{ "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-05", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "tool": { "slug": "ripple-65b-preview", "name": "Ripple-65B Preview", "vendor": "Ricursive Intelligence", "vendorUrl": "https://www.ricursive.com/ripple", "kind": "weights", "category": "open-weights", "summary": "Ricursive Intelligence's 65B dense model, pretrained from scratch and post-trained for agentic coding, on Hugging Face in BF16 and NVFP4 under Apache 2.0. Its custom architecture needs Ricursive's own model code to run.", "url": "https://www.anchorterminal.com/tools/ripple-65b-preview", "markdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json", "dns": "ripple-65b-preview.grade.anchorterminal.com", "repo": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ricursiveAI/Ripple-65B-Preview" }, { "registry": "huggingface", "name": "ricursiveAI/Ripple-65B-Preview-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the BF16 nor the NVFP4 repository is gated (checked 2026-11-04).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 129.6 GB for the BF16 files and 57.2 GB for the NVFP4 build by the Hub's file pages. Ricursive's launch post names Together AI, GMI Cloud and OpenRouter as hosts, but Together's model page says coming soon (checked 2026-11-04).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-04).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-04" }, "docsUrl": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "reasoning", "tool-calling", "coding", "apache-2.0", "safetensors", "remote-code", "self-hosted", "new" ], "lastRelease": "2026-11-04", "graded": true, "anchor": { "graded": true, "score": 53.3, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1350, "categoryRank": 78, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 75, "reach": 45, "risk": 55, "upkeep": 49 }, "pending": [ "performance", "tasks" ], "breakdown": [ { "key": "capability", "name": "Capability", "weight": 35, "effectiveWeight": 0, "pending": true, "points": 0, "reason": "Pending for this listing. Nobody independent has published results for it in at least two areas yet, so its weight is shared across the other four and the total is capped at 61 until they do or our own runs start." }, { "key": "reach", "name": "Reach", "weight": 25, "effectiveWeight": 38.46, "score": 45, "points": 17.31, "reason": "Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read.", "checked": "2026-11-04" }, { "key": "risk", "name": "Risk", "weight": 20, "effectiveWeight": 30.77, "score": 55, "points": 16.92, "reason": "File safety 15, safetensors only, less 10 because the documented way to run it loads Ricursive's Python. Provenance 15, the files are in the organisation ricursive.com links to, with a checksum on every file. Safety evaluation 10, Ricursive's own FORTRESS and StrongREJECT results. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, Apache 2.0. Security programme 0, no way to report a flaw.", "checked": "2026-11-04" }, { "key": "interface", "name": "Agent interface", "weight": 10, "effectiveWeight": 15.38, "score": 75, "points": 11.54, "reason": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M.", "checked": "2026-11-04" }, { "key": "upkeep", "name": "Documentation and upkeep", "weight": 10, "effectiveWeight": 15.38, "score": 49, "points": 7.54, "reason": "Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M.", "checked": "2026-11-04" } ], "assessment": { "date": "2026-11-04", "basis": "public evidence", "confidence": "low", "notes": { "capability": "Pending for this listing. Independent results were found in 0 areas (none), and Capability needs 2. Its weight is shared across the other four categories and the total is capped at 61 until there are more, or our own runs start.", "interface": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M.", "reach": "Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read.", "risk": "File safety 15, safetensors only, less 10 because the documented way to run it loads Ricursive's Python. Provenance 15, the files are in the organisation ricursive.com links to, with a checksum on every file. Safety evaluation 10, Ricursive's own FORTRESS and StrongREJECT results. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, Apache 2.0. Security programme 0, no way to report a flaw.", "upkeep": "Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M." }, "sources": [ { "what": "launch post, Terminal-Bench 2.1 claim, cost, architecture, hosts", "url": "https://www.ricursive.com/ripple", "seen": "2026-11-04" }, { "what": "home page, company name, links", "url": "https://www.ricursive.com/", "seen": "2026-11-04" }, { "what": "legal pages, rendered by script and unreadable", "url": "https://www.ricursive.com/legal/terms", "seen": "2026-11-04" }, { "what": "security.txt, 404", "url": "https://www.ricursive.com/.well-known/security.txt", "seen": "2026-11-04" }, { "what": "model card, benchmark tables, stage table, safety figures", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "seen": "2026-11-04" }, { "what": "Hub model