktop app for running large language models locally on your own machine \u2014 it has no Chicago office, no Chicago server region, and no city-specific build. Searches for &#8220;lm studio chicago&#8221; almost always mean one of three things: running LM Studio on a workstation in Chicago, keeping data inside Illinois for compliance reasons, or a similarly named local studio business.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_86 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a8f681d48140\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #000000;color:#000000\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #000000;color:#000000\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a8f681d48140\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/convly.ai\/ar\/lm-studio-chicago\/#Which_%E2%80%9CLM_Studio_Chicago%E2%80%9D_are_you_looking_for\" >Which &#8220;LM Studio Chicago&#8221; are you looking for?<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/ar\/lm-studio-chicago\/#Why_there_is_no_Chicago_region\" >Why there is no Chicago region<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/ar\/lm-studio-chicago\/#Installing_LM_Studio_on_Windows\" >Installing LM Studio on Windows<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/ar\/lm-studio-chicago\/#Installing_LM_Studio_on_macOS\" >Installing LM Studio on macOS<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/ar\/lm-studio-chicago\/#Installing_LM_Studio_on_Linux\" >Installing LM Studio on Linux<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/ar\/lm-studio-chicago\/#Serving_models_to_the_rest_of_your_office\" >Serving models to the rest of your office<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/ar\/lm-studio-chicago\/#Sizing_hardware_before_you_buy\" >Sizing hardware before you buy<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/ar\/lm-studio-chicago\/#The_Illinois_data-residency_angle\" >The Illinois data-residency angle<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/ar\/lm-studio-chicago\/#If_you_actually_want_a_Chicago-hosted_endpoint\" >If you actually want a Chicago-hosted endpoint<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/convly.ai\/ar\/lm-studio-chicago\/#Frequently_asked_questions\" >Frequently asked questions<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Which_%E2%80%9CLM_Studio_Chicago%E2%80%9D_are_you_looking_for\"><\/span>Which &#8220;LM Studio Chicago&#8221; are you looking for?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table>\n<thead>\n<tr>\n<th>What you probably meant<\/th>\n<th>What you actually need<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Run LLMs locally on a machine in Chicago<\/td>\n<td>Install LM Studio normally \u2014 nothing about setup is city-specific<\/td>\n<\/tr>\n<tr>\n<td>A Chicago server region or low-latency endpoint<\/td>\n<td>Not applicable. LM Studio has no hosted service. Use a cloud API with a US-Central region, or colocate your own box<\/td>\n<\/tr>\n<tr>\n<td>Illinois data residency or compliance<\/td>\n<td>Local inference \u2014 the data stays on the device you control<\/td>\n<\/tr>\n<tr>\n<td>Buy a GPU or workstation in the Chicago area<\/td>\n<td>Local retail (Micro Center&#8217;s Westmont store is the usual answer) or online; see the GPU guide below<\/td>\n<\/tr>\n<tr>\n<td>A Chicago design, photography, or architecture firm called LM Studio<\/td>\n<td>A different business entirely \u2014 this page is about the LLM app<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"Why_there_is_no_Chicago_region\"><\/span>Why there is no Chicago region<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>LM Studio is a GUI wrapper around local inference engines \u2014 llama.cpp for GGUF models on all platforms, and Apple&#8217;s MLX framework on Apple Silicon. The only outbound network traffic is model search and download (weights are pulled from Hugging Face over HTTPS), app update checks, and optional runtime downloads. Once a model file is on disk, you can pull the network cable and it keeps working.<\/p>\n<p>That architecture is why &#8220;region&#8221; is a category error here: there is no control plane, no tenant, and no account to create. The latency you care about is memory bandwidth on your own GPU, not a round trip to a data center. For background on the app itself, see the <a href=\"https:\/\/convly.ai\/lm-studio-complete-guide-2026\/\">LM Studio complete guide<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Installing_LM_Studio_on_Windows\"><\/span>Installing LM Studio on Windows<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Download the <code>.exe<\/code> from lmstudio.ai. Recent builds ship both x64 and ARM64 (Snapdragon X) installers, and the x64 build expects an AVX2-capable CPU.<\/p>\n<ul>\n<li>The installer is per-user by default and lands under <code>%LOCALAPPDATA%Programs<\/code> \u2014 no admin rights needed in most setups.<\/li>\n<li>Models default to <code>%USERPROFILE%.lmstudiomodels<\/code>. Change the directory in the <strong>My Models<\/strong> tab if you want them on a second drive; a handful of 30B quants will eat 100 GB fast.<\/li>\n<li>NVIDIA GPUs use the CUDA runtime, which LM Studio downloads as a separate runtime package. AMD and Intel GPUs fall back to Vulkan, with ROCm available on some AMD cards. Which runtimes are offered changes between releases, so check the runtime\/hardware panel in your version rather than assuming.<\/li>\n<li>Enable the CLI once with <code>cmd \/c %USERPROFILE%.lmstudiobinlms.exe bootstrap<\/code>, then use <code>lms<\/code> from any shell.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Installing_LM_Studio_on_macOS\"><\/span>Installing LM Studio on macOS<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Current versions require Apple Silicon (M1 or later) and a reasonably recent macOS \u2014 13.4 is the usual stated floor. Intel Macs were only supported by older releases, so if you are on a 2019 iMac you are looking at an archived build, not the latest.<\/p>\n<ul>\n<li>Open the <code>.dmg<\/code> and drag LM Studio to <code>\/Applications<\/code>.<\/li>\n<li>Prefer <strong>MLX<\/strong> builds of a model over GGUF when both exist. MLX is Apple&#8217;s own array framework and is usually faster on unified memory, though model availability is narrower.<\/li>\n<li>macOS caps how much unified memory the GPU may wire down \u2014 roughly two-thirds to three-quarters of installed RAM. There is a <code>sysctl<\/code> to raise that ceiling, but the exact key has changed across macOS releases, so look it up for your specific version instead of copying an old command from a forum.<\/li>\n<li>CLI: <code>~\/.lmstudio\/bin\/lms bootstrap<\/code>.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Installing_LM_Studio_on_Linux\"><\/span>Installing LM Studio on Linux<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Linux ships as an x86-64 AppImage. There is no first-party ARM Linux build at time of writing, so a Raspberry Pi or Ampere box is out.<\/p>\n<pre><code>chmod +x LM-Studio-*.AppImage\n.\/LM-Studio-*.AppImage<\/code><\/pre>\n<p>On Ubuntu 22.04 and newer you may need <code>libfuse2<\/code> for AppImages to mount; the alternative is <code>.\/LM-Studio-*.AppImage --appimage-extract-and-run<\/code>. For NVIDIA, install the proprietary driver first and confirm with <code>nvidia-smi<\/code> before launching. Models live under <code>~\/.lmstudio\/models<\/code> on recent versions \u2014 older builds used a path under <code>~\/.cache<\/code>, so verify in the app rather than guessing.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Serving_models_to_the_rest_of_your_office\"><\/span>Serving models to the rest of your office<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>This is the part most &#8220;local LLM in Chicago&#8221; questions are really about: one strong workstation, several developers hitting it. Turn on the server from the <strong>Developer<\/strong> tab, or from the CLI.<\/p>\n<pre><code>lms server start --port 1234\nlms ls          # installed models\nlms ps          # currently loaded models\nlms load qwen2.5-coder-7b-instruct\nlms unload --all\nlms log stream  # watch requests live<\/code><\/pre>\n<p>The server speaks the OpenAI wire format, so most SDKs work with a base-URL change and a dummy key.