info, gating, likes, parameters, created date, file list, providers (none)", "url": "https://huggingface.co/api/models/ricursiveAI/Ripple-65B-Preview", "seen": "2026-11-04" }, { "what": "file page, sizes, SHA-256 and last commits", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/tree/main", "seen": "2026-11-04" }, { "what": "config.json", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/raw/main/config.json", "seen": "2026-11-04" }, { "what": "tokenizer_config.json", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/raw/main/tokenizer_config.json", "seen": "2026-11-04" }, { "what": "LICENSE, Apache 2.0", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/raw/main/LICENSE", "seen": "2026-11-04" }, { "what": "server README, SGLang 0.5.19, parsers, tool calling", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/raw/main/inference/server/README.md", "seen": "2026-11-04" }, { "what": "discussions, none", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/discussions", "seen": "2026-11-04" }, { "what": "NVFP4 card", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview-NVFP4", "seen": "2026-11-04" }, { "what": "NVFP4 Hub model info", "url": "https://huggingface.co/api/models/ricursiveAI/Ripple-65B-Preview-NVFP4", "seen": "2026-11-04" }, { "what": "NVFP4 file page, sizes and the in-place replacement commit", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview-NVFP4/tree/main", "seen": "2026-11-04" }, { "what": "NVFP4 config.json, quantisation groups", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview-NVFP4/raw/main/config.json", "seen": "2026-11-04" }, { "what": "Spark loader, SGLang plugin registration", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview-NVFP4/raw/main/inference/local/spark.py", "seen": "2026-11-04" }, { "what": "base model Hub info, created 14 October 2026", "url": "https://huggingface.co/api/models/ricursiveAI/Ripple-65B-Base-Preview", "seen": "2026-11-04" }, { "what": "Hugging Face organisation page", "url": "https://huggingface.co/ricursiveAI", "seen": "2026-11-04" }, { "what": "Together AI model page, coming soon", "url": "https://www.together.ai/models/ripple-65b-preview", "seen": "2026-11-04" } ], "openQuestions": [ "Capability is pending. Ricursive's 80.52 on Terminal-Bench 2.1 and the \"U.S. open-weight SOTA\" and \"surpassing\" claims are its own and couldn't be checked against an independent result, so no deduction. Re-check the Terminal-Bench 2.1 leaderboard, Vals AI and Epoch AI in a week", "Hosted is 0. Together says coming soon, GMI Cloud's console couldn't be read under robots.txt, and OpenRouter isn't requested. If two providers serve it, hosted is 5", "Runtimes is 0 on release day. If SGLang, vLLM or Transformers ship ricursive support in a release, each is 5 and the remote-code 10 off file safety may lift", "Tool-calling format is read at 25 because Ricursive documents the format and names its parser in SGLang 0.5.19. Read as the template-only 10 the interface score is 60 and the total about 51.0", "The base model repository was created on 14 October 2026, before release; the instruct repositories on 4 November", "www.ricursive.com is a Framer site, and Framer's own Acceptable Use Policy bars automated access to content it hosts (framer re-check, 4 November). The framer reading paused framer.com hosts only; whether it reaches customers' sites on Framer is for the founder" ] }, "negative": 0, "verdict": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.", "bestFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.", "strengths": [ "Apache 2.0 with nothing added, ungated, from the organisation Ricursive's site links to", "A tools-aware chat template with several calls a turn, and a server that returns reasoning, content and tool calls separately", "Trained for long context at 2M-token sequences, with 1M stated", "Base model and a speculative-decoding draft published beside the instruct model", "Safetensors only, with a checksum on every file" ], "weaknesses": [ "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code", "The smallest build is 57.2 GB NVFP4, for NVIDIA Blackwell only, and there is no GGUF", "No independent benchmark yet, and the top-ranking claim rests on Ricursive's own Terminal-Bench 2.1 run", "Thinking can't be turned off or budgeted", "No safety guidance for agent use, no security contact and no knowledge cut-off" ], "agentNotes": [ "Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default", "The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags", "For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields", "Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on", "Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.3 } ], "editorialScores": { "interface": 75, "reach": 45, "risk": 55, "upkeep": 49 }, "provenanceScore": 36, "checked": { "categories": { "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 }, "scoreHistory": [ { "date": "2026-11-04", "grade": "D", "kind": "listed", "method": "0.7", "score": 53.3 } ] }, "connect": { "install": "hf download ricursiveAI/Ripple-65B-Preview --local-dir ./ripple-65b\nhf download ricursiveAI/Ripple-65B-Preview-DFlash2 --local-dir ./ripple-dflash2\ncd ripple-65b/inference/server\nCUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py \\\n --model-path ../.. \\\n --speculative-draft-model-path ../../../ripple-dflash2", "http": "curl http://127.0.0.1:30000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"ripple\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"max_tokens\": 128}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ripple-65b-preview" }, "weights": { "trainedBy": "Ricursive Intelligence", "params": "65B", "context": 1048576, "goodFor": "A dense 65B model post-trained for agentic coding and terminal work, with several tool calls a turn and a long context, for teams with Blackwell GPUs or a DGX Spark.", "runtimes": [], "builds": [ { "name": "Ripple-65B-Preview-NVFP4", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 57.2, "memoryGB": 69, "format": "safetensors", "note": "NVFP4 MLP weights and activations with BF16 attention, 6 shards summed from the Hub's file page. For NVIDIA Blackwell, with Ricursive's DGX Spark loader. The checkpoint was replaced in place on release day" }, { "name": "Ripple-65B-Preview", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "who": "publisher", "precision": "bf16", "sizeGB": 129.6, "memoryGB": 160, "format": "safetensors", "note": "16 shards summed from the Hub's file page. Served with Ricursive's SGLang 0.5.19 plugin on two B200 GPUs" } ], "run": "CUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py --model-path /path/to/Ripple-65B-Preview --speculative-draft-model-path /path/to/Ripple-65B-Preview-DFlash2", "runBuild": "Ripple-65B-Preview", "checked": "2026-11-04", "minMemoryGB": 69 }, "notable": [ "Ricursive says Ripple-65B Preview sets a U.S. open-weight state of the art on agentic coding, 80.52 on Terminal-Bench 2.1, above Reflection Beam 501B (80.1), Muse Spark Open 816B (76.4), Inkling 975B (63.8) and Nemotron 3 Ultra 550B (56.4). These are Ricursive's own runs set beside the other labs' self-reported numbers, not our measurement, and no independent result for Ripple was found on 4 November 2026 (https://www.ricursive.com/ripple)", "On Ricursive's own table Reflection Beam is ahead of Ripple on SWE-bench Verified (81.6 against 76.5), SWE-bench Pro v1.0 (66.1 against 61.42) and GPQA Diamond, and Muse Spark Open on DeepSWE 1.1, MCP Atlas and SciCode, so the top-ranking claim rests on Terminal-Bench 2.1 alone. Ricursive notes its italic figures for other models are its own in-house runs (https://huggingface.co/ricursiveAI/Ripple-65B-Preview)", "Ricursive says it trained the model for a full R\u0026D cost of $32M, from an Olmo 3 model ladder, with a team that grew from one to five people. Ricursive's claim (https://www.ricursive.com/ripple)", "The architecture is Ricursive's own (RicursiveForCausalLM, model_type ricursive), 80 layers with four sliding-window layers of 1,024 tokens to each global one, gated attention and value residuals. It loads through the repository's Python files or Ricursive's SGLang plugin, not a runtime's own code (https://huggingface.co/ricursiveAI/Ripple-65B-Preview/raw/main/config.json)", "88 likes on the BF16 repository and 14 on the NVFP4 one on 4 November 2026, the day of release, with no downloads counted yet and no discussions (https://huggingface.co/api/models/ricursiveAI/Ripple-65B-Preview)" ], "area": "models", "details": [ { "label": "Surface graded", "value": "The open weights at huggingface.co/ricursiveAI/Ripple-65B-Preview and its NVFP4 build, read from the model cards, the Hub's model info and file pages, config.json, tokenizer_config.json, the licence, the server README and the Spark loader on 4 November 2026. Nothing was downloaded or run. Hosted copies aren't graded" }, { "label": "Model", "value": "RicursiveForCausalLM, 64,782,689,360 BF16 parameters per the Hub. Dense, 80 layers, hidden size 5,120, 80 attention heads and 8 KV heads, sliding window 1,024 on four layers of every five, vocabulary 100,352. Text in, text out. Pretrained from scratch, not built on another model, starting from Olmo 3's model ladder" }, { "label": "Builds", "value": "BF16 instruct (16 safetensors shards, 129.6 GB), NVFP4 instruct (6 shards, 57.2 GB, NVFP4 MLPs and BF16 attention, which its card calls experimental and conservative), a BF16 pretrain-only base model and a DFlash2 draft model for speculative decoding, each in its own repository. No GGUF or other build from Ricursive" }, { "label": "Files", "value": "Safetensors with a SHA-256 for every LFS file on the Hub, chat_template.jinja, tokenizer files, configuration_ricursive.py and modeling_ricursive.py (loaded through auto_map), and inference folders of Python (serve.py, model.py, ripple_format.py, and spark.py with a notebook in the NVFP4 repository). No pickle files" }, { "label": "Runtimes", "value": "Ricursive's server registers its own model.py and parsers into SGLang 0.5.19 on two B200 GPUs, and its Spark loader does the same for the NVFP4 build. Transformers loads it only through