<\/p>\n<table>\n<thead>\n<tr>\n<th>Endpoint<\/th>\n<th>Purpose<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><code>GET \/v1\/models<\/code><\/td>\n<td>List loaded and available models<\/td>\n<\/tr>\n<tr>\n<td><code>POST \/v1\/chat\/completions<\/code><\/td>\n<td>Chat, streaming supported<\/td>\n<\/tr>\n<tr>\n<td><code>POST \/v1\/completions<\/code><\/td>\n<td>Raw text completion<\/td>\n<\/tr>\n<tr>\n<td><code>POST \/v1\/embeddings<\/code><\/td>\n<td>Embeddings, when an embedding model is loaded<\/td>\n<\/tr>\n<tr>\n<td><code>\/api\/v0\/*<\/code><\/td>\n<td>LM Studio&#8217;s native REST API, added in later 0.3.x builds; exposes extra load state<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<pre><code>curl http:\/\/localhost:1234\/v1\/chat\/completions \n  -H \"Content-Type: application\/json\" \n  -d '{\"model\": \"qwen2.5-coder-7b-instruct\", \"messages\": [{\"role\": \"user\", \"content\": \"Summarize this repo.\"}]}'<\/code><\/pre>\n<pre><code>from openai import OpenAI\n\nclient = OpenAI(base_url=\"http:\/\/localhost:1234\/v1\", api_key=\"lm-studio\")\nresp = client.chat.completions.create(\n    model=\"qwen2.5-coder-7b-instruct\",\n    messages=[{\"role\": \"user\", \"content\": \"Hello\"}],\n)<\/code><\/pre>\n<p><strong>Security note:<\/strong> the LM Studio server has no authentication. Serving on the local network is a toggle away, but do not port-forward it or bind it to a public interface. If a team needs access, put it behind a reverse proxy that enforces auth, or reach it over Tailscale or a VPN.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Sizing_hardware_before_you_buy\"><\/span>Sizing hardware before you buy<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Roughly what a 4-bit quant needs, before context. Treat these as planning numbers, not guarantees \u2014 quantization method, KV cache size, and context length all move the total.<\/p>\n<table>\n<thead>\n<tr>\n<th>Model size<\/th>\n<th>Approx. memory at ~Q4<\/th>\n<th>Practical target<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>7\u20138B<\/td>\n<td>5\u20136 GB<\/td>\n<td>8 GB GPU, or any 16 GB Mac<\/td>\n<\/tr>\n<tr>\n<td>13\u201314B<\/td>\n<td>9\u201310 GB<\/td>\n<td>12 GB GPU<\/td>\n<\/tr>\n<tr>\n<td>24B<\/td>\n<td>14\u201316 GB<\/td>\n<td>16\u201324 GB GPU<\/td>\n<\/tr>\n<tr>\n<td>30\u201332B<\/td>\n<td>19\u201322 GB<\/td>\n<td>24 GB GPU (3090 \/ 4090 class)<\/td>\n<\/tr>\n<tr>\n<td>70B<\/td>\n<td>40\u201345 GB<\/td>\n<td>2\u00d7 24 GB, or 64 GB+ unified memory<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Run your exact configuration through the <a href=\"https:\/\/convly.ai\/llm-vram-calculator\/\">VRAM calculator<\/a> before ordering anything, and cross-check against the <a href=\"https:\/\/convly.ai\/vram-requirements-every-major-llm-2026\/\">per-model VRAM requirements table<\/a>. If you have not chosen a card yet, the <a href=\"https:\/\/convly.ai\/best-gpus-for-local-llms-2026\/\">best GPUs for local LLMs<\/a> comparison covers where the price-per-usable-GB breakpoints fall. Chicago has one advantage worth using: Micro Center&#8217;s Westmont store means you can buy a GPU in person and return it in person if it does not fit your case or PSU budget \u2014 check current stock before driving out.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_Illinois_data-residency_angle\"><\/span>The Illinois data-residency angle<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Illinois is an unusually aggressive privacy jurisdiction. The Biometric Information Privacy Act (BIPA, 740 ILCS 14) carries a private right of action and has driven large settlements; the Artificial Intelligence Video Interview Act imposes notice and consent duties for AI analysis of candidate interviews; and amendments to the Illinois Human Rights Act extend discrimination liability to AI used in employment decisions. Local inference removes the third-party processor from the picture entirely, which is why regulated Chicago shops \u2014 hospital systems, insurers, law firms, trading firms \u2014 often land on LM Studio or Ollama for internal tooling. It does not remove your notice, consent, or nondiscrimination obligations. Confirm the specifics with counsel rather than with a settings toggle.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"If_you_actually_want_a_Chicago-hosted_endpoint\"><\/span>If you actually want a Chicago-hosted endpoint<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Two options, neither of which is LM Studio. Rent a hosted model from a commercial API and pick a US-Central region \u2014 priced out with the <a href=\"https:\/\/convly.ai\/ai-api-cost-calculator\/\">API cost calculator<\/a>. Or colocate your own GPU server; Chicago is a serious interconnect market, with 350 E. Cermak among the best-known carrier hotels in the country. Before committing capital, run the numbers through the <a href=\"https:\/\/convly.ai\/self-hosting-vs-api-calculator\/\">self-hosting vs API break-even calculator<\/a> \u2014 for low-volume internal use, API tokens usually beat owning hardware, and the crossover point arrives later than most teams expect.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Frequently_asked_questions\"><\/span>Frequently asked questions<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>Is there an LM Studio server or data center in Chicago?<\/h3>\n<p>No. LM Studio has no hosted inference service anywhere, so there is nothing to place in Chicago or any other region. All generation happens on the machine running the app. The only servers involved are Hugging Face for model downloads and LM Studio&#8217;s own update endpoints.<\/p>\n<h3>Does LM Studio work fully offline?<\/h3>\n<p>Yes, once models are downloaded. You need connectivity to search for and fetch model weights and runtime packages, but after that the app loads and runs models with no network access. This is the usual reason it clears review in air-gapped or heavily restricted corporate environments.<\/p>\n<h3>Is LM Studio free for commercial use at a Chicago company?<\/h3>\n<p>LM Studio dropped its earlier personal-use-only restriction and now permits use at work at no cost, but licensing terms are the kind of thing that changes between releases. Read the current terms on lmstudio.ai before you deploy it across a team. Note separately that model weights carry their own licenses \u2014 some open-weight models restrict commercial use regardless of what the runner allows.<\/p>\n<h3>LM Studio or Ollama for a small team?<\/h3>\n<p>LM Studio wins on discovery and iteration: a real GUI, a model browser, per-model parameter tuning, and a chat interface non-engineers can use. Ollama wins on headless deployment and scripting, with a cleaner story for Docker and CI. Many teams run both \u2014 LM Studio on developer laptops, Ollama on the shared box. See the <a href=\"https:\/\/convly.ai\/what-is-ollama-complete-guide-2026\/\">Ollama guide<\/a> for that side of the comparison.<\/p>\n<h3>Can the whole office connect to one LM Studio machine?<\/h3>\n<p>Technically yes \u2014 enable network serving and point clients at <code>http:\/\/&lt;host&gt;:1234\/v1<\/code>. Practically, remember there is no built-in authentication and no request queuing designed for many concurrent users. For anything beyond a few developers, put a proxy with auth in front, or move to a purpose-built serving stack such as vLLM.<\/p>\n<h3>How do I know which model to download first?<\/h3>\n<p>Start with a 7\u20138B instruct model at Q4_K_M \u2014 it fits nearly any modern GPU and tells you quickly whether your hardware path (CUDA, Metal, Vulkan) is working. From there, scale up until you hit your memory ceiling. The <a href=\"https:\/\/convly.ai\/llm-leaderboard\/\">LLM leaderboard<\/a> and <a href=\"https:\/\/convly.ai\/models\/\">models database<\/a> are useful for comparing capability against size before you spend an hour on a download.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>There is no &#8220;LM Studio Chicago&#8221; server, region, or edition. LM Studio is a desktop app for Windows, macOS and [\u2026]<\/p>\n","protected":false},"author":1,"featured_media":2409,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[7],"tags":[],"class_list":["post-2408","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-news"],"_links":{"self":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/posts\/2408","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/comments?post=2408"}],"version-history":[{"count":1,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/posts\/2408\/revisions"}],"predecessor-version":[{"id":2410,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/posts\/2408\/revisions\/2410"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/media\/2409"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/media?parent=2408"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/categories?post=2408"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/tags?post=2408"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}