trust_remote_code. No released llama.cpp, Ollama, vLLM, SGLang, LM Studio or Transformers version was found to run the ricursive architecture with its own code" }, { "label": "Context", "value": "Up to 1M tokens per the card, after long-context stages at 2M-token sequences. config.json allows 2,097,152 positions, tokenizer_config.json says 131,072, and the server defaults to 16K" }, { "label": "Tools and reasoning", "value": "The tools-aware ChatML template takes tools in tools tags and renders one or more tool_call blocks per turn. With Ricursive's ripple parsers the server returns reasoning_content, content and tool_calls separately. The template has no thinking switch and no reasoning-effort levels" }, { "label": "Hardware", "value": "The server README targets two B200 GPUs at TP2 with a 16K context. The cards say the model fits an NVIDIA DGX Spark with the NVFP4 build, and give 250 tokens a second on 2 × B200 at batch 8 in BF16" }, { "label": "Training data", "value": "12T tokens of text-only, predominantly English and code, then 200B midtraining, 6.5B long-context, 183B SFT and 13.5B long-context SFT tokens and RL, per the card's stage table. No sources, filtering or knowledge cut-off" }, { "label": "Ricursive's figures", "value": "Vendor-reported at full BF16 and maximum effort. Terminal-Bench 2.1 80.5, SWE-bench Verified 76.5, SWE-bench Pro v1.0 61.42, DeepSWE 1.1 44.3, MCP Atlas 71.67, GPQA Diamond 85.2, MMLU-Pro 84.2, IFBench 79.2, AA-LCR 75.0. Safety FORTRESS adversarial 83.8, FORTRESS benign 95.5, StrongREJECT 97.5. Harnesses are OpenCode or Terminus 2" } ], "provenance": { "legalEntity": "Ricursive Intelligence, Inc.", "domain": "ricursive.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-04", "notes": [ "Every page on www.ricursive.com ends with \"© 2026 Ricursive Intelligence, Inc.\". The site links the Hugging Face organisation ricursiveAI (fullname Ricursive Intelligence), which publishes the four Ripple repositories. RDAP wasn't queried, by the run's rules.", "The Apache 2.0 LICENSE in the repository governs the weights and stands in for terms of service. The site's own legal pages (/legal and /legal/terms, titled Model Attribution Terms) render their text by script only and couldn't be read.", "Nothing is hosted by Ricursive, so no privacy policy or status page is scored.", "No changelog. The repositories' commit messages are uploads and one in-place swap of the NVFP4 checkpoint on release day.", "www.ricursive.com/.well-known/security.txt is 404 on 4 November 2026, and the only contact on the card and site is a general one." ], "score": 36, "checks": [ { "check": "Legal entity named", "value": "Ricursive Intelligence, Inc.", "points": 20, "max": 20, "state": "ok" }, { "check": "Domain age", "value": "ricursive.com, no registry record we could read", "points": 0, "max": 15, "state": "no" }, { "check": "Endpoint on the vendor's domain", "value": "no hosted endpoint", "points": 0, "max": 0, "state": "na" }, { "check": "Terms of service", "value": "published, not read by us yet", "points": 4, "max": 10, "state": "part" }, { "check": "Privacy policy", "value": "nothing hosted, not scored", "points": 0, "max": 0, "state": "na" }, { "check": "Status page", "value": "not found", "points": 0, "max": 10, "state": "no" }, { "check": "Changelog", "value": "not found", "points": 0, "max": 10, "state": "no" }, { "check": "security.txt", "value": "not found", "points": 0, "max": 10, "state": "no" }, { "check": "People named", "value": "not researched yet", "points": 0, "max": 0, "state": "na" }, { "check": "Support route", "value": "help pages, a community", "points": 5, "max": 5, "state": "ok" } ] }, "humans": { "slug": "ripple-65b-preview", "checked": "2026-11-04", "company": "Ricursive Intelligence, Inc.", "peopleNote": "No person is named with a role on a page we could read. www.ricursive.com names no one; its home page links a TIME100 AI 2026 entry whose address names Azalia Mirhoseini and Anna Goldie, but time.com answered 406 and the entry wasn't read, so their roles aren't given here. The model card is authored by \"Ricursive Intelligence\" only, and the Hugging Face organisation keeps its member list private. TechCrunch, linked from the home page about the company's funding, disallows ClaudeBot in robots.txt and wasn't read; The New York Times and The Wall Street Journal articles it links are paywalled. Roster empty pending a first-hand page. No LinkedIn or X was read.", "companySocial": {}, "support": { "urls": [ "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/discussions" ], "community": [ { "label": "Hugging Face discussions", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/discussions", "kind": "forum" } ] }, "sources": [ "https://www.ricursive.com/", "https://www.ricursive.com/ripple", "https://huggingface.co/ricursiveAI", "https://huggingface.co/ricursiveAI/Ripple-65B-Preview" ], "ownership": { "status": "private", "checked": "2026-11-04", "sources": [ "https://www.ricursive.com/" ] } }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.json